Emergent Behaviour and the Adoption of Community-Based Water Purification Technology in Rural India

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While traditional technology adoption frameworks assume a structured pathway for behavior change, this study highlights the role of emergent factors in shaping both psychological determinants and adoption patterns. Using data from 54 communities and applying logistic and linear regression models, our analysis shows that major external disruptions, like political shifts and large-scale nationwide initiatives play a significant role in shaping adoption decisions. Furthermore, linear regression analysis shows that these emergent factors also impact key psychological determinants, which mediate the decision-making process: perceived risk, trust and social norms. These results suggest that external disturbances not only alter adoption but also change the behavioral paths through which it happens. We propose an Enhanced ToC framework that incorporates determinants of emergent behavior as contextual factors that dynamically shape both individual decision-making and more general adoption patterns. Our results suggest that interventions aimed at improving safe water access should remain responsive to emergent contextual factors, as community responses evolve dynamically in response to political, environmental, and programmatic changes. Humanities/Complex networks Social science/Complex networks Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Community-based water management Emergent Behavior Technology Adoption Behavioral Pathways Community interventions Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION The United Nations 2030 Agenda for Sustainable Development stresses on water security through Sustainable Development Goal (SDG) 6, which aims to ensure universal access to clean and affordable drinking water. Target 6.1 requires every nation to achieve safe and universally accessible drinking water standards by 2030 (United Nations, 2015). Achieving Sustainable Development Goal 6 (SDG 6) remains a significant task, particularly in low- and middle-income countries, due to ongoing financial, infrastructural and human resource constraints (Aman et al., 2024). Despite these, countries such as India have made significant progress toward achieving SDG 6. However, it remains water-stressed, continuing to face challenges in providing safely managed water services (WHO & UNICEF, 2021). While increasing pollution contaminates 70% of the accessible water, rapid urbanization puts great demand on water supply and sanitation systems, therefore aggravating water shortage (Sarker et al.,2021; Kanyagui & Viswanathan, 2022; Ajith et al., 2023). These challenges highlight the need for alternative, decentralized solutions that can complement large-scale water infrastructure projects (Liddle et al., 2016). Community-based water purification technologies serve as dependable solutions, particularly in rural areas where access to safely managed drinking water remains limited (Ghosal & Ruj, 2023). These technologies allow underdeveloped rural regions to acquire cost-effective as well as environmentally friendly water treatment (Thompson, 2015). How communities adopt water purification technologies depends on factors like socioeconomic conditions, trust in institutions, perceived risks, and social norms (Mosler, 2012). Most theories of technology adoption rely on behavioral intention to predict actual use and have certain drawbacks (Moghavvemi et al., 2017). While previous research has examined the psychological determinants of adoption (Mosler, 2012), most frameworks assume a linear and structured process of behaviour change. Daniel et al. (2022) posed adoption as emergent of feedback dynamics between psychological factors and behavioural outcomes that are endogenous. For example, Daniel et al. (2022) provide evidence that while social norms influence technology adoption, increased adoption itself influences the norm of using the technology, thereby accentuating the influence of norms on adoption. In order to disentangle the bidirectional feedback and isolate the effect of psychological variables adoption outcomes, Daniel et al. (2022) use ‘slow’ moving determinants of culture that influence adoption indirectly through the psychological variables and proposed two-stage (culture as) instruments to remove reverse causality. However, Daniel et al. (2022) assumed that conditions external to the water human system, i.e. outside the behavioural adoption dynamics, such as the cultural conditions remains relatively unchanged or at least it changes very slowly. Often rapid changes in external conditions or unforeseen events can disrupt the endogenous adoption dynamics over time. Emergent behavior in community systems refers to collective patterns of response that emerge from the interaction of individuals, groups or institutions, without being deliberately designed or controlled (Chiles et al., 2004). These patterns are disrupted and re-emerge when conditions external to the system (or exogenous) change abruptly, often in unpredictable ways. For example, community responses to crises, such as during the COVID-19 pandemic, often evolved in unpredictable ways, shaped by shifting perceptions of risk, institutional trust, and evolving norms (Bavel et al., 2020). Emergent behaviour, a concept explored in both science and philosophy, describes how complex behaviors arise unexpectedly from simple interactions. This interesting phenomenon is evident in diverse fields, from natural ecosystems to the evolving landscape of artificial intelligence (Corradini & O'Connor, 2010; Macaulay, 2016). Emergent behavior is a key consideration in the adoption of technologies within sectors like transportation and logistics, where intricate interactions among various agents may result in unforeseen consequences (Li et al. 2006). Successful deployment of such technologies depends on understanding the emerging patterns which appear during their operation. In the field of energy transition, emergent behavior refers to large-scale patterns which originate when different stakeholders including people, government and other stakeholders execute independent but connected decisions (Chappin & Blomme, 2022). The results of these unpredictable actions may be challenging to regulate, resulting in uncertainty regarding the speed and direction of change. Factors such as social norms, institutional trust, economic incentives and unexpected obstacles contribute to these patterns. Understanding these factors can help identify relevant interventions aimed at alleviating obstacles and promoting facilitative factors, leading to more effective and sustainable adoption. In this paper, emergent behaviour is conceptualised as community-level responses that emerge from the feedback dynamics between psychological factors and behavioural outcomes, which disrupts in reaction to external shocks to give rise to new ones (Chiles et al., 2004; Chappin & Blomme, 2022) and cannot be entirely predicted by individual-level attributes (Bavel et al., 2020). These behaviours can take several forms: adaptive responses such as the rapid uptake of new interventions; resistant responses, like delayed or defiant actions; and substitutive responses, such as shifting to alternate water sources (see Fig 1). As depicted in Figure 1, the “actions and interactions” of community members, water committee, local government and other stakeholders are modelled through specific psychological determinants that reflect institutional trust, shared norms and perceived responsibility. Trust in the implementing agency (e.g., trust in the agency’s ability to ensure the quality and safety of the water provided) and trust in the water committee (e.g., belief that the committee effectively manages maintenance schedules and coordinates timely repairs) reflect institutional trust and governance responsiveness. Social norms (e.g., perceptions that most community members use the system responsibly and are expected to contribute to its upkeep) model how expectations around behavior are socially constructed and reinforced. Psychological ownership (e.g., feeling of personal stake in the system) translates into behaviors like reporting faults or volunteering for maintenance. By modelling these variables, the study captures how collective decision-making, coordination, and sustained engagement emerge from community-level interactions in response to both external disruptions and programmatic interventions. New emergent behavior thus can result from unexpected, decentralized responses that arise in communities during periods of disruption, where local actors self-organize in ways that are not planned or directed through formal interventions. Events such as the COVID-19 pandemic, natural hazards (e.g., floods, cyclones), introduction of national water programs and local political changes trigger reshaping of how communities perceive risk, trust institutions, and act collectively around technologies. That is, it brings about changes in how psychological factors and technology adoption feedback on each other leading to new emergent behaviour with respect to psychological determinants of technology adoption. Drawing on Fromm’s typology of emergence, these emergent behaviors represent Type II (Weak Emergence) when collective psychological changes feed back into local decision-making without centralized orchestration, and Type IV (Strong Emergence) when actors are consciously aware of their coordinated response and its systemic effects (Provitolo et al., 2011). For instance, COVID-19 pandemic and the introduction of national water programs reflect Type IV emergence, where actors (community members, water committees etc) are aware of the broader institutional shifts and consciously adapt their behavior, either by embracing hygiene practices and purified water or by reevaluating the role of existing systems. In contrast, natural hazards (e.g., floods, droughts) and local political changes represent Type II emergence, where decentralized, self-organized behaviors, such as shifting water priorities after disasters or disengagement due to political distrust, arise organically from individual experiences and peer influence, without centralized planning (see Table 1). Emergent behavior is well studied in social networks and complex systems (Dumitrescu et al., 2017), but its role in water purification technology adoption in rural areas remains underexplored. While much of the research on community-based water purification technologies focuses on structured interventions like education campaigns (Hunter et al., 2010) and subsidies (Mandri-Perrott, 2008), there is a significant gap in understanding how unplanned external factors such as pandemics, political transitions, and natural hazards impact the adoption process. This leaves a critical gap in understanding how such disruptions may impact adoption, particularly in rural settings (for an exception, see States, 2020). Further, there is limited research on the interaction between competing water purification initiatives specifically, how multiple, often conflicting, water programs influence community adoption decisions. While some studies have investigated government-led and NGO-driven projects in isolation, the combined effect of these initiatives, whether they are viewed as complementary or as substitutes, on community decision-making remains largely unexplored (for an exception, see Longworth, 2022). Lastly, while emergent behavior in sectors such as energy transition, transportation, and logistics has been shown to play a critical role in shaping adoption patterns, where small shifts in perception or stakeholder actions can trigger larger, often unexpected changes through feedback loops and collective responses, this remains underexplored in the water sector. Specifically, there is little research on how emergent behaviors influence the adoption of water purification technologies, especially when driven by factors such as trust, social influence, under local political changes. Our research aims to fill these gaps by exploring the impact of unstructured external events like pandemics and natural hazards on the adoption of community-based water purification technology in rural India. It further examines the influence of shifts in political leadership on community adoption behaviour, and investigates how competing government-led and non-government-led programmes interact to shape local decision-making. To the best of our knowledge, no study has specifically examined the effects of external disruptions, political transitions, and competing interventions in driving changes in behavior related to the adoption of community-based water purification technology in developing countries. 1.1 Theory of Change Understanding why some communities adopt water purification technologies while others do not, requires insights from well-established psychological theories. The Risk-Attitude-Norm-Ability-Self-regulation (RANAS) model elaborates how personal norms, risk perception, and self-regulation shape adoption behaviors (Mosler, 2012). Likewise, the Health Belief Model (HBM) (Rainey & Harding, 2005) emphasizes the role of perceived severity and benefits in motivating health-related behaviors. The Integrated Behavioural Model for WASH (IBM-WASH) (Dreibelbis et al., 2013) is a comprehensive framework and incorporates individual, societal, and contextual determinants of behavior. In this paper, we apply the Theory of Change (ToC) framework presented by Contzen et al. (2023), since it provides a comprehensive view of the psychological factors and contextual impacts on the adoption of decentralized water treatment technologies (see Fig. 2). According to the Theory of Change (ToC), contextual factors influence both psychological determinants, such as beliefs, perceived ability, social norms etc as well as the pathways through which behavioral change occurs. Emergent behavior plays a critical role in both of these processes. First, unexpected events modify psychological determinants by changing how people perceive risks and trust levels (Pirla, 2024). For example, when natural hazards like floods or cyclones occur, communities instinctively make water safety their main concern without any external intervention campaigns. Second, emergent behavior impacts the pathways of change by facilitating new routes to adoption that extend outside structured intervention models (Li et al. 2006). For example, the installation of local water purification unit and traditional water harvesting revival in drought-hit areas demonstrates rapid adoption of vital water programs without any government agency involvement. Technology adoption models, including the Theory of Change (ToC), typically assume a rational decision-making process, in which predefined interventions or change techniques such as awareness campaigns, financial subsidies and training programs lead to behavioral change and sustained adoption (Contzen et al., 2023). These models stress structured, top-down approaches, where stakeholders such as governments, NGOs and development agencies design interventions based on expected psychological and contextual factors. But such models often ignore the role played by external disturbances and spontaneous public reactions that can greatly change paths of adoption. The ToC approach does not specifically consider emergent behavior ie unplanned reactions to external shocks that may impact the adoption of new technology. Political changes create quick alterations to public trust within decentralized water systems which impacts community participation beyond what intervention planners expected (Schmidt & Shrubsole, 2013). Similarly, global crises such as the COVID-19 pandemic have demonstrated how external shocks can fundamentally reshape adoption patterns through unexpected behavioral shifts, independent of planned interventions (Erjavec & Manfreda, 2022). To address this gap, we propose integrating emergent behavior as a contextual factor within the ToC pathway. Unlike structured interventions, emergent behavior arises from bottom-up, unplanned processes that can significantly influence adoption pathways. These dynamics are particularly relevant in developing countries, where local communities frequently deal with unpredictable socio-political landscapes that influence their decision-making processes regarding water purification technologies (Kausley et al., 2019). Our main assumption is that external events such as pandemics, political transitions, natural hazards and large-scale national initiatives trigger different adoption patterns that are inherently context-dependent and often do not adhere strictly to traditional theoretical models. The development of adaptive and resilient interventions requires knowledge of the emerging patterns since they also contribute to the actual processes of technology adoption in communities. While we did not conduct formal social network analysis or apply complex systems modelling, our Theory of Change (ToC) is conceptually informed by both traditions. We model emergent behavior through psychological determinants that reflect interactions among community members, water committee, implementing agency and other stakeholders. These variables capture decentralized, networked processes through which adoption decisions emerge. In line with Complex Systems Theory, adoption is treated as a non-linear outcome shaped by feedback loops from programmatic interventions and external shocks, illustrating principles of adaptivity and emergence (Thurner et al., 2018). Likewise, Social Network Theory informs our treatment of trust and norms as relational constructs that mediate adoption and collective action (Sih et al., 2009). Thus, our framework implicitly integrates both perspectives to explain how local interactions shape system-level adoption patterns. 