Assessing Judicial Readiness for Artificial Intelligence Adoption: Functional, Normative, and Ethical Drivers from Bangladesh

Assessing Judicial Readiness for Artificial Intelligence Adoption: Functional, Normative, and Ethical Drivers from Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Judicial Readiness for Artificial Intelligence Adoption: Functional, Normative, and Ethical Drivers from Bangladesh Shiakh Md Mujahid Ul Islam, Dr. Muhammad Mehedi Masud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9275906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Globally, Artificial Intelligence (AI) is being used consistently as a means of improving procedural transparency, uniformity, and efficiency in conventional judicial processes designed by substantive and procedural laws of the countries. With an emphasis on six areas—evidence analysis, sentencing, case management, transparency and accountability, investigation and interagency coordination, and ethical safeguards—this study empirically investigates the judicial preparedness for AI adoption in the judicial process in Bangladesh. The study examines how views of judges regarding AI use are influenced by functional benefits and ethical readiness using Partial Least Squares Structural Equation Modeling (PLS-SEM) and a quantitative, cross-sectional survey of 100 judicial officers from various court tiers. The results show that while AI-assisted sentencing and investigative cooperation have non-significant effects on court readiness, views of AI-assisted case management, ethical protections, transparency, and rule-of-law compliance have a substantial impact. The significance of ethical governance, human oversight, and institutional trust is further highlighted by qualitative interviews, which show that judges largely see AI as a decision-support tool rather than a replacement for judicial discretion. By incorporating socio-legal and technological governance viewpoints into a model of judicial AI adoption, this paper makes a theoretical contribution. It also offers empirical support by presenting data from emerging nations. From a practical standpoint, the findings help judicial administrators and lawmakers create AI interventions that improve judicial efficiency, preserve procedural integrity, and protect public trust. The small sample size, Bangladeshi emphasis, and absence of other stakeholders like attorneys and court employees are among the limitations, which point to areas for further study on the deployment of AI in judicial and institutional settings. Clinical trial number: not applicable. Artificial Intelligence and Machine Learning Criminal Law Artificial Intelligence Judiciary Judicial Readiness Case Management Transparency Ethics Bangladesh PLS-SEM Figures Figure 1 Figure 2 1. Introduction Judicial institutions in technologically advanced nations have been incorporating artificial intelligence (AI) more and more into their fundamental operations, such as case administration, evidence evaluation, and, in certain situations, sentence assistance. To address systemic inefficiencies and mounting caseload pressures, nations including the US, China, and EU members have used AI-driven solutions (Choi et al., 2020 ; Zhang & Kloza, 2021 ). AI has the potential to increase administrative effectiveness, procedural consistency, and transparency in the administration of justice as a tool for technological rationalization (Surden, 2019 ; Sourdin, 2021 ). It commonly alleges that AI threatens judicial discretion, procedural fairness, accountability, explainability, and data ethics, even while technology may speed up adjudication and reduce some human errors (Carothers, 1998 ; Dicey, 2013 ; Ji, 2020 ). These issues have an impact on judicial legitimacy, which depends on independence, reasoned judgment, and public trust in addition to efficiency. Thus, a major theme in current discussions about AI-assisted justice is the conflict between legal normativity and technological usefulness. AI usage in court systems is still relatively low in the Global South. In the context of Bangladesh, several initiatives are taken, such as Judicial Monitoring Dashboard (My Court App), while creating a platform ( www.judiciary.gov.bd ) to give information relating to judicial services, such as an inheritance calculator, judipay, e-filing, e-certified copy, etc. (Sourav, 2025 ). Judicial digitalization has been hampered by structural issues such as insufficient infrastructure, institutional inertia, procedural conservatism, and shortages in technological capacity. Calls for change have increased due to persistent backlogs of more than four million cases, as well as perceptions of political interference and inefficiency (Rahman, 2020). AI integration is still mostly in the exploratory stage, despite recent e-judiciary initiatives showing a desire to embrace digital transformation (New Age, 2022). Importantly, there is a dearth of empirical studies on how judges view the use of AI, specifically concerning practicality, moral protections, institutional compatibility, and consequences for the rule of law. By empirically examining judicial perceptions of AI deployment in Bangladesh, with an emphasis on case management and evidence analysis as crucial technical interventions, this study fills this knowledge gap. Judicial readiness, which is based on a socio-legal framework of technological governance, is said to be influenced by both practical advantages and moral considerations. While ethical considerations include judicial autonomy, procedural fairness, accountability, and data protection, functional benefits include efficiency, transparency, uniformity, and interagency coordination. The main research issue is whether overall judicial readiness is influenced by perceived functional benefits of AI that outweigh ethical concerns. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the structural relationships among six latent constructs: (B1) AI in Evidence Analysis; (B2) AI in Sentencing; (B3) AI in Case Management; (B4) Transparency, Accountability, and Rule-of-Law; (B5) AI in Investigation and Inter-Agency Collaboration; and (B6) Ethical Concerns. The study involved a quantitative, cross-sectional survey of 100 judicial officers. The dependent variable is judicial preparedness for adopting AI. Three main contributions are made by this work. First, it closes a significant empirical gap by offering unique factual data on judges' opinions of AI deployment in emerging nations. Second, by showing how judicial attitudes toward technological innovation are mediated by legal culture, institutional trust, and ethical norms, it advances socio-legal knowledge of AI governance. Thirdly, it provides a model-based analytical approach that connects empirical measures of institutional preparation with normative legal theory. Overall, the results help guide Bangladesh's judicial modernization efforts and add to larger discussions about the morally sound and law-abiding use of AI in legal systems. 2. Conceptual Framework The conceptual framework of this study explores judicial readiness for AI deployment in Bangladesh, incorporating socio-legal theories of technological governance and the digital rule of law (Ji, 2020 ; Sourdin, 2021 ; Sun & Xiao, 2024 ). Judicial preparedness is defined as a product of an evaluation process where projected functional benefits of AI are assessed against ethical protections and institutional conditions. Acceptance of AI is thus understood as a socio-institutional construct, mediated by legal culture, ethical awareness, and trust in governance, rather than a simply technological or efficiency-driven consequence. The conceptual framework comprises six dimensions: AI in Evidence Analysis, AI in Sentencing, AI in Case Management, Transparency, Accountability and Rule of Law, AI in Investigation and Inter-Agency Collaboration, and Ethical Concerns, which are considered factors that influence Judicial Readiness for AI Adoption. 