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Full Research Article

Modelling the Structural Determinants of Households’ Indebtedness among Agricultural Households: An ISM-MICMAC Approach

R
Raj Kumar1,*
0009-0006-8565-6755
M
Monika Kalani1
T
Tabish Hashmi2
H
Hassan Wali3
1Mittal School of Business, Lovely Professional University, Phagwara-144 411, Punjab, India.
2School of Liberal Sciences, Lovely Professional University, Phagwara-144 411, Punjab, India.
3Department of Economics, Sule Lamido University, Kafin Hausa, Nigeria.

ABSTRACT

Background: Agricultural households’ indebtedness is influenced by interconnected farm, financial and household conditions, making it important to understand relationships among its structural elements rather than examining them independently. This study develops a systemic representation of these interrelationships using literature-derived structural elements.

Methods: Seven structural elements were assessed through expert judgement using interpretive structural modelling (ISM) and MICMAC analysis. 40 experts were contacted, 38 responded and 37 responses were considered valid. Three senior experts subsequently reviewed and consolidated the pairwise judgements. Twenty-one unique pairwise relationships were assessed and used to construct the Structured Self-Interaction Matrix, reachability structure and hierarchical model.

Result: The final structure comprised two levels. Landholding Size occupied Level 2 and had driving and dependence powers of 7 and 1, respectively. Production capacity, cost burden, access to formal credit, farm income, borrowing level and household indebtedness occupied Level 1, each with driving and dependence powers of 6 and 7. The findings indicate an interconnected structure in which Landholding Size forms the foundational element.

INTRODUCTION

Agricultural households’ indebtedness remains an important rural-finance problem because agricultural production and income are uncertain, while expenditures often precede the realisation of returns and access to financial support may be limited (Catanach, 2021; Maurya and Vishwakarma, 2021; Narayanamoorthy, 2017). Credit can support productive investment and consumption smoothing, but financial vulnerability may arise when repayment obligations exceed productive and income-generating capacity (Osborne, 2006; Rosenzweig and Wolpin, 1993). In developing agricultural economies, formal and informal credit markets operate alongside household, farm and credit-access characteristics that shape borrowing and indebtedness (Kondratjeva, 2021; Kumar et al., 2013; Manogna and Mishra, 2022).

The National Statistical Office’s 77th-round Situation Assessment Survey reported that 50.2% of agricultural households were indebted in 2019, with an average outstanding debt of INR 74,121 per agricultural household (National Statistical Office, 2021). Indian evidence further links indebtedness with socioeconomic characteristics, farm size, resource endowment and sources of credit (Kumar et al., 2017; Ravita et al., 2022). Recent evidence also indicates that resource-poor households, households experiencing crop losses and those relying on casual labour are more likely to borrow, including for non-farm financial needs as well as agricultural investment (Pavithra et al., 2025). Access to institutional credit is also unequal, with socioeconomic and demographic characteristics associated with participation and many small and marginal farmers remain outside the institutional credit system (Kumar et al., 2020).

The previous literature has focused more on the prevalence and correlates of indebtedness and less on the relationships among production capacity, cost burden, formal credit access, farm income, borrowing level, household indebtedness and landholding size-all within a single system. In particular, the contextual direction and hierarchical position of these elements have not been sufficiently examined. This work attempts to fill this gap by establishing a structural representation of these interrelationships, derived from expert input using Interpretive Structural Modelling (ISM) and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis. The study creates a new foundation in which elements of indebtedness were identified and their structural modelling was done to find driving and dependence powers among them.

Thus, the aim of the study is:
(i) To identify the relevant structural elements associated with agricultural household indebtedness from the literature.
(ii) To examine the contextual relationship among them by applying expert judgement using ISM.
(iii) To establish the hierarchical position by reachability analysis.
(iv) To classify the elements based on the driving and dependence powers using the MICMAC analysis.
 
Literature review
 
Agricultural indebtedness has long been associated with structural constraints in rural economies, including dependence on uncertain agricultural conditions, limited collateral, rising production costs and unstable returns (Blunt, 1937; Darling, 1925; Narayanamoorthy and Kalamkar, 2005; Reddy et al., 2020). Drawing on this literature, the present study identifies seven structural elements relevant to agricultural households’ indebtedness, which are explained below:
 
Landholding size (V1)
 
It influences agricultural households’ resource position, income stability, collateral availability and access to credit. Small, resource-constrained holdings are associated with greater financial vulnerability, whereas land also serves as a tangible asset for securing agricultural credit (Diagne, 1999; Maurya and Vishwakarma, 2021; Padmaja and Ali, 2019; Kale et al., 2012). Landholding size is therefore considered a structural element of agricultural households’ financial and borrowing conditions.
 
Production capacity (V2)
 
It represents the productive potential of agricultural land under given conditions (Lu et al., 2022). Agricultural productivity is associated with borrowing behaviour and credit dependence, while evidence indicates that output value per hectare is positively related to debt per hectare (Sidhu and Gill, 2006). Production capacity was therefore included as a link between productive conditions and household financial behaviour.
 