2. RESULTS 2.1 Direct predictors of adoption Table 3 presents the findings from the unweighted logistic regression analysis examining the influence of SEC, psychological, contextual and emergent factors on the adoption of the community-based water purification technology. Higher perceived severity of waterborne infections, greater perceived cost-effectiveness and ease of access, stronger descriptive norms, and more trust in the implementing agency were all significantly associated with the adoption of the community-based water purification technology. Furthermore, it was discovered that having family members help with water collection was substantially linked to the adoption. Adoption was significantly influenced by emergent factors, with political changes showing strong significance (p = 0.001), while the Jal Jeevan Mission had a weaker yet notable, negative effect (p = 0.1) 2.2 Impact of emergent factors on psychological variables Linear regression analysis (Table 4) shows that emergent factors greatly impact several psychological variables associated with the adoption of community-based water purification systems. Among these, COVID-19 and the implementation of large-scale water initiatives by the government such as the Jal Jeevan Mission (JJM) exhibited the most widespread and statistically significant effects across multiple psychological constructs. COVID-19 was found to have a strong positive effect on numerous psychological determinants. It was associated with higher perceived safety (β = 0.39, p < 0.001), taste (β = 0.38, p < 0.001), accessibility (β = 0.54, p < 0.001), time benefits (β = 0.59, p < 0.001), cost-effectiveness (β = 0.53, p < 0.001), and ability (β = 0.48, p < 0.001) of the community-based water purification technology. Additionally, it was linked to greater psychological ownership (β = 0.60, p < 0.001) and trust in the water committee (β = 0.42, p < 0.001), likely due to increased reliance on and appreciation for community-based services during the pandemic. Interestingly, COVID-19 was also associated with reduced perceptions of vulnerability (β = -0.27, p < 0.001) and severity (β = -0.21, p < 0.001) related to waterborne illness, possibly because of enhanced health awareness and preventive behaviors during the pandemic, which may have made individuals feel more protected from water-related health threats. JJM, in contrast, had negative associations with several psychological variables, suggesting a more complex influence. It was strongly associated with reduced perceptions of safety (β = -0.92, p < 0.001), taste (β = -0.89, p < 0.001), access (β = -1.09, p < 0.001), time benefit (β = -1.04, p < 0.001), cost-effectiveness (β = -1.10, p < 0.001), and ability (β = -1.04, p < 0.001) of the community-based water purification technology. Additionally, JJM implementation was significantly associated with lower levels of psychological ownership (β = -1.04, p < 0.001), trust in both the implementing agency (β = -0.60, p < 0.001) and the water committee (β = -0.86, p < 0.001), as well as decreased perceptions of descriptive norms (β = -0.45, p < 0.001) and injunctive norms (β = -0.98, p < 0.001). These results may indicate that the top-down implementation of JJM could have undermined local ownership and trust, thereby reducing psychological engagement with the community-based technology. Political changes had relatively limited influence on psychological variables. However, significant positive associations were observed for perceived severity (β = 0.19, p < 0.01) and descriptive norms (β = 0.25, p < 0.05). Natural hazards, had a significant negative impact on several psychological factors including: increased perceived vulnerability (β = 0.21, p < 0.1), perceived severity (β = 0.18, p < 0.01), and perceived ability (β = 0.30, p < 0.01), potentially reflecting a heightened awareness of water-related risks and a greater sense of personal efficacy following crisis events. At the same time, hazards were linked to lower perceived cost-effectiveness (β = -0.39, p < 0.001), time benefit (β = -0.28, p < 0.01), and ease of access (β = -0.27, p < 0.01), suggesting that logistical and economic concerns after the hazard, may have been exacerbated. Notably, hazards also eroded injunctive norms (β = -0.29, p < 0.01), trust in the implementing agency (β = -0.29, p < 0.001), trust in the water committee (β = -0.25, p < 0.01), and psychological ownership (β = -0.28, p < 0.01). These statistically measurable effects on psychological determinants illustrate how emergent behavior, arising from unplanned shocks, modifies key drivers of behavior change. These shifts in psychological determinants lay the groundwork for examining their indirect effects on adoption, which we explore in the next section. 2.3 Indirect Pathways to Adoption: Path Analysis Using a Two-Stage Regression Approach Logistic regression analyses using JJM-predicted psychological factors (variables derived from the first-stage regression model where emergent factors e.g., JJM, COVID, HAZARD are used to estimate their influence on psychological determinants – see Table 5) revealed that descriptive norms, access, cost-effectiveness, and ability significantly predicted Jivamritam adoption, even after controlling for socioeconomic and other variables. In contrast, models using COVID-predicted psychological factors (Table 6) did not show significant associations with adoption outcomes. Similarly, models using hazard-predicted psychological factors (Table 7) revealed significant negative associations through norms, access, cost-effectiveness and ability, indicating that the logistical and institutional disruptions following hazards may have weakened confidence in and feasibility of using the technology. The results suggests that JJM had a stronger indirect influence on Jivamritam adoption through psychological determinants, whereas natural hazard-related impacts were associated with a significant but negative indirect influence and COVID-related influences showed no significant indirect effect. Together, these findings demonstrate that emergent factors shape adoption not just directly, but through their influence on key psychological mediators. To further interpret these findings, we compared the coefficients of key psychological predictors across the unweighted logistic regression model and those using psychological factors predicted by emergent conditions (Table 8). JJM-predicted psychological variables exhibit substantially stronger positive effects on adoption than in the baseline model. This suggests that large-scale policy initiatives amplify key psychological drivers—such as descriptive norms, perceived access, and ability—by enhancing visibility, institutional legitimacy, and confidence in adoption. Although COVID-19 improved psychological perceptions, these positive orientations did not translate into significant behavioral adoption. This indicates that favourable perceptions alone were insufficient to overcome operational or institutional constraints during the pandemic. Under hazard-predicted conditions, psychological variables exhibit strong negative coefficients, implying that disruptions can invert behavioral relationships. Logistical breakdowns and uncertainty appear to have weakened community trust and perceived capability, turning previously positive drivers into deterrents to adoption. 3. DISCUSSION Community-based water purification systems are widely employed in rural India to ensure that people have access to safe drinking water. This study examined how emergent contextual factors, including political changes, national government-led water initiative-JJM, COVID-19 pandemic and natural hazards, influenced both the psychological determinants and the adoption of a community-based water purification technology. The findings demonstrate that certain factors have an immediate, direct effect on adoption behavior, while others operate indirectly by modifying key psychological determinants that mediate the process of behavior change. Political changes exhibited a strong and direct effect on technology adoption, indicating that changes in local leadership or party affiliations play an instrumental role in shaping community behaviors. When leadership changes occur, trust in existing water programs may be re-evaluated, which can either accelerate or hinder adoption. This finding is consistent with prior studies emphasizing the role of political factors in shaping public perceptions of new technologies (McDonald, 2010 ; Okada & Samreth, 2024 ). However, the indirect influence of political changes on psychological determinants appears more limited. Significant associations were found only with perceived severity and descriptive norms, suggesting that politically driven narratives might have heightened public awareness of water-related risks and established community expectations around technology use. This divergence between strong direct and limited indirect effects underscores the idea that while political context can be pivotal in shaping immediate adoption behaviors, it may not necessarily align with the internal psychological mechanisms typically emphasized in behavior change models. Although COVID-19 did not emerge as a significant direct predictor of Jivamritam adoption in the logistic regression model, it was associated with a strong indirect influence on adoption by improving multiple psychological variables associated with behavior change. COVID-19 was associated with significant increases in perceived safety, taste, ease of access, time savings, cost-effectiveness, and ability related to the water purification technology. Additionally, it enhanced trust in the water committee and psychological ownership, both important constructs in community-based interventions. Interestingly, COVID-19 was also associated with decreased perceptions of vulnerability and severity related to waterborne diseases. This counterintuitive result could be explained by greater health awareness and hygiene practices during the pandemic, which may have made individuals feel more protected, thereby lowering their perceived risk from water-related illnesses. Despite these positive psychological shifts, the lack of a significant direct effect of COVID-19 on adoption behavior implies that while the pandemic improved many enabling and motivational factors, it was not sufficient on its own to translate these changes into higher adoption rates, in line with past studies (Sahin & Sahin, 2022). This underscores the importance of active institutional or policy support in transforming favourable perceptions into tangible behavior change. In contrast to COVID-19, JJM was associated with a direct negative influence on adoption and showed strong negative associations with multiple psychological factors, indicating that the presence of alternative government-led initiatives may deter engagement with community-based systems like Jivamritam. Specifically, JJM significantly reduced psychological ownership and trust in both the implementing agency and the local water committee, while also lowering perceived descriptive and injunctive norms. Although the direct negative effect of JJM on adoption was only marginally significant, its strong negative associations with key psychological factors, many of which significantly predicted adoption, suggest that government-led water initiatives shaped behavior through both direct and indirect pathways by altering core psychological drivers, in line with past studies (Pahl-Wostl, et al., 2010 ) Finally, natural hazards showed a more complex and predominantly indirect influence on adoption. Natural hazards were associated with increased perceptions of vulnerability and ability, suggesting that exposure to crisis events may heighten risk awareness and foster a sense of personal efficacy. Hazard experiences undermine individuals’ psychological readiness by weakening social norms, reducing perceived access and ability, and increasing perceived costs, thereby discouraging adoption of community-based water purification technology. These adverse perceptions could stem from disruptions in water services or increased logistical barriers during and after hazard events, which may have made access to the technology appear less reliable or desirable. The absence of a significant direct effect on adoption, highlights that while crises can alter psychological orientations, they may not be sufficient to drive behavioral change in the absence of supportive infrastructure or sustained trust, again inline with past studies (Grothmann & Reusswig, 2006 ). Natural hazards trigger emergent behavioral shifts by increasing perceived vulnerability and self-efficacy, reflecting a spontaneous prioritization of water safety even without external interventions. While these psychological changes do not always translate into direct adoption, often due to heightened access or cost barriers, they highlight how emergent behavior can redirect the adoption pathway through alternative, unstructured routes shaped by crisis exposure rather than formal programmatic efforts. A comparative analysis of coefficient magnitudes further revealed that emergent factors modify the strength and direction of psychological factors—amplifying them under JJM, attenuating them under COVID-19, and reversing them under hazard conditions—demonstrating that the relationship between psychology and behavior is dynamic rather than static. Overall, these findings emphasize that adoption is influenced by planned interventions as well as unplanned emergent behaviors. Communities do not simply follow a linear path toward adoption dictated by structured behavior change models. Instead, they respond adaptively to political, environmental, and programmatic disruptions, reshaping both psychological orientations and behavioral outcomes. This adaptive response is consistent with the concept of emergent behavior, wherein communities self-organize in reaction to shifting external conditions. Such insights necessitate a reconceptualization of the traditional Theory of Change (ToC) to include emergent behavior as a contextual factor that modifies both psychological determinants and the mechanisms of adoption. Contextual factors within ToC influence psychological determinants (for example beliefs, perceived ability and social norms) together with pathways of change (mechanisms through which adoption takes place). Emergent behavior operates in a similar way; it modifies psychological determinants by reshaping risk perceptions, social norms, and trust in response to external disruptions. At the same time, it alters paths of change by generating unplanned adoption pathways beyond those anticipated in structured interventions. Since contextual factors in ToC influences both psychological determinants and paths of change, classifying emergent behavior as a contextual factor maintains theoretical consistency. Traditional ToC framework (Contzen et al., 2023 ) view interventions as following predictable, step-by-step processes leading to behavioural change. The Enhanced Theory of Change (ToC) proposed in this study extends the traditional ToC by explicitly recognizing emergent behavior as a key driver of adoption and psychological factors, that interacts with structured interventions. Rather than assuming a sequential path from input to behavior, the Enhanced ToC acknowledges that adoption can stem from the interplay between top-down programs and bottom-up community responses. Psychological determinants such as beliefs, perceived ability and social norms are not static; they evolve in response to fluctuating external conditions. For example, a public health emergency can significantly reshape risk perceptions and institutional trust, which in turn alter patterns of adoption in ways not predicted by conventional ToC logic. Figure 4 represents the Enhanced ToC, illustrating how emergent factors interact with psychological determinants to shape technology adoption. This approach emphasizes that adoption pathways evolve under changing contextual conditions, rather than following fixed sequences. Recognizing such dynamics can help refine how interventions are adapted over time, while acknowledging that not all emergent community responses are predictable or can be directly managed. Ultimately, this paper argues for a Theory of Change that is not only predictive but also capable of accounting for the emergent and context-dependent dynamics that shape behavioral change. The findings offer useful guidance for policymakers, NGOs, and program implementers, highlighting the importance of emergent community behaviors when designing interventions to improve the adoption of community-based water purification technologies. Introduction of large-scale government schemes like JJM may unintentionally influence the adoption of community-based systems by altering local psychological and social drivers. It is important for such programs to better align with local interventions to avoid conflicting messaging or undermining community ownership. Communities often make adoption decisions not solely based on the practical benefits of a technology but also in response to broader social and political cues. Political leadership can influence adoption, especially when local leaders visibly support the intervention. Engaging them in outreach efforts may help build credibility and trust. Findings also highlight that beyond physical infrastructure improvements, interventions must also focus on rebuilding psychological confidence, reinforcing positive norms, and addressing fear arising from past natural hazards, to effectively promote community-based water technology uptake in vulnerable