3. Literature Review and Hypothesis Development The literature on artificial intelligence (AI) in judicial systems indicates an increasing global engagement with algorithmic technology as a tool of judicial modernization. AI has been employed to help case management, evidence analysis, and procedural administration, aiming to promote efficiency, consistency, and access to justice. Yet, literature also highlights normative concerns, like judicial independence, accountability, transparency, and respect for the rule of law (Surden, 2019 ; Sourdin, 2021 ; Ji, 2020 ). AI adoption is thus regarded as a governance challenge at the nexus of law, ethics, and institutional architecture. Comparative evidence from advanced jurisdictions suggests that AI is most extensively employed in administrative and decision-support roles, including docket management, predictive analytics, and digital evidence processing (Choi et al., 2020 ; Zhang & Kloza, 2021 ). While these applications improve efficiency, questions exist around algorithmic opacity, bias, data quality, and the potential erosion of judicial discretion. Judicial acceptance of AI is therefore dependent upon rigorous legal safeguards, openness, and ethical oversight, ensuring AI complements rather than substitutes judicial reasoning. In Bangladesh, judicial digitalization has advanced through legal and institutional measures, including the Usage of Information and Communication Technology by Courts Act 2020, the National AI Strategy 2020, and the e-Judiciary Programme. Practical solutions such as the MyCourt App and digital case filing aim to increase procedural efficiency. However, empirical studies reveal that digitization alone has not appreciably decreased backlogs or boosted judicial performance, stressing the significance of judicial readiness and institutional trust in facilitating AI implementation. Building on this context, judicial readiness for AI deployment is characterized as affected by perceived functional benefits, ethical legitimacy, and compliance with judicial norms, including independence, discretion, procedural fairness, and accountability. The study investigates hypotheses across six dimensions: AI in evidence analysis, AI in sentencing, AI in case management, transparency and accountability, AI in investigation and inter-agency collaboration, and ethical problems. 3.1 Global Integration of AI in Judicial Processes AI integration represents a radical shift in judicial systems, supporting case management, evidence analysis, sentencing, and procedural administration. These techniques are meant to promote efficiency, consistency, and transparency, but must be carefully placed inside normative frameworks to ensure judicial discretion and accountability (Ji, 2020 ; Sun & Xiao, 2024 ). Comparative studies reveal that adoption is context-dependent, impacted by legal tradition, institutional capability, procedural safeguards, and ethical monitoring. Common law jurisdictions emphasize administrative and decision-support applications, although civil law systems have sometimes dabbled with predictive analytics and standardized decision-making. Across contexts, transparency, explainability, accountability, and human monitoring are crucial to sustaining legitimacy. Against this context, judicial readiness for AI adoption is defined as a multidimensional entity, operationalized through six interconnected dimensions (B1–B6), covering both functional and normative variables. 3.1.1 AI in Evidence Analysis AI-assisted evidence analysis has emerged as a transformational tool in contemporary court processes, giving increased skills in data extraction, document inspection, pattern identification, and forensic analytics. These technologies are particularly beneficial in handling complicated digital evidence, boosting accuracy, consistency, and procedural efficiency, while keeping judicial oversight and discretion. By automating repetitive processes and facilitating quick analysis, AI assists courts in rendering better-informed and speedy rulings without supplanting human judgment. China exhibits the large-scale deployment of AI in judicial evidence evaluation. In over 1,500 criminal cases, methods such as the “206 System” in Shanghai have shown considerable reductions in trial duration and gains in transparency (The Star, 2020). In the United States, AI is extensively employed in e-discovery and digital evidence analysis, where machine learning tools assist judges and litigants in efficiently managing vast volumes of electronically stored information, enabling faster and more precise identification of relevant evidence (Remus & Levy, 2017 ). Metrics such as citation counts, download trends, and adoption rates reflect increased academic and institutional interest, demonstrating that courts worldwide are gradually appreciating the importance of AI-assisted evidence tools (Kerdvibulvech, 2024 ). These studies further reveal that judicial acceptability is increased when AI is viewed as a supportive, rather than a replacement, technology, boosting evidential evaluation while maintaining judicial autonomy. Collectively, these findings show a positive correlation between judicial impressions of AI-assisted evidence processing and institutional preparedness to incorporate AI in judicial procedures, and based on these studies, the first hypothesis is developed; H1: Perception of judges on AI-assisted evidence analysis favorably affects judicial preparedness for AI deployment. 3.1.2 AI in Sentencing AI-assisted sentencing is inherently normatively sensitive, given its implications for judicial discretion, proportionality, and procedural fairness. Artificial Intelligence (AI) technology is increasingly being integrated into judicial systems to provide decision-support tools that enhance the efficiency, consistency, and accuracy of sentencing, while eliminating human prejudice (Pixelplex, 2021 ). These systems do not replace judicial authority but instead give direction, predictive analytics, and document automation to inform court thinking. China and Estonia exhibit large-scale implementation of AI-assisted sentencing. In some jurisdictions, AI systems create suggestive sentencing ranges, indicate procedural abnormalities, and automate administrative documentation, while preserving the final decision-making power with human judges (Ji, 2020 ; Hern, 2019 ). Similarly, common law jurisdictions typically deploy AI for procedural and administrative support, rather than automated determination of penalties, suggesting a careful approach that respects judicial autonomy and due process. Empirical and socio-legal studies reveal that judicial readiness for AI adoption is largely influenced by perceptions of system reliability, fairness, and procedural integrity. Judges are more inclined to adopt AI-assisted sentencing when technology is regarded as boosting consistency, impartiality, and efficiency, without weakening discretion or respect to legal norms. Positive judicial impressions thus serve as a substantial mediator between technical availability and institutional acceptance, and the following hypothesis arises from it. H2: Perceptions of Judges on AI-assisted sentencing favorably influence court readiness for AI adoption. 