Cost burden (V3)
 
Agricultural production entails recurring and non-recurring cultivation costs that vary with crop and production conditions (Dev and Rao, 2010; Narayanamoorthy, 2013; Rawal, 2013; Jose and Ponnusamy, 2025). Empirical evidence reports a positive relationship between cultivation costs and indebtedness (Sajjad and Chauhan, 2012). Consequently, cost burden was included to represent the expenditure pressure associated with agricultural production.
 
Access to formal credit (V4)
 
Formal credit access represents a household’s ability to borrow from institutional sources and can support financial inclusion and productive activity while also affecting borrowing exposure (Mutsonziwa and Fanta, 2019; Kishore et al., 2012). Its implications depend on actual participation and borrowing conditions rather than access alone (Diagne et al., 2000; Dinger et al., 2026). Accordingly, access to formal credit was included as a structural element linking financial availability to borrowing behaviour and indebtedness.
 
Farm income (V5)
 
This affects both borrowing requirements and repayment capacity. Uncertain or inadequate agricultural returns can increase dependence on borrowing, while higher income allows households to service higher debt with comparatively less financial stress (Briggeman, 2010; Deogharia, 2016; Narayanamoorthy, 2017; Jain and Hazarika, 2026). Farm income was therefore included as a structural element linking agricultural returns with household financial capacity.
 
Borrowing level (V6)
 
Borrowing level reflects the magnitude of borrowing relative to households’ production needs and repayment capacity. Borrowing varies with farm size, income and collateral availability, while excessive borrowing relative to output, landholding or income may increase financial vulnerability and debt distress (Bell, 1990; Sidhu and Gill, 2006). Conversely, productive borrowing may support farm investment when institutional credit is accessible and borrowing costs remain manageable (Kochar, 1997). Borrowing level was therefore retained as a structural element representing households’ credit utilisation and financial exposure.
 
Household indebtedness (V7)
 
Household indebtedness is treated as one of the seven structural elements rather than as an independent statistical determinant. The literature associates indebtedness with landholding, production costs, farm income, credit access and borrowing conditions, indicating that it is embedded within a broader set of rural financial relationships (Binswanger and Rosenzweig, 1986; Diagne, 1999). Its inclusion therefore permits the structural model to examine its contextual position among elements.

In this framework, the concepts of borrowing level and household indebtedness are distinct, but related. Borrowing level measures the amount of credit a household has borrowed in the reference period, while household indebtedness is the total stock of indebtedness from borrowing in all the relevant sources and periods. Therefore, even if the current borrowing is high, it does not necessarily indicate that the indebtedness is high if the previous borrowing has been paid off; likewise, the current borrowing may be small, but the outstanding debt may be high. Hence, the former gives the level of borrowing activity and the latter gives the burden of debt of the household.

Taken together, the literature indicates that indebtedness is embedded in a network of productive capacity, expenditure pressure, income generation, land resources, credit access and borrowing behaviour. These elements are therefore not conceptually independent, providing a rationale for examining their contextual interrelationships rather than treating them solely as separate correlates.

MATERIALS AND METHODS

Research design and identification of structural elements
 
Interpretative structural modelling (ISM) is essentially an interpretive learning process that represents the ordering of variables and converts them into a structured framework (Mathiyazhagan et al., 2013). The concept of ISM was introduced by John Nelson Warfield (Warfield, 1973) and is fundamentally based on the systematic application of graph theory (Sindhu et al., 2016). As a qualitative approach, ISM converts complex and articulated structural models into clearly defined conceptual models that demonstrate the interrelationships between variables (Gupta and Sahu, 2013). The resulting model supports the identification of solutions to the defined problem and provides an objective and understandable representation of the system (Sindhu et al., 2016).

The study employed Interpretive Structural Modelling (ISM) to examine the contextual relationships among structural elements associated with agricultural households’ indebtedness. The elements were first identified from the literature and then assessed through expert judgement. ISM was selected because it enables interdependent elements to be organised into a hierarchical structure based on contextual relationships rather than estimating their independent statistical effects (Attri et al., 2013; Sushil, 2012; Janes, 1988). MICMAC analysis was subsequently used to complement the ISM by assessing the driving and dependence characteristics of the elements.
 
Expert consultation and pairwise assessment
 
Expert judgement established the direction of relationships among the elements. 40 experts were contacted; 38 returned questionnaires and 37 were used for analysis. One response was dropped due to not meeting the response-completeness criteria that were established. Subsequently, three senior experts from the selected panel participated in a Focus Group Discussion (FGD) to review and consolidate the pairwise judgements and establish the final contextual relationships.

Experts were purposively selected for their substantive knowledge of agricultural finance, rural credit, agricultural economics and/or agricultural household financial conditions. Eligibility required a minimum of 5 Years of academic or professional experience in relevant professional, research, or field experience and the panel comprises Academic Professors, Statisticians, Lead Bank Managers and Field Officers from the Government of Haryana. The 21 pairwise relationships among the seven structural elements (see Equation 1) were established.
 
 
      
 Where,
N = Number of elements (N = 7).

RESULTS AND DISCUSSION

The ISM process starts by identifying the variables and establishing the contextual relationships between them. The ISM approach draws upon the practical knowledge and understanding of experts within a specific field (Dubey and Ali, 2014; Yadav and Sagar, 2021). Discussions with experts help identify the variables and the interrelationships existing among them. Various techniques, including literature surveys, open brainstorming sessions, questionnaires, open discussions among expert panels, workshops, nominal group techniques and idea engineering workshops, can be used to identify the contextual variables (Ali et al., 2018).

