Behavioral Drivers of Farmers’ Adaptation and Maladaptation to Natural Disasters in Coastal Odisha, India

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This study applied structural equation modeling (SEM) using AMOS 20 to examine the determinants of farmers’ adaptation decisions, drawing on data collected from 240 respondents across Puri and Khordha districts. The conceptual model tested eight latent constructs: risk evaluation, adaptation evaluation, maladaptation, social discourse, exact adaptive capacity, adaptation incentives, trust in national adaptation plans, and adaptation decision. Results revealed that the most influential predictor of adaptation decision was maladaptation (β = 0.650, p < 0.001), followed by adaptation evaluation (β = 0.248, p < 0.001) and risk evaluation (β = 0.155, p < 0.001). Social discourse significantly influenced adaptation evaluation (β = 0.598, p < 0.001), while risk evaluation negatively predicted maladaptation (β = -0.246, p < 0.001). Trust, adaptive capacity, and adaptation incentives were found to be statistically non-significant in the decision-making process. These findings suggest that adaptation behavior is shaped not only by rational assessments of risk and strategy efficacy, but also by deep-seated psychological constructs such as fatalism and denial. The study contributes to the climate adaptation literature by empirically validating a comprehensive behavioral model specific to high-risk agrarian systems and offers actionable insights for designing context-specific, psychologically informed interventions that support long-term adaptation in disaster-prone regions. Adaptation decision Climate change Natural disasters Structural equation model Protection motivation theory Figures Figure 1 Figure 2 Figure 3 1. Introduction The intensification of climate change over the past two decades has emerged as one of the most pressing challenges for global agriculture. Climate change characterized by increasing temperatures, erratic rainfall patterns, and frequent natural disasters such as floods, droughts, cyclones, and heatwaves; continues to threaten both food security and rural livelihoods, especially in tropical and subtropical regions (UNFCCC, 2009 ; IPCC, 2013; World Economic Forum, 2022 ). The impacts on agricultural production are significant; estimates suggest a reduction of crop yields by 1–5% per decade in the absence of adaptation interventions (Porter et al., 2014 ; O'neill et al., 2017). For developing countries like India, where agriculture forms the economic backbone for millions, the challenge is even more acute. The state of Odisha, situated along India’s eastern coastline, is particularly vulnerable to climate-induced natural hazards. It has historically experienced high exposure to cyclonic storms, floods, and droughts. Out of India’s 30 most climate-vulnerable districts, several are located in Odisha, especially along its coastal belt (Bahinipati, 2014 ). According to IMD data, the frequency of cyclones making landfall in Odisha has risen sharply in recent decades, with events like Cyclones Phailin (2013), Fani (2019), Amphan (2020), and Yaas (2021) causing severe damage to crops and rural infrastructure (Hazra, et al.,2022). Cyclones during the monsoon and post-monsoon periods, which coincide with the Kharif crop season, have particularly devastating effects on rice and other rain-fed crops. Inland regions of Odisha, on the other hand, suffer predominantly from recurring droughts and extreme heat, further accentuating the state’s climate fragility. Climate change-induced shocks in Odisha adversely affect not only agricultural yields but also the socio-economic fabric of farming communities. Over 70% of Odisha’s population depends on agriculture for their livelihood, and a vast majority of them are small and marginal farmers with limited adaptive capacity (Panda, 2017 ). Fragmented landholdings, lack of access to irrigation, weak market linkages, and limited social safety nets further exacerbate their vulnerability (Aryal et al., 2018 ). Odisha is classified as one of the most vulnerable Indian states, given its fragile ecosystems, high climate exposure, and socio-economic constraints (Mishra 2017 ). To address these challenges, farmers have gradually adopted various adaptation strategies, including changing sowing dates, introducing drought- or flood-tolerant crop varieties, diversifying income sources, using improved irrigation practices, and accessing crop insurance (Habiba et al., 2012 ; Udmale et al., 2014 ; Aryal et al., 2020 ). However, the extent and effectiveness of these adaptations vary significantly across regions and households. This heterogeneity is often influenced by the socio-economic status, asset base, institutional support, and access to information among farming households (Sarker et al., 2013 ; Alam, 2017 ). Despite growing interest in climate change adaptation, most previous studies have predominantly relied on econometric models such as regression, logit, or multinomial logit analyses to identify socio-economic determinants of adaptation (e.g., landholding size, income, education, and access to credit). While these approaches provide important insights into observable characteristics, they often neglect the cognitive and psychological processes that shape adaptation decisions at the individual and community level (Grothmann & Patt, 2005 ; Lopez-Marrero, 2010; Osberghaus et al., 2010 ). Psychological factors, including perceptions of climate risk, beliefs about the efficacy of adaptation strategies, and motivations to act, are increasingly recognized as critical to understanding adaptive behavior. Protection Motivation Theory (PMT) offers a robust framework for analyzing these psychological constructs. Originally developed in health psychology, PMT explains how individuals appraise threats and assess coping options, which in turn influence protective behaviors. In the context of climate change, PMT has been adapted to examine how farmers perceive climatic threats, evaluate adaptation responses, and decide on adaptive actions (Grothmann & Reusswig, 2006 ). However, very few studies in the Indian context have incorporated PMT or similar psychological models into empirical research. Even fewer have focused on regions like coastal Odisha, which experience compound vulnerabilities due to both socio-economic and climatic stressors. Moreover, concepts such as maladaptation (adoption of measures that inadvertently increase vulnerability) and social discourse (the role of community discussion, peer learning, and information exchange) remain underexplored in existing literature, despite their growing relevance (Clayton et al., 2015 ; Chanie et al., 2018). Against this backdrop, the current study seeks to fill this critical gap by applying an extended Protection Motivation Theory framework to examine the determinants of farmers’ adaptation decisions in the coastal districts of Puri and Khordha in Odisha. By integrating psychological and social dimensions with traditional socio-economic factors, this research offers a more holistic understanding of adaptation behavior in vulnerable agrarian contexts. It highlights the need for climate change policies and programs that move beyond asset-based vulnerability assessments to also address the cognitive, motivational, and informational needs of farmers. 2. Materials and Methods 2.1 Theoretical Framework A number of behavioral theories have been employed to explain environmental decision-making, especially in the context of climate change. Among the most commonly used are the Theory of Planned Behavior (TPB) (Ajzen, 1991 ) and the Value-Belief-Norm (VBN) Theory (Stern, 2000 ). TPB posits that behavior is shaped by intentions, which in turn are influenced by attitudes, subjective norms, and perceived behavioral control. VBN theory, meanwhile, emphasizes the role of individuals’ values, ecological beliefs, and moral norms in shaping pro-environmental behaviors. However, both frameworks fall short when it comes to predicting behavior under conditions of uncertainty and climatic risk (Le Dang et al., 2014), a key feature of adaptation decision-making in disaster-prone agrarian settings. To address this gap, this study is grounded in the Protection Motivation Theory (PMT) proposed by Rogers ( 1975 ), which explains how individuals perceive and respond to threats based on two core cognitive processes: risk appraisal (perceived severity and vulnerability) and coping appraisal (self-efficacy, response efficacy, and response cost). A growing body of literature has supported the applicability of PMT in climate change adaptation contexts. For instance, Le Dang et al. (2014) used SEM to examine the adaptation intentions of 598 rice farmers in Vietnam and found that risk perception and belief in adaptive strategy effectiveness positively influenced adaptation decisions, while maladaptive responses such as fatalism and denial hindered them. Van Duinen et al. (2015) applied PMT to explore drought adaptation behavior in the Netherlands and found it superior to TPB in accounting for farmers' behavioral responses to climate threats, explaining about 43% of the variance. Lam ( 2015 ) found that self-efficacy, response efficacy, and perceived benefit significantly predicted public support for climate policies in Taiwan using a PMT framework. Similarly, Keshavarz and Karami ( 2016 ) employed partial least squares modeling and concluded that pro-environmental behavior among farmers in drought-hit regions of Iran was influenced by response efficacy, perceived severity, perceived vulnerability, response costs, and social environment. Delfiyan et al. ( 2020 ) also used SEM in Southwestern Iran and found that PMT constructs such as perceived vulnerability, response cost, and response efficacy together explained nearly 50% of the variation in adaptation behavior. Ghanian et al. ( 2020 ) extended PMT to include eight constructs and demonstrated that risk perception, belief in climate change, and disincentives significantly explained 57% of the variance in adaptation intentions. Neisi et al. ( 2020 ) applied PMT to evaluate farmers’ drought management in the Karkheh Dam Basin and found that self-efficacy had the strongest influence among the six core PMT constructs, which together explained 47.3% of behavioral variance. Pakmehr et al. ( 2020 ) also reported that PMT core variables accounted for 39% of the variance in drought adaptation in Shushtar, Iran, and observed an 11% improvement in explanatory power with the inclusion of collective efficacy. These studies suggest that PMT not only provides a robust explanation of climate risk behavior but also benefits from extensions that include socio-environmental variables. As proposed by Grothmann and Patt ( 2005 ), this study incorporates additional constructs such as maladaptation—defined as behavior that undermines long-term adaptation when risk is high but coping appraisal is low—social discourse (e.g., peer and community influence), trust in national adaptation plans, exact adaptive capacity (actual ability to cope), and adaptation incentives (e.g., subsidies or support schemes). Finally, research by Warnatzsch and Reay ( 2020 ) confirms that maladaptive behavior can suppress the likelihood of positive adaptation. Thus, the theoretical framework for this study is built upon the original PMT by integrating cognitive, psychological, and contextual factors to explain farmers’ adaptation decision-making in the face of climate-induced natural disasters as given in figure no 1. 2.2 Survey Area, Sampling and Data Collection The study was conducted in Odisha, a state on the eastern coast of India, widely recognized for its high vulnerability to climate-induced hazards. According to the Council on Energy, Environment and Water ( 2021 ), 26 districts in Odisha are highly susceptible to extreme climate events, including cyclones, floods, and droughts. The frequency of cyclones in the state has tripled since 1970, while the incidence of storm surges has also witnessed a threefold increase over the same period (Open Government Data Platform India, 2023 ). Annually, more than 12.6 million people in Odisha are affected by severe flood events (Council on Energy, Environment and Water, 2021 ). Severe cyclonic storms have increasingly battered the state in recent years—Cyclone Aila (2009), Phailin (2013), Fani and Bulbul (2018), Amphan (2020), and Yaas and Jawad (2021)—causing extensive damage to agriculture, infrastructure, and livelihoods, particularly in the coastal districts. In light of this recurring vulnerability, Puri and Khordha districts were purposively selected for the study as both regions are frequently affected by cyclones and floods. Within each district, two blocks were chosen, and from each block, two villages were randomly selected, making a total of eight villages. From each village, 30 farmers who had experienced the adverse impacts of cyclones and floods were randomly selected, resulting in a total sample of 240 respondents. Data were collected through personal interviews using a structured interview schedule. The interviews were conducted during March and April 2023, with the assistance of trained enumerators to ensure data accuracy, cultural sensitivity, and clarity of responses. 2.3 Research Instrument The study utilized a structured interview schedule developed to examine farmers’ climate change adaptation behavior in vulnerable coastal districts of Odisha. The instrument consisted of two parts: a demographic section and a scale-based section to measure the latent constructs of the conceptual model. The demographic section collected essential information such as age, gender, education level, landholding size, income source, and farming experience. The main section operationalized eight theoretical constructs- Risk Evaluation (RE), Adaptation Evaluation (AE), Trust in National Adaptation Plans (TR), Social Discourse (SD), Exact Adaptive Capacity (EAC), Adaptation Incentives (AI), Maladaptation (MA), and Adaptation Decisions (AD)- via 36 carefully phrased statements. These statements were evaluated using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), allowing the quantification of farmers’ agreement with each item. To enhance clarity, relevance, and contextual suitability, the schedule was reviewed by experts from the State Agriculture Department, Krishi Vigyan Kendras, and ICAR institutes and pretested with a pilot sample of 30 farmers. 