3.1.3 AI in Case Management AI- assisted case management has become an important instrument for boosting administrative efficiency inside judicial systems. By optimizing scheduling, docket control, and workflow monitoring, AI solutions speed regular operations, eliminate human error, and help courts to handle caseloads more effectively. Within the legal profession, AI-driven case management systems improve efficiency, productivity, accuracy, and client satisfaction by automating repetitive tasks, facilitating faster and more precise evidence analysis, and allowing legal professionals to dedicate greater attention to strategic and client-focused work (Access, n.d.).Empirical research from China, India, and Singapore reveals that AI-assisted case management contributes to reduced case backlogs, faster processing, and increased institutional performance (China Daily, 2025 ; Times of India, 2023). Judges regard these instruments as supporting administrative aids, improving efficiency without encroaching upon adjudicative authority. This impression boosts institutional trust, strengthens confidence in technology integration, and positively affects judge preparation to deploy AI technologies across broader portions of court administration and the third hypothesis is formed on this premise. H3: Perception of Judges on AI-assisted case management favorably influences court readiness for AI implementation. 3.1.4 Transparency, Accountability, and Rule of Law The institutional legitimacy of AI in judicial processes depends on the integration of robust governance structures that assure openness, accountability, and compliance with the rule of law. AI-driven predictive justice systems employ algorithmic analysis and Big Data to anticipate legal choices, thereby lowering ambiguity and boosting consistency in judicial rulings (Masum, 2025 ). Practical experiences in China and Estonia indicate that explainable, auditable AI systems boost judicial oversight, promote procedural fairness, and enhance public confidence in algorithmically supported decisions (Ji, 2020 ). Similarly, programs in Bangladesh, such as the e-Judiciary Programme, expressly emphasize accountable, transparent, and ethical use of AI in judicial processes. Scholars contend that while AI might offer efficiency improvements through automation, its integration must be carefully calibrated to maintain transparency, eliminate prejudice, safeguard due process, and uphold fairness (Tahura & Selvadurai, 2025 ). Collectively, these findings imply that judicial perceptions of the transparency, accountability, and rule-of-law compliance of AI systems are significant predictors of institutional readiness for AI implementation, and the following hypothesis can be drawn on this basis. H4: Court perceptions of openness, accountability, and rule-of-law compliance in AI systems favorably influence court readiness for AI implementation. 3.1.5 AI in Investigation and Inter-Agency Collaboration Artificial Intelligence (AI) has increasingly been applied to judicial investigations and inter-agency collaboration, enabling increased data integration, evidence verification, and coordination across numerous governmental and judicial institutions. By simplifying information flows between courts, law enforcement agencies, and administrative bodies, AI increases institutional efficiency, procedural coherence, and the trustworthiness of evidence management. Experiences in China and India highlight the practical benefits of AI in inter-agency operations. In many jurisdictions, AI technologies offer real-time information sharing, case tracking, and verification of complex evidence, thereby minimizing redundancies, alleviating delays, and boosting the overall effectiveness of the court process (China Daily, 2024; The Business Standard, 2024 ). These data reveal that court preparedness to use AI is positively influenced when these technologies are regarded as boosting oversight and inter-agency efficiency, while protecting judicial independence and procedural integrity. Based on this analysis, the following hypothesis can be developed. H5: Court perceptions of AI-assisted inquiry and inter-agency collaboration positively influence court readiness for AI implementation. 3.1.6 Ethical Concerns Ethical readiness constitutes a fundamental facet of judicial AI adoption, encompassing values like justice, bias mitigation, openness, data protection, human oversight, and gradual implementation. The introduction of rigorous ethical protections boosts the legitimacy and acceptance of AI systems within judicial institutions, increasing the impact of functional benefits—such as efficiency, accuracy, and administrative support—on overall judicial readiness (Masum, 2025 ).AI is most effectively integrated as a complementary decision-support tool, rather than a substitute for human judgment, ensuring that ethical, legal, and procedural norms are upheld. In this context, Judges are more willing to employ AI-assisted systems when they are confident that these technologies are morally calibrated, responsible, and procedurally sound, therefore lowering risks of prejudice, injustice, or loss of due process and this leads to the following hypothesis. H6: Judicial perceptions of ethical protections and readiness positively moderate the association between functional benefits of AI and judicial readiness for AI deployment. The literature highlights that judicial readiness for AI adoption depends on both functional utility and normative validity. Globally, AI promotes efficiency, uniformity, and transparency, while ethical governance, accountability, and adherence to the rule of law are vital for confidence and legitimacy. This study operationalizes six hypotheses reflecting these dimensions, giving an experimentally tested framework to evaluate how judges’ perceptions influence AI deployment inside Bangladesh’s judiciary. The subsequent PLS-SEM analysis examines these correlations, delivering theoretical and practical insights for AI-enabled judicial modernization. 4. Methodology This study adopts a quantitative, cross-sectional survey approach to empirically assess judicial attitudes on artificial intelligence (AI) adoption across Bangladesh’s judiciary. It focuses on AI’s potential contributions to evidence analysis, sentencing, case management, investigative collaboration, transparency, and ethical governance. By combining socio-legal inquiry with empirical modeling, the methodology assesses how functional benefits and normative legitimacy interact in building judicial readiness, coinciding with contemporary ideas on the digital rule of law and technological governance. 4.1 Sampling and Representativeness The target population includes about 1,600 judicial personnel across Bangladesh’s subordinate and superior courts. Through official communication with the Judicial Administration Training Institute (JATI) and follow-ups, 100 judges engaged voluntarily, giving a 6.25% response rate. The sample covers numerous judicial tiers, such as district courts, metropolitan magistrates’ courts, and higher forums. A stratified purposive sampling strategy maintained variation in court level, jurisdiction, and judicial experience. Despite its modest size, the sample is sufficient for Partial Least Squares Structural Equation Modeling (PLS-SEM), which supports smaller samples and intricate path architectures under non-normal data conditions. Bootstrapping with 5,000 resamples boosts inferential reliability. Early–late response comparisons and demographic balance checks were done to mitigate potential biases. The results are seen as suggestive, presenting empirically grounded insights into factors impacting judicial readiness for AI deployment. 