Fig 1 presents the sequence of procedures used to develop the ISM-MICMAC model.

Fig 1: Flow diagram of the ISM-MICMAC procedure.


 
Expert validation and structural relationships
 
Expert assessment helped draw the 1st stage of SSIM based on the Model (Z) value; this is similar to the way individual VAXO judgments have been consolidated in known applications of SSIM, which use the most frequently occurring (Modal) value to calculate a final SSIM (Shaukat et al., 2021; Dohale et al., 2024; Desingh and Baskaran, 2022). FGD further refined these relationships where context required reconsideration. Because the SSIM was constructed from judgments obtained independently from multiple experts, the individual expert responses for each pairwise relationship were consolidated using the Modal (Z) response. The Z was defined as the response category occurring with the highest frequency among the experts. Accordingly, the V, A, X, or O category receiving the highest number of expert responses was assigned to the corresponding cell of the integrated SSIM (Sarikhani et al., 2020; Saeedi et al., 2022).

In Table 1, the V2-V7 relationship was revised from O to X, while V4-V5 and V5-V7 involved tied initial responses and were resolved through expert consensus in FGD. The resulting relationships were used to construct the final SSIM.

Table 1: Expert validation of contextual relationships among the determinants.


 
Structured self-interaction matrix
 
The expert assessments were represented through a Structured Self-Interaction Matrix (SSIM). Four conventional ISM symbols were used to express the direction of relationships: V when element i influences element j, A when  influences , X when both elements influence one another and O when no contextual relationship was identified (Mandal and Deshmukh, 1994; Singh and Samuel, 2018). The responses were aggregated using the modal response for each pair. When a relationship required contextual reconsideration, the pair was reviewed during the FGD and the final expert consensus was incorporated into the SSIM.

The final SSIM in Table 2 indicates that the structural elements are interconnected rather than isolated. V1 has directional relationships with all V2-V7 elements, while the remaining relationships include both unidirectional and reciprocal associations. The final structure comprised 18 unidirectional and 3 bidirectional relationships among the 21 unique pairwise comparisons.

Table 2: Final structured self-interaction matrix (SSIM).


 
Reachability analysis and level partitioning
 
The SSIM was converted into an initial reachability matrix (IRM), with 1 representing a directional relationship and 0 representing its absence. V, A, X and O relationships were converted according to the conventional ISM rules (Mathiyazhagan et al., 2013; Sushil, 2012). The IRM was then subjected to transitivity analysis to incorporate indirect relationships and obtain the final reachability matrix (FRM). Reachability and antecedent sets were subsequently used to partition the elements into hierarchical levels.

The final reachability matrix (FRM) (Table 3) is employed for calculating reachability and antecedent sets for each factor (Warfield, 1973). The reachability set is a specific factor and other factors which can be reached by it, while the antecedent set comprises the factor, as well as other factors which can help achieve it. For each factor, the intersection set is computed as the common elements of the reachability and antecedent sets. The reachability set and intersection set are the same for factors and they are placed at the top of the ISM hierarchy. It doesn’t help to achieve anything higher than their level for such factors. The top-level factors are therefore recognised and distinguished from the other factors. This is then repeated for the other factors so that the next level is found. In the current ISM system, the process gives rise to a two-level hierarchical structure where six variables are placed at the first level and one at the second level (Fig 2). The final ISM model and digraph are subsequently built on these identified levels.

Table 3: Final reachability matrix.



Fig 2: Interpretive structural model of the structural elements of agricultural households’ indebtedness.


 
MICMAC analysis
 
MICMAC introduces the concepts of driving and dependence power of variables (Godet, 1986; Sharma et al., 1995), which can be used to identify and classify significant variables and thus gain knowledge about the enablers of voice assistant adoption. It is based on the characteristics of matrix multiplication (Nandal et al., 2019).  In the current study, the variables are categorised based on their driving and dependence power. Quadrant 1 is for autonomous variables, where the driving power is low and the dependency is also low. They are regarded as being independent and somewhat separate from the rest of the design. Quadrant 2 is the dependent quadrant, in which the driving power is low and the dependency is high. Quadrant 3 is for linkage variables, having high dependence and high driving power. These can affect other variables and be affected by other variables. Quadrant 4 has independent variables with low dependence and high driving power.

The MICMAC graph is then built on the basis of these principles (Fig 3), which shows the driving power on the y-axis and the dependence power on the x-axis. The graph depicts the hierarchy of variables and a conceptual model.

Fig 3: MICMAC classification of the structural elements of agricultural households’ indebtedness.


 
Structural interpretation and comparison with previous studies
 
The ISM-MICMAC analysis indicates that the analysis is of two tiers, where Landholding Size (V1) is the base and Production Capacity (V2), Cost Burden (V3), Access to Formal Credit (V4), Farm Income (V5), Borrowing Level (V6) and Household Indebtedness (V7) are the upper tier. V1 has the highest driving power (7) and the lowest dependence power (1), while V2 to V7 have a driving power of (6) and dependence power of (7). This set-up suggests that landholding has a relatively basic role in the overall structure, with the other components being highly interrelated.