2.4 Measurement of Constructs Each of the eight constructs in the model was measured using multiple items, as shown in Table 1 . Table 1 The items included in the questionnaire Construct Operational Definition Code Items Risk evaluation (RE) The farmer's perception of the severity and impact of climate change and natural disasters on agricultural productivity, livelihood, pest outbreaks, and migration RE1 Climate change has increased the frequency and severity of natural disasters in your area over the past few years RE2 Climate change and natural disasters has increased poverty and unemployment RE3 Climate change and natural disasters have increased pests and diseases in the fields RE4 Climate change and natural disasters have reduced product yield RE5 Climate change and natural disasters have increased displacement or migration among farmers. Adaptation incentives (AI) The farmers’ perception of government support measures—such as tolerant varieties, insurance schemes, early warning systems, and pricing policies—that enhance their capacity to adapt to climate change and natural disasters AI1 Government support for providing tolerant varieties will make the farmers adapt to the climate change and natural disasters AI2 Government support for agricultural insurance will improve the farmers' capacity to adapt to the climate change and natural disasters AI3 Government provision of information and warnings at the time of climate related disasters will improve the farmers' capacity to adapt to the climate change and natural disasters AI4 Increase in electricity and water price will improve the farmers' capacity to adapt to the climate change and natural disasters Trust in national adaptation plans (TR) The farmers’ confidence in the effectiveness, reliability, and usefulness of government-led climate-resilient agricultural practices, disaster warning systems, and adaptation measures. TR1 I have full trust in climate resilient agricultural practices TR2 I perceive the disaster warning system useful TR3 I perceive that actions taken by government are appropriate and useful Social discourse (SD) the influence of media, public communication, and interpersonal interactions with friends, relatives, and neighbors on a farmer’s perception of climate change-related natural disasters and their motivation to adopt coping or adaptive strategies SD1 Climate change is increasing the frequency and intensity of natural disasters since the media and public agencies have mentioned it. SD2 My livelihood will be affected by climate change led natural disasters since my friends, relatives, and neighbors believed in it. SD3 A coping strategy should be taken since my friends, relatives, and neighbors took it. SD4 My friends, families and relatives expect me to engage in climate change-friendly behaviours. Exact adaptive capacity (EAC) a farmer’s perceived ability, in terms of knowledge, skills, and access to resources, to effectively manage and respond to the impacts of climate change-induced natural disasters EAC1 I have enough motivation and energy to deal with climate change led disasters EAC2 I have enough money and resource to apply strategies for adapting to climate change led disasters EAC3 I think I have the ability to deal with the potential dangers of climate change and natural disasters EAC4 Climate change is not such a big challenge, and human intervention can be able to cope with it Adaptation evaluation (AE) a farmer’s assessment of the applicability and effectiveness of various adaptation strategies—such as tolerant varieties, crop rotation, and income diversification—in managing the risks posed by climate change and natural disasters AE1 Using tolerant varieties is applicable and effective AE2 Crop diversity is applicable and effective AE3 Crop rotation is applicable and effective AE4 Changing timing of irrigation is applicable and effective AE5 Using short duration varieties is applicable and effective AE6 Diversifying income earning activities is applicable and effective Maladaptation (MA) a farmer’s belief that taking adaptive actions is either ineffective or unnecessary due to fatalism, religious determinism, or uncertainty about the future MA1 It is not necessary to use adaptive measures, because they will not make any difference MA2 Everything is decided by fate and are unchangeable MA3 God will protect me, my lands and my family against climate change MA4 The future is too uncertain for a person to make serious plan Adaptation decisions (AD) a farmer’s expressed willingness and intention to adopt various agricultural strategies—such as tolerant crop varieties, crop diversification, crop rotation, adjusted irrigation timing, short-duration varieties, and income diversification—in order to reduce the risks posed by climate change and natural disasters AD1 I would like to use tolerant crop varieties AD2 I would like to cultivate more than one crop to reduce risk (crop diversification) AD3 I would like to grow different types of crops sequentially (crop rotation) AD4 I would like to change timing of irrigation AD5 I would like to use short duration crop varieties AD6 I would like to diversify income earning activities 2.5 Hypothesis Testing The hypothesized relationships among the constructs were tested using Structural Equation Modeling (SEM). Figure 1 illustrates the conceptual model and Table 2 summarizes the hypotheses. Table 2 Research hypotheses regarding farmers’ adaptation decision using extended PMT model No. Hypothesis H1 Trust in national adaptation plan (TR) has positive and significant effect on farmer's Risk evaluation (RE) H2 Social discourse (SD) has positive and significant effect on farmer's Risk evaluation (RE) H3 Social discourse (SD) has positive and significant effect on farmer's Adaptation evaluation (RE) H4 Exact adaptive capacity (EAC) has positive and significant effect on farmer's Adaptation evaluation (AE) H5 Risk evaluation (RE) has negative and significant effect on farmer's maladaptation (MA) H6 Risk evaluation (RE) has positive and significant effect on farmer's Adaptation decisions (AD) H7 Adaptation evaluation (AE) has negative and significant effect on farmer's Maladaptation (MA) H8 Adaptation evaluation (AE) has positive and significant effect on farmer's Adaptation decisions (AD) H9 Adaptation incentives (AI) has positive and significant effect on farmer's Adaptation decisions (AD) H10 Maladaptation (MA) has negative and significant effect on farmer's Adaptation decisions (AD) 2.6 Data Analysis The analysis proceeded in three stages: Stage 1: Exploratory Factor Analysis (EFA) —Maximum Likelihood method with Varimax rotation was employed to examine the factor structure and assess inter-item correlations. Stage 2: Confirmatory Factor Analysis (CFA) —AMOS 20 was used to validate the measurement model. Fit indices such as CFI, GFI, AGFI, RMSEA, and normed Chi-square (χ²/df) were used to evaluate model adequacy. Construct validity was assessed using standardized factor loadings, Average Variance Extracted (AVE), and Construct Reliability (CR). Stage 3: Structural Model Estimation — SEM was performed in AMOS to test hypothesized causal paths among latent constructs. Path coefficients, regression weights, and overall model fit statistics were examined to determine the significance and strength of relationships. This rigorous multi-step approach ensured a robust estimation of the latent constructs and offered comprehensive insights into the psychological, institutional, and contextual variables shaping farmers’ adaptation behavior in the face of climate-induced challenges. 3. Results The study followed the two-stage approach for Structural Equation Modelling (Anderson & Gerbing, 1988 ). First, the model was evaluated to ensure the reliability and validity of the constructs using Cronbach’s alpha, composite reliability, average variance extracted (AVE), factor loadings, and discriminant validity through the Fornell-Larcker criterion and cross-loadings. Only after confirming the measurement model's adequacy was the structural model tested for hypothesis validation and path significance 3.1 Sampling Adequacy To determine the suitability of the dataset for factor analysis, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity were conducted. The KMO value was 0.873, indicating excellent sampling adequacy, while Bartlett’s Test was highly significant (χ² = 7289.221, df = 861, p < 0.001), suggesting that the inter-item correlations were sufficient for factor analysis. 3.2 Exploratory Factor Analysis(EFA) EFA was performed to examine the latent factor structure and to assess the data’s suitability for factor extraction (Table 3 ). The analysis revealed eight distinct factors explaining 69.2% of the total variance, with each item loading strongly on its intended construct. All factor loadings were above the acceptable cut-off of 0.50, ranging from 0.502 to 0.977, indicating satisfactory construct representation. Table 3 Factor loadings of the constructs Rotated Factors Variable Factor loading coefficients Rotated Factors Variable Factor loading coefficients Risk Evaluation (RE) RE1 RE2 RE3 RE4 RE5 0.973 0.925 0.977 0.930 0.964 Adaptation evaluation (AE) AE1 AE2 AE3 AE4 AE5 AE6 0.751 0.744 0.702 0.796 0.733 0.837 Social discourse (SD) SD1 SD2 SD3 SD4 0.728 0.805 0.832 0.788 Exact adaptive capacity (EAC) EAC1 EAC2 EAC3 EAC4 0.754 0.694 0.801 0.765 Adaptation incentives (AI) AI1 AI2 AI3 AI4 0.822 0.882 0.820 0.502 Maladaptation (MA) MA1 MA2 MA3 MA4 0.726 0.706 0.736 0.724 Trust in national adaptation plan (TR) TR1 TR2 TR3 0.790 0.932 0.782 Adaptation decision (AD) AD1 AD2 AD3 AD4 AD5 AD6 0.816 0.819 0.792 0.783 0.804 0.797 3.3 Confirmatory Factor Analysis Confirmatory Factor Analysis (CFA) was conducted using the Maximum Likelihood Estimation method to evaluate the validity and reliability of the measurement model. The model fit indices indicated an acceptable level of fit between the observed data and the hypothesized structure. Specifically, the chi-square to degrees of freedom ratio (χ²/df) was 2.86, which is within the acceptable threshold of less than 3.0 (Browne & Cudeck, 1992 ). The Root Mean Square Error of Approximation (RMSEA) was 0.08, the Root Mean Square Residual (RMR) was 0.04, and the Comparative Fit Index (CFI) was 0.86. While the CFI was slightly below the recommended value of 0.90 (Hu & Bentler, 1999 ), the other indices confirmed an overall acceptable model fit. 3.3.1 Composite Reliability The composite reliability (CR) values for all constructs (Table 4 ) exceeded the recommended threshold of 0.70 (Hair et al., 2017 ), indicating strong internal consistency of the measurement model. For instance, the CR for ‘Risk Evaluation’ was 0.992, and for ‘Adaptation Decisions’, it was 0.925. These results demonstrate that the constructs are reliably measured by their respective items. 3.3.2 Convergent Validity Convergent validity was established through the Average Variance Extracted (AVE) scores, all of which were above the threshold of 0.50. The AVE for ‘Risk Evaluation’ was particularly strong at 0.962, while other constructs like ‘Maladaptation’ and ‘Adaptation Evaluation’ had AVE scores of 0.531 and 0.584, respectively. Additionally, all constructs satisfied the condition that the Maximum Shared Variance (MSV) was lower than their respective AVE values, which further confirmed convergent validity (Fornell & Larcker, 1981 ). These values are summarised in Table 4 . Table 4 Reliability and convergent validity of the constructs and the model fitness Constructs Composite Reliability Average Variance Extracted Maximum Shared Variance Risk evaluation 0.992 0.962 0.127 Adaptation incentives 0.850 0.600 0.020 Trust in national adaptation plans 0.877 0.705 0.020 Social discourse 0.870 0.626 0.018 Exact adaptive capacity 0.843 0.574 0.061 Adaptation evaluation 0.894 0.584 0.033 Maladaptation 0.819 0.531 0.033 Adaptation decisions 0.925 0.674 0.127 Model Fitness : ϰ 2 /df = 2.86, RMSEA = 0.08, RMR = 0.04, CFI = 0.86 3.3.3 Discriminant Validity Discriminant validity was assessed using the Fornell-Larcker criterion. In Table 5 , the diagonal elements represent the square root of the AVE for each construct, while the off-diagonal elements show inter-construct correlations. The results confirmed that each construct shared more variance with its own indicators than with any other construct in the model. For instance, the square root of AVE for ‘Risk Evaluation’ (0.981) exceeded its correlation with all other constructs. Similarly, ‘Adaptation Decision’ showed a AVE of 0.821, which was higher than its correlations with constructs such as ‘Trust in National Adaptation Plans’ (0.071) or ‘Adaptation Incentives’ (0.098). These findings collectively support the discriminant validity of the measurement model (Fornell & Larcker, 1981 ; Hair et al., 2014). Table 5 Discriminant validity of the constructs AD AI TR SD EAC AE MA RE AD 0.821 AI 0.098 0.775 TR 0.071 0.140 0.840 SD -0.003 -0.136 -0.119 0.791 EAC 0.247 -0.131 0.017 -0.031 0.758 AE -0.044 0.088 0.067 0.130 0.016 0.764 MA 0.072 -0.066 -0.083 -0.006 0.077 -0.182 0.729 RE 0.356 0.009 -0.079 -0.052 -0.010 -0.121 -0.160 0.981 3.4 Structural Model Analysis The structural model was tested using AMOS path analysis to examine hypothesised causal relationships between the constructs. The model (Fig. 3) demonstrated a good overall fit, with Chi-square divided by degrees of freedom (χ²/df) = 2.173, Root Mean Square Error of Approximation (RMSEA) = 0.07, Root Mean Square Residual (RMR) = 0.017, Goodness-of-Fit Index (GFI) = 0.974, Adjusted Goodness-of-Fit Index (AGFI) = 0.923, and Comparative Fit Index (CFI) = 0.923.These indices indicate a well-fitting structural model based on established benchmarks (Hu & Bentler, 1999 ). Table 6 Regression weights and model fitness of the structural model Hypothesis Causal relationships Regression weights P Remarks H1 TR → RE − .022 .694 H1 not supported H2 SD → RE − .132 .265 H2 not supported H3 SD → AE .598 *** H3 supported H4 EAC → AE .046 .415 H4 not supported H5 RE → MA − .246 *** H5 supported H6 RE → AD .155 *** H6 supported H7 AE → MA − .621 *** H7 supported H8 AE → AD .248 *** H8 supported H9 AI → AD .007 .732 H9 not supported H10 MA → AD .065 ** H10 not supported Model fitness: ϰ 2 /df: 2.173, RMR: 0.017, GFI: 0.974, AGFI: 0.923, CFI: 0.923, RMSEA: 0.07 Note: ***<.001, **<.01, *<.05 The path coefficients provided insights into the significance and direction of the hypothesised relationships. As shown in Table 6 , Five out of ten hypothesised relationships were statistically significant (p < 0.05). Social discourse had a strong positive effect on adaptation evaluation (β = 0.598, p < .001), and adaptation evaluation, in turn, had a significant negative effect on maladaptation (β = − 0.621, p < .001). Risk evaluation was positively associated with adaptation decisions (β = 0.155, p < .001) and negatively related to maladaptation (β = − 0.246, p < .001). Furthermore, adaptation evaluation positively influenced adaptation decisions (β = 0.248, p < .001), while maladaptation also emerged as a significant positive predictor of adaptation decisions (β = 0.065, p < .01). Conversely, the effect of trust in national adaptation plans on risk evaluation (β = − 0.022, p = .694), the effect of social discourse on risk evaluation (β = − 0.132, p = .265), the relationship between exact adaptive capacity and adaptation evaluation (β = 0.046, p = .415), and the influence of adaptation incentives on adaptation decisions (β = 0.007, p = .732) were all found to be non-significant. Based on these outcomes, hypotheses H3, H5, H6, H7, and H8 were supported, while H1, H2, H4, H9, and H10 were not supported. 