4.2 Instrument Development Based on an exhaustive assessment of worldwide literature on the integration of artificial intelligence into judicial systems, a structured survey instrument was carefully constructed and contextualized for the Bangladeshi judiciary. The questionnaire was informed by seminal works from scholars such as Remus and Levy (2016), Sourdin ( 2021 ), and Sun and Xiao ( 2024 ), as well as policy guidelines from the European Commission (2021), ensuring its alignment with global discourse while remaining sensitive to local institutional realities. The instrument was arranged into three coherent portions, each aiming to capture various characteristics of judicial vision and readiness. The first segment focused on the Demographic Profile of respondents, obtaining crucial background information such as judge rank, years of tenure, and past exposure to or expertise with digital court management systems. This data was designed to contextualize responses and discover potential relationships between judge seniority, technological familiarity, and opinions regarding AI deployment. The core of the questionnaire comprised 30 Likert-scale items, systematically divided over six latent variables developed from the study’s conceptual framework. These structures were: AI in Evidence Analysis — examining perceptions of AI’s utility in analyzing digital evidence, document analysis, and forensic data. AI in Sentencing - examining viewpoints on the use of algorithmic technologies in sentencing recommendations and consistency. AI in Case Management – evaluating opinions on AI-driven scheduling, backlog reduction, and process automation. Transparency, Accountability, and Rule of Law - assessing concerns relating to explainability, judicial scrutiny, and adherence to legal norms. AI in Investigation and Inter-Agency Collaboration — evaluating openness to AI-facilitated coordination between courts, law enforcement, and other government entities. Ethical Concerns and Readiness — exploring apprehensions around prejudice, data privacy, human dignity, and institutional preparedness for ethical AI governance. All items featured a six-point Likert scale, ranging from Strongly Disagree (1) to Strongly Agree (5), purposely excluding a neutral midpoint to encourage deliberate stance-taking by respondents. This scaling strategy was used to enhance measurement clarity and eliminate ambivalence in responses. Finally, the instrument contained a section of Open-ended Questions, allowing participants to expound qualitatively on institutional constraints, ethical concerns, training needs, and the practical viability of AI-driven judicial changes in Bangladesh. These comments were meant to provide nuanced insights beyond quantitative measurements, capturing the perspectives and reflections of judicial officers in their own words. Collectively, these constructs operationalize the study’s central hypothesis: that judicial readiness for AI adoption is not merely a function of technological access, but is fundamentally shaped by the perceived functional benefits of AI applications in judicial work, balanced against ethical preparedness and institutional confidence in maintaining transparency, accountability, and the rule of law. The instrument thus acts as both a diagnostic tool and a foundational resource for policy creation targeted at responsible and context-sensitive AI integration in the Bangladeshi judicial system. 4.3 Data Collection and Analysis Data were collected over six weeks in November and December 2025 using safe online platforms and formal correspondence. Initial screening and coding were performed in SPSS to address missing values, assess normalcy, and create descriptive statistics. Subsequent analyses were undertaken in SmartPLS 4, permitting simultaneous estimation of measurement and structural models within the PLS-SEM framework. 4.4 Measurement Model Assessment The measurement model was carefully validated to confirm the reliability and validity of the built latent variables, in accordance with accepted psychometric standards for structural equation modelling. This review entailed the use of three major criteria to confirm that the conceptions were both theoretically sound and empirically robust. First, internal consistency reliability was assessed using two complementary indicators: Cronbach’s Alpha and Composite Reliability (CR). Both metrics were calculated for each latent construct to verify that the items inside each dimension consistently measured the same underlying notion. Acceptable thresholds were maintained, with values exceeding the specified minimum of 0.70, validating the scale's dependability and the coherence of the theoretical notions. Second, convergent validity was examined by the Average Variance Extracted (AVE). This measure analyzes the extent to which items within a construct converge or share a significant proportion of variance. An AVE value larger than 0.50 was retained as the benchmark, showing that more than half of the variance in the observed variables was accounted for by the latent construct, thereby proving appropriate convergent validity and validating the soundness of the measurement model. Finally, discriminant validity was assessed using the Heterotrait-Monotrait ratio (HTMT) of correlations. This advanced criterion evaluates whether theoretically distinct conceptions are truly experimentally distinguished. An HTMT value below the cautious threshold of 0.85 was used, ensuring that each construct recorded a unique dimension of judicial perception toward AI adoption, without unnecessary overlap with other constructs in the model. Together, these analytical steps—evaluating internal consistency, convergent validity, and discriminant validity—provided a full validation of the measurement model. This comprehensive assessment guarantees that the latent constructs are not only statistically trustworthy but also conceptually different, so building a solid empirical foundation for subsequent hypothesis testing and structural analysis within the study 4.5 Structural Model Evaluation The structural model examined hypothesized associations (H1–H6) using path coefficients, t-statistics, and p-values obtained by bootstrapping with 5,000 resamples. Explanatory power was assessed by the coefficient of determination (R²), and effect sizes (f²) evaluated the substantive influence of each predictor. Predictive relevance (Q²) was examined using the blindfolding process. The moderating influence of Ethical Concerns and Readiness was explored by introducing interaction terms between ethical protections and each functional component, analyzing how ethical preparedness shapes the link between AI functionality and judicial readiness. 5. Empirical Analysis This study uses Partial Least Squares Structural Equation Modeling (PLS-SEM), a robust and commonly used technique for evaluating causal links across latent constructs obtained via survey data. PLS-SEM is particularly useful for exploratory research using complicated models. Both reflective measurement model assessment and structural model evaluation were accomplished within this paradigm. The study’s independent variables contained six latent constructs: CMD: AI in Case Management and Disposal of Non-Cognizable Offences. EA: AI in Evidence Analysis. TAR: Transparency, Accountability, and Rule of Law. SENT: AI in Sentencing. INV&C: AI in Investigation and Inter-Agency Collaboration. ETH: Ethical Concerns and Readiness. The dependent variable was Judicial Readiness for AI Adoption. Functional constructs (CMD, EA, TAR, SENT, INV&C) were modeled as direct predictors, while ETH served as a moderator impacting the strength of functional factors on Judicial Readiness. To detect any common method bias coming from self-reported survey data, diagnostic tests were undertaken in SPSS before the PLS-SEM analysis. 