communities. In post-crisis settings, such as after a pandemic, restoring trust in the implementing agency and water committees becomes essential. Lastly, behavioral strategies that enhance perceptions of safety, ease of access, cost-effectiveness and community endorsement may support adoption. To our knowledge, this study is among the first to integrate emergent behaviour into a behavior change framework to understand the adoption of community-based water purification technology in rural India. By examining how external influences, such as national water programs and political shifts, interact with psychological determinants, the study provides a more nuanced understanding of technology adoption. It’s focus on both direct and indirect pathways to adoption, strengthens its empirical contribution, while the incorporation of emergent contextual behaviour represents theoretical advancement. While this study does not include longitudinal data to capture abrupt behavioral shifts before and after external events (e.g., COVID-19, natural hazards), it quantifies significant changes in psychological determinants such as perceived access, cost-effectiveness, ability, and trust, that are statistically linked to these events. Drawing on Fromm’s typology, we interpret these patterns as Type II (weak emergence) and Type IV (strong emergence), depending on whether the community’s responses were self-organized or consciously coordinated. This typological framing offers a valid lens through which emergent behavior can be understood, even in the absence of time-series data, by highlighting how perceptions and social processes evolve adaptively in response to systemic disruptions. Future research should differentiate between types of emergent community responses, such as adaptive, resistant, or substitutive behaviours to better understand their distinct effects on adoption. Third, while we propose an Enhanced Theory of Change framework that incorporates emergent factors, the conceptual integration could be further refined, for example, a clearer differentiation between emergent behavioural drivers and traditional structural factors, along with further theoretical refinement to improve its broader applicability. Additionally, political and hazard-related variables were necessarily broad, which may have masked more localized or nuanced patterns of influence. Future research could benefit from incorporating qualitative methods to explore how communities interpret and respond to government initiatives like JJM. Longitudinal designs would also help clarify how emergent factors influence adoption over time, and tools such as network analysis or agent-based modelling could provide insights into how community-level interactions shape behavioral change. This study, based on data from 54 communities, highlights the need for continuous learning and flexibility in designing water security strategies. 4. METHODS 4.1 Study Sample : Jivamritam Jivamritam represents a community-based water purification program operating in India that empowers communities to analyse and control their water issues to improve overall water sustainability. Since its launch in 2017, Jivamritam has been introduced in approximately 300 rural communities (Ajith et al., 2022). The initiative is a joint effort that involves Amrita University, local government agencies, NGO and communities. The adoption process occurred in phases; some communities embraced the technology readily, while others required additional support before accepting it. We carried out a quantitative analysis to investigate the effects of psychological variables, contextual factors and emergent behavior on Jivamritam adoption, drawing on the Theory of Change (ToC) framework (Contzen et al., 2023) and qualitative observations from the field. Using logistic regression, we examined these associations through a cross-sectional study across six Indian states—Kerala, Karnataka, Andhra Pradesh, Odisha, Uttar Pradesh, and Himachal Pradesh—where the technology was already in place. From an initial pool of 300 communities, 75 with severe water contamination were shortlisted. Of these, 64 showed interest, and 54 were ultimately selected for data collection (Fig.3), ensuring diverse cultural, geographical and environmental representation (Dheer et al., 2015). 4.2. Sampling Strategy and Data Collection The data collection process happened through structured in-person interviews with household members of participating communities, over six weeks during July and August 2023. A modified random route sampling technique (Hoffmeyer-Zlotnik, 2003) served as a method to ensure impartial household selection. The survey assigned five zones for each community by using recognizable landmarks and randomly assigned interviewers across these zones. Each survey zone began at a randomly selected house after which interviewers followed a predetermined sequence to talk with every alternate house. Of all surveyed households, only 89 (8%) declined to participate in the study while the rest contributed to a very high survey response rate. Interviews were conducted with the people in charge of collecting water for the family. Given the high rates of illiteracy in some communities, informed consent was sought verbally or in writing, with the former being documented by the interviewer through a signed declaration and participation was completely voluntary. Ethical clearance for this study was obtained from the Institutional Human Ethics Committee (IHEC), Amrita Vishwa Vidyapeetham (Ref. No. IHEC/2023/10/01). All methods were carried out in accordance with relevant guidelines and regulations. The survey research required written permission from local government authorities who granted authorization in each participating region. The interview process lasted around 45 minutes while the respondents could choose between Malayalam, Hindi, Kannada, Telugu or Odia based on their native language. The interviews were conducted by a team of four interviewers, including the local village coordinator, university employee, a member of the Jivamritam implementation team and the first author. The local field research facilitator along with the first author provided oversight to all interviewers during data collection while each interviewer underwent particular training for interview methods. The Empower mobile application served as the tool for survey questionnaire (Aiswarya et al., 2023 https://empower.amrita.edu/#) to speed up the interview process. In all, 894 participants from 54 distinct rural Indian communities took part in the study. With ages ranging from 19 to 77 years (M = 48.66, SD = 8.32), 70% of the respondents were female. In terms of education, 28% had finished primary school, 64% had completed secondary school, 4% had no formal education, and 4% had a university degree or equivalent higher credential. Additionally, 91% of the households in question were considered to be below the poverty line. 4.3. Questionnaire and measures Interviews for the study were conducted using a structured survey, with an emphasis on demographic data, drinking water sources and psychological factors that contributed to the adoption of Jivamritam. In a brainstorming session with members of the Jivamritam implementation team and technical experts, questions were developed in English and discussed collectively. To ensure the accuracy of the translations, the final questionnaire was back-translated into English after being translated into five regional languages spoken by the participating communities. After a pretest with 15 participants from communities where Jivamritam had been deployed, additional changes were made to improve the clarity of some of the questions. The particular measurements used in this study are explained in the sections that follow. 4.3.1 ToC : Psychological and contextual factors The adoption of water purification technology is motivated by people’s understanding of health risks because individuals with knowledge regarding waterborne illnesses view themselves at greater risk, by consuming untreated water (Anthonj et al., 2022; Daniel et al., 2020; Démolis et al., 2018; Huber & Mosler, 2013). The adoption rates of water purification technology increase when users perceive various benefits such as cost-effectiveness, time-savings and easy access to these technologies (Huber et al., 2014; Lilje & Mosler, 2018; Boone et al., 2011; Contzen & Marks, 2018). People who trust the water committee members and implementing agency responsible for water system maintenance will likely adopt the technology because they perceive reduced risks (Contzen et al., 2023). According to perceived descriptive norms, community members become more inclined to adopt and sustain use of water purification technology when they believe many of their peers already accept it (Altherr et al., 2008; Contzen & Marks, 2018; Huber et al., 2014; Lilje et al., 2015; Ajith et al., 2022). According to perceived injunctive norms, people are more likely to adopt technology when they believe that others expect them to (Contzen et al., 2023; Lilje & Mosler, 2018). When community members treat the water purification system as if it were their own and take full responsibility for its upkeep and operation, this is referred to as psychological ownership of the technology. Technology adoption should increase through the development of psychological ownership among end-users (Ambuehl et al., 2021; Contzen & Marks, 2018). The adoption rate is noticeably impacted by perceived ability which integrates technical troubleshooting skills with familiarity towards technology and resource provisioning with the ability to handle obstacles steadfastly (Contzen & Marks, 2018; Daniel et al., 2019; Heri & Mosler, 2008; Huber et al., 2012; Murray et al., 2020). RANAS model was used to assess the psychological aspects influencing the adoption of Jivamritam (Mosler, 2012; Mosler & Contzen, 2016). RANAS model integrates psychological elements from prominent behavior change theories into five comprehensive factor groups: Risk, Attitudes, Norms, Abilities, and Self-Regulation. It was created for the water, sanitation, and hygiene sector in developing nations. Additionally, three particular elements were included: psychological ownership, trust, and perceived risks of utilizing or not using the technology. Table 1-SD in the supplementary material contains comprehensive details regarding the wording and response scales used to measure these psychological factors as well as the sources. Except for psychological ownership, which had three items, each ToC element was represented by a single item. These three items had a high level of internal consistency (Cronbach's alpha = 0.95), so their answers were aggregated to get a composite score. A 5-point Likert scale was used for responses on all items. The adoption of community-based water purification technology is also explained by contextual factors. For example, a household's distance from the technology may affect how much effort is thought to be necessary to utilize it because a greater proximity lowers the time and physical demands of collecting water, making the system more convenient and accessible and increasing adoption (Boone et al., 2011). Also, the perceived difficulty of getting water is reduced when more family members participate, which encourages the use of water purification technologies (Boone et al., 2011). Additionally, the socioeconomic characteristics (SEC) of community members were included. Two SEC variables were chosen from previous research because of their proven correlations with the adoption of household water treatment (HWT): wealth (Opryszko et al., 2010; Roma et al., 2014) and education level (Fotue et al., 2012; Freeman et al., 2012; Nauges & Van Den Berg, 2009) (Daniel et al., 2022). The government's ration card system was utilized to categorize households and distinguished between those living below and above the poverty line (Boo et al., 2024). To determine the respondents' highest level of education, the response categories "None," "Primary," "Secondary," and "Graduate or Above" were employed. 4.3.2 Determinants of Emergent Behaviour In the case of Jivamritam adoption, emergent behaviour may be the outcome of policy changes, dynamic reactions to external events or social interactions. Four binary variables were used to capture emergent contextual factors, based on community responses to whether the adoption of Jivamritam was affected by the COVID-19 pandemic, the national government led initiative - Jal Jeevan Mission, political changes, or natural hazards, with each variable coded as 1 for “yes” and 0 for “no” based on specific survey questions (see Table 2). Impact of COVID-19 COVID-19 pandemic has majorly impacted WASH services, highlighting that more investment, better access, and community engagement are needed to protect public health (Desye, 2021). The risk of health hazards has made local communities demand for more purified water as an adaptive response. This is an example of emergent behavior as a result of collective decision-making rather than direct policy interventions (Brown et al. 2014). COVID-19 pandemic made people more conscious of good hygiene and the need for safe drinking water, which increased the demand for purified water in some communities. It, however, also raised concerns about the use of shared facilities (Hayashi et al. 2022). The use of community-based water purification systems faces resistance from people because they worry about viruses spreading through common water points. This again is an example of emergent behavior, which shows that groups' reactions to external shocks can change adoption patterns in ways that are hard to predict. Impact of Jal Jeevan Mission (JJM) Introduction The Jal Jeevan Mission (JJM) is a national government-led initiative aimed at providing tap water to all rural households in India by 2024 with the help of sustainable water management and community participation (Balamurugan et al. 2024). But its impact on community-based water purification technology adoption depends on how communities interpret and integrate this policy within their local context. Studies show that policy changes produce unexpected behavioral changes such as, causing people to use alternative technologies (Koessler & Engel, 2021). This best aligns with the emergent behavior concept, where systemic responses to new regulations evolve through decentralized social interactions (Muneepeerakul & Anderies, 2017). Impact of Political Changes Political changes, such as changes in local governance, influence public trust, allocation of funds and the level of community participation in community service initiatives (Kumar Singh, 2021). With the world's second-largest population and a rich history of local governance reforms, India provides valuable insights into these interconnections. These factors may indirectly shape technology adoption patterns, even though Jivamritam itself is not directly influenced by political changes. Emergent behavior characterizes the unpredictable reactions of communities toward governance changes (Lawlor & Neal, 2016). Impact of Natural Hazards Local priorities shift when environmental shocks such as floods or droughts occur because they create increased need for water purification solutions and necessitate hazard relief efforts (Herlambang, 2010). This aligns with studies on crisis-driven behavioral shifts, where communities self-organize and adapt their decision-making in response to external stressors (Clavijo & Montaño, 2022). In the case of Jivamritam, such events may trigger unexpected adoption patterns, illustrating how emergent behavior arises from adaptive responses rather than planned interventions. 