4. Discussion This study contributes significantly to understanding the behavioral dynamics underlying farmers’ adaptation decisions to climate change and natural disasters. It captures the cognitive, social, and institutional variables influencing how smallholder farmers evaluate climate risks and choose adaptation strategies by employing structural equation modelling (SEM). The results provide both confirmations of established theories and region-specific deviations that illuminate the complex socio-psychological reality of agrarian adaptation. The eight hypothesized constructs—risk evaluation, adaptation evaluation, social discourse, maladaptation, adaptation incentives, trust in national adaptation plans, exact adaptive capacity, and adaptation decision—were modelled to capture the complex interplay between perception, belief systems, and behavioral intention in one of India’s most exposed agro-ecological zones. The strongest and most significant path in the model was from social discourse to adaptation evaluation (β = 0.598, p < .001), emphasizing the vital role that peer influence, family expectations, and community interaction play in shaping how farmers evaluate the usefulness and feasibility of adaptation strategies. Farmers in Odisha, especially those in the coastal districts of Puri and Khordah, often rely heavily on informal channels for information—family discussions, neighbors' experiences, and community observations—when making decisions related to climate resilience. This aligns with previous studies by Grothmann and Patt ( 2005 ) and Dang et al. (2014), who noted that social pressure or communal consensus was a major influence in shaping risk behavior and the adoption of adaptive strategies. In this context, collective experiences serve as both a validation mechanism and a motivator for behavioral change. The fact that social discourse significantly influences adaptation evaluation implies that leveraging community-based education and peer demonstrations could effectively reinforce perception about the value of adaptation interventions. The second strongest relationship in the model was the negative association between adaptation evaluation and maladaptation (β = − 0.621, p < .001). This inverse relationship indicates that farmers who evaluate adaptation strategies as effective are less likely to hold maladaptive beliefs such as fatalism, denial, or the perception that adaptation measures are futile. This finding aligns with previous work by Grothmann and Patt ( 2005 ); Grothmann and Reusswig ( 2006 ); and Mitter et al. ( 2019 ), who argued that positive assessments of adaptation options could counteract maladaptive responses. It further implies that transparent and contextual evidence about the success of adaptive practices—like changing irrigation schedules or using short-duration crop varieties—can help dismantle fatalistic mindsets that perceive climate impacts as uncontrollable or preordained. Another significant finding is the positive influence of adaptation evaluation on adaptation decision (β = 0.248, p < .001), suggesting that when farmers believe that certain strategies (e.g., crop diversification, rotation, or stress-tolerant seeds) are effective and applicable, they are more likely to translate these evaluations into behavioral intentions. This is consistent with previous research by Dang et al. (2014), Mitter et al. ( 2019 ), and Azhari et al. ( 2025 ), who emphasized that perceived efficacy is a key predictor of climate action. In our study, farmers who positively assessed the utility of adaptation measures were more inclined to adopt them, affirming the theoretical pathway between perceived outcome and behavior as proposed in Protection Motivation Theory (Grothmann & Patt, 2005 ). This also suggests that field demonstrations and participatory trials could be an essential mechanism to influence farmers’ perceptions positively. Risk evaluation also significantly influenced adaptation decisions (β = 0.155, p < .001), indicating that farmers who perceive higher levels of threat—such as damage to livelihood, loss of assets, and threats to health—are more likely to take proactive steps to adapt. These results mirror those of Dang et al. (2014), and Ghazali et al. ( 2021 ), who found that heightened risk perception increases the likelihood of adaptation. Farmers in Odisha are particularly sensitive to climate risks due to their lived experiences with repeated extreme events such as cyclones Fani (2018), Amphan (2020), and Yaas (2021). Thus, personal exposure and memory of loss serve as a cognitive trigger that motivates action. These findings reaffirm the central role of risk appraisal in adaptation frameworks, particularly in high-impact zones like coastal Odisha. Additionally, risk evaluation had a significant negative effect on maladaptation (β = − 0.246, p < .001), implying that higher perceived risks of climate change discourage fatalism and passivity. This supports earlier conclusions by Grothmann and Reusswig ( 2006 ) and Mitter et al. ( 2019 ), who argued that awareness of danger can override feelings of helplessness. However, these results also contrast with earlier research by Milne et al. (2000) and Grothmann and Patt ( 2005 ), who reported that in some contexts, high threat perception could lead to denial or defensive avoidance, especially when coping resources are perceived as insufficient. The divergence observed in our study could be due to improved climate communication efforts in Odisha, where increasing institutional efforts and repeated experience of disaster have possibly shifted farmers from denial to action. Interestingly, the path from maladaptation to adaptation decision was weak but positive (β = 0.065, p < .01), which appears paradoxical. This may reflect a form of cognitive dissonance (Festinger, 1957 ), wherein farmers who deny climate change risk or feel that outcomes are preordained still take action to protect their livelihoods. Such behavior may be influenced by external pressures or habits, or it may represent a coping strategy to reduce psychological discomfort. Another explanation could be confirmation bias: while initially dismissive of climate change, farmers may encounter new information that encourages action, even as their beliefs remain unchanged. While this result does not support our original hypothesis, it adds a layer of complexity and suggests that behavioral intentions can sometimes occur independently of stated beliefs, especially under survival pressures. Among the non-significant paths, trust in national adaptation plans did not significantly influence risk evaluation (β = − 0.022, ns). This reflects a disconnect between top-down institutional messaging and farmers’ ground-level realities. In Odisha, despite the presence of government schemes like crop insurance (PMFBY), the accessibility and perceived relevance of such plans remain limited. This distrust may stem from bureaucratic hurdles, low transparency, or poor on-ground implementation, as highlighted by Sharma et al. ( 2024 ). Therefore, trust in adaptation policies needs to be rebuilt through participatory approaches and localized implementation. Likewise, social discourse did not influence risk evaluation (β = − 0.132, ns), suggesting that perceptions of climate risk are primarily shaped by personal experience rather than peer discussion. While community conversations are pivotal in evaluating solutions, they appear to have limited influence on farmers' recognition of climate threats. This is particularly true in high-risk zones like Odisha, where farmers are constantly exposed to real and visible impacts, making direct observation a more powerful force than hearsay. The path from exact adaptive capacity to adaptation evaluation was also non-significant (β = 0.046, ns). This could be because most of the adaptation practices evaluated—such as crop rotation, timing adjustment, or diversification—are relatively low-cost and do not require high skill or investment. Similar observations were made by van Duinen et al. (2015), who argued that when adaptation is simple and widely known, perceived capacity becomes less relevant in determining behavior. Finally, adaptation incentives did not significantly affect adaptation decisions (β = 0.007, ns). This finding stands in contrast to previous studies (e.g., Grothmann & Patt, 2005 ; Ghazali et al., 2021 ), which reported that institutional support positively shaped adaptation. In the Odisha context, poor infrastructure, administrative complexity, and ambiguous eligibility criteria may be discouraging smallholders from availing these schemes. For example, the cumbersome process of claiming benefits under crop insurance schemes or the lack of timely dissemination of subsidies can erode trust and reduce usage, despite their theoretical benefits. Overall, this study highlights the need to tailor adaptation strategies that go beyond information dissemination and incentives. Farmers in vulnerable zones like coastal Odisha are more influenced by how their peers evaluate interventions and how relevant these interventions appear in their lived context. Trust in institutions, individual capacity, and incentives only work when coupled with direct experience and community-driven validation. The findings validate and extend the propositions of Grothmann and Patt ( 2005 ) and other adaptation scholars, while also introducing cognitive dissonance and confirmation bias as possible explanatory mechanisms for seemingly contradictory behavioral outcomes. 4.1 Practical Implications The practical implications of this study are highly relevant for climate policy makers, development practitioners, and local extension agencies working in disaster-prone agrarian settings like coastal Odisha. 1. Shift from Risk Awareness to Evaluation Efficacy: While raising risk perception remains important, this study demonstrates that evaluation of adaptation strategies plays a far more crucial role in motivating farmers. Hence, interventions should not just highlight climate threats, but actively demonstrate the effectiveness, feasibility, and outcomes of adaptive practices through localized field demonstrations, testimonial videos, and farmer-led knowledge exchanges. 2. Leverage Peer Influence and Social Norms: Given the strong influence of social discourse on adaptation evaluation, programs should capitalize on community-based approaches. Training progressive farmers as “climate champions,” using group-based learning models (like Farmer Field Schools), and facilitating peer-to-peer knowledge sharing will likely increase credibility and adoption rates. 3. Reassess the Use of Financial Incentives in Isolation: The finding that adaptation incentives did not significantly affect adaptation decisions suggests that financial schemes alone (e.g., subsidies, insurance, input support) are not sufficient. Implementation agencies must simplify access, reduce bureaucratic hurdles, and bundle incentives with training, follow-up support, and local relevance. Otherwise, even well-funded programs will suffer from under-utilization. 4. Address Maladaptive Beliefs with Caution: The positive association between maladaptation and adaptation intention implies that fatalism and denial do not always translate into inaction. This nuance requires a psychologically informed communication strategy. Instead of confronting religious or fatalistic beliefs directly, interventions should build on existing cultural narratives and gently redirect fatalism towards action-oriented hope, such as community prayers combined with flood preparedness. 5. Build Trust Through Decentralized Action: The non-significance of trust in national adaptation plans indicates a disconnect between top-down schemes and grassroots realities. Strengthening decentralized governance, empowering Panchayati Raj institutions, and ensuring visible and timely delivery of adaptation services can help restore confidence. Involving farmers in the co-design of adaptation programs, especially in early warning dissemination and resource allocation, would further enhance local ownership. 6. Tailor Adaptation Support to Simple Practices First: Since exact adaptive capacity was not a significant predictor of adaptation evaluation, and most farmers reported using low-cost, low-skill practices like changing irrigation timing or using short-duration varieties, support services should prioritize scaling up these simple strategies before introducing complex innovations. This gradualist approach respects both existing capacity levels and cultural preferences. 