5.1 Demographic Profile of the Respondents One hundred judicial officers took part. The composition of the judiciary was mirrored in the gender distribution, which was 80.3% male (n = 80), 18.2% female (n = 18), and 1.5% (n = 2) "prefer not to say." The age distribution showed that the majority were in the mid-career range: 63.6% were between the ages of 35 and 45, 25.8% were under 35, 7.6% were between the ages of 46 and 55, and 1.5% were over the age of 56. In terms of professional readiness, 19.7% (n = 15) had some training in legal technology or artificial intelligence, but 80.3% (n = 85) had no formal training. Digital court systems, such as e-filing, virtual hearings, and digital case management platforms, were at least somewhat familiar to all of the respondents. The magnitude of the judicial workload varied greatly among jurisdictions, with pending cases ranging from 170 to 11,000 and annual case disposals from 100 to 4,000. Table 1 presents the demographic data of the judges. Category Sub-category Frequency (n) Percentage (%) Gender Male 80 80.3% Female 18 18.2% Prefer not to say 2 1.5% Age Group 35 26 25.8% 35–45 64 63.6% 46–55 8 7.6% 56+ 2 1.5% Training in AI / Legal Technology Yes 15 15.2% No 85 84.8% Case Load (Pending) Lowest pending cases — 170 cases Highest pending cases — 11,000 cases Typical range (majority of respondents) — 800–3,200 cases 5.2 Reflective Measurement Model Results—Reliability and Validity Reliability was evaluated by looking at the objects' outer loadings. According to Chin (1998), loadings greater than 0.707 signify sufficient dependability. Table 2 confirms satisfactory item validity by demonstrating that all constructs satisfy this requirement. Table 2 Outer loadings of the measurement model Variables Outer Loadings AI in Evidence Analysis E A1 0.759 E A1 0.792 E A1 0.831 E A1 0.798 AI in Sentencing Sentencing 1 0.751 Sentencing 2 0.735 Sentencing 3 0.526 Sentencing 4 0.582 AI in Case Management and Disposal of Non-Cognizable Offences CMD 1 0.893 CMD 1 0.816 CMD 1 0.875 CMD 1 0.914 Transparency, Accountability, and Rule of Law T R A 1 0.870 T R A 2 0.839 T R A 3 0.798 T R A 4 0.838 AI in Investigation and Inter-Agency Collaboration Inv & C 1 0.847 Inv & C 1 0.863 Inv & C 1 0.824 Inv & C 1 0.698 Ethical Concerns and Readiness Ethical 1 0.786 Ethical 1 0.909 Ethical 1 0.799 Judicial Readiness Judicial Readiness 1 0.776 Judicial Readiness 2 0.724 Judicial Readiness 3 0.665 Judicial Readiness 4 0.529 Judicial Readiness 5 0.795 Judicial Readiness 6 0.797 Judicial Readiness 7 0.649 Average Variance Extracted (AVE) and Composite Reliability (CR) were used to evaluate convergent validity (Hair et al., 2010). Except for SENT, all constructs had CR values greater than 0.70 and AVE values greater than 0.50, showing acceptable reliability and convergent validity. Table 3 Construct reliability and validity CMD Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) 0.898 0.901 0.929 0.766 E A 0.807 0.811 0.873 0.633 Ethical 0.784 0.859 0.872 0.694 Inv & C 0.823 0.825 0.884 0.657 Readiness 0.833 0.843 0.876 0.505 Sentencing 0.767 0.703 0.747 0.500 T A R 0.857 0.860 0.903 0.700 HTMT, cross-loadings, and Fornell-Larcker (1981) approaches were used to evaluate discriminant validity. Items loaded higher on their intended constructs, as proven by cross-loadings (Table 5 ) and the Fornell-Larcker criterion (Table 4 ). With the exception of CMD–Readiness (0.934) and CMD–SENT (0.988), most HTMT values were below 0.90. The absence of 1 in bootstrapped confidence intervals supported empirical distinctiveness. Since these constructs reflect interconnected elements of judicial decision-making, elevated HTMT scores were theoretically warranted. Table 4 Discriminant Validity: Employing the Fornell-Larcker criterion CMD CMD E A Ethical Inv & C Readiness Sentencing T A R 0.875 E A 0.486 0.795 Ethical 0.478 0.151 0.833 Inv & C 0.725 0.566 0.388 0.810 Readiness 0.808 0.587 0.550 0.728 0.711 Sentencing 0.727 0.552 0.367 0.527 0.694 0.656 T A R 0.588 0.661 0.190 0.641 0.685 0.535 0.836 The second method of assessing discriminant validity is to compare the "cross-loadings" between variables. Cross-loadings were calculated to evaluate the items' equal loading on their targeted constructs and other constructs (see Table 5 ). The intended construct must have a greater loading than other constructs in order for cross-validated items to be included in the final dataset. The current study met this condition, guaranteeing that the analysis contained cross-validated items. Table 5 Cross-loadings CMD 1 CMD E A Ethical Inv & C Readiness Sentencing T A R 0.893 0.345 0.433 0.599 0.733 0.668 0.485 CMD 2 0.816 0.496 0.325 0.612 0.648 0.524 0.531 CMD 3 0.875 0.421 0.429 0.667 0.722 0.651 0.538 CMD 4 0.914 0.448 0.478 0.663 0.721 0.693 0.508 E A 1 0.322 0.759 0.025 0.433 0.449 0.418 0.544 E A 2 0.367 0.792 0.057 0.308 0.434 0.336 0.449 E A 3 0.416 0.831 0.180 0.516 0.436 0.480 0.534 E A 4 0.431 0.798 0.201 0.524 0.533 0.507 0.565 Ethical 2 0.364 -0.019 0.786 0.220 0.374 0.159 0.000 Ethical 1(21) 0.531 0.222 0.909 0.464 0.585 0.417 0.278 Ethical 3 0.239 0.124 0.799 0.218 0.362 0.291 0.136 Inv & C 1 0.654 0.431 0.299 0.847 0.575 0.441 0.559 Inv & C 2 0.538 0.608 0.262 0.863 0.637 0.497 0.597 Inv & C 3 0.502 0.531 0.194 0.824 0.513 0.343 0.555 Inv & C 4 0.641 0.260 0.479 0.698 0.610 0.405 0.364 Readiness 1 0.664 0.431 0.614 0.562 0.776 0.650 0.497 Readiness 2 0.485 0.487 0.420 0.446 0.724 0.490 0.553 Readiness 3 0.634 0.361 0.390 0.564 0.665 0.368 0.440 Readiness 4 0.479 0.323 0.198 0.398 0.529 0.258 0.415 Readiness 5 0.520 0.404 0.388 0.493 0.795 0.491 0.441 Readiness 6 0.613 0.488 0.458 0.556 0.797 0.587 0.481 Readiness 7 0.598 0.403 0.186 0.575 0.649 0.523 0.574 Sentencing 1 0.532 0.661 0.183 0.489 0.575 0.751 0.633 Sentencing 2 0.575 0.215 0.507 0.331 0.550 0.735 0.261 Sentencing 3 0.424 0.147 0.116 0.237 0.292 0.526 0.062 Sentencing 4 0.335 0.340 0.029 0.269 0.299 0.582 0.323 T A R 1 0.524 0.526 0.151 0.507 0.566 0.464 0.870 T A R 2 0.470 0.592 0.074 0.489 0.500 0.492 0.839 T A R 3 0.473 0.487 0.222 0.495 0.589 0.360 0.798 T A R 4 0.497 0.606 0.175 0.637 0.622 0.477 0.838 The Heterotrait–Monotrait (HTMT) ratio was used to further evaluate discriminant validity (Henseler, Ringle, & Sarstedt, 2015). All of the HTMT ratios are below the suggested cut-off of 0.90, as seen in Table 6 . Adequate discriminant validity is supported by bootstrapped confidence intervals, which verified that none of the HTMT estimations include 1. Table 6 Heterotrait–Monotrait ratio (HTMT) CMD CMD E A Ethical Inv & C Readiness Sentencing T A R E A 0.571 Ethical 0.537 0.222 Inv & C 0.641 0.687 0.441 Readiness 0.734 0.709 0.636 0.672 Sentencing 0.788 0.756 0.511 0.728 0.724 T A R 0.671 0.791 0.227 0.758 0.809 0.699 5.3 Assessment of the Structural Model Results Inner VIF values, which range from 1.359 to 3.526 and are all below the crucial threshold of 5.0, were used to analyze predictor collinearity. This suggests that multicollinearity is not a significant concern. 