The MICMAC classification reiterates this distinction. V1 is independent/driver - high driving and low dependence power, while V2-V7 are linkage - high driving and high dependence powers. The role of Landholding Size is generally congruent with earlier studies on the relationship between farm size and land endowment and financial and credit activities of agricultural households (Diagne, 1999; Maurya and Vishwakarma, 2021; Padmaja and Ali, 2019). The current research, however, introduces a structural perspective to landholding as a basic component of a complex system of interconnected components, where it is not only understood as an individual correlate of indebtedness.

The linkage position of production capacity and cost burden is also consistent with the earlier evidence of linkage between production conditions and cultivation expenditure and agriculture indebtedness (Narayanamoorthy and Kalamkar, 2005; Sajjad and Chauhan, 2012; Sidhu and Gill, 2006). But these factors are not isolated but are all in one connected layer of structure, as are credit, income, borrowing and indebtedness. Likewise, earlier studies have reported a relationship between institutional credit access, income situation, credit constraints and credit behaviour (Kumar et al., 2013; Kumar et al., 2017; Kumar et al., 2020; Narayanamoorthy, 2017). The present ISM adds to this evidence by introducing the linkage elements of Access to Formal Credit, Farm Income and Borrowing Level in the credit-income-borrowing structure.

This suggests the multidimensional nature of agricultural indebtedness, which has been identified in previous studies (Padmaja and Ali, 2019; Manogna and Mishra, 2022; Ravita et al., 2022; Pavithra et al., 2025). Its linkage position shows that indebtedness is not an isolated phenomenon but has a structural relationship with production, cost, credit conditions, income and borrowing conditions. The overall results support and expand on the existing evidence, presenting a hierarchy of the dimensions in context and their driving-dependence configuration.

CONTRIBUTION, LIMITATIONS AND FUTURE SCOPE

The results indicate that the solution to agricultural household indebtedness is through an integrated approach and not stand-alone credit interventions. Landholding size is the base variable and policies should take into account differences in farm-resource endowment. In addition to this, for productivity and income-stabilising interventions, households with limited land resources may need credit support. Further, the linkage between Production capacity and Cost burden seems to indicate that the emphasis of agricultural extension programmes should be on productive efficiency, suitable and appropriate input utilisation, cost management and better utilisation of resources, thereby reducing the need for borrowings.

There is a high level of financial vulnerability shown in the relationships between Access to Formal Credit, Farm Income and Level of Borrowing. Credit delivery should thus be linked to suitable repayment plans, financial advisement and credit and repayment facilities. The interconnectedness of the position of Household Indebtedness also points to the need for coordinated interventions from credit institutions, extension agencies and rural-development programmes.

The MICMAC results can serve as a basis on which to prioritise interventions: Landholding Size is the main intervening variable, whereas the other variables are in the linkage category. Therefore, indebtedness should be taken up as a subject of concerted action by focusing on an integrated approach towards land resources, productive potential, costs, formal credit and income generation and borrowing. The model provides practitioners with a diagnostic tool to show when a person is vulnerable before they enter into persistent debt.

This study is based on expert judgement and ISM-MICMAC analysis, so the relationships identified are not statistically estimated causal effects but rather an interpretation of the relationships within a particular context or structure. Future studies may confirm these relationships based on household survey data, statistical modelling and comparative studies between regions and agricultural situations.

CONCLUSION

The study developed an ISM-MICMAC framework to examine the contextual interrelationships among seven structural elements of agricultural households’ indebtedness identified from the literature. Expert assessment involved 37 valid responses, followed by focused review and consolidation by three senior experts and included 21 pairwise comparisons, yielding 18 unidirectional and 3 bidirectional relationships. The resulting structure demonstrates that the elements are interconnected rather than independent.

The final reachability structure and level partitioning distinguish Landholding Size from the other six elements. Landholding Size has a driving power of 7 and a dependence power of 1 and occupies Level 2, whereas Production Capacity, Cost Burden, Access to Formal Credit, Farm Income, Borrowing Level and Household Indebtedness each have a driving power of 6 and a dependence power of 7 and occupy Level 1. Thus, the ISM represents Landholding Size as the foundational element, with the remaining six forming an interconnected upper-level structure.

The findings provide a systemic representation of agricultural households’ indebtedness by showing how the identified elements are structurally positioned and interconnected. The ISM-MICMAC results should, however, be interpreted as contextual and expert-derived structural relationships rather than statistical evidence of causality.

ACKNOWLEDGEMENT

The authors thank the 40 experts for their knowledge, experience and professional views. They also appreciate the focus group experts for their suggestions and insights that helped refine the study. Overall, they are grateful to all experts for their time, cooperation and contributions.
 
Ethical declaration
 
No ethical permission was required for the study.
 
Data availability
 
The authors may provide any data and calculation sheets upon reasonable request by email to the corresponding author.
 
Funding
 
The study received no funding; it was self-financed.

CONFLICT OF INTEREST

The authors declare no competing interests that could influence this work. All authors participated in the research and manuscript preparation and approved the final version of the manuscript.