4.4 Data analysis procedure "Do you regularly use water from Jivamritam in your community?" was the question used to gauge the response variable, Jivamritam adoption, and the answer was binary: 0 indicates no, whereas 1 indicates yes. It is hypothesized that a logistic distribution governs the likelihood of adopting Jivamritam. R version 4.2.3 was used for all analyses (R Core Team 2023). An unweighted logistic regression model was used to assess the influence of contextual, psychological, socioeconomic (SEC) factors as well as determinants of emergent behavior on the adoption of water purification technology (Daniel et al., 2022; Kraemer & Mosler, 2010). To characterize the respondents' overall socio-economic status (SEC), we combined two factors—wealth and education level—using Principal Component Analysis (PCA) (Houweling et al., 2003). The first main component scores, or SEC, were then used in the study. Among the independent variables were SEC, psychological effects, contextual factors and determinants of emergent behavior. Additionally, unweighted multiple linear regression was used to evaluate how emergent factors influence individual psychological determinants. This helped to quantitatively assess the impacts that external disruptions like political changes and nationwide water programs, have psychological determinants of technology adoption. Each psychological factor (e.g. perceived vulnerability, severity, safety, taste, access, cost, time, ability, descriptive norms, injunctive norms, trust in implementing agency and water committee, psychological ownership) was modelled individually using emergent factors as predictors, with statistical significance assessed through p-values and corresponding coefficient estimates reported. The regression model took the following form. Yi = β0 + β1(COVID-19) + β2(JJM) + β3(Political) + β4(Hazards) + εi where Yi represents the psychological determinant for a community member and βk are the estimated coefficients representing the influence of each emergent factor. This approach allows us to examine whether emergent factors influence not only adoption outcomes (as shown through logistic regression) but also the psychological determinants that shape behavioral decisions. Similar approaches have been used in behavioral and environmental studies to assess the role of contextual and psychological factors in shaping technology adoption (Gong et al., 2020). Following this, a second-stage logistic regression was conducted using only those emergent factors that had shown significant effects on psychological determinants in the first-stage analysis. To address potential multicollinearity among psychological predictors and to isolate the unique contribution of each psychological determinant influenced by emergent contextual factors (e.g. JJM), we employed a series of separate logistic regression models. This approach allowed us to examine the individual impact of each emergent-predicted psychological factor on adoption outcomes while controlling for key covariates such as SEC, political changes, COVID-19 and natural hazards. From the list of significantly affected psychological variables, we prioritized those that have been identified in prior literature (e.g., Mosler, 2012; Daniel et al., 2022) as core determinants of adoption in the context of water-related technologies. Specifically, perceived access, cost-effectiveness, ability, and descriptive norms have been consistently shown to influence adoption decisions in low-resource settings. This two-stage approach enabled the identification of both the indirect and direct pathways through which emergent events influence technology adoption behavior. Emergent factors were not treated as formal instrumental variables intended to correct for endogeneity. Instead, they were employed to simulate how external disruptions reshape psychological determinants and, in turn, influence technology adoption behavior. By comparing coefficient magnitudes from the baseline (unweighted) logistic regression with those from models using emergent-predicted psychological variables, we captured the behavioral change that occurs before and after external shocks. Declarations FUNDING This project has been funded by the E4LIFE International Ph.D. Fellowship Program offered by Amrita Vishwa Vidyapeetham ACKNOWLEDGEMENT The authors express their immense gratitude to Sri. Mata Amritanandamayi Devi (Amma), Chancellor of Amrita Vishwa Vidyapeetham, who has inspired them in performing selfless service to society. We extend our gratitude to the Amrita Live-in-Labs® academic program for providing all the support. This research is part of the Joint Center of Excellence in Water Sustainability, a collaborative initiative between Amrita Vishwa Vidyapeetham and Delft University of Technology. AUTHOR CONTRIBUTIONS Conceived and designed the research: M.R., S.P. and M.V. Conducted the research: M.R. 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(2023). https://washdata.org Yu, Y., & Wei, J. (2022). Analysis of the influence of agricultural natural HAZARD on farmers’ technology adoption decision. Frontiers in Environmental Science, 10, 923694. Tables Table 1 . Typology of Emergent Behaviour. Adapted from (Provitolo et al., 2011) Emergent Type Definition Characteristics Examples Type II (Weak Emergence) Behaviour emerges from local interactions without central coordination. Decentralised, self-organising, often unplanned or subconscious. Natural hazards - spontaneous reprioritisation of water needs Political change - reduced trust and disengagement Type IV (Strong Emergence) Behaviour reflects conscious awareness of systemic change and collective action. Deliberate, coordinated or semi-coordinated adaptation to known external shocks. COVID-19 - adoption of hygiene practices and water safety measures JJM - intentional re-evaluation of community water systems Table 2. Determinants of Emergent Behavior Determinants of Emergent Behavior Question wording Source Impact of COVID-19 Do you think the COVID-19 pandemic has had an impact on the adoption of the Jivamritam water purification system in your community? (Berglund et al., 2022) Impact of Jal Jeevan Mission (JJM) Has the introduction of the Jal Jeevan Mission (JJM) by the Indian government influenced the adoption of Jivamritam in your community? (Barstow et al., 2016) Impact of Political Changes Have any political changes, such as a change in the ruling party at the community level, influenced the adoption of Jivamritam in your community? (Ajith et al., 2022) Impact of Natural hazards Have external events such as natural hazards (e.g., floods, droughts, cyclones) influenced the adoption of Jivamritam in your community? (Yu & Wei, 2022) Table 3 . Unweighted logistic regression of SEC, contextual, psychological and emergent behavior factors on Jivamritam adoption (N=894) Independent variables Estimate Std. Error z value Perceived Vulnerability -0.26 0.18 -1.44 Perceived Severity 0.50 * 0.21 2.37 Perceived Safety 0.42 0.22 1.92 Perceived good taste -0.47 0.24 -1.95 Perceived ease of access 0.99 *** 0.26 3.81 Perceived time benefit -0.26 0.26 -1.01 Perceived cost effectiveness 0.49 * 0.25 1.91 Perceived ability 0.11 0.25 0.43 Descriptive Norms 0.74 *** 0.18 4.03 Injunctive norms -0.07 0.29 -0.23 Proximity -0.59 0.33 -1.79 Family members assistance 2.56 ** 0.97 2.68 Trust in implementing agency 0.89 *** 0.28 3.19 Trust in water committee 0.14 0.30 0.45 Psychological ownership 0.48 0.36 1.36 SEC -0.07 0.14 -0.49 COVID-19 0.05 0.41 0.13 JJM -0.78 * 0.47 -1.65 Political 1.18 ** 0.45 2.62 Hazards 0.53 0.37 1.41 AIC = 371.7 ; BIC = 468.3 ; R2 = 0.67 ; * p < .1. ** p< .01. *** p< .001 Table 4. Linear regression results showing the impact of emergent factors on psychological determinants (N=894) Psychological Determinants (DV) COVID-19 (IV) JJM (IV) Political (IV) Hazards (IV) Estimate S.E Estimate S.E Estimate S.E Estimate S.E Perceived Vulnerability -0.27 *** 0.08 0.29 *** 0.09 0.11 0.09 0.21 * 0.08 Perceived Severity -0.21 *** 0.06 0.27 *** 0.06 0.19 ** 0.07 0.18 ** 0.06 Perceived Safety 0.39 *** 0.09 -0.92 *** 0.09 -0.17 0.10 0.22 * 0.09 Perceived good taste 0.38 *** 0.09 -0.89 *** 0.09 -0.08 0.11 -0.18 0.09 Perceived ease of access 0.54 *** 0.10 -1.09 *** 0.09 0.08 0.11 -0.27 ** 0.10 Perceived time benefit 0.59 *** 0.10 -1.04 *** 0.09 -0.03 0.11 -0.28 ** 0.10 Perceived cost effectiveness 0.53 *** 0.11 -1.10 *** 0.09 -0.15 0.11 -0.39 *** 0.10 Perceived ability 0.48 *** 0.10 -1.04 *** 0.09 -0.05 0.11 0.30 ** 0.10 Descriptive Norms 0.12 *** 0.10 -0.45 *** 0.10 0.25 * 0.11 -0.14 0.10 Injunctive Norms 0.49 *** 0.09 -0.98 *** 0.09 0.01 0.10 -0.29 ** 0.10 Trust in implementing agency 0.15 0.08 -0.60 *** 0.09 -0.15 0.09 -0.29 *** 0.09 Trust in water committee 0.42 *** 0.08 -0.86 *** 0.08 -0.15 0.10 -0.25 ** 0.09 Psychological ownership 0.60 *** 0.09 -1.04 *** 0.09 -0.13 0.11 -0.28 ** 0.10 DV – Dependent variable; IV – Independent variable; * p < .1. ** p< .01. *** p< .001 Table 5 . Logistic regression results showing Jivamritam adoption using JJM-predicted psychological Factors Model Independent variables Estimate Std. Error Model 1 pred_DESC_JJM 3.90 *** 0.51 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 2 pred_ACCESS_JJM 1.61 *** 0.21 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 3 pred_COST_JJM 1.59 *** 0.21 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 4 pred_ABILITY_JJM 1.69 *** 0.22 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Note : pred_DESC_JJM, pred_ACCESS_JJM, pred_COST_JJM, and pred_ABILITY_JJM are predicted psychological factors based on regressions using JJM as the independent variable. * p < .1. ** p< .01. *** p< .001 Table 6. Logistic regression results showing Jivamritam adoption using COVID-predicted psychological Factors Model Independent variables Estimate Std. Error Model 1 pred_DESC_COVID -2.58 1.58 SEC -0.08 0.07 JJM -1.76 *** 0.23 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 2 pred_ACCESS_COVID -0.58 0.35 SEC -0.08 0.06 JJM -1.76 *** 0.23 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 3 pred_COST_COVID -0.59 0.36 SEC -0.08 0.07 JJM -1.76 *** 0.23 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Model 4 pred_ABILITY_COVID -0.65 0.39 SEC -0.08 0.07 JJM -1.76 *** 0.23 POLITICAL -0.002 0.19 HAZARD 0.55 ** 0.19 Note: pred_DESC_COVID, pred_ACCESS_COVID, pred_COST_COVID, and pred_ABILITY_COVID are predicted psychological factors based on regressions using COVID as the independent variable. * p < .1. ** p< .01. *** p< .001 Table 7 . Logistic regression results showing Jivamritam adoption using JJM-predicted psychological Factors Model Independent variables Estimate Std. Error Model 1 pred_DESC_HAZARD -4.05 ** 1.41 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 JJM -1.76 *** 0.23 Model 2 pred_ACCESS_HAZARD -2.07 ** 0.72 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 JJM -1.76 *** 0.23 Model 3 pred_COST_HAZARD -1.40 ** 0.49 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 JJM -1.76 *** 0.23 Model 4 pred_ABILITY_HAZARD -1.84 ** 0.64 SEC -0.08 0.07 COVID -0.31 0.19 POLITICAL -0.002 0.19 JJM -1.76 *** 0.23 Note : pred_DESC_HAZARD, pred_ACCESS_HAZARD, pred_COST_HAZARD, and pred_ABILITY_HAZARD are predicted psychological factors based on regressions using HAZARD as the independent variable. * p < .1. ** p< .01. *** p< .001 Table 8 . Comparison of coefficients for psychological factors across models Psychological Factor Unweighted logistic regression coefficient JJM-predicted coefficient COVID-predicted coefficient Hazard-predicted coefficient Descriptive Norms 0.74 *** 3.90 *** –2.58 –4.05 ** Perceived ease of access 0.99 *** 1.61 *** –0.58 –2.07 ** Perceived cost effectiveness 0.49 * 1.59 *** –0.59 –1.40 ** Perceived ability 0.11 1.69 *** –0.65 –1.84 ** Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":68161,"visible":true,"origin":"","legend":"\u003cp\u003eEmergent system-level behaviors in adoption of community-based water purification technology. On the system level, patterns such as the adoption of technology emerge from the actions and interactions of community members and stakeholders. Figure adapted from (Chappin \u0026nbsp;\u0026amp; Blomme, 2022)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8186808/v1/6325126605d475647e72f1c6.jpeg"},{"id":98803338,"identity":"9b8367fd-193f-40bb-8a45-6e41dcd54f71","added_by":"auto","created_at":"2025-12-22 14:20:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":311047,"visible":true,"origin":"","legend":"\u003cp\u003eThe Theory of Change (ToC) showing possible pathways leading to the adoption of community-based water purification technology. Figure adapted with permission from (Contzen et al., 2023)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8186808/v1/800fbbc43254263fe2de5fb5.png"},{"id":98803363,"identity":"d952143f-441f-4f42-9540-cd09931ea0b2","added_by":"auto","created_at":"2025-12-22 14:20:25","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":286828,"visible":true,"origin":"","legend":"\u003cp\u003eA map highlighting 54 Jivamritam rural communities in India\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8186808/v1/a7dcc31f0e41c7e7f836e826.jpeg"},{"id":98803227,"identity":"6be2d9cb-db82-4bb8-bcb9-b15ab44c85a3","added_by":"auto","created_at":"2025-12-22 14:20:14","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216009,"visible":true,"origin":"","legend":"\u003cp\u003eEnhanced Theory of Change (ToC)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8186808/v1/c0dbf64ffb7777408338a70c.jpeg"},{"id":104808616,"identity":"440fdbe1-9fbd-4e21-8b56-32a7171bb20b","added_by":"auto","created_at":"2026-03-17 12:39:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1891616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8186808/v1/24f3b940-eeb0-4f9c-a725-7c22201eeaae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Emergent Behaviour and the Adoption of Community-Based Water Purification Technology in Rural India","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe United Nations 2030 Agenda for Sustainable Development stresses on water security through Sustainable Development Goal (SDG) 6, which aims to ensure universal access to clean and affordable drinking water. Target 6.1 requires every nation to achieve safe and universally accessible drinking water standards by 2030 (United Nations, 2015). Achieving Sustainable Development Goal 6 (SDG 6) remains a significant task, particularly in low- and middle-income countries, due to ongoing financial, infrastructural and human resource constraints (Aman et al., 2024). Despite these, countries such as India have made significant progress toward achieving SDG 6. However, it remains water-stressed, continuing to face challenges in providing safely managed water services (WHO \u0026amp; UNICEF, 2021). While increasing pollution contaminates 70% of the accessible water, rapid urbanization puts great demand on water supply and sanitation systems, therefore aggravating water shortage (Sarker et al.,2021; Kanyagui \u0026amp; Viswanathan, 2022; Ajith et al., 2023).\u003c/p\u003e\n\u003cp\u003eThese challenges highlight the need for alternative, decentralized solutions that can complement large-scale water infrastructure projects (Liddle et al., 2016). Community-based water purification technologies serve as dependable solutions, particularly in rural areas where access to safely managed drinking water remains limited (Ghosal \u0026amp; Ruj, 2023). These technologies allow underdeveloped rural regions to acquire cost-effective as well as environmentally friendly water treatment (Thompson, 2015). How communities adopt water purification technologies depends on factors like socioeconomic conditions, trust in institutions, perceived risks, and social norms (Mosler, 2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost theories of technology adoption rely on behavioral intention to predict actual use and have certain drawbacks (Moghavvemi et al., 2017). While previous research has examined the psychological determinants of adoption (Mosler, 2012), most frameworks assume a linear and structured process of behaviour change. Daniel et al. (2022) posed adoption as emergent of feedback dynamics between psychological factors and behavioural outcomes that are endogenous. For example, Daniel et al. (2022) provide evidence that while social norms influence technology adoption, increased adoption itself influences the norm of using the technology, thereby accentuating the influence of norms on adoption. In order to disentangle the bidirectional feedback and isolate the effect of psychological variables adoption outcomes, Daniel et al. (2022) use \u0026lsquo;slow\u0026rsquo; moving determinants of culture that influence adoption indirectly through the psychological variables and proposed two-stage (culture as) instruments to remove reverse causality. However, Daniel et al. (2022) assumed that conditions external to the water human system, i.e. outside the behavioural adoption dynamics, such as the cultural conditions remains relatively unchanged or at least it changes very slowly. Often rapid changes in external conditions or unforeseen events can disrupt the endogenous adoption dynamics over time.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Emergent behavior in community systems refers to collective patterns of response that emerge from the interaction of individuals, groups or institutions, without being deliberately designed or controlled (Chiles et al., 2004). These patterns are disrupted and re-emerge when conditions external to the system (or exogenous) change abruptly, often in unpredictable ways. For example, community responses to crises, such as during the COVID-19 pandemic, often evolved in unpredictable ways, shaped by shifting perceptions of risk, institutional trust, and evolving norms (Bavel et al., 2020). Emergent behaviour, a concept explored in both science and philosophy, describes how complex behaviors arise unexpectedly from simple interactions. This interesting phenomenon is evident in diverse fields, from natural ecosystems to the evolving landscape of artificial intelligence (Corradini \u0026amp; O\u0026apos;Connor, 2010; Macaulay, 2016).\u003c/p\u003e\n\u003cp\u003eEmergent behavior is a key consideration in the adoption of technologies within sectors like transportation and logistics, where intricate interactions among various agents may result in unforeseen consequences (Li et al. 2006). Successful deployment of such technologies depends on understanding the emerging patterns which appear during their operation. In the field of energy transition, emergent behavior refers to large-scale patterns which originate when different stakeholders including people, government and other stakeholders execute independent but connected decisions (Chappin \u0026amp; Blomme, 2022). The results of these unpredictable actions may be challenging to regulate, resulting in uncertainty regarding the speed and direction of change. Factors such as social norms, institutional trust, economic incentives and unexpected obstacles contribute to these patterns. Understanding these factors can help identify relevant interventions aimed at alleviating obstacles and promoting facilitative factors, leading to more effective and sustainable adoption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this paper, \u003cem\u003eemergent behaviour\u003c/em\u003e is conceptualised as community-level responses that emerge from the feedback dynamics between psychological factors and behavioural outcomes, which disrupts in reaction to external shocks to give rise to new ones (Chiles et al., 2004; Chappin \u0026amp; Blomme, 2022) and cannot be entirely predicted by individual-level attributes (Bavel et al., 2020). These behaviours can take several forms: adaptive responses such as the rapid uptake of new interventions; resistant responses, like delayed or defiant actions; and substitutive responses, such as shifting to alternate water sources (see Fig 1). As depicted in Figure 1, the \u0026ldquo;actions and interactions\u0026rdquo; of community members, water committee, local government and other stakeholders are modelled through specific psychological determinants that reflect institutional trust, shared norms and perceived responsibility. Trust in the implementing agency (e.g., trust in the agency\u0026rsquo;s ability to ensure the quality and safety of the water provided) and trust in the water committee (e.g., belief that the committee effectively manages maintenance schedules and coordinates timely repairs) reflect institutional trust and governance responsiveness. Social norms (e.g., perceptions that most community members use the system responsibly and are expected to contribute to its upkeep) model how expectations around behavior are socially constructed and reinforced. Psychological ownership (e.g., feeling of personal stake in the system) translates into behaviors like reporting faults or volunteering for maintenance. By modelling these variables, the study captures how collective decision-making, coordination, and sustained engagement emerge from community-level interactions in response to both external disruptions and programmatic interventions.