500 resamples were bootstrapped to test for path significance. Judicial Readiness is strongly influenced by CMD (β = 0.324, t = 3.320, p = 0.001), ETH (β = 0.240, t = 3.405, p = 0.001), and TAR (β = 0.238, t = 2.809, p = 0.005), according to the data (Table 7 ). There was no statistical significance for SENT, INV&C, or EA. Figure 2 presents the structural model of AI use by judges. Table 7 The Results of the Structural Model Hypothesis coefficient Std error Standard deviation (STDEV) T statistics (|O/STDEV|) P values CMD -> Readiness 0.324 0.307 0.098 3.320 0.001 E A -> Readiness 0.096 0.094 0.098 0.978 0.328 Ethical -> Readiness 0.240 0.248 0.071 3.405 0.001 Inv & C -> Readiness 0.128 0.130 0.094 1.366 0.172 Sentencing -> Readiness 0.123 0.139 0.088 1.396 0.163 T A R -> Readiness 0.238 0.234 0.085 2.809 0.005 5.4 Structural Path Coefficients, R², f², and Predictive Relevance According to Cohen (2013), the model has a modest explanatory power, accounting for 42% of the variance in judicial readiness (R2 = 0.42). According to effect sizes (f2), TAR, ETH, and CMD appear to have minor to moderate effects. Adequate predictive capacity is confirmed by predictive relevance (Q² > 0). Additionally examined were the control factors of location, education, gender, and age. Judicial Readiness was strongly impacted by geography (β = 0.171, p < 0.05), education (β = 0.071, p < 0.05), and gender (β = 0.106, p Readiness VIF 3.526 E A -> Readiness 2.098 Ethical -> Readiness 1.359 Inv & C -> Readiness 2.690 Sentencing -> Readiness 2.459 T A R -> Readiness 2.310 6. Analysis and Findings 6.1 Finding 1: AI-Assisted Evidence Analysis and Judicial Readiness The study demonstrates that judicial views of AI-assisted evidence analysis (EA) exert a beneficial influence on court preparedness for AI adoption (β = 0.096), suggesting convergence between quantitative PLS-SEM results and qualitative insights. Judges consider AI as a tool that can improve the speed and accuracy of evidence review (EA1; Mean = 3.369), reduce human error (EA2; Mean = 3.692), and enhance judgment quality in complicated cases (EA3; Mean = 3.492). The strongest recommendation was for judicial access to AI tools for examining evidence (EA4; Mean = 4.000), demonstrating institutional openness toward integrating AI inside ordinary judicial operations. Interview data reflect these findings, with judges stressing AI’s capacity to help analytical clarity in cases requiring massive digital records, forensic datasets, and electronic conversations. Critically, AI was portrayed as a decision-support mechanism rather than a substitute for judicial discretion. This accords with prevalent jurisprudential norms that defend adjudicative autonomy, indicating that acceptance is predicated upon AI operating as an augmentative rather than intrusive device. The conclusion highlights that AI readiness is function-specific, stronger when it enhances procedural efficiency without encroaching on basic judicial judgment. 6.2 Finding 2: AI-Assisted Sentencing and Judicial Readiness Judicial readiness was not substantially impacted by perceptions of AI-assisted sentencing (β = 0.123, t = 1.396, p = 0.163). According to descriptive statistics, judges have cautious attitudes. There is moderate agreement on other measures (Means = 3.185–3.262) and strong agreement only on AI's capacity to improve sentencing uniformity (Sentencing 2; Mean = 4.108). These findings imply that judges distinguish between adjudicative responsibility and operational support. Sentencing is still firmly within the human realm as a field that calls for moral reasoning, contextual judgment, and subjective assessment. Despite the recognized technical advantages, AI-assisted sentencing does not determine overall preparedness, which reflects the judiciary's normative commitment to moral accountability and procedural fairness. This illustrates professional boundaries where technology enhances rather than replaces human judgment, supporting a function-specific model of AI acceptance. 6.3 Finding 3: AI-Assisted Case Management and Judicial Readiness Judicial impressions of AI in case management and disposition of non-cognizable offences (CMD) considerably boost readiness (β = 0.324, t = 3.320, p = 0.001). High mean ratings across CMD indicators (3.738–4.123) demonstrate broad unanimity on AI’s potential to improve administrative efficiency, workflow optimization, and quick disposal of routine cases. Qualitative statistics reinforce this view, emphasizing AI’s involvement in backlog reduction, procedural tracking, and clerical workload alleviation, freeing judges to concentrate on substantive legal thinking. Unlike sentencing, case management responsibilities are non-discretionary, making them particularly susceptible to automation. This places CMD as a crucial entry point for AI deployment in court systems, indicating that acceptance is strongest where efficiency improvements correspond with professional norms and do not threaten judicial independence. 6.4 Finding 4: Transparency, Accountability, and Rule-of-Law Compliance (TAR) The readiness of AI systems is positively and statistically significantly impacted by judicial opinions of TAR compliance (β = 0.238, t = 2.809, p = 0.005). As prerequisites for adoption, judges repeatedly underlined the importance of responsibility, transparency, and conformity to legal standards (Means = 3.492–3.754). According to qualitative findings, institutional trust is crucial; for AI outputs to be accepted by judges, they must be comprehensible, accountable, and compliant with the law. "Black-box" AI models have been found to threaten public trust and procedural justice. According to these findings, adoption is significantly facilitated by governance-oriented elements such as verifiable accountability, ethical and legal compliance, and procedural clarity. As a result, judicial preparedness is firmly rooted in institutional and moral validity rather than being exclusively a consequence of technological capabilities. 6.5 Finding 5: AI-Assisted Investigation and Inter-Agency Collaboration (INV&C) Readiness for AI use was not substantially impacted by judges' positive ratings of AI-assisted investigative and inter-agency collaboration capabilities (Means = 3.831–4.200) (β = 0.128, t = 1.366, p = 0.172). This implies that judges uphold distinct institutional boundaries: executive-stage duties like coordination and inquiry are seen as separate from judicial duties. The direct effect of AI on readiness is constrained by issues with the contestability, procedural integrity, and evidential reliability of AI-generated outputs. Judges underlined the judiciary's cautious attitude to tasks that overlap with prosecutorial or administrative authorities by emphasizing that AI in investigative contexts does not transfer into higher adoption willingness without transparency and verifiability. 6.6 Finding 6: Ethical Safeguards and Judicial Readiness (ETH) Overall preparedness of judges for AI use is strongly influenced by judicial views on ethical protections (β = 0.240, t = 3.405, p = 0.001). High mean scores (4.385–4.569) show that justice, accountability, transparency, and bias mitigation are strongly supported as prerequisites for the adoption of AI. Research indicates that judicial receptivity requires ethical protections. The lack of normative guarantees leads to doubts about judicial independence and the defense of fundamental rights, even in cases when AI provides technical efficiency. Thus, ethical readiness acts as a major facilitator of adoption across domains and modifies opinions regarding the validity of AI's functional advantages. The broader idea that legitimacy supports technology acceptance in public governance is reflected in this study, which emphasizes that the judiciary's adoption of AI is ethically contingent. All of the results point to a model of judicial preparedness for AI adoption that is function-specific, governance-sensitive, and morally grounded. Applications like evidence analysis and case management that improve operational effectiveness and decision support without compromising discretionary authority are the most prepared. On the other hand, even in cases when technical advantages are present, topics that are thought to be fundamental to adjudicative judgment—like punishment or investigatory oversight—evoke cautious involvement. The analysis emphasizes how crucial it is to include AI into frameworks that are open, accountable, and compliant with the law, all while maintaining strong ethical standards. These results imply that a balanced approach is needed for the judiciary to deploy AI effectively: focusing on high-acceptance functions, maintaining judicial autonomy, and integrating governance and ethical standards into the technology's use. This strategy is consistent with the larger body of jurisprudential discussion on responsible technological governance, procedural justice, and the rule of law in developing nations. 