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    Full Research Article

    Modelling the Structural Determinants of Households’ Indebtedness among Agricultural Households: An ISM-MICMAC Approach

    R
    Raj Kumar1,*
    0009-0006-8565-6755
    M
    Monika Kalani1
    T
    Tabish Hashmi2
    H
    Hassan Wali3
    1Mittal School of Business, Lovely Professional University, Phagwara-144 411, Punjab, India.
    2School of Liberal Sciences, Lovely Professional University, Phagwara-144 411, Punjab, India.
    3Department of Economics, Sule Lamido University, Kafin Hausa, Nigeria.

    ABSTRACT

    Background: Agricultural households’ indebtedness is influenced by interconnected farm, financial and household conditions, making it important to understand relationships among its structural elements rather than examining them independently. This study develops a systemic representation of these interrelationships using literature-derived structural elements.

    Methods: Seven structural elements were assessed through expert judgement using interpretive structural modelling (ISM) and MICMAC analysis. 40 experts were contacted, 38 responded and 37 responses were considered valid. Three senior experts subsequently reviewed and consolidated the pairwise judgements. Twenty-one unique pairwise relationships were assessed and used to construct the Structured Self-Interaction Matrix, reachability structure and hierarchical model.

    Result: The final structure comprised two levels. Landholding Size occupied Level 2 and had driving and dependence powers of 7 and 1, respectively. Production capacity, cost burden, access to formal credit, farm income, borrowing level and household indebtedness occupied Level 1, each with driving and dependence powers of 6 and 7. The findings indicate an interconnected structure in which Landholding Size forms the foundational element.

    INTRODUCTION

    Agricultural households’ indebtedness remains an important rural-finance problem because agricultural production and income are uncertain, while expenditures often precede the realisation of returns and access to financial support may be limited (Catanach, 2021; Maurya and Vishwakarma, 2021; Narayanamoorthy, 2017). Credit can support productive investment and consumption smoothing, but financial vulnerability may arise when repayment obligations exceed productive and income-generating capacity (Osborne, 2006; Rosenzweig and Wolpin, 1993). In developing agricultural economies, formal and informal credit markets operate alongside household, farm and credit-access characteristics that shape borrowing and indebtedness (Kondratjeva, 2021; Kumar et al., 2013; Manogna and Mishra, 2022).

    The National Statistical Office’s 77th-round Situation Assessment Survey reported that 50.2% of agricultural households were indebted in 2019, with an average outstanding debt of INR 74,121 per agricultural household (National Statistical Office, 2021). Indian evidence further links indebtedness with socioeconomic characteristics, farm size, resource endowment and sources of credit (Kumar et al., 2017; Ravita et al., 2022). Recent evidence also indicates that resource-poor households, households experiencing crop losses and those relying on casual labour are more likely to borrow, including for non-farm financial needs as well as agricultural investment (Pavithra et al., 2025). Access to institutional credit is also unequal, with socioeconomic and demographic characteristics associated with participation and many small and marginal farmers remain outside the institutional credit system (Kumar et al., 2020).

    The previous literature has focused more on the prevalence and correlates of indebtedness and less on the relationships among production capacity, cost burden, formal credit access, farm income, borrowing level, household indebtedness and landholding size-all within a single system. In particular, the contextual direction and hierarchical position of these elements have not been sufficiently examined. This work attempts to fill this gap by establishing a structural representation of these interrelationships, derived from expert input using Interpretive Structural Modelling (ISM) and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis. The study creates a new foundation in which elements of indebtedness were identified and their structural modelling was done to find driving and dependence powers among them.

    Thus, the aim of the study is:
    (i) To identify the relevant structural elements associated with agricultural household indebtedness from the literature.
    (ii) To examine the contextual relationship among them by applying expert judgement using ISM.
    (iii) To establish the hierarchical position by reachability analysis.
    (iv) To classify the elements based on the driving and dependence powers using the MICMAC analysis.
     
    Literature review
     
    Agricultural indebtedness has long been associated with structural constraints in rural economies, including dependence on uncertain agricultural conditions, limited collateral, rising production costs and unstable returns (Blunt, 1937; Darling, 1925; Narayanamoorthy and Kalamkar, 2005; Reddy et al., 2020). Drawing on this literature, the present study identifies seven structural elements relevant to agricultural households’ indebtedness, which are explained below:
     
    Landholding size (V1)
     
    It influences agricultural households’ resource position, income stability, collateral availability and access to credit. Small, resource-constrained holdings are associated with greater financial vulnerability, whereas land also serves as a tangible asset for securing agricultural credit (Diagne, 1999; Maurya and Vishwakarma, 2021; Padmaja and Ali, 2019; Kale et al., 2012). Landholding size is therefore considered a structural element of agricultural households’ financial and borrowing conditions.
     
    Production capacity (V2)
     
    It represents the productive potential of agricultural land under given conditions (Lu et al., 2022). Agricultural productivity is associated with borrowing behaviour and credit dependence, while evidence indicates that output value per hectare is positively related to debt per hectare (Sidhu and Gill, 2006). Production capacity was therefore included as a link between productive conditions and household financial behaviour.
     