\u003c/p\u003e\n\u003cp\u003eNew emergent behavior thus can result from unexpected, decentralized responses that arise in communities during periods of disruption, where local actors self-organize in ways that are not planned or directed through formal interventions. Events such as the COVID-19 pandemic, natural hazards (e.g., floods, cyclones), introduction of national water programs and local political changes trigger reshaping of how communities perceive risk, trust institutions, and act collectively around technologies. That is, it brings about changes in how psychological factors and technology adoption feedback on each other leading to new emergent behaviour with respect to psychological determinants of technology adoption. Drawing on Fromm\u0026rsquo;s typology of emergence, these emergent behaviors represent Type II (Weak Emergence) when collective psychological changes feed back into local decision-making without centralized orchestration, and Type IV (Strong Emergence) when actors are consciously aware of their coordinated response and its systemic effects (Provitolo et al., 2011). For instance, COVID-19 pandemic and the introduction of national water programs reflect Type IV emergence, where actors (community members, water committees etc) are aware of the broader institutional shifts and consciously adapt their behavior, either by embracing hygiene practices and purified water or by reevaluating the role of existing systems. In contrast, natural hazards (e.g., floods, droughts) and local political changes represent Type II emergence, where decentralized, self-organized behaviors, such as shifting water priorities after disasters or disengagement due to political distrust, arise organically from individual experiences and peer influence, without centralized planning (see Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmergent behavior is well studied in social networks and complex systems (Dumitrescu et al., 2017), but its role in water purification technology adoption in rural areas remains underexplored. While much of the research on community-based water purification technologies focuses on structured interventions like education campaigns (Hunter et al., 2010) and subsidies (Mandri-Perrott, 2008), there is a significant gap in understanding how unplanned external factors such as pandemics, political transitions, and natural hazards impact the adoption process. This leaves a critical gap in understanding how such disruptions may impact adoption, particularly in rural settings (for an exception, see States, 2020). Further, there is limited research on the interaction between competing water purification initiatives specifically, how multiple, often conflicting, water programs influence community adoption decisions. While some studies have investigated government-led and NGO-driven projects in isolation, the combined effect of these initiatives, whether they are viewed as complementary or as substitutes, on community decision-making remains largely unexplored (for an exception, see Longworth, 2022). Lastly, while emergent behavior in sectors such as energy transition, transportation, and logistics has been shown to play a critical role in shaping adoption patterns, where small shifts in perception or stakeholder actions can trigger larger, often unexpected changes through feedback loops and collective responses, this remains underexplored in the water sector. Specifically, there is little research on how emergent behaviors influence the adoption of water purification technologies, especially when driven by factors such as trust, social influence, under local political changes.\u003c/p\u003e\n\u003cp\u003eOur research aims to fill these gaps by exploring the impact of unstructured external events like pandemics and natural hazards on the adoption of community-based water purification technology in rural India. It further examines the influence of shifts in political leadership on community adoption behaviour, and investigates how competing government-led and non-government-led programmes interact to shape local decision-making. To the best of our knowledge, no study has specifically examined the effects of external disruptions, political transitions, and competing interventions in driving changes in behavior related to the adoption of community-based water purification technology in developing countries.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e1.1 Theory of Change\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eUnderstanding why some communities adopt water purification technologies while others do not, requires insights from well-established psychological theories. The Risk-Attitude-Norm-Ability-Self-regulation (RANAS) model elaborates how personal norms, risk perception, and self-regulation shape adoption behaviors (Mosler, 2012). Likewise, the Health Belief Model (HBM) (Rainey \u0026amp; Harding, 2005) emphasizes the role of perceived severity and benefits in motivating health-related behaviors. The Integrated Behavioural Model for WASH (IBM-WASH) (Dreibelbis et al., 2013) is a comprehensive framework and incorporates individual, societal, and contextual determinants of behavior. In this paper, we apply the Theory of Change (ToC) framework presented by Contzen et al. (2023), since it provides a comprehensive view of the psychological factors and contextual impacts on the adoption of decentralized water treatment technologies (see Fig. 2).\u003c/p\u003e\n\u003cp\u003eAccording to the Theory of Change (ToC), contextual factors influence both psychological determinants, such as beliefs, perceived ability, social norms etc as well as the pathways through which behavioral change occurs. Emergent behavior plays a critical role in both of these processes. First, unexpected events modify psychological determinants by changing how people perceive risks and trust levels (Pirla, 2024). For example, when natural hazards like floods or cyclones occur, communities instinctively make water safety their main concern without any external intervention campaigns. Second, emergent behavior impacts the pathways of change by facilitating new routes to adoption that extend outside structured intervention models (Li et al. 2006). For example, the installation of local water purification unit and traditional water harvesting revival in drought-hit areas demonstrates rapid adoption of vital water programs without any government agency involvement.\u003c/p\u003e\n\u003cp\u003eTechnology adoption models, including the Theory of Change (ToC), typically assume a rational decision-making process, in which predefined interventions or change techniques such as awareness campaigns, financial subsidies and training programs lead to behavioral change and sustained adoption (Contzen et al., 2023). These models stress structured, top-down approaches, where stakeholders such as governments, NGOs and development agencies design interventions based on expected psychological and contextual factors. But such models often ignore the role played by external disturbances and spontaneous public reactions that can greatly change paths of adoption. \u0026nbsp; The ToC approach does not specifically consider emergent behavior ie unplanned reactions to external shocks that may impact the adoption of new technology. Political changes create quick alterations to public trust within decentralized water systems which impacts community participation beyond what intervention planners expected (Schmidt \u0026amp; Shrubsole, 2013). Similarly, global crises such as the COVID-19 pandemic have demonstrated how external shocks can fundamentally reshape adoption patterns through unexpected behavioral shifts, independent of planned interventions (Erjavec \u0026amp; Manfreda, 2022).\u003c/p\u003e\n\u003cp\u003eTo address this gap, we propose integrating emergent behavior as a contextual factor within the ToC pathway. Unlike structured interventions, emergent behavior arises from bottom-up, unplanned processes that can significantly influence adoption pathways. These dynamics are particularly relevant in developing countries, where local communities frequently deal with unpredictable socio-political landscapes that influence their decision-making processes regarding water purification technologies (Kausley et al., 2019). Our main assumption is that external events such as pandemics, political transitions, natural hazards and large-scale national initiatives trigger different adoption patterns that are inherently context-dependent and often do not adhere strictly to traditional theoretical models. The development of adaptive and resilient interventions requires knowledge of the emerging patterns since they also contribute to the actual processes of technology adoption in communities. While we did not conduct formal social network analysis or apply complex systems modelling, our Theory of Change (ToC) is conceptually informed by both traditions. We model emergent behavior through psychological determinants that reflect interactions among community members, water committee, implementing agency and other stakeholders. These variables capture decentralized, networked processes through which adoption decisions emerge. In line with Complex Systems Theory, adoption is treated as a non-linear outcome shaped by feedback loops from programmatic interventions and external shocks, illustrating principles of adaptivity and emergence (Thurner et al., 2018). Likewise, Social Network Theory informs our treatment of trust and norms as relational constructs that mediate adoption and collective action (Sih et al., 2009). Thus, our framework implicitly integrates both perspectives to explain how local interactions shape system-level adoption patterns.\u003c/p\u003e"},{"header":"2. RESULTS","content":"\u003ch2\u003e\u003cem\u003e2.1 Direct predictors of adoption\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTable 3 presents the findings from the unweighted logistic regression analysis examining the influence of SEC, psychological, contextual and emergent factors on the adoption of the community-based water purification technology. Higher perceived severity of waterborne infections, greater perceived cost-effectiveness and ease of access, stronger descriptive norms, and more trust in the implementing agency were all significantly associated with the adoption of the community-based water purification technology. Furthermore, it was discovered that having family members help with water collection was substantially linked to the adoption. Adoption was significantly influenced by emergent factors, with political changes showing strong significance (p = 0.001), while the Jal Jeevan Mission had a weaker yet notable, negative effect (p = 0.1)\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2 Impact of emergent factors on psychological variables\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eLinear regression analysis (Table 4) shows that emergent factors greatly impact several psychological variables associated with the adoption of community-based water purification systems. Among these, COVID-19 and the implementation of large-scale water initiatives by the government such as the Jal Jeevan Mission (JJM) exhibited the most widespread and statistically significant effects across multiple psychological constructs. COVID-19 was found to have a strong positive effect on numerous psychological determinants. It was associated with higher perceived safety (\u0026beta; = 0.39, p \u0026lt; 0.001), taste (\u0026beta; = 0.38, p \u0026lt; 0.001), accessibility (\u0026beta; = 0.54, p \u0026lt; 0.001), time benefits (\u0026beta; = 0.59, p \u0026lt; 0.001), cost-effectiveness (\u0026beta; = 0.53, p \u0026lt; 0.001), and ability (\u0026beta; = 0.48, p \u0026lt; 0.001) of the community-based water purification technology. Additionally, it was linked to greater psychological ownership (\u0026beta; = 0.60, p \u0026lt; 0.001) and trust in the water committee (\u0026beta; = 0.42, p \u0026lt; 0.001), likely due to increased reliance on and appreciation for community-based services during the pandemic. Interestingly, COVID-19 was also associated with reduced perceptions of vulnerability (\u0026beta; = -0.27, p \u0026lt; 0.001) and severity (\u0026beta; = -0.21, p \u0026lt; 0.001) related to waterborne illness, possibly because of enhanced health awareness and preventive behaviors during the pandemic, which may have made individuals feel more protected from water-related health threats. JJM, in contrast, had negative associations with several psychological variables, suggesting a more complex influence. It was strongly associated with reduced perceptions of safety (\u0026beta; = -0.92, p \u0026lt; 0.001), taste (\u0026beta; = -0.89, p \u0026lt; 0.001), access (\u0026beta; = -1.09, p \u0026lt; 0.001), time benefit (\u0026beta; = -1.04, p \u0026lt; 0.001), cost-effectiveness (\u0026beta; = -1.10, p \u0026lt; 0.001), and ability (\u0026beta; = -1.04, p \u0026lt; 0.001) of the community-based water purification technology. Additionally, JJM implementation was significantly associated with lower levels of psychological ownership (\u0026beta; = -1.04, p \u0026lt; 0.001), trust in both the implementing agency (\u0026beta; = -0.60, p \u0026lt; 0.001) and the water committee (\u0026beta; = -0.86, p \u0026lt; 0.001), as well as decreased perceptions of descriptive norms (\u0026beta; = -0.45, p \u0026lt; 0.001) and injunctive norms (\u0026beta; = -0.98, p \u0026lt; 0.001). These results may indicate that the top-down implementation of JJM could have undermined local ownership and trust, thereby reducing psychological engagement with the community-based technology.\u003c/p\u003e\n\u003cp\u003ePolitical changes had relatively limited influence on psychological variables. However, significant positive associations were observed for perceived severity (\u0026beta; = 0.19, p \u0026lt; 0.01) and descriptive norms (\u0026beta; = 0.25, p \u0026lt; 0.05). Natural hazards, had a significant negative impact on several psychological factors including: increased perceived vulnerability (\u0026beta; = 0.21, p \u0026lt; 0.1), perceived severity (\u0026beta; = 0.18, p \u0026lt; 0.01), and perceived ability (\u0026beta; = 0.30, p \u0026lt; 0.01), potentially reflecting a heightened awareness of water-related risks and a greater sense of personal efficacy following crisis events. At the same time, hazards were linked to lower perceived cost-effectiveness (\u0026beta; = -0.39, p \u0026lt; 0.001), time benefit (\u0026beta; = -0.28, p \u0026lt; 0.01), and ease of access (\u0026beta; = -0.27, p \u0026lt; 0.01), suggesting that logistical and economic concerns after the hazard, may have been exacerbated. Notably, hazards also eroded injunctive norms (\u0026beta; = -0.29, p \u0026lt; 0.01), trust in the implementing agency (\u0026beta; = -0.29, p \u0026lt; 0.001), trust in the water committee (\u0026beta; = -0.25, p \u0026lt; 0.01), and psychological ownership (\u0026beta; = -0.28, p \u0026lt; 0.01). These statistically measurable effects on psychological determinants illustrate how emergent behavior, arising from unplanned shocks, modifies key drivers of behavior change. These shifts in psychological determinants lay the groundwork for examining their indirect effects on adoption, which we explore in the next section.