7. Discussion and Conclusion This paper presents a detailed evaluation of judicial readiness for AI deployment across Bangladesh’s judiciary, combining empirical survey data with qualitative insights from judicial interviews. The analysis underlines the connection between technological functionality, ethical governance, and rule-of-law compliance, revealing insights of both theoretical and practical value for AI integration in judicial systems. 7.1 Function-Specific Patterns of AI Adoption Results show that judicial adoption of AI is rather function-specific. Judges' recognition of AI as a tool to optimize workflows, reduce clerical burdens, and improve evidentiary review is reflected in their positive perceptions of AI applications that support decision-making and enhance administrative efficiency, such as evidence analysis (EA; β = 0.096) and case management (CMD; β = 0.324, t = 3.320, p = 0.001). Judges emphasize AI as a decision-support tool that maintains judicial discretion while enhancing analytical clarity, especially in instances involving extensive records or intricate forensic evidence, according to qualitative interviews. Conversely, inter-agency investigative functions (Inv & C; β = 0.128, p = 0.172) and AI-assisted sentencing (Sentencing; β = 0.123, p = 0.163) generated cautious or non-significant answers. These findings highlight the continued significance of contextual judgment and judicial discretion in adjudicative procedures. The institutional and normative limits of AI acceptance were highlighted by judges' perceptions of AI interventions in these areas as potentially invasive. By showing that perceived utility depends on both technological performance and the nature of judicial functions, this task-specific adoption pattern expands on current technology adoption models like the Technology Acceptance Model (TAM) and Task-Technology Fit (TTF). 7.2 Ethical and Governance Considerations Critical factors of judicial preparation were shown to be normative governance and ethical protections (ETH; β = 0.240, t = 3.405, p = 0.001), highlighting the fact that institutional legitimacy and ethical preparedness are fundamental to AI deployment rather than incidental. On measures of fairness, accountability, transparency, and bias mitigation, judges expressed high agreement (Means = 4.385–4.569), suggesting that normative protections are necessary for fostering trust in AI systems. Qualitative findings also show that ethical frameworks serve as a lens through which functional benefits are evaluated, highlighting the significance of auditability, traceable outputs, and "ethics-by-design" in preserving judicial independence. Rule-of-law alignment, accountability, and transparency all had a similar impact on readiness (TAR; β = 0.238, t = 2.809, p = 0.005), demonstrating the importance of governance-oriented design for AI adoption. Collectively, these results expand socio-legal research on technology integration in crucial public institutions by indicating that ethical and governance factors influence the legitimacy of AI adoption across functional domains. 7.3 Theoretical and Practical Implications The study adds to the body of literature in several ways: Task-Specific Legitimacy: The perceived suitability of AI in relation to task sensitivity determines judicial preparedness. Adjudicative discretion is still safeguarded, but administrative and supportive duties are widely accepted. This provides empirical support for the theoretical claim that, in situations involving power and accountability, institutional legitimacy moderates the adoption of technology. Integration of Functionality, Ethics, and Governance: The study presents a socio-technical framework for judicial AI adoption by integrating normative protections, institutional trust, and functional performance (AI capabilities). It shows that adoption is influenced by a combination of legal compliance, ethical readiness, and technological usefulness. Context-Specific Insights: Using a developing-country judiciary as its setting, the study shows how readiness is influenced by judge training, caseload demands, and demographic characteristics. It also offers a context-sensitive model for the adoption of AI in developing legal systems. Practically speaking, the results guide implementation and policy strategies: Phased Deployment: AI should be introduced initially in high-acceptance domains (case management, evidence analysis) to build confidence before expanding to sensitive adjudicative areas (sentencing, investigation). Governance and Ethics Safeguards: To promote legitimacy and trust, it is crucial to establish audit procedures, ethics-by-design, and open AI workflows. Capacity Building: AI literacy, ethical comprehension, and useful abilities for using AI tools should all be developed in judicial training programs. Infrastructure and Multi-Stakeholder Engagement: Adoption, procedural justice, and institutional legitimacy will all be improved by sufficient technology platforms and collaborative design procedures involving judges, court employees, attorneys, and legislators. 7.4 Limitations and Future Research Scope Despite its contributions, the study contains shortcomings that suggest paths for further research: Limited Global Literature: Scarce empirical research on judicial AI impedes cross-national contextualization. Future comparative research could investigate adoption, ethics, and governance practices across jurisdictions. Contextual Focus: The findings are peculiar to Bangladesh, and caution is needed in generalizing conclusions to other legal systems. Replication in varied institutional settings is warranted. Stakeholder Scope: The study focuses on judges’ perceptions. Multi-stakeholder viewpoints, including lawyers, court workers, and technology vendors, could offer a more thorough knowledge of adoption dynamics. Longitudinal and Impact Studies: Future studies might track court readiness and AI adoption over time, and assess real-world effectiveness, procedural fairness, and ethical outcomes. 