    Cost burden (V3)
     
    Agricultural production entails recurring and non-recurring cultivation costs that vary with crop and production conditions (Dev and Rao, 2010; Narayanamoorthy, 2013; Rawal, 2013; Jose and Ponnusamy, 2025). Empirical evidence reports a positive relationship between cultivation costs and indebtedness (Sajjad and Chauhan, 2012). Consequently, cost burden was included to represent the expenditure pressure associated with agricultural production.
     
    Access to formal credit (V4)
     
    Formal credit access represents a household’s ability to borrow from institutional sources and can support financial inclusion and productive activity while also affecting borrowing exposure (Mutsonziwa and Fanta, 2019; Kishore et al., 2012). Its implications depend on actual participation and borrowing conditions rather than access alone (Diagne et al., 2000; Dinger et al., 2026). Accordingly, access to formal credit was included as a structural element linking financial availability to borrowing behaviour and indebtedness.
     
    Farm income (V5)
     
    This affects both borrowing requirements and repayment capacity. Uncertain or inadequate agricultural returns can increase dependence on borrowing, while higher income allows households to service higher debt with comparatively less financial stress (Briggeman, 2010; Deogharia, 2016; Narayanamoorthy, 2017; Jain and Hazarika, 2026). Farm income was therefore included as a structural element linking agricultural returns with household financial capacity.
     
    Borrowing level (V6)
     
    Borrowing level reflects the magnitude of borrowing relative to households’ production needs and repayment capacity. Borrowing varies with farm size, income and collateral availability, while excessive borrowing relative to output, landholding or income may increase financial vulnerability and debt distress (Bell, 1990; Sidhu and Gill, 2006). Conversely, productive borrowing may support farm investment when institutional credit is accessible and borrowing costs remain manageable (Kochar, 1997). Borrowing level was therefore retained as a structural element representing households’ credit utilisation and financial exposure.
     
    Household indebtedness (V7)
     
    Household indebtedness is treated as one of the seven structural elements rather than as an independent statistical determinant. The literature associates indebtedness with landholding, production costs, farm income, credit access and borrowing conditions, indicating that it is embedded within a broader set of rural financial relationships (Binswanger and Rosenzweig, 1986; Diagne, 1999). Its inclusion therefore permits the structural model to examine its contextual position among elements.

    In this framework, the concepts of borrowing level and household indebtedness are distinct, but related. Borrowing level measures the amount of credit a household has borrowed in the reference period, while household indebtedness is the total stock of indebtedness from borrowing in all the relevant sources and periods. Therefore, even if the current borrowing is high, it does not necessarily indicate that the indebtedness is high if the previous borrowing has been paid off; likewise, the current borrowing may be small, but the outstanding debt may be high. Hence, the former gives the level of borrowing activity and the latter gives the burden of debt of the household.

    Taken together, the literature indicates that indebtedness is embedded in a network of productive capacity, expenditure pressure, income generation, land resources, credit access and borrowing behaviour. These elements are therefore not conceptually independent, providing a rationale for examining their contextual interrelationships rather than treating them solely as separate correlates.

    MATERIALS AND METHODS

    Research design and identification of structural elements
     
    Interpretative structural modelling (ISM) is essentially an interpretive learning process that represents the ordering of variables and converts them into a structured framework (Mathiyazhagan et al., 2013). The concept of ISM was introduced by John Nelson Warfield (Warfield, 1973) and is fundamentally based on the systematic application of graph theory (Sindhu et al., 2016). As a qualitative approach, ISM converts complex and articulated structural models into clearly defined conceptual models that demonstrate the interrelationships between variables (Gupta and Sahu, 2013). The resulting model supports the identification of solutions to the defined problem and provides an objective and understandable representation of the system (Sindhu et al., 2016).

    The study employed Interpretive Structural Modelling (ISM) to examine the contextual relationships among structural elements associated with agricultural households’ indebtedness. The elements were first identified from the literature and then assessed through expert judgement. ISM was selected because it enables interdependent elements to be organised into a hierarchical structure based on contextual relationships rather than estimating their independent statistical effects (Attri et al., 2013; Sushil, 2012; Janes, 1988). MICMAC analysis was subsequently used to complement the ISM by assessing the driving and dependence characteristics of the elements.
     
    Expert consultation and pairwise assessment
     
    Expert judgement established the direction of relationships among the elements. 40 experts were contacted; 38 returned questionnaires and 37 were used for analysis. One response was dropped due to not meeting the response-completeness criteria that were established. Subsequently, three senior experts from the selected panel participated in a Focus Group Discussion (FGD) to review and consolidate the pairwise judgements and establish the final contextual relationships.

    Experts were purposively selected for their substantive knowledge of agricultural finance, rural credit, agricultural economics and/or agricultural household financial conditions. Eligibility required a minimum of 5 Years of academic or professional experience in relevant professional, research, or field experience and the panel comprises Academic Professors, Statisticians, Lead Bank Managers and Field Officers from the Government of Haryana. The 21 pairwise relationships among the seven structural elements (see Equation 1) were established.
     
     
          
     Where,
    N = Number of elements (N = 7).

    RESULTS AND DISCUSSION

    The ISM process starts by identifying the variables and establishing the contextual relationships between them. The ISM approach draws upon the practical knowledge and understanding of experts within a specific field (Dubey and Ali, 2014; Yadav and Sagar, 2021). Discussions with experts help identify the variables and the interrelationships existing among them. Various techniques, including literature surveys, open brainstorming sessions, questionnaires, open discussions among expert panels, workshops, nominal group techniques and idea engineering workshops, can be used to identify the contextual variables (Ali et al., 2018).