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3 Indirect Pathways to Adoption: Path Analysis Using a Two-Stage Regression Approach\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eLogistic regression analyses using JJM-predicted psychological factors (variables derived from the first-stage regression model where emergent factors e.g., JJM, COVID, HAZARD are used to estimate their influence on psychological determinants \u0026ndash; see Table 5) revealed that descriptive norms, access, cost-effectiveness, and ability significantly predicted Jivamritam adoption, even after controlling for socioeconomic and other variables. In contrast, models using COVID-predicted psychological factors (Table 6) did not show significant associations with adoption outcomes. Similarly, models using hazard-predicted psychological factors (Table 7) revealed significant negative associations through norms, access, cost-effectiveness and ability, indicating that the logistical and institutional disruptions following hazards may have weakened confidence in and feasibility of using the technology. The results suggests that JJM had a stronger indirect influence on Jivamritam adoption through psychological determinants, whereas natural hazard-related impacts were associated with a significant but negative indirect influence and COVID-related influences showed no significant indirect effect. Together, these findings demonstrate that emergent factors shape adoption not just directly, but through their influence on key psychological mediators. To further interpret these findings, we compared the coefficients of key psychological predictors across the unweighted logistic regression model and those using psychological factors predicted by emergent conditions (Table 8). JJM-predicted psychological variables exhibit substantially stronger positive effects on adoption than in the baseline model. This suggests that large-scale policy initiatives amplify key psychological drivers\u0026mdash;such as descriptive norms, perceived access, and ability\u0026mdash;by enhancing visibility, institutional legitimacy, and confidence in adoption. Although COVID-19 improved psychological perceptions, these positive orientations did not translate into significant behavioral adoption. This indicates that favourable perceptions alone were insufficient to overcome operational or institutional constraints during the pandemic. Under hazard-predicted conditions, psychological variables exhibit strong negative coefficients, implying that disruptions can invert behavioral relationships. Logistical breakdowns and uncertainty appear to have weakened community trust and perceived capability, turning previously positive drivers into deterrents to adoption.\u003c/p\u003e"},{"header":"3. DISCUSSION","content":"\u003cp\u003eCommunity-based water purification systems are widely employed in rural India to ensure that people have access to safe drinking water. This study examined how emergent contextual factors, including political changes, national government-led water initiative-JJM, COVID-19 pandemic and natural hazards, influenced both the psychological determinants and the adoption of a community-based water purification technology. The findings demonstrate that certain factors have an immediate, direct effect on adoption behavior, while others operate indirectly by modifying key psychological determinants that mediate the process of behavior change.\u003c/p\u003e \u003cp\u003ePolitical changes exhibited a strong and direct effect on technology adoption, indicating that changes in local leadership or party affiliations play an instrumental role in shaping community behaviors. When leadership changes occur, trust in existing water programs may be re-evaluated, which can either accelerate or hinder adoption. This finding is consistent with prior studies emphasizing the role of political factors in shaping public perceptions of new technologies (McDonald, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Okada \u0026amp; Samreth, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the indirect influence of political changes on psychological determinants appears more limited. Significant associations were found only with perceived severity and descriptive norms, suggesting that politically driven narratives might have heightened public awareness of water-related risks and established community expectations around technology use. This divergence between strong direct and limited indirect effects underscores the idea that while political context can be pivotal in shaping immediate adoption behaviors, it may not necessarily align with the internal psychological mechanisms typically emphasized in behavior change models.\u003c/p\u003e \u003cp\u003eAlthough COVID-19 did not emerge as a significant direct predictor of Jivamritam adoption in the logistic regression model, it was associated with a strong indirect influence on adoption by improving multiple psychological variables associated with behavior change. COVID-19 was associated with significant increases in perceived safety, taste, ease of access, time savings, cost-effectiveness, and ability related to the water purification technology. Additionally, it enhanced trust in the water committee and psychological ownership, both important constructs in community-based interventions. Interestingly, COVID-19 was also associated with decreased perceptions of vulnerability and severity related to waterborne diseases. This counterintuitive result could be explained by greater health awareness and hygiene practices during the pandemic, which may have made individuals feel more protected, thereby lowering their perceived risk from water-related illnesses. Despite these positive psychological shifts, the lack of a significant direct effect of COVID-19 on adoption behavior implies that while the pandemic improved many enabling and motivational factors, it was not sufficient on its own to translate these changes into higher adoption rates, in line with past studies (Sahin \u0026amp; Sahin, 2022). This underscores the importance of active institutional or policy support in transforming favourable perceptions into tangible behavior change.\u003c/p\u003e \u003cp\u003eIn contrast to COVID-19, JJM was associated with a direct negative influence on adoption and showed strong negative associations with multiple psychological factors, indicating that the presence of alternative government-led initiatives may deter engagement with community-based systems like Jivamritam. Specifically, JJM significantly reduced psychological ownership and trust in both the implementing agency and the local water committee, while also lowering perceived descriptive and injunctive norms. Although the direct negative effect of JJM on adoption was only marginally significant, its strong negative associations with key psychological factors, many of which significantly predicted adoption, suggest that government-led water initiatives shaped behavior through both direct and indirect pathways by altering core psychological drivers, in line with past studies (Pahl-Wostl, et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFinally, natural hazards showed a more complex and predominantly indirect influence on adoption. Natural hazards were associated with increased perceptions of vulnerability and ability, suggesting that exposure to crisis events may heighten risk awareness and foster a sense of personal efficacy. Hazard experiences undermine individuals\u0026rsquo; psychological readiness by weakening social norms, reducing perceived access and ability, and increasing perceived costs, thereby discouraging adoption of community-based water purification technology. These adverse perceptions could stem from disruptions in water services or increased logistical barriers during and after hazard events, which may have made access to the technology appear less reliable or desirable. The absence of a significant direct effect on adoption, highlights that while crises can alter psychological orientations, they may not be sufficient to drive behavioral change in the absence of supportive infrastructure or sustained trust, again inline with past studies (Grothmann \u0026amp; Reusswig, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Natural hazards trigger emergent behavioral shifts by increasing perceived vulnerability and self-efficacy, reflecting a spontaneous prioritization of water safety even without external interventions. While these psychological changes do not always translate into direct adoption, often due to heightened access or cost barriers, they highlight how emergent behavior can redirect the adoption pathway through alternative, unstructured routes shaped by crisis exposure rather than formal programmatic efforts. A comparative analysis of coefficient magnitudes further revealed that emergent factors modify the strength and direction of psychological factors\u0026mdash;amplifying them under JJM, attenuating them under COVID-19, and reversing them under hazard conditions\u0026mdash;demonstrating that the relationship between psychology and behavior is dynamic rather than static.\u003c/p\u003e \u003cp\u003eOverall, these findings emphasize that adoption is influenced by planned interventions as well as unplanned emergent behaviors. Communities do not simply follow a linear path toward adoption dictated by structured behavior change models. Instead, they respond adaptively to political, environmental, and programmatic disruptions, reshaping both psychological orientations and behavioral outcomes. This adaptive response is consistent with the concept of emergent behavior, wherein communities self-organize in reaction to shifting external conditions. Such insights necessitate a reconceptualization of the traditional Theory of Change (ToC) to include emergent behavior as a contextual factor that modifies both psychological determinants and the mechanisms of adoption. Contextual factors within ToC influence psychological determinants (for example beliefs, perceived ability and social norms) together with pathways of change (mechanisms through which adoption takes place). Emergent behavior operates in a similar way; it modifies psychological determinants by reshaping risk perceptions, social norms, and trust in response to external disruptions. At the same time, it alters paths of change by generating unplanned adoption pathways beyond those anticipated in structured interventions. Since contextual factors in ToC influences both psychological determinants and paths of change, classifying emergent behavior as a contextual factor maintains theoretical consistency. Traditional ToC framework (Contzen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) view interventions as following predictable, step-by-step processes leading to behavioural change. The Enhanced Theory of Change (ToC) proposed in this study extends the traditional ToC by explicitly recognizing emergent behavior as a key driver of adoption and psychological factors, that interacts with structured interventions. Rather than assuming a sequential path from input to behavior, the Enhanced ToC acknowledges that adoption can stem from the interplay between top-down programs and bottom-up community responses. Psychological determinants such as beliefs, perceived ability and social norms are not static; they evolve in response to fluctuating external conditions. For example, a public health emergency can significantly reshape risk perceptions and institutional trust, which in turn alter patterns of adoption in ways not predicted by conventional ToC logic. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the Enhanced ToC, illustrating how emergent factors interact with psychological determinants to shape technology adoption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis approach emphasizes that adoption pathways evolve under changing contextual conditions, rather than following fixed sequences. Recognizing such dynamics can help refine how interventions are adapted over time, while acknowledging that not all emergent community responses are predictable or can be directly managed. Ultimately, this paper argues for a Theory of Change that is not only predictive but also capable of accounting for the emergent and context-dependent dynamics that shape behavioral change.\u003c/p\u003e \u003cp\u003eThe findings offer useful guidance for policymakers, NGOs, and program implementers, highlighting the importance of emergent community behaviors when designing interventions to improve the adoption of community-based water purification technologies. Introduction of large-scale government schemes like JJM may unintentionally influence the adoption of community-based systems by altering local psychological and social drivers. It is important for such programs to better align with local interventions to avoid conflicting messaging or undermining community ownership. Communities often make adoption decisions not solely based on the practical benefits of a technology but also in response to broader social and political cues. Political leadership can influence adoption, especially when local leaders visibly support the intervention. Engaging them in outreach efforts may help build credibility and trust. Findings also highlight that beyond physical infrastructure improvements, interventions must also focus on rebuilding psychological confidence, reinforcing positive norms, and addressing fear arising from past natural hazards, to effectively promote community-based water technology uptake in vulnerable communities. In post-crisis settings, such as after a pandemic, restoring trust in the implementing agency and water committees becomes essential. Lastly, behavioral strategies that enhance perceptions of safety, ease of access, cost-effectiveness and community endorsement may support adoption.\u003c/p\u003e \u003cp\u003eTo our knowledge, this study is among the first to integrate emergent behaviour into a behavior change framework to understand the adoption of community-based water purification technology in rural India. By examining how external influences, such as national water programs and political shifts, interact with psychological determinants, the study provides a more nuanced understanding of technology adoption. It\u0026rsquo;s focus on both direct and indirect pathways to adoption, strengthens its empirical contribution, while the incorporation of emergent contextual behaviour represents theoretical advancement. While this study does not include longitudinal data to capture abrupt behavioral shifts before and after external events (e.g., COVID-19, natural hazards), it quantifies significant changes in psychological determinants such as perceived access, cost-effectiveness, ability, and trust, that are statistically linked to these events. Drawing on Fromm\u0026rsquo;s typology, we interpret these patterns as Type II (weak emergence) and Type IV (strong emergence), depending on whether the community\u0026rsquo;s responses were self-organized or consciously coordinated. This typological framing offers a valid lens through which emergent behavior can be understood, even in the absence of time-series data, by highlighting how perceptions and social processes evolve adaptively in response to systemic disruptions. Future research should differentiate between types of emergent community responses, such as adaptive, resistant, or substitutive behaviours to better understand their distinct effects on adoption. Third, while we propose an Enhanced Theory of Change framework that incorporates emergent factors, the conceptual integration could be further refined, for example, a clearer differentiation between emergent behavioural drivers and traditional structural factors, along with further theoretical refinement to improve its broader applicability. Additionally, political and hazard-related variables were necessarily broad, which may have masked more localized or nuanced patterns of influence. Future research could benefit from incorporating qualitative methods to explore how communities interpret and respond to government initiatives like JJM. Longitudinal designs would also help clarify how emergent factors influence adoption over time, and tools such as network analysis or agent-based modelling could provide insights into how community-level interactions shape behavioral change. This study, based on data from 54 communities, highlights the need for continuous learning and flexibility in designing water security strategies.\u003c/p\u003e"},{"header":"4. METHODS","content":"\u003ch2\u003e\u003cem\u003e4.1 Study Sample : Jivamritam\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eJivamritam represents a community-based water purification program operating in India that empowers communities to analyse and control their water issues to improve overall water sustainability. Since its launch in 2017, Jivamritam has been introduced in approximately 300 rural communities (Ajith et al., 2022). The initiative is a joint effort that involves Amrita University, local government agencies, NGO and communities. The adoption process occurred in phases; some communities embraced the technology readily, while others required additional support before accepting it. We carried out a quantitative analysis to investigate the effects of psychological variables, contextual factors and emergent behavior on Jivamritam adoption, drawing on the Theory of Change (ToC) framework (Contzen et al., 2023) and qualitative observations from the field. Using logistic regression, we examined these associations through a cross-sectional study across six Indian states\u0026mdash;Kerala, Karnataka, Andhra Pradesh, Odisha, Uttar Pradesh, and Himachal Pradesh\u0026mdash;where the technology was already in place. From an initial pool of 300 communities, 75 with severe water contamination were shortlisted. Of these, 64 showed interest, and 54 were ultimately selected for data collection (Fig.3), ensuring diverse cultural, geographical and environmental representation (Dheer et al., 2015).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.2. Sampling Strategy and Data Collection\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe data collection process happened through structured in-person interviews with household members of participating communities, over six weeks during July and August 2023. A modified random route sampling technique (Hoffmeyer-Zlotnik, 2003) served as a method to ensure impartial household selection. The survey assigned five zones for each community by using recognizable landmarks and randomly assigned interviewers across these zones. Each survey zone began at a randomly selected house after which interviewers followed a predetermined sequence to talk with every alternate house. Of all surveyed households, only 89 (8%) declined to participate in the study while the rest contributed to a very high survey response rate.