8. Conclusion This study shows that judicial preparedness for the use of artificial intelligence (AI) is function-specific, founded in ethics, and cognizant of governance issues. According to empirical data from Bangladesh, the successful integration of AI in court systems hinges on compatibility with judicial discretion, procedural fairness, and institutional legitimacy under the rule of law, rather than just technological prowess or efficiency advantages. In the areas of case management and evidence analysis, where AI is seen as improving productivity, analytical clarity, and administrative efficacy without interfering with fundamental adjudicative duties, judges show the greatest preparedness for AI applications. On the other hand, judicial preparedness is not greatly impacted by AI-assisted sentencing or investigative cooperation, which reflects enduring worries about personalized justice, accountability, and the indispensable role of human judgment in legally delicate areas. These results highlight how judges make a clear distinction between AI as a tool for decision-making and AI as a possible replacement for judicial authority. One of the key enabling conditions influencing judicial approval is the presence of ethical protections. The normative lenses of accountability, explainability, transparency, and human oversight are used to assess the functional benefits. Therefore, trust in governance structures that guarantee legal observance, procedural integrity, and the defense of fundamental rights is essential to judicial preparedness. By showing how functionality, ethical legitimacy, and institutional trust interact to influence AI adoption in courts, the work theoretically improves socio-legal and technological governance scholarship. Practically speaking, the results encourage a gradual, function-sensitive approach to AI adoption, giving low-risk administrative and analytical applications priority, as well as consistent investment in digital infrastructure, judicial training, and ethics-by-design procedures. Notwithstanding sample and contextual constraints, the study offers empirically supported insights into the adoption of judicial AI in developing nations and identifies avenues for responsible, rule-of-law-compliant judicial modernization. Declarations Conflict of Interest The corresponding author declares that there is no conflict of interest. Ethics Approval and Consent to Participate Following the established ethical standards and with the informed consent of the individual participants, this research was conducted. The participants joined in this data collection process voluntarily and This study was conducted in accordance with established ethical standards and was approved by the relevant institutional review body of the authors’ affiliated institution. Informed consent was obtained from all individual participants prior to their participation in the study. Participation was voluntary, and respondents were informed of their right to withdraw at any stage without any consequences. All data were collected and analyzed anonymously to ensure confidentiality. AI-Assisted Writing Large language models were used solely to support language polishing and structural refinement during the preparation of this manuscript. AI tools were not used for conceptual development, framework design, metric formulation, threshold determination, data interpretation, or substantive analysis. All theoretical choices, analytical judgments, and interpretive conclusions are the sole responsibility of the author. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Arthanari A, Raj SS, Vignesh R (2025) A narrative review in application of artificial intelligence in forensic science: Enhancing accuracy in crime scene analysis and evidence interpretation. J Int Oral Health 17(1):16–16. https://doi.org/10.4103/jioh.jioh_162_24 Journal ABA (2024) Access to justice 2.0: How AI-powered software can bridge the gap. American Bar Association. https://www.abajournal.com/columns/article/access-to-justice-20-how-ai-powered-software-can-bridge-the-gap , accessed 6 February 2026 Access (n.d.). 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Legal Veda. https://legal-veda.com/supace-ai-tool-in-the-supreme-court-of-india-transforming-legal-research-and-case-management accessed 20 February 2026 The Business Standard (TBS News) (2025), February Draft by Sept to enact law checking AI misuse: Anisul. https://www.tbsnews.net/tech/draft-sept-enact-law-checking-ai-misuse-anisul-823051 accessed 20 February 2026 The Business Standard (2024) Digital justice initiatives in Bangladesh. https://tbsnews.net/thoughts/balancing-justice-and-algorithm-ai-accountability-and-future-bangladeshs-judiciary-1150101?m accessed 6 February 2026 The Daily Star (2024) Can AI help mitigate long pending legal cases? https://www.thedailystar.net/business/economy/news/can-ai-help-mitigate-long-pending-legal-cases-3717406 accessed 6 February 2026 The Times of India (2023) Soon, India may have 24x7 virtual courts deciding cases. https://timesofindia.indiatimes.com/india/soon-india-may-have-24x7-virtual-courts-deciding-cases/articleshow/101582046.cms accessed 6 February 2026 The Times of India (2025) E-research library with AI tools to assist lawyers. https://timesofindia.indiatimes.com/city/delhi/e-research-library-with-ai-tools-to-assist-lawyers/articleshow/122348095.cms accessed 6 February 2026 Thomson, Reuters (2023) Chatbots for justice: Building AI-powered legal solutions. https://www.thomsonreuters.com/en-us/posts/ai-in-courts/chatbots-for-justice-building-ai-powered-legal-solutions accessed 6 February 2026 Tiarks E (2021) The impact of algorithms on legitimacy in sentencing. J Law Technol Trust 2(1):1–23. https://doi.org/10.19164/jltt.v2i1.1151 Wasi AT, Faisal W, Islam MR, Bappy MM (2024) Exploring possibilities of AI-powered legal assistance in Bangladesh through large language modeling. https://doi.org/10.48550/arXiv.2410.17210 . arXiv Xinhua News Agency (2017) Big data, artificial intelligence aid China’s judicial reforms. Xinhua. https://www.xinhuanet.com/english/2017-11/01/c_136721038.htm accessed 6 February 2026 Xinhua (2025) China’s local judicial systems embrace AI to improve efficiency. Xinhua NewsAgency. https://english.news.cn/20250101/94c58c6b4ae544f8b5840c835a2eff34/c.html accessed 6 February 2026 Zhang B, Kloza D (2021) AI in the judiciary: A comparative analysis. Comput Law Secur Rev 41:105–125. https://doi.org/10.1016/j.clsr.2021.105525 Zouhir S (2025) AI and the judiciary: Balancing innovation with integrity. UNESCO. https://www.unesco.org/en/articles/ai-and-judiciary-balancing-innovation-integrity accessed 6 February 2026 Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9275906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615069229,"identity":"4523e996-3841-4d89-b5be-037fd7aecae4","order_by":0,"name":"Shiakh Md Mujahid Ul Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3QsUvDQBTH8RcCNz3J+kpo/4aDg4hYEP+THMJNUQSXDg4JgbiIXTv4LwiCkDnloF0CXQNZLK4OhkK3Vo2DFCRH3UTuOz748OMOwGb7g3lectO8jYYDD5z4+0om0pvomUulEr14X8ILpeAg05IXO1cjgaIMnom5oVjodIXXIB9id1oj5DLuEE56e8gvkV0Elcx8nIHMC3Z2jFB3EhdKTkR4FVRO5kasJRj4JsIg4oSc5NN4mq6ibUu8tZEgKEUYcvkIMvbPs68VZiREWgsqQkHtWzZ3JHLNxNE9r0UXOVkkybLZvg+88fylmayH/XyeLqvXUd3vIj9W2x/5jMPpvmRn/9fCZrPZ/msfzVpcvuJ94pwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-6630-4265","institution":"Department of Science and Technology Studies Faculty of Science, Universiti Malaya, Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Shiakh","middleName":"Md Mujahid Ul","lastName":"Is","suffix":"Md"},{"id":615077225,"identity":"1f7a28ea-8322-4a32-b0ee-b97797cb018e","order_by":1,"name":"Dr. Muhammad Mehedi Masud","email":"","orcid":"https://orcid.org/0000-0003-0476-4481","institution":"The Faculty of Islamic Economics and Finance Sultan Sharif Ali Islamic University, Brunei","correspondingAuthor":false,"prefix":"Dr.","firstName":"Muhammad","middleName":"Mehedi","lastName":"Masud","suffix":""}],"badges":[],"createdAt":"2026-03-31 07:13:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9275906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9275906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105911916,"identity":"f7a7129e-7d65-45d6-9c44-47146a52d54d","added_by":"auto","created_at":"2026-04-01 10:55:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual Framework of the Research\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9275906/v1/