    Fig 1 presents the sequence of procedures used to develop the ISM-MICMAC model.

    Fig 1: Flow diagram of the ISM-MICMAC procedure.


     
    Expert validation and structural relationships
     
    Expert assessment helped draw the 1st stage of SSIM based on the Model (Z) value; this is similar to the way individual VAXO judgments have been consolidated in known applications of SSIM, which use the most frequently occurring (Modal) value to calculate a final SSIM (Shaukat et al., 2021; Dohale et al., 2024; Desingh and Baskaran, 2022). FGD further refined these relationships where context required reconsideration. Because the SSIM was constructed from judgments obtained independently from multiple experts, the individual expert responses for each pairwise relationship were consolidated using the Modal (Z) response. The Z was defined as the response category occurring with the highest frequency among the experts. Accordingly, the V, A, X, or O category receiving the highest number of expert responses was assigned to the corresponding cell of the integrated SSIM (Sarikhani et al., 2020; Saeedi et al., 2022).

    In Table 1, the V2-V7 relationship was revised from O to X, while V4-V5 and V5-V7 involved tied initial responses and were resolved through expert consensus in FGD. The resulting relationships were used to construct the final SSIM.

    Table 1: Expert validation of contextual relationships among the determinants.


     
    Structured self-interaction matrix
     
    The expert assessments were represented through a Structured Self-Interaction Matrix (SSIM). Four conventional ISM symbols were used to express the direction of relationships: V when element i influences element j, A when  influences , X when both elements influence one another and O when no contextual relationship was identified (Mandal and Deshmukh, 1994; Singh and Samuel, 2018). The responses were aggregated using the modal response for each pair. When a relationship required contextual reconsideration, the pair was reviewed during the FGD and the final expert consensus was incorporated into the SSIM.

    The final SSIM in Table 2 indicates that the structural elements are interconnected rather than isolated. V1 has directional relationships with all V2-V7 elements, while the remaining relationships include both unidirectional and reciprocal associations. The final structure comprised 18 unidirectional and 3 bidirectional relationships among the 21 unique pairwise comparisons.

    Table 2: Final structured self-interaction matrix (SSIM).


     
    Reachability analysis and level partitioning
     
    The SSIM was converted into an initial reachability matrix (IRM), with 1 representing a directional relationship and 0 representing its absence. V, A, X and O relationships were converted according to the conventional ISM rules (Mathiyazhagan et al., 2013; Sushil, 2012). The IRM was then subjected to transitivity analysis to incorporate indirect relationships and obtain the final reachability matrix (FRM). Reachability and antecedent sets were subsequently used to partition the elements into hierarchical levels.

    The final reachability matrix (FRM) (Table 3) is employed for calculating reachability and antecedent sets for each factor (Warfield, 1973). The reachability set is a specific factor and other factors which can be reached by it, while the antecedent set comprises the factor, as well as other factors which can help achieve it. For each factor, the intersection set is computed as the common elements of the reachability and antecedent sets. The reachability set and intersection set are the same for factors and they are placed at the top of the ISM hierarchy. It doesn’t help to achieve anything higher than their level for such factors. The top-level factors are therefore recognised and distinguished from the other factors. This is then repeated for the other factors so that the next level is found. In the current ISM system, the process gives rise to a two-level hierarchical structure where six variables are placed at the first level and one at the second level (Fig 2). The final ISM model and digraph are subsequently built on these identified levels.

    Table 3: Final reachability matrix.



    Fig 2: Interpretive structural model of the structural elements of agricultural households’ indebtedness.


     
    MICMAC analysis
     
    MICMAC introduces the concepts of driving and dependence power of variables (Godet, 1986; Sharma et al., 1995), which can be used to identify and classify significant variables and thus gain knowledge about the enablers of voice assistant adoption. It is based on the characteristics of matrix multiplication (Nandal et al., 2019).  In the current study, the variables are categorised based on their driving and dependence power. Quadrant 1 is for autonomous variables, where the driving power is low and the dependency is also low. They are regarded as being independent and somewhat separate from the rest of the design. Quadrant 2 is the dependent quadrant, in which the driving power is low and the dependency is high. Quadrant 3 is for linkage variables, having high dependence and high driving power. These can affect other variables and be affected by other variables. Quadrant 4 has independent variables with low dependence and high driving power.

    The MICMAC graph is then built on the basis of these principles (Fig 3), which shows the driving power on the y-axis and the dependence power on the x-axis. The graph depicts the hierarchy of variables and a conceptual model.

    Fig 3: MICMAC classification of the structural elements of agricultural households’ indebtedness.


     
    Structural interpretation and comparison with previous studies
     
    The ISM-MICMAC analysis indicates that the analysis is of two tiers, where Landholding Size (V1) is the base and Production Capacity (V2), Cost Burden (V3), Access to Formal Credit (V4), Farm Income (V5), Borrowing Level (V6) and Household Indebtedness (V7) are the upper tier. V1 has the highest driving power (7) and the lowest dependence power (1), while V2 to V7 have a driving power of (6) and dependence power of (7). This set-up suggests that landholding has a relatively basic role in the overall structure, with the other components being highly interrelated.