\u003c/p\u003e\n\u003cp\u003eInterviews were conducted with the people in charge of collecting water for the family. Given the high rates of illiteracy in some communities, informed consent was sought verbally or in writing, with the former being documented by the interviewer through a signed declaration and participation was completely voluntary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical clearance for this study was obtained from the Institutional Human Ethics Committee (IHEC), Amrita Vishwa Vidyapeetham (Ref. No. IHEC/2023/10/01). All methods were carried out in accordance with relevant guidelines and regulations. The survey research required written permission from local government authorities who granted authorization in each participating region. The interview process lasted around 45 minutes while the respondents could choose between Malayalam, Hindi, Kannada, Telugu or Odia based on their native language. The interviews were conducted by a team of four interviewers, including the local village coordinator, university employee, a member of the Jivamritam implementation team and the first author. The local field research facilitator along with the first author provided oversight to all interviewers during data collection while each interviewer underwent particular training for interview methods. The Empower mobile application served as the tool for survey questionnaire (Aiswarya et al., 2023 https://empower.amrita.edu/#) to speed up the interview process.\u003c/p\u003e\n\u003cp\u003eIn all, 894 participants from 54 distinct rural Indian communities took part in the study. With ages ranging from 19 to 77 years (M = 48.66, SD = 8.32), 70% of the respondents were female. In terms of education, 28% had finished primary school, 64% had completed secondary school, 4% had no formal education, and 4% had a university degree or equivalent higher credential. Additionally, 91% of the households in question were considered to be below the poverty line.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3. Questionnaire and measures \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eInterviews for the study were conducted using a structured survey, with an emphasis on demographic data, drinking water sources and psychological factors that contributed to the adoption of Jivamritam. In a brainstorming session with members of the Jivamritam implementation team and technical experts, questions were developed in English and discussed collectively. To ensure the accuracy of the translations, the final questionnaire was back-translated into English after being translated into five regional languages spoken by the participating communities. After a pretest with 15 participants from communities where Jivamritam had been deployed, additional changes were made to improve the clarity of some of the questions. The particular measurements used in this study are explained in the sections that follow.\u003c/p\u003e\n\u003ch3\u003e4.3.1 ToC : Psychological and contextual factors\u003c/h3\u003e\n\u003cp\u003eThe adoption of water purification technology is motivated by people\u0026rsquo;s understanding of health risks because individuals with knowledge regarding waterborne illnesses view themselves at greater risk, by consuming untreated water (Anthonj et al., 2022; Daniel et al., 2020; D\u0026eacute;molis et al., 2018; Huber \u0026amp; Mosler, 2013). The adoption rates of water purification technology increase when users perceive various benefits such as cost-effectiveness, time-savings and easy access to these technologies (Huber et al., 2014; Lilje \u0026amp; Mosler, 2018; Boone et al., 2011; Contzen \u0026amp; Marks, 2018). People who trust the water committee members and implementing agency responsible for water system maintenance will likely adopt the technology because they perceive reduced risks (Contzen et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to perceived descriptive norms, community members become more inclined to adopt and sustain use of water purification technology when they believe many of their peers already accept it (Altherr et al., 2008; Contzen \u0026amp; Marks, 2018; Huber et al., 2014; Lilje et al., 2015; Ajith et al., 2022). According to perceived injunctive norms, people are more likely to adopt technology when they believe that others expect them to (Contzen et al., 2023; Lilje \u0026amp; Mosler, 2018). When community members treat the water purification system as if it were their own and take full responsibility for its upkeep and operation, this is referred to as psychological ownership of the technology. Technology adoption should increase through the development of psychological ownership among end-users (Ambuehl et al., 2021; Contzen \u0026amp; Marks, 2018). The adoption rate is noticeably impacted by perceived ability which integrates technical troubleshooting skills with familiarity towards technology and resource provisioning with the ability to handle obstacles steadfastly (Contzen \u0026amp; Marks, 2018; Daniel et al., 2019; Heri \u0026amp; Mosler, 2008; Huber et al., 2012; Murray et al., 2020).\u003c/p\u003e\n\u003cp\u003eRANAS model was used to assess the psychological aspects influencing the adoption of Jivamritam (Mosler, 2012; Mosler \u0026amp; Contzen, 2016). RANAS model integrates psychological elements from prominent behavior change theories into five comprehensive factor groups: Risk, Attitudes, Norms, Abilities, and Self-Regulation. It was created for the water, sanitation, and hygiene sector in developing nations. Additionally, three particular elements were included: psychological ownership, trust, and perceived risks of utilizing or not using the technology. Table 1-SD in the supplementary material contains comprehensive details regarding the wording and response scales used to measure these psychological factors as well as the sources. Except for psychological ownership, which had three items, each ToC element was represented by a single item. These three items had a high level of internal consistency (Cronbach\u0026apos;s alpha = 0.95), so their answers were aggregated to get a composite score. A 5-point Likert scale was used for responses on all items.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe adoption of community-based water purification technology is also explained by contextual factors. For example, a household\u0026apos;s distance from the technology may affect how much effort is thought to be necessary to utilize it because a greater proximity lowers the time and physical demands of collecting water, making the system more convenient and accessible and increasing adoption (Boone et al., 2011). Also, the perceived difficulty of getting water is reduced when more family members participate, which encourages the use of water purification technologies (Boone et al., 2011). Additionally, the socioeconomic characteristics (SEC) of community members were included. Two SEC variables were chosen from previous research because of their proven correlations with the adoption of household water treatment (HWT): wealth (Opryszko et al., 2010; Roma et al., 2014) and education level (Fotue et al., 2012; Freeman et al., 2012; Nauges \u0026amp; Van Den Berg, 2009) (Daniel et al., 2022). The government\u0026apos;s ration card system was utilized to categorize households and distinguished between those living below and above the poverty line (Boo et al., 2024). To determine the respondents\u0026apos; highest level of education, the response categories \u0026quot;None,\u0026quot; \u0026quot;Primary,\u0026quot; \u0026quot;Secondary,\u0026quot; and \u0026quot;Graduate or Above\u0026quot; were employed.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e4.3.2 Determinants of Emergent Behaviour\u003c/h3\u003e\n\u003cp\u003eIn the case of Jivamritam adoption, emergent behaviour may be the outcome of policy changes, dynamic reactions to external events or social interactions. Four binary variables were used to capture emergent contextual factors, based on community responses to whether the adoption of Jivamritam was affected by the COVID-19 pandemic, the national government led initiative - Jal Jeevan Mission, political changes, or natural hazards, with each variable coded as 1 for \u0026ldquo;yes\u0026rdquo; and 0 for \u0026ldquo;no\u0026rdquo; based on specific survey questions (see Table 2).\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eImpact of COVID-19\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCOVID-19 pandemic has majorly impacted WASH services, highlighting that more investment, better access, and community engagement are needed to protect public health (Desye, 2021). The risk of health hazards has made local communities demand for more purified water as an adaptive response. This is an example of emergent behavior as a result of collective decision-making rather than direct policy interventions (Brown et al. 2014). COVID-19 pandemic made people more conscious of good hygiene and the need for safe drinking water, which increased the demand for purified water in some communities. It, however, also raised concerns about the use of shared facilities (Hayashi et al. 2022). \u0026nbsp;The use of community-based water purification systems faces resistance from people because they worry about viruses spreading through common water points. This again is an example of emergent behavior, which shows that groups\u0026apos; reactions to external shocks can change adoption patterns in ways that are hard to predict.\u003c/p\u003e\n\u003col start=\"2\" style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eImpact of Jal Jeevan Mission (JJM) Introduction\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe Jal Jeevan Mission (JJM) is a national government-led initiative aimed at providing tap water to all rural households in India by 2024 with the help of sustainable water management and community participation (Balamurugan et al. 2024). But its impact on community-based water purification technology adoption depends on how communities interpret and integrate this policy within their local context. Studies show that policy changes produce unexpected behavioral changes such as, causing people to use alternative technologies (Koessler \u0026amp; Engel, 2021). This best aligns with the emergent behavior concept, where systemic responses to new regulations evolve through decentralized social interactions (Muneepeerakul \u0026amp; Anderies, 2017).\u003c/p\u003e\n\u003col start=\"3\" style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eImpact of Political Changes\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePolitical changes, such as changes in local governance, influence public trust, allocation of funds and the level of community participation in community service initiatives (Kumar Singh, 2021). With the world\u0026apos;s second-largest population and a rich history of local governance reforms, India provides valuable insights into these interconnections. These factors may indirectly shape technology adoption patterns, even though Jivamritam itself is not directly influenced by political changes. Emergent behavior characterizes the unpredictable reactions of communities toward governance changes (Lawlor \u0026amp; Neal, 2016).\u0026nbsp;\u003c/p\u003e\n\u003col start=\"4\" style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eImpact of Natural Hazards\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eLocal priorities shift when environmental shocks such as floods or droughts occur because they create increased need for water purification solutions and necessitate hazard relief efforts (Herlambang, 2010). This aligns with studies on crisis-driven behavioral shifts, where communities self-organize and adapt their decision-making in response to external stressors (Clavijo \u0026amp; Monta\u0026ntilde;o, 2022). In the case of Jivamritam, such events may trigger unexpected adoption patterns, illustrating how emergent behavior arises from adaptive responses rather than planned interventions.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.4 Data analysis procedure\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e\u0026quot;Do you regularly use water from Jivamritam in your community?\u0026quot; was the question used to gauge the response variable, Jivamritam adoption, and the answer was binary: 0 indicates no, whereas 1 indicates yes. It is hypothesized that a logistic distribution governs the likelihood of adopting Jivamritam. R version 4.2.3 was used for all analyses (R Core Team 2023). An unweighted logistic regression model was used to assess the influence of contextual, psychological, socioeconomic (SEC) factors as well as determinants of emergent behavior on the adoption of water purification technology (Daniel et al., 2022; Kraemer \u0026amp; Mosler, 2010). To characterize the respondents\u0026apos; overall socio-economic status (SEC), we combined two factors\u0026mdash;wealth and education level\u0026mdash;using Principal Component Analysis (PCA) (Houweling et al., 2003). The first main component scores, or SEC, were then used in the study. Among the independent variables were SEC, psychological effects, contextual factors and determinants of emergent behavior.\u003c/p\u003e\n\u003cp\u003eAdditionally, unweighted multiple linear regression was used to evaluate how emergent factors influence individual psychological determinants. This helped to quantitatively assess the impacts that external disruptions like political changes and nationwide water programs, have psychological determinants of technology adoption. Each psychological factor (e.g. perceived vulnerability, severity, safety, taste, access, cost, time, ability, descriptive norms, injunctive norms, trust in implementing agency and water committee, psychological ownership) was modelled individually using emergent factors as predictors, with statistical significance assessed through p-values and corresponding coefficient estimates reported. The regression model took the following form.\u003c/p\u003e\n\u003cp\u003eYi = \u0026beta;0 + \u0026beta;1(COVID-19) + \u0026beta;2(JJM) + \u0026beta;3(Political) + \u0026beta;4(Hazards) + \u0026epsilon;i\u003c/p\u003e\n\u003cp\u003ewhere Yi represents the psychological determinant for a community member and \u0026beta;k are the estimated coefficients representing the influence of each emergent factor. This approach allows us to examine whether emergent factors influence not only adoption outcomes (as shown through logistic regression) but also the psychological determinants that shape behavioral decisions. Similar approaches have been used in behavioral and environmental studies to assess the role of contextual and psychological factors in shaping technology adoption (Gong et al., 2020). Following this, a second-stage logistic regression was conducted using only those emergent factors that had shown significant effects on psychological determinants in the first-stage analysis. To address potential multicollinearity among psychological predictors and to isolate the unique contribution of each psychological determinant influenced by emergent contextual factors (e.g. JJM), we employed a series of separate logistic regression models. This approach allowed us to examine the individual impact of each emergent-predicted psychological factor on adoption outcomes while controlling for key covariates such as SEC, political changes, COVID-19 and natural hazards. From the list of significantly affected psychological variables, we prioritized those that have been identified in prior literature (e.g., Mosler, 2012; Daniel et al., 2022) as core determinants of adoption in the context of water-related technologies. Specifically, perceived access, cost-effectiveness, ability, and descriptive norms have been consistently shown to influence adoption decisions in low-resource settings. This two-stage approach enabled the identification of both the indirect and direct pathways through which emergent events influence technology adoption behavior. Emergent factors were not treated as formal instrumental variables intended to correct for endogeneity. Instead, they were employed to simulate how external disruptions reshape psychological determinants and, in turn, influence technology adoption behavior. By comparing coefficient magnitudes from the baseline (unweighted) logistic regression with those from models using emergent-predicted psychological variables, we captured the behavioral change that occurs before and after external shocks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis project has been funded by the E4LIFE International Ph.D. Fellowship Program offered by Amrita Vishwa Vidyapeetham\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e\n\u003cp\u003eThe authors express their immense gratitude to Sri. Mata Amritanandamayi Devi (Amma), Chancellor of Amrita Vishwa Vidyapeetham, who has inspired them in performing selfless service to society. We extend our gratitude to the Amrita Live-in-Labs\u0026reg; academic program for providing all the support. This research is part of the Joint Center of Excellence in Water Sustainability, a collaborative initiative between Amrita Vishwa Vidyapeetham and Delft University of Technology.\u003c/p\u003e\n\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e\n\u003cp\u003eConceived and designed the research: M.R., S.P. and M.V. Conducted the research: M.R. Analyzed the results: M.R., S.P. Writing\u0026mdash;original draft: M.R. Writing\u0026mdash;review \u0026amp; editing: M.R., S.P. and M.V.\u003c/p\u003e\n\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAiswarya, A., Guntha, R., Ajith, V., Reshma, A. S., Suresh, S., Amrita, K., ... \u0026amp; Charan, P. S. V. V. (2023, October). Community empowerment through geospatial data collection. Presenting the Empower App: A secure, scalable and configurable mobile platform. In 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 1100\u0026ndash;1105). 