7. Region-Specific Planning and Resource Allocation: The findings reaffirm the need for place-based adaptation strategies. Coastal Odisha’s exposure to cyclones, storm surges, and flooding warrants robust local planning, such as investment in climate-resilient infrastructure, community seed banks, weather-indexed insurance, and mobile-based advisory services. These should be implemented in tandem with social empowerment initiatives, recognizing that adaptation is both a technical and social process. 8. Monitoring Psychological Shifts in Climate Perception: Programs should incorporate periodic measurement of farmers’ attitudes and beliefs, especially regarding fatalism, trust, and peer influence. Such psychological monitoring can provide early indicators of intervention success or stagnation and allow for course correction in messaging, methods, and delivery. This study not only provides evidence-based recommendations for more culturally sensitive, community-anchored, and cognitively attuned adaptation strategies, but also challenges one-size-fits-all approaches that ignore the socio-psychological diversity among smallholder farmers. By drawing on real-world behavior rather than abstract rationality, these implications offer a roadmap for grounded and impactful climate adaptation policies in India and similar vulnerable regions. 5. Conclusion Climate change remains a deeply entrenched challenge that continues to compromise the livelihood security of smallholder farmers, especially in fragile ecosystems such as the coastal districts of Odisha. Given the recurrent exposure to extreme climate events—cyclones, floods, and erratic rainfall—it becomes essential not just to understand the farmers’ behavioral responses, but also the layered psychosocial and institutional contexts that shape their adaptation decisions. This study affirms the idea that climate adaptation is not only a technical issue but an adaptive process requiring social learning, trust-building, and behavioral transformation. In Odisha’s coastal farming systems, the farmers’ perceptions of risk, their belief in the utility of adaptation strategies, and their ability to overcome maladaptive thinking form the triad that shapes their resilience pathway. The findings strongly signal that adaptation behavior is underpinned by how people evaluate the immediacy of risk and the feasibility of solutions—factors that are shaped by both subjective belief systems and systemic support mechanisms. This emphasizes the need for policies that prioritize behavioral insights alongside infrastructural and technological interventions. Future adaptation policies in vulnerable regions like Odisha should thus focus on psychological readiness, credible risk communication, and capacity-building at the grassroots. Strengthening social networks, trust in institutional adaptation plans, and farmer-to-farmer learning can create conditions for sustained adaptation. Moreover, region-specific strategies that integrate scientific guidance with local knowledge—tailored to the climatic realities of Odisha—are imperative for transitioning from individual resilience to systemic climate robustness across farming communities. Declarations Author contribution Conceptualisation: SKG, RNP, RRB and SNK. Data collection: SKG. Statistical analysis: SKG, MY, AS, SWQ and AL. Writing- original draft preparation: SKG, AS, BG and BB. Writing- review and editing: RNP, RRB, SR, PP, SS, TGC and SM. All authors provided their valuable insights on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The author(s) declare that no financial support was received for the research and/or publication of this article. Clinical trial number Not applicable Ethical Approval and Accordance Ethical approval for this study was granted by the Division of Agricultural Extension, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, which serves as the authorized Ethics Committee for social science research involving human participants within the institute. The study was conducted in strict accordance with ICAR and IARI ethical standards and the 1964 Helsinki Declaration (Approval Reference: File No. Ag.Extn/2023/339). Consent to Participate Verbal informed consent was obtained from all participants prior to data collection. Participants were briefed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without any negative consequences. No identifying personal information (such as names, addresses, or contact details) was collected, ensuring complete confidentiality and anonymity of the responses. Consent to Publish Not applicable Conflict of interest The authors declare no competing interests. Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Generative AI statement The author(s) declare that Generative AI was used in the creation of this manuscript. The authors confirm that generative tools were used only to enhance the clarity and readability of certain sections of this manuscript, including language refinement and structural suggestions. All research design, data collection, analysis, interpretation, and core intellectual content remain solely the responsibility of the authors. The final manuscript reflects the authors’ original work, with AI-assisted edits carefully reviewed for accuracy and adherence to scientific integrity. References Ajzen I. The theory of planned behavior. 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16:41:52","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165491,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8542601/v1/99ef2d6112524f6a4942ecae.html"},{"id":100910096,"identity":"787b9a42-e88e-426c-89d7-e850e56f241d","added_by":"auto","created_at":"2026-01-22 16:41:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216222,"visible":true,"origin":"","legend":"\u003cp\u003eThe extended PMT model to explain determinants of farmer's adaptation decision under climate change and natural disasters\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8542601/v1/5ff0d96f14ed8c0a7a3c5fbf.jpeg"},{"id":100910081,"identity":"88148115-520e-464e-9da2-cd1abaaa33c1","added_by":"auto","created_at":"2026-01-22 16:41:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60707,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area (Puri and Khordah district of Odisha)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8542601/v1/a8dc2344faaede9a79c0d266.png"},{"id":100910104,"identity":"a56aa734-9a00-4bf1-8699-f588e72a1668","added_by":"auto","created_at":"2026-01-22 16:41:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97409,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model for farmer’s adaptation decision\u003c/p\u003e\n\u003cp\u003eNote: ***\u0026lt;.001, **\u0026lt;.01, *\u0026lt;.05\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8542601/v1/5b434c1dd85138ecb364cb3e.png"},{"id":100950241,"identity":"01759058-5d71-4c51-bcc2-868c2c5f76ec","added_by":"auto","created_at":"2026-01-23 07:07:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8542601/v1/29289d1f-e2e7-4e4b-95ed-020240b02750.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioral Drivers of Farmers’ Adaptation and Maladaptation to Natural Disasters in Coastal Odisha, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe intensification of climate change over the past two decades has emerged as one of the most pressing challenges for global agriculture. Climate change characterized by increasing temperatures, erratic rainfall patterns, and frequent natural disasters such as floods, droughts, cyclones, and heatwaves; continues to threaten both food security and rural livelihoods, especially in tropical and subtropical regions (UNFCCC, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; IPCC, 2013; World Economic Forum, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The impacts on agricultural production are significant; estimates suggest a reduction of crop yields by 1\u0026ndash;5% per decade in the absence of adaptation interventions (Porter et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; O'neill et al., 2017). For developing countries like India, where agriculture forms the economic backbone for millions, the challenge is even more acute.\u003c/p\u003e \u003cp\u003eThe state of Odisha, situated along India\u0026rsquo;s eastern coastline, is particularly vulnerable to climate-induced natural hazards. It has historically experienced high exposure to cyclonic storms, floods, and droughts. Out of India\u0026rsquo;s 30 most climate-vulnerable districts, several are located in Odisha, especially along its coastal belt (Bahinipati, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). According to IMD data, the frequency of cyclones making landfall in Odisha has risen sharply in recent decades, with events like Cyclones Phailin (2013), Fani (2019), Amphan (2020), and Yaas (2021) causing severe damage to crops and rural infrastructure (Hazra, et al.,2022). Cyclones during the monsoon and post-monsoon periods, which coincide with the Kharif crop season, have particularly devastating effects on rice and other rain-fed crops. Inland regions of Odisha, on the other hand, suffer predominantly from recurring droughts and extreme heat, further accentuating the state\u0026rsquo;s climate fragility. Climate change-induced shocks in Odisha adversely affect not only agricultural yields but also the socio-economic fabric of farming communities. Over 70% of Odisha\u0026rsquo;s population depends on agriculture for their livelihood, and a vast majority of them are small and marginal farmers with limited adaptive capacity (Panda, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Fragmented landholdings, lack of access to irrigation, weak market linkages, and limited social safety nets further exacerbate their vulnerability (Aryal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Odisha is classified as one of the most vulnerable Indian states, given its fragile ecosystems, high climate exposure, and socio-economic constraints (Mishra \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address these challenges, farmers have gradually adopted various adaptation strategies, including changing sowing dates, introducing drought- or flood-tolerant crop varieties, diversifying income sources, using improved irrigation practices, and accessing crop insurance (Habiba et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Udmale et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Aryal et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the extent and effectiveness of these adaptations vary significantly across regions and households. This heterogeneity is often influenced by the socio-economic status, asset base, institutional support, and access to information among farming households (Sarker et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Alam, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite growing interest in climate change adaptation, most previous studies have predominantly relied on econometric models such as regression, logit, or multinomial logit analyses to identify socio-economic determinants of adaptation (e.g., landholding size, income, education, and access to credit). While these approaches provide important insights into observable characteristics, they often neglect the cognitive and psychological processes that shape adaptation decisions at the individual and community level (Grothmann \u0026amp; Patt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lopez-Marrero, 2010; Osberghaus et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Psychological factors, including perceptions of climate risk, beliefs about the efficacy of adaptation strategies, and motivations to act, are increasingly recognized as critical to understanding adaptive behavior. Protection Motivation Theory (PMT) offers a robust framework for analyzing these psychological constructs. Originally developed in health psychology, PMT explains how individuals appraise threats and assess coping options, which in turn influence protective behaviors. In the context of climate change, PMT has been adapted to examine how farmers perceive climatic threats, evaluate adaptation responses, and decide on adaptive actions (Grothmann \u0026amp; Reusswig, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, very few studies in the Indian context have incorporated PMT or similar psychological models into empirical research. Even fewer have focused on regions like coastal Odisha, which experience compound vulnerabilities due to both socio-economic and climatic stressors. Moreover, concepts such as maladaptation (adoption of measures that inadvertently increase vulnerability) and social discourse (the role of community discussion, peer learning, and information exchange) remain underexplored in existing literature, despite their growing relevance (Clayton et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chanie et al., 2018). Against this backdrop, the current study seeks to fill this critical gap by applying an extended Protection Motivation Theory framework to examine the determinants of farmers\u0026rsquo; adaptation decisions in the coastal districts of Puri and Khordha in Odisha. By integrating psychological and social dimensions with traditional socio-economic factors, this research offers a more holistic understanding of adaptation behavior in vulnerable agrarian contexts. It highlights the need for climate change policies and programs that move beyond asset-based vulnerability assessments to also address the cognitive, motivational, and informational needs of farmers.