    The MICMAC classification reiterates this distinction. V1 is independent/driver - high driving and low dependence power, while V2-V7 are linkage - high driving and high dependence powers. The role of Landholding Size is generally congruent with earlier studies on the relationship between farm size and land endowment and financial and credit activities of agricultural households (Diagne, 1999; Maurya and Vishwakarma, 2021; Padmaja and Ali, 2019). The current research, however, introduces a structural perspective to landholding as a basic component of a complex system of interconnected components, where it is not only understood as an individual correlate of indebtedness.

    The linkage position of production capacity and cost burden is also consistent with the earlier evidence of linkage between production conditions and cultivation expenditure and agriculture indebtedness (Narayanamoorthy and Kalamkar, 2005; Sajjad and Chauhan, 2012; Sidhu and Gill, 2006). But these factors are not isolated but are all in one connected layer of structure, as are credit, income, borrowing and indebtedness. Likewise, earlier studies have reported a relationship between institutional credit access, income situation, credit constraints and credit behaviour (Kumar et al., 2013; Kumar et al., 2017; Kumar et al., 2020; Narayanamoorthy, 2017). The present ISM adds to this evidence by introducing the linkage elements of Access to Formal Credit, Farm Income and Borrowing Level in the credit-income-borrowing structure.

    This suggests the multidimensional nature of agricultural indebtedness, which has been identified in previous studies (Padmaja and Ali, 2019; Manogna and Mishra, 2022; Ravita et al., 2022; Pavithra et al., 2025). Its linkage position shows that indebtedness is not an isolated phenomenon but has a structural relationship with production, cost, credit conditions, income and borrowing conditions. The overall results support and expand on the existing evidence, presenting a hierarchy of the dimensions in context and their driving-dependence configuration.

    CONTRIBUTION, LIMITATIONS AND FUTURE SCOPE

    The results indicate that the solution to agricultural household indebtedness is through an integrated approach and not stand-alone credit interventions. Landholding size is the base variable and policies should take into account differences in farm-resource endowment. In addition to this, for productivity and income-stabilising interventions, households with limited land resources may need credit support. Further, the linkage between Production capacity and Cost burden seems to indicate that the emphasis of agricultural extension programmes should be on productive efficiency, suitable and appropriate input utilisation, cost management and better utilisation of resources, thereby reducing the need for borrowings.

    There is a high level of financial vulnerability shown in the relationships between Access to Formal Credit, Farm Income and Level of Borrowing. Credit delivery should thus be linked to suitable repayment plans, financial advisement and credit and repayment facilities. The interconnectedness of the position of Household Indebtedness also points to the need for coordinated interventions from credit institutions, extension agencies and rural-development programmes.

    The MICMAC results can serve as a basis on which to prioritise interventions: Landholding Size is the main intervening variable, whereas the other variables are in the linkage category. Therefore, indebtedness should be taken up as a subject of concerted action by focusing on an integrated approach towards land resources, productive potential, costs, formal credit and income generation and borrowing. The model provides practitioners with a diagnostic tool to show when a person is vulnerable before they enter into persistent debt.

    This study is based on expert judgement and ISM-MICMAC analysis, so the relationships identified are not statistically estimated causal effects but rather an interpretation of the relationships within a particular context or structure. Future studies may confirm these relationships based on household survey data, statistical modelling and comparative studies between regions and agricultural situations.

    CONCLUSION

    The study developed an ISM-MICMAC framework to examine the contextual interrelationships among seven structural elements of agricultural households’ indebtedness identified from the literature. Expert assessment involved 37 valid responses, followed by focused review and consolidation by three senior experts and included 21 pairwise comparisons, yielding 18 unidirectional and 3 bidirectional relationships. The resulting structure demonstrates that the elements are interconnected rather than independent.

    The final reachability structure and level partitioning distinguish Landholding Size from the other six elements. Landholding Size has a driving power of 7 and a dependence power of 1 and occupies Level 2, whereas Production Capacity, Cost Burden, Access to Formal Credit, Farm Income, Borrowing Level and Household Indebtedness each have a driving power of 6 and a dependence power of 7 and occupy Level 1. Thus, the ISM represents Landholding Size as the foundational element, with the remaining six forming an interconnected upper-level structure.

    The findings provide a systemic representation of agricultural households’ indebtedness by showing how the identified elements are structurally positioned and interconnected. The ISM-MICMAC results should, however, be interpreted as contextual and expert-derived structural relationships rather than statistical evidence of causality.

    ACKNOWLEDGEMENT

    The authors thank the 40 experts for their knowledge, experience and professional views. They also appreciate the focus group experts for their suggestions and insights that helped refine the study. Overall, they are grateful to all experts for their time, cooperation and contributions.
     
    Ethical declaration
     
    No ethical permission was required for the study.
     
    Data availability
     
    The authors may provide any data and calculation sheets upon reasonable request by email to the corresponding author.
     
    Funding
     
    The study received no funding; it was self-financed.

    CONFLICT OF INTEREST

    The authors declare no competing interests that could influence this work. All authors participated in the research and manuscript preparation and approved the final version of the manuscript.

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