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Social network theory: new insights and issues for behavioral ecologists. Behavioral Ecology and Sociobiology, 63, 975-988. \u003c/li\u003e\n\u003cli\u003eStates, S. (2020). Epidemic/pandemic emergency planning for water utilities. Journal - American Water Works Association, 112(12), 26.\u003c/li\u003e\n\u003cli\u003eThompson, M. (2015). A critical review of water purification technology appropriate for developing countries: Northern Ghana as a case study. Desalination and Water Treatment, 54(13), 3487\u0026ndash;3493.\u003c/li\u003e\n\u003cli\u003eThurner, S., Hanel, R., \u0026amp; Klimek, P. (2018). Introduction to the theory of complex systems. Oxford University Press. \u003c/li\u003e\n\u003cli\u003eWHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene. (2023). https://washdata.org\u003c/li\u003e\n\u003cli\u003eYu, Y., \u0026amp; Wei, J. (2022). Analysis of the influence of agricultural natural HAZARD on farmers\u0026rsquo; technology adoption decision. Frontiers in Environmental Science, 10, 923694.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Typology of Emergent Behaviour. Adapted from (Provitolo et al., 2011)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmergent Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExamples\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType II (Weak Emergence)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eBehaviour emerges from local interactions without central coordination.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eDecentralised, self-organising, often unplanned or subconscious.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eNatural hazards - spontaneous reprioritisation of water needs\u003cbr\u003e\u0026nbsp;Political change - reduced trust and disengagement\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType IV (Strong Emergence)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eBehaviour reflects conscious awareness of systemic change and collective action.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eDeliberate, coordinated or semi-coordinated adaptation to known external shocks.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 176px;\"\u003e\n \u003cp\u003eCOVID-19 - adoption of hygiene practices and water safety measures\u003cbr\u003e\u0026nbsp;JJM - intentional re-evaluation of community water systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Determinants of Emergent Behavior\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003eDeterminants of Emergent Behavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 306px;\"\u003e\n \u003cp\u003eQuestion wording\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eImpact of COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eDo you think the COVID-19 pandemic has had an impact on the adoption of the Jivamritam water purification system in your community?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(Berglund et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eImpact of Jal Jeevan Mission (JJM)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eHas the introduction of the Jal Jeevan Mission (JJM) by the Indian government influenced the adoption of Jivamritam in your community?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(Barstow et al., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eImpact of Political Changes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eHave any political changes, such as a change in the ruling party at the community level, influenced the adoption of Jivamritam in your community?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(Ajith et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 208px;\"\u003e\n \u003cp\u003eImpact of Natural hazards\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 306px;\"\u003e\n \u003cp\u003eHave external events such as natural hazards (e.g., floods, droughts, cyclones) influenced the adoption of Jivamritam in your community?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(Yu \u0026amp; Wei, 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Unweighted logistic regression of SEC, contextual, psychological and emergent behavior factors on Jivamritam adoption (N=894)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"664\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.50 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived Safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived good taste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived ease of access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.99 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived time benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived cost effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.49 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePerceived ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eDescriptive Norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.74 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e4.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eInjunctive norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eProximity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eFamily members assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e2.56 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eTrust in implementing agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.89 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eTrust in water committee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePsychological ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e-0.78 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e-1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003ePolitical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e1.18 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 229px;\"\u003e\n \u003cp\u003eHazards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 201px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 664px;\"\u003e\n \u003cp\u003eAIC = 371.7 ; BIC = 468.3 ; R2 = 0.67 ; * p \u0026lt; .1. \u0026nbsp; ** p\u0026lt; .01. \u0026nbsp; *** p\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Linear regression results showing the impact of emergent factors on psychological determinants (N=894)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"868\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 197px;\"\u003e\n \u003cp\u003ePsychological Determinants (DV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCOVID-19 (IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 158px;\"\u003e\n \u003cp\u003eJJM (IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePolitical (IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003eHazards (IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.27 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.29 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.21 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived Severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.21 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.27 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.19 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.18 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived Safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.39 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.92 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.22 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived good taste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.38 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.89 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived ease of access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.54 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.09 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.27 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived time benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.59 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.04 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.28 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived cost effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.53 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.10 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.39 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePerceived ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.48 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.04 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.30 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eDescriptive Norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.12 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.45 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.25 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eInjunctive Norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.49 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.98 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.29 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eTrust in implementing agency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.60 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.29 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eTrust in water committee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.42 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.86 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.25 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003ePsychological ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.60 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-1.04 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e-0.28 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 868px;\"\u003e\n \u003cp\u003eDV \u0026ndash; Dependent variable; IV \u0026ndash; Independent variable; * p \u0026lt; .1. \u0026nbsp; ** p\u0026lt; .01. \u0026nbsp; *** p\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e. Logistic regression results showing Jivamritam adoption using JJM-predicted psychological Factors\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_DESC_JJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.90 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_ACCESS_JJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.61 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_COST_JJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_ABILITY_JJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.69 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 592px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: pred_DESC_JJM, pred_ACCESS_JJM, pred_COST_JJM, and pred_ABILITY_JJM are predicted psychological factors based on regressions using JJM as the independent variable. * p \u0026lt; .1. \u0026nbsp; ** p\u0026lt; .01. \u0026nbsp; *** p\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e Logistic regression results showing Jivamritam adoption using COVID-predicted psychological Factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 160px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003epred_DESC_COVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 160px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003epred_ACCESS_COVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 160px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003epred_COST_COVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 160px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003epred_ABILITY_COVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.55 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 595px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e pred_DESC_COVID, pred_ACCESS_COVID, pred_COST_COVID, and pred_ABILITY_COVID are predicted psychological factors based on regressions using COVID as the independent variable. * p \u0026lt; .1. \u0026nbsp;** p\u0026lt; .01. \u0026nbsp; *** p\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e. Logistic regression results showing Jivamritam adoption using JJM-predicted psychological Factors\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 177px;\"\u003e\n \u003cp\u003eIndependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_DESC_HAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.05 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_ACCESS_HAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.07 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_COST_HAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.40 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003epred_ABILITY_HAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.84 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePOLITICAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.76 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 592px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: pred_DESC_HAZARD, pred_ACCESS_HAZARD, pred_COST_HAZARD, and pred_ABILITY_HAZARD are predicted psychological factors based on regressions using HAZARD as the independent variable. * p \u0026lt; .1. \u0026nbsp; ** p\u0026lt; .01. \u0026nbsp; *** p\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8\u003c/strong\u003e. Comparison of coefficients for psychological factors across models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePsychological Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eUnweighted logistic regression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eJJM-predicted coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCOVID-predicted coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eHazard-predicted coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDescriptive Norms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.74 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3.90 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;2.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;4.05 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePerceived ease of access\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.99 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.61 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;0.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;2.07 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePerceived cost effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.49 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.59 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;0.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;1.40 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePerceived ability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.69 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ndash;1.84 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community-based water management, Emergent Behavior, Technology Adoption, Behavioral Pathways, Community interventions","lastPublishedDoi":"10.21203/rs.3.rs-8186808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8186808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe adoption of community-based water purification technologies in India is often influenced by external disruptions, such as political transitions, large-scale national water initiatives and public health crises. While traditional technology adoption frameworks assume a structured pathway for behavior change, this study highlights the role of emergent factors in shaping both psychological determinants and adoption patterns. Using data from 54 communities and applying logistic and linear regression models, our analysis shows that major external disruptions, like political shifts and large-scale nationwide initiatives play a significant role in shaping adoption decisions. Furthermore, linear regression analysis shows that these emergent factors also impact key psychological determinants, which mediate the decision-making process: perceived risk, trust and social norms. These results suggest that external disturbances not only alter adoption but also change the behavioral paths through which it happens. We propose an Enhanced ToC framework that incorporates determinants of emergent behavior as contextual factors that dynamically shape both individual decision-making and more general adoption patterns. Our results suggest that interventions aimed at improving safe water access should remain responsive to emergent contextual factors, as community responses evolve dynamically in response to political, environmental, and programmatic changes.\u003c/p\u003e","manuscriptTitle":"Emergent Behaviour and the Adoption of Community-Based Water Purification Technology in Rural India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 14:06:38","doi":"10.21203/rs.3.rs-8186808/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80535e98-49bd-4593-b97b-2229ab971268","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60009777,"name":"Humanities/Complex networks"},{"id":60009778,"name":"Social science/Complex networks"},{"id":60009779,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":60009780,"name":"Social science/Environmental studies"},{"id":60009781,"name":"Biological sciences/Psychology"},{"id":60009782,"name":"Social science/Psychology"},{"id":60009783,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-03-17T05:10:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 14:06:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8186808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8186808","identity":"rs-8186808","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}