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Framework\u003c/h2\u003e \u003cp\u003eA number of behavioral theories have been employed to explain environmental decision-making, especially in the context of climate change. Among the most commonly used are the Theory of Planned Behavior (TPB) (Ajzen, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and the Value-Belief-Norm (VBN) Theory (Stern, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). TPB posits that behavior is shaped by intentions, which in turn are influenced by attitudes, subjective norms, and perceived behavioral control. VBN theory, meanwhile, emphasizes the role of individuals\u0026rsquo; values, ecological beliefs, and moral norms in shaping pro-environmental behaviors. However, both frameworks fall short when it comes to predicting behavior under conditions of uncertainty and climatic risk (Le Dang et al., 2014), a key feature of adaptation decision-making in disaster-prone agrarian settings. To address this gap, this study is grounded in the Protection Motivation Theory (PMT) proposed by Rogers (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), which explains how individuals perceive and respond to threats based on two core cognitive processes: risk appraisal (perceived severity and vulnerability) and coping appraisal (self-efficacy, response efficacy, and response cost). A growing body of literature has supported the applicability of PMT in climate change adaptation contexts. For instance, Le Dang et al. (2014) used SEM to examine the adaptation intentions of 598 rice farmers in Vietnam and found that risk perception and belief in adaptive strategy effectiveness positively influenced adaptation decisions, while maladaptive responses such as fatalism and denial hindered them. Van Duinen et al. (2015) applied PMT to explore drought adaptation behavior in the Netherlands and found it superior to TPB in accounting for farmers' behavioral responses to climate threats, explaining about 43% of the variance. Lam (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that self-efficacy, response efficacy, and perceived benefit significantly predicted public support for climate policies in Taiwan using a PMT framework. Similarly, Keshavarz and Karami (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) employed partial least squares modeling and concluded that pro-environmental behavior among farmers in drought-hit regions of Iran was influenced by response efficacy, perceived severity, perceived vulnerability, response costs, and social environment. Delfiyan et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also used SEM in Southwestern Iran and found that PMT constructs such as perceived vulnerability, response cost, and response efficacy together explained nearly 50% of the variation in adaptation behavior. Ghanian et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) extended PMT to include eight constructs and demonstrated that risk perception, belief in climate change, and disincentives significantly explained 57% of the variance in adaptation intentions. Neisi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) applied PMT to evaluate farmers\u0026rsquo; drought management in the Karkheh Dam Basin and found that self-efficacy had the strongest influence among the six core PMT constructs, which together explained 47.3% of behavioral variance. Pakmehr et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also reported that PMT core variables accounted for 39% of the variance in drought adaptation in Shushtar, Iran, and observed an 11% improvement in explanatory power with the inclusion of collective efficacy. These studies suggest that PMT not only provides a robust explanation of climate risk behavior but also benefits from extensions that include socio-environmental variables. As proposed by Grothmann and Patt (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), this study incorporates additional constructs such as maladaptation\u0026mdash;defined as behavior that undermines long-term adaptation when risk is high but coping appraisal is low\u0026mdash;social discourse (e.g., peer and community influence), trust in national adaptation plans, exact adaptive capacity (actual ability to cope), and adaptation incentives (e.g., subsidies or support schemes). Finally, research by Warnatzsch and Reay (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) confirms that maladaptive behavior can suppress the likelihood of positive adaptation. Thus, the theoretical framework for this study is built upon the original PMT by integrating cognitive, psychological, and contextual factors to explain farmers\u0026rsquo; adaptation decision-making in the face of climate-induced natural disasters as given in figure no 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Survey Area, Sampling and Data Collection\u003c/h2\u003e \u003cp\u003eThe study was conducted in Odisha, a state on the eastern coast of India, widely recognized for its high vulnerability to climate-induced hazards. According to the Council on Energy, Environment and Water (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), 26 districts in Odisha are highly susceptible to extreme climate events, including cyclones, floods, and droughts. The frequency of cyclones in the state has tripled since 1970, while the incidence of storm surges has also witnessed a threefold increase over the same period (Open Government Data Platform India, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Annually, more than 12.6\u0026nbsp;million people in Odisha are affected by severe flood events (Council on Energy, Environment and Water, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Severe cyclonic storms have increasingly battered the state in recent years\u0026mdash;Cyclone Aila (2009), Phailin (2013), Fani and Bulbul (2018), Amphan (2020), and Yaas and Jawad (2021)\u0026mdash;causing extensive damage to agriculture, infrastructure, and livelihoods, particularly in the coastal districts.\u003c/p\u003e \u003cp\u003eIn light of this recurring vulnerability, Puri and Khordha districts were purposively selected for the study as both regions are frequently affected by cyclones and floods. Within each district, two blocks were chosen, and from each block, two villages were randomly selected, making a total of eight villages. From each village, 30 farmers who had experienced the adverse impacts of cyclones and floods were randomly selected, resulting in a total sample of 240 respondents. Data were collected through personal interviews using a structured interview schedule. The interviews were conducted during March and April 2023, with the assistance of trained enumerators to ensure data accuracy, cultural sensitivity, and clarity of responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research Instrument\u003c/h2\u003e \u003cp\u003eThe study utilized a structured interview schedule developed to examine farmers\u0026rsquo; climate change adaptation behavior in vulnerable coastal districts of Odisha. The instrument consisted of two parts: a demographic section and a scale-based section to measure the latent constructs of the conceptual model. The demographic section collected essential information such as age, gender, education level, landholding size, income source, and farming experience.\u003c/p\u003e \u003cp\u003eThe main section operationalized eight theoretical constructs- Risk Evaluation (RE), Adaptation Evaluation (AE), Trust in National Adaptation Plans (TR), Social Discourse (SD), Exact Adaptive Capacity (EAC), Adaptation Incentives (AI), Maladaptation (MA), and Adaptation Decisions (AD)- via 36 carefully phrased statements. These statements were evaluated using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), allowing the quantification of farmers\u0026rsquo; agreement with each item. To enhance clarity, relevance, and contextual suitability, the schedule was reviewed by experts from the State Agriculture Department, Krishi Vigyan Kendras, and ICAR institutes and pretested with a pilot sample of 30 farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Measurement of Constructs\u003c/h2\u003e \u003cp\u003eEach of the eight constructs in the model was measured using multiple items, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eThe items included in the questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperational Definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRisk evaluation (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eThe farmer's perception of the severity and impact of climate change and natural disasters on agricultural productivity, livelihood, pest outbreaks, and migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change has increased the frequency and severity of natural disasters in your area over the past few years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change and natural disasters has increased poverty and unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change and natural disasters have increased pests and diseases in the fields\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change and natural disasters have reduced product yield\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change and natural disasters have increased displacement or migration among farmers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAdaptation incentives\u003c/p\u003e \u003cp\u003e(AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eThe farmers\u0026rsquo; perception of government support measures\u0026mdash;such as tolerant varieties, insurance schemes, early warning systems, and pricing policies\u0026mdash;that enhance their capacity to adapt to climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovernment support for providing tolerant varieties will make the farmers adapt to the climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovernment support for agricultural insurance will improve the farmers' capacity to adapt to the climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGovernment provision of information and warnings at the time of climate related disasters will improve the farmers' capacity to adapt to the climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncrease in electricity and water price will improve the farmers' capacity to adapt to the climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTrust in national adaptation plans\u003c/p\u003e \u003cp\u003e(TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eThe farmers\u0026rsquo; confidence in the effectiveness, reliability, and usefulness of government-led climate-resilient agricultural practices, disaster warning systems, and adaptation measures.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI have full trust in climate resilient agricultural practices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI perceive the disaster warning system useful\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI perceive that actions taken by government are appropriate and useful\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSocial discourse\u003c/p\u003e \u003cp\u003e(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ethe influence of media, public communication, and interpersonal interactions with friends, relatives, and neighbors on a farmer\u0026rsquo;s perception of climate change-related natural disasters and their motivation to adopt coping or adaptive strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change is increasing the frequency and intensity of natural disasters since the media and public agencies have mentioned it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy livelihood will be affected by climate change led natural disasters since my friends, relatives, and neighbors believed in it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA coping strategy should be taken since my friends, relatives, and neighbors took it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMy friends, families and relatives expect me to engage in climate change-friendly behaviours.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eExact adaptive capacity\u003c/p\u003e \u003cp\u003e(EAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ea farmer\u0026rsquo;s perceived ability, in terms of knowledge, skills, and access to resources, to effectively manage and respond to the impacts of climate change-induced natural disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI have enough motivation and energy to deal with climate change led disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI have enough money and resource to apply strategies for adapting to climate change led disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI think I have the ability to deal with the potential dangers of climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate change is not such a big challenge, and human intervention can be able to cope with it\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAdaptation evaluation\u003c/p\u003e \u003cp\u003e(AE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ea farmer\u0026rsquo;s assessment of the applicability and effectiveness of various adaptation strategies\u0026mdash;such as tolerant varieties, crop rotation, and income diversification\u0026mdash;in managing the risks posed by climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing tolerant varieties is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrop diversity is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrop rotation is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChanging timing of irrigation is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing short duration varieties is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiversifying income earning activities is applicable and effective\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMaladaptation\u003c/p\u003e \u003cp\u003e(MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ea farmer\u0026rsquo;s belief that taking adaptive actions is either ineffective or unnecessary due to fatalism, religious determinism, or uncertainty about the future\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIt is not necessary to use adaptive measures, because they will not make any difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEverything is decided by fate and are unchangeable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGod will protect me, my lands and my family against climate change\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe future is too uncertain for a person to make serious plan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAdaptation decisions\u003c/p\u003e \u003cp\u003e(AD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ea farmer\u0026rsquo;s expressed willingness and intention to adopt various agricultural strategies\u0026mdash;such as tolerant crop varieties, crop diversification, crop rotation, adjusted irrigation timing, short-duration varieties, and income diversification\u0026mdash;in order to reduce the risks posed by climate change and natural disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to use tolerant crop varieties\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to cultivate more than one crop to reduce risk (crop diversification)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to grow different types of crops sequentially (crop rotation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to change timing of irrigation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to use short duration crop varieties\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI would like to diversify income earning activities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Hypothesis Testing\u003c/h2\u003e \u003cp\u003eThe hypothesized relationships among the constructs were tested using Structural Equation Modeling (SEM). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the conceptual model and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the hypotheses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eResearch hypotheses regarding farmers\u0026rsquo; adaptation decision using extended PMT model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust in national adaptation plan (TR) has positive and significant effect on farmer's Risk evaluation (RE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial discourse (SD) has positive and significant effect on farmer's Risk evaluation (RE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial discourse (SD) has positive and significant effect on farmer's Adaptation evaluation (RE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExact adaptive capacity (EAC) has positive and significant effect on farmer's Adaptation evaluation (AE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk evaluation (RE) has negative and significant effect on farmer's maladaptation (MA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk evaluation (RE) has positive and significant effect on farmer's Adaptation decisions (AD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaptation evaluation (AE) has negative and significant effect on farmer's Maladaptation (MA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaptation evaluation (AE) has positive and significant effect on farmer's Adaptation decisions (AD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdaptation incentives (AI) has positive and significant effect on farmer's Adaptation decisions (AD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaladaptation (MA) has negative and significant effect on farmer's Adaptation decisions (AD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data Analysis\u003c/h2\u003e \u003cp\u003eThe analysis proceeded in three stages:\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 1: Exploratory Factor Analysis (EFA)\u003c/b\u003e \u0026mdash;Maximum Likelihood method with Varimax rotation was employed to examine the factor structure and assess inter-item correlations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 2: Confirmatory Factor Analysis (CFA)\u003c/b\u003e \u0026mdash;AMOS 20 was used to validate the measurement model. Fit indices such as CFI, GFI, AGFI, RMSEA, and normed Chi-square (χ\u0026sup2;/df) were used to evaluate model adequacy. Construct validity was assessed using standardized factor loadings, Average Variance Extracted (AVE), and Construct Reliability (CR).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStage 3: Structural Model Estimation\u003c/b\u003e \u0026mdash; SEM was performed in AMOS to test hypothesized causal paths among latent constructs. Path coefficients, regression weights, and overall model fit statistics were examined to determine the significance and strength of relationships. This rigorous multi-step approach ensured a robust estimation of the latent constructs and offered comprehensive insights into the psychological, institutional, and contextual variables shaping farmers\u0026rsquo; adaptation behavior in the face of climate-induced challenges.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe study followed the two-stage approach for Structural Equation Modelling (Anderson \u0026amp; Gerbing, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). First, the model was evaluated to ensure the reliability and validity of the constructs using Cronbach\u0026rsquo;s alpha, composite reliability, average variance extracted (AVE), factor loadings, and discriminant validity through the Fornell-Larcker criterion and cross-loadings. Only after confirming the measurement model's adequacy was the structural model tested for hypothesis validation and path significance\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sampling Adequacy\u003c/h2\u003e \u003cp\u003eTo determine the suitability of the dataset for factor analysis, the Kaiser-Meyer-Olkin (KMO) measure and Bartlett\u0026rsquo;s Test of Sphericity were conducted. The KMO value was 0.873, indicating excellent sampling adequacy, while Bartlett\u0026rsquo;s Test was highly significant (χ\u0026sup2; = 7289.221, df\u0026thinsp;=\u0026thinsp;861, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the inter-item correlations were sufficient for factor analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.2 Exploratory Factor Analysis(EFA)\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eEFA was performed to examine the latent factor structure and to assess the data\u0026rsquo;s suitability for factor extraction (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis revealed eight distinct factors explaining 69.2% of the total variance, with each item loading strongly on its intended construct. All factor loadings were above the acceptable cut-off of 0.50, ranging from 0.502 to 0.977, indicating satisfactory construct representation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loadings of the constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRotated Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor loading coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRotated Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor loading coefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Evaluation (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRE1\u003c/p\u003e \u003cp\u003eRE2\u003c/p\u003e \u003cp\u003eRE3\u003c/p\u003e \u003cp\u003eRE4\u003c/p\u003e \u003cp\u003eRE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003cp\u003e0.925\u003c/p\u003e \u003cp\u003e0.977\u003c/p\u003e \u003cp\u003e0.930\u003c/p\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdaptation evaluation (AE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAE1\u003c/p\u003e \u003cp\u003eAE2\u003c/p\u003e \u003cp\u003eAE3\u003c/p\u003e \u003cp\u003eAE4\u003c/p\u003e \u003cp\u003eAE5\u003c/p\u003e \u003cp\u003eAE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003cp\u003e0.744\u003c/p\u003e \u003cp\u003e0.702\u003c/p\u003e \u003cp\u003e0.796\u003c/p\u003e \u003cp\u003e0.733\u003c/p\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial discourse (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD1\u003c/p\u003e \u003cp\u003eSD2\u003c/p\u003e \u003cp\u003eSD3\u003c/p\u003e \u003cp\u003eSD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003cp\u003e0.805\u003c/p\u003e \u003cp\u003e0.832\u003c/p\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExact adaptive capacity (EAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEAC1\u003c/p\u003e \u003cp\u003eEAC2\u003c/p\u003e \u003cp\u003eEAC3\u003c/p\u003e \u003cp\u003eEAC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003cp\u003e0.694\u003c/p\u003e \u003cp\u003e0.801\u003c/p\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation incentives (AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI1\u003c/p\u003e \u003cp\u003eAI2\u003c/p\u003e \u003cp\u003eAI3\u003c/p\u003e \u003cp\u003eAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003cp\u003e0.882\u003c/p\u003e \u003cp\u003e0.820\u003c/p\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaladaptation (MA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMA1\u003c/p\u003e \u003cp\u003eMA2\u003c/p\u003e \u003cp\u003eMA3\u003c/p\u003e \u003cp\u003eMA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003cp\u003e0.706\u003c/p\u003e \u003cp\u003e0.736\u003c/p\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in national adaptation plan (TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u003c/p\u003e \u003cp\u003eTR2\u003c/p\u003e \u003cp\u003eTR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003cp\u003e0.932\u003c/p\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdaptation decision (AD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAD1\u003c/p\u003e \u003cp\u003eAD2\u003c/p\u003e \u003cp\u003eAD3\u003c/p\u003e \u003cp\u003eAD4\u003c/p\u003e \u003cp\u003eAD5\u003c/p\u003e \u003cp\u003eAD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003cp\u003e0.819\u003c/p\u003e \u003cp\u003e0.792\u003c/p\u003e \u003cp\u003e0.783\u003c/p\u003e \u003cp\u003e0.804\u003c/p\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 Confirmatory Factor Analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eConfirmatory Factor Analysis (CFA) was conducted using the Maximum Likelihood Estimation method to evaluate the validity and reliability of the measurement model. The model fit indices indicated an acceptable level of fit between the observed data and the hypothesized structure. Specifically, the chi-square to degrees of freedom ratio (χ\u0026sup2;/df) was 2.86, which is within the acceptable threshold of less than 3.0 (Browne \u0026amp; Cudeck, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The Root Mean Square Error of Approximation (RMSEA) was 0.08, the Root Mean Square Residual (RMR) was 0.04, and the Comparative Fit Index (CFI) was 0.86. While the CFI was slightly below the recommended value of 0.90 (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the other indices confirmed an overall acceptable model fit.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Composite Reliability\u003c/h2\u003e \u003cp\u003eThe composite reliability (CR) values for all constructs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) exceeded the recommended threshold of 0.70 (Hair et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), indicating strong internal consistency of the measurement model. For instance, the CR for \u0026lsquo;Risk Evaluation\u0026rsquo; was 0.992, and for \u0026lsquo;Adaptation Decisions\u0026rsquo;, it was 0.925. These results demonstrate that the constructs are reliably measured by their respective items.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Convergent Validity\u003c/h2\u003e \u003cp\u003eConvergent validity was established through the Average Variance Extracted (AVE) scores, all of which were above the threshold of 0.50. The AVE for \u0026lsquo;Risk Evaluation\u0026rsquo; was particularly strong at 0.962, while other constructs like \u0026lsquo;Maladaptation\u0026rsquo; and \u0026lsquo;Adaptation Evaluation\u0026rsquo; had AVE scores of 0.531 and 0.584, respectively. Additionally, all constructs satisfied the condition that the Maximum Shared Variance (MSV) was lower than their respective AVE values, which further confirmed convergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). These values are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and convergent validity of the constructs and the model fitness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Variance Extracted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum Shared Variance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation incentives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrust in national adaptation plans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial discourse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExact adaptive capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaladaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptation decisions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eModel Fitness\u003c/b\u003e: ϰ\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;2.86, RMSEA\u0026thinsp;=\u0026thinsp;0.08, RMR\u0026thinsp;=\u0026thinsp;0.04, CFI\u0026thinsp;=\u0026thinsp;0.86\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Discriminant Validity\u003c/h2\u003e \u003cp\u003eDiscriminant validity was assessed using the Fornell-Larcker criterion. In Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the diagonal elements represent the square root of the AVE for each construct, while the off-diagonal elements show inter-construct correlations. The results confirmed that each construct shared more variance with its own indicators than with any other construct in the model. For instance, the square root of AVE for \u0026lsquo;Risk Evaluation\u0026rsquo; (0.981) exceeded its correlation with all other constructs. Similarly, \u0026lsquo;Adaptation Decision\u0026rsquo; showed a AVE of 0.821, which was higher than its correlations with constructs such as \u0026lsquo;Trust in National Adaptation Plans\u0026rsquo; (0.071) or \u0026lsquo;Adaptation Incentives\u0026rsquo; (0.098). These findings collectively support the discriminant validity of the measurement model (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Hair et al., 2014).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity of the constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.821\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.775\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.840\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.791\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.758\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.764\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.729\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.981\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Structural Model Analysis\u003c/h2\u003e \u003cp\u003eThe structural model was tested using AMOS path analysis to examine hypothesised causal relationships between the constructs. The model (Fig.\u0026nbsp;3) demonstrated a good overall fit, with Chi-square divided by degrees of freedom (χ\u0026sup2;/df)\u0026thinsp;=\u0026thinsp;2.173, Root Mean Square Error of Approximation (RMSEA)\u0026thinsp;=\u0026thinsp;0.07, Root Mean Square Residual (RMR)\u0026thinsp;=\u0026thinsp;0.017, Goodness-of-Fit Index (GFI)\u0026thinsp;=\u0026thinsp;0.974, Adjusted Goodness-of-Fit Index (AGFI)\u0026thinsp;=\u0026thinsp;0.923, and Comparative Fit Index (CFI)\u0026thinsp;=\u0026thinsp;0.923.These indices indicate a well-fitting structural model based on established benchmarks (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression weights and model fitness of the structural model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCausal relationships\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression weights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR \u0026rarr; RE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH1 not supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD \u0026rarr; RE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH2 not supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD \u0026rarr; AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH3 supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEAC \u0026rarr; AE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH4 not supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRE \u0026rarr; MA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH5 supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRE \u0026rarr; AD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH6 supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE \u0026rarr; MA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH7 supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE \u0026rarr; AD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH8 supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI \u0026rarr; AD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH9 not supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMA \u0026rarr; AD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH10 not supported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel fitness: ϰ\u003csup\u003e2\u003c/sup\u003e/df: 2.173, RMR: 0.017, GFI: 0.974, AGFI: 0.923, CFI: 0.923, RMSEA: 0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***\u0026lt;.001, **\u0026lt;.01, *\u0026lt;.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe path coefficients provided insights into the significance and direction of the hypothesised relationships. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Five out of ten hypothesised relationships were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Social discourse had a strong positive effect on adaptation evaluation (β\u0026thinsp;=\u0026thinsp;0.598, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and adaptation evaluation, in turn, had a significant negative effect on maladaptation (β = \u0026minus;\u0026thinsp;0.621, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Risk evaluation was positively associated with adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.155, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and negatively related to maladaptation (β = \u0026minus;\u0026thinsp;0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, adaptation evaluation positively influenced adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), while maladaptation also emerged as a significant positive predictor of adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.065, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Conversely, the effect of trust in national adaptation plans on risk evaluation (β = \u0026minus;\u0026thinsp;0.022, p\u0026thinsp;=\u0026thinsp;.694), the effect of social discourse on risk evaluation (β = \u0026minus;\u0026thinsp;0.132, p\u0026thinsp;=\u0026thinsp;.265), the relationship between exact adaptive capacity and adaptation evaluation (β\u0026thinsp;=\u0026thinsp;0.046, p\u0026thinsp;=\u0026thinsp;.415), and the influence of adaptation incentives on adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;=\u0026thinsp;.732) were all found to be non-significant. Based on these outcomes, hypotheses H3, H5, H6, H7, and H8 were supported, while H1, H2, H4, H9, and H10 were not supported.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study contributes significantly to understanding the behavioral dynamics underlying farmers\u0026rsquo; adaptation decisions to climate change and natural disasters. It captures the cognitive, social, and institutional variables influencing how smallholder farmers evaluate climate risks and choose adaptation strategies by employing structural equation modelling (SEM). The results provide both confirmations of established theories and region-specific deviations that illuminate the complex socio-psychological reality of agrarian adaptation. The eight hypothesized constructs\u0026mdash;risk evaluation, adaptation evaluation, social discourse, maladaptation, adaptation incentives, trust in national adaptation plans, exact adaptive capacity, and adaptation decision\u0026mdash;were modelled to capture the complex interplay between perception, belief systems, and behavioral intention in one of India\u0026rsquo;s most exposed agro-ecological zones.\u003c/p\u003e \u003cp\u003eThe strongest and most significant path in the model was from social discourse to adaptation evaluation (β\u0026thinsp;=\u0026thinsp;0.598, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), emphasizing the vital role that peer influence, family expectations, and community interaction play in shaping how farmers evaluate the usefulness and feasibility of adaptation strategies. Farmers in Odisha, especially those in the coastal districts of Puri and Khordah, often rely heavily on informal channels for information\u0026mdash;family discussions, neighbors' experiences, and community observations\u0026mdash;when making decisions related to climate resilience. This aligns with previous studies by Grothmann and Patt (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Dang et al. (2014), who noted that social pressure or communal consensus was a major influence in shaping risk behavior and the adoption of adaptive strategies. In this context, collective experiences serve as both a validation mechanism and a motivator for behavioral change. The fact that social discourse significantly influences adaptation evaluation implies that leveraging community-based education and peer demonstrations could effectively reinforce perception about the value of adaptation interventions.\u003c/p\u003e \u003cp\u003eThe second strongest relationship in the model was the negative association between adaptation evaluation and maladaptation (β = \u0026minus;\u0026thinsp;0.621, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). This inverse relationship indicates that farmers who evaluate adaptation strategies as effective are less likely to hold maladaptive beliefs such as fatalism, denial, or the perception that adaptation measures are futile. This finding aligns with previous work by Grothmann and Patt (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Grothmann and Reusswig (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); and Mitter et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who argued that positive assessments of adaptation options could counteract maladaptive responses. It further implies that transparent and contextual evidence about the success of adaptive practices\u0026mdash;like changing irrigation schedules or using short-duration crop varieties\u0026mdash;can help dismantle fatalistic mindsets that perceive climate impacts as uncontrollable or preordained.\u003c/p\u003e \u003cp\u003eAnother significant finding is the positive influence of adaptation evaluation on adaptation decision (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that when farmers believe that certain strategies (e.g., crop diversification, rotation, or stress-tolerant seeds) are effective and applicable, they are more likely to translate these evaluations into behavioral intentions. This is consistent with previous research by Dang et al. (2014), Mitter et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Azhari et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who emphasized that perceived efficacy is a key predictor of climate action. In our study, farmers who positively assessed the utility of adaptation measures were more inclined to adopt them, affirming the theoretical pathway between perceived outcome and behavior as proposed in Protection Motivation Theory (Grothmann \u0026amp; Patt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This also suggests that field demonstrations and participatory trials could be an essential mechanism to influence farmers\u0026rsquo; perceptions positively.\u003c/p\u003e \u003cp\u003eRisk evaluation also significantly influenced adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.155, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that farmers who perceive higher levels of threat\u0026mdash;such as damage to livelihood, loss of assets, and threats to health\u0026mdash;are more likely to take proactive steps to adapt. These results mirror those of Dang et al. (2014), and Ghazali et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who found that heightened risk perception increases the likelihood of adaptation. Farmers in Odisha are particularly sensitive to climate risks due to their lived experiences with repeated extreme events such as cyclones Fani (2018), Amphan (2020), and Yaas (2021). Thus, personal exposure and memory of loss serve as a cognitive trigger that motivates action. These findings reaffirm the central role of risk appraisal in adaptation frameworks, particularly in high-impact zones like coastal Odisha.\u003c/p\u003e \u003cp\u003eAdditionally, risk evaluation had a significant negative effect on maladaptation (β = \u0026minus;\u0026thinsp;0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), implying that higher perceived risks of climate change discourage fatalism and passivity. This supports earlier conclusions by Grothmann and Reusswig (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Mitter et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who argued that awareness of danger can override feelings of helplessness. However, these results also contrast with earlier research by Milne et al. (2000) and Grothmann and Patt (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), who reported that in some contexts, high threat perception could lead to denial or defensive avoidance, especially when coping resources are perceived as insufficient. The divergence observed in our study could be due to improved climate communication efforts in Odisha, where increasing institutional efforts and repeated experience of disaster have possibly shifted farmers from denial to action.\u003c/p\u003e \u003cp\u003eInterestingly, the path from maladaptation to adaptation decision was weak but positive (β\u0026thinsp;=\u0026thinsp;0.065, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), which appears paradoxical. This may reflect a form of cognitive dissonance (Festinger, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), wherein farmers who deny climate change risk or feel that outcomes are preordained still take action to protect their livelihoods. Such behavior may be influenced by external pressures or habits, or it may represent a coping strategy to reduce psychological discomfort. Another explanation could be confirmation bias: while initially dismissive of climate change, farmers may encounter new information that encourages action, even as their beliefs remain unchanged. While this result does not support our original hypothesis, it adds a layer of complexity and suggests that behavioral intentions can sometimes occur independently of stated beliefs, especially under survival pressures.\u003c/p\u003e \u003cp\u003eAmong the non-significant paths, trust in national adaptation plans did not significantly influence risk evaluation (β = \u0026minus;\u0026thinsp;0.022, ns). This reflects a disconnect between top-down institutional messaging and farmers\u0026rsquo; ground-level realities. In Odisha, despite the presence of government schemes like crop insurance (PMFBY), the accessibility and perceived relevance of such plans remain limited. This distrust may stem from bureaucratic hurdles, low transparency, or poor on-ground implementation, as highlighted by Sharma et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, trust in adaptation policies needs to be rebuilt through participatory approaches and localized implementation. Likewise, social discourse did not influence risk evaluation (β = \u0026minus;\u0026thinsp;0.132, ns), suggesting that perceptions of climate risk are primarily shaped by personal experience rather than peer discussion. While community conversations are pivotal in evaluating solutions, they appear to have limited influence on farmers' recognition of climate threats. This is particularly true in high-risk zones like Odisha, where farmers are constantly exposed to real and visible impacts, making direct observation a more powerful force than hearsay. The path from exact adaptive capacity to adaptation evaluation was also non-significant (β\u0026thinsp;=\u0026thinsp;0.046, ns). This could be because most of the adaptation practices evaluated\u0026mdash;such as crop rotation, timing adjustment, or diversification\u0026mdash;are relatively low-cost and do not require high skill or investment. Similar observations were made by van Duinen et al. (2015), who argued that when adaptation is simple and widely known, perceived capacity becomes less relevant in determining behavior. Finally, adaptation incentives did not significantly affect adaptation decisions (β\u0026thinsp;=\u0026thinsp;0.007, ns). This finding stands in contrast to previous studies (e.g., Grothmann \u0026amp; Patt, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ghazali et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which reported that institutional support positively shaped adaptation. In the Odisha context, poor infrastructure, administrative complexity, and ambiguous eligibility criteria may be discouraging smallholders from availing these schemes. For example, the cumbersome process of claiming benefits under crop insurance schemes or the lack of timely dissemination of subsidies can erode trust and reduce usage, despite their theoretical benefits.\u003c/p\u003e \u003cp\u003eOverall, this study highlights the need to tailor adaptation strategies that go beyond information dissemination and incentives. Farmers in vulnerable zones like coastal Odisha are more influenced by how their peers evaluate interventions and how relevant these interventions appear in their lived context. Trust in institutions, individual capacity, and incentives only work when coupled with direct experience and community-driven validation. The findings validate and extend the propositions of Grothmann and Patt (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and other adaptation scholars, while also introducing cognitive dissonance and confirmation bias as possible explanatory mechanisms for seemingly contradictory behavioral outcomes.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Practical Implications\u003c/h2\u003e \u003cp\u003eThe practical implications of this study are highly relevant for climate policy makers, development practitioners, and local extension agencies working in disaster-prone agrarian settings like coastal Odisha.\u003c/p\u003e \u003cp\u003e \u003cp\u003e1. Shift from Risk Awareness to Evaluation Efficacy: While raising risk perception remains important, this study demonstrates that evaluation of adaptation strategies plays a far more crucial role in motivating farmers. Hence, interventions should not just highlight climate threats, but actively demonstrate the effectiveness, feasibility, and outcomes of adaptive practices through localized field demonstrations, testimonial videos, and farmer-led knowledge exchanges.\u003c/p\u003e \u003cp\u003e2. Leverage Peer Influence and Social Norms: Given the strong influence of social discourse on adaptation evaluation, programs should capitalize on community-based approaches. Training progressive farmers as \u0026ldquo;climate champions,\u0026rdquo; using group-based learning models (like Farmer Field Schools), and facilitating peer-to-peer knowledge sharing will likely increase credibility and adoption rates.\u003c/p\u003e \u003cp\u003e3. Reassess the Use of Financial Incentives in Isolation: The finding that adaptation incentives did not significantly affect adaptation decisions suggests that financial schemes alone (e.g., subsidies, insurance, input support) are not sufficient. Implementation agencies must simplify access, reduce bureaucratic hurdles, and bundle incentives with training, follow-up support, and local relevance. Otherwise, even well-funded programs will suffer from under-utilization.\u003c/p\u003e \u003cp\u003e4. Address Maladaptive Beliefs with Caution: The positive association between maladaptation and adaptation intention implies that fatalism and denial do not always translate into inaction. This nuance requires a psychologically informed communication strategy. Instead of confronting religious or fatalistic beliefs directly, interventions should build on existing cultural narratives and gently redirect fatalism towards action-oriented hope, such as community prayers combined with flood preparedness.\u003c/p\u003e \u003cp\u003e5. Build Trust Through Decentralized Action: The non-significance of trust in national adaptation plans indicates a disconnect between top-down schemes and grassroots realities. Strengthening decentralized governance, empowering Panchayati Raj institutions, and ensuring visible and timely delivery of adaptation services can help restore confidence. Involving farmers in the co-design of adaptation programs, especially in early warning dissemination and resource allocation, would further enhance local ownership.\u003c/p\u003e \u003cp\u003e6. Tailor Adaptation Support to Simple Practices First: Since exact adaptive capacity was not a significant predictor of adaptation evaluation, and most farmers reported using low-cost, low-skill practices like changing irrigation timing or using short-duration varieties, support services should prioritize scaling up these simple strategies before introducing complex innovations. This gradualist approach respects both existing capacity levels and cultural preferences.\u003c/p\u003e \u003cp\u003e7. Region-Specific Planning and Resource Allocation: The findings reaffirm the need for place-based adaptation strategies. Coastal Odisha\u0026rsquo;s exposure to cyclones, storm surges, and flooding warrants robust local planning, such as investment in climate-resilient infrastructure, community seed banks, weather-indexed insurance, and mobile-based advisory services. These should be implemented in tandem with social empowerment initiatives, recognizing that adaptation is both a technical and social process.\u003c/p\u003e \u003cp\u003e8. Monitoring Psychological Shifts in Climate Perception: Programs should incorporate periodic measurement of farmers\u0026rsquo; attitudes and beliefs, especially regarding fatalism, trust, and peer influence. Such psychological monitoring can provide early indicators of intervention success or stagnation and allow for course correction in messaging, methods, and delivery.\u003c/p\u003e \u003cp\u003eThis study not only provides evidence-based recommendations for more culturally sensitive, community-anchored, and cognitively attuned adaptation strategies, but also challenges one-size-fits-all approaches that ignore the socio-psychological diversity among smallholder farmers. By drawing on real-world behavior rather than abstract rationality, these implications offer a roadmap for grounded and impactful climate adaptation policies in India and similar vulnerable regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eClimate change remains a deeply entrenched challenge that continues to compromise the livelihood security of smallholder farmers, especially in fragile ecosystems such as the coastal districts of Odisha. Given the recurrent exposure to extreme climate events\u0026mdash;cyclones, floods, and erratic rainfall\u0026mdash;it becomes essential not just to understand the farmers\u0026rsquo; behavioral responses, but also the layered psychosocial and institutional contexts that shape their adaptation decisions. This study affirms the idea that climate adaptation is not only a technical issue but an adaptive process requiring social learning, trust-building, and behavioral transformation. In Odisha\u0026rsquo;s coastal farming systems, the farmers\u0026rsquo; perceptions of risk, their belief in the utility of adaptation strategies, and their ability to overcome maladaptive thinking form the triad that shapes their resilience pathway. The findings strongly signal that adaptation behavior is underpinned by how people evaluate the immediacy of risk and the feasibility of solutions\u0026mdash;factors that are shaped by both subjective belief systems and systemic support mechanisms. This emphasizes the need for policies that prioritize behavioral insights alongside infrastructural and technological interventions. Future adaptation policies in vulnerable regions like Odisha should thus focus on psychological readiness, credible risk communication, and capacity-building at the grassroots. Strengthening social networks, trust in institutional adaptation plans, and farmer-to-farmer learning can create conditions for sustained adaptation. Moreover, region-specific strategies that integrate scientific guidance with local knowledge\u0026mdash;tailored to the climatic realities of Odisha\u0026mdash;are imperative for transitioning from individual resilience to systemic climate robustness across farming communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualisation: SKG, RNP, RRB and SNK. Data collection: SKG. Statistical analysis: SKG, MY, AS, SWQ and AL. Writing- original draft preparation: SKG, AS, BG and BB. Writing- review and editing: RNP, RRB, SR, PP, SS, TGC and SM. All authors provided their valuable insights on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Accordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Division of Agricultural Extension, ICAR-Indian Agricultural Research Institute (IARI), New Delhi, which serves as the authorized Ethics Committee for social science research involving human participants within the institute. The study was conducted in strict accordance with ICAR and IARI ethical standards and the 1964 Helsinki Declaration\u0026nbsp;(Approval Reference: File No. Ag.Extn/2023/339).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVerbal informed consent was obtained from all participants prior to data collection. Participants were briefed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without any negative consequences. No identifying personal information (such as names, addresses, or contact details) was collected, ensuring complete confidentiality and anonymity of the responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenerative AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that Generative AI was used in the creation of this manuscript. The authors confirm that generative tools were used only to enhance the clarity and readability of certain sections of this manuscript, including language refinement and structural suggestions. All research design, data collection, analysis, interpretation, and core intellectual content remain solely the responsibility of the authors. The final manuscript reflects the authors\u0026rsquo; original work, with AI-assisted edits carefully reviewed for accuracy and adherence to scientific integrity.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjzen I. The theory of planned behavior. 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Accessed 9 July 2025\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adaptation decision, Climate change, Natural disasters, Structural equation model, Protection motivation theory","lastPublishedDoi":"10.21203/rs.3.rs-8542601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8542601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the behavioral dimensions of climate change adaptation is critical for building resilient farming systems, particularly in regions like coastal Odisha, India, where smallholder farmers face recurring climatic threats such as cyclones, floods, and erratic rainfall. This study applied structural equation modeling (SEM) using AMOS 20 to examine the determinants of farmers\u0026rsquo; adaptation decisions, drawing on data collected from 240 respondents across Puri and Khordha districts. The conceptual model tested eight latent constructs: risk evaluation, adaptation evaluation, maladaptation, social discourse, exact adaptive capacity, adaptation incentives, trust in national adaptation plans, and adaptation decision. Results revealed that the most influential predictor of adaptation decision was maladaptation (β\u0026thinsp;=\u0026thinsp;0.650, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by adaptation evaluation (β\u0026thinsp;=\u0026thinsp;0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and risk evaluation (β\u0026thinsp;=\u0026thinsp;0.155, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Social discourse significantly influenced adaptation evaluation (β\u0026thinsp;=\u0026thinsp;0.598, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while risk evaluation negatively predicted maladaptation (β = -0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Trust, adaptive capacity, and adaptation incentives were found to be statistically non-significant in the decision-making process. These findings suggest that adaptation behavior is shaped not only by rational assessments of risk and strategy efficacy, but also by deep-seated psychological constructs such as fatalism and denial. The study contributes to the climate adaptation literature by empirically validating a comprehensive behavioral model specific to high-risk agrarian systems and offers actionable insights for designing context-specific, psychologically informed interventions that support long-term adaptation in disaster-prone regions.\u003c/p\u003e","manuscriptTitle":"Behavioral Drivers of Farmers’ Adaptation and Maladaptation to Natural Disasters in Coastal Odisha, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 16:40:02","doi":"10.21203/rs.3.rs-8542601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T15:22:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T04:33:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175153389956693158037969976572319178576","date":"2026-04-03T03:43:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T06:16:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98018592073416578921719311637985651088","date":"2026-02-24T06:20:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4322973948214547041772580081664848856","date":"2026-02-22T09:09:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T20:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183437696199096256155657752154086193401","date":"2026-01-24T07:07:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187067442307341945486539127697328370556","date":"2026-01-21T18:54:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137309853752506179781934639874276421522","date":"2026-01-21T04:24:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210357320054401305068120387578057729999","date":"2026-01-21T03:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T23:25:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T17:39:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T05:06:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Management","date":"2026-01-07T14:16:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emvm","sideBox":"Learn more about [Environmental Management](http://link.springer.com/journal/267)","snPcode":"267","submissionUrl":"https://submission.nature.com/new-submission/267/3","title":"Environmental Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1883616e-5802-40b3-8cc1-3f68e64f6f8b","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 16:40:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8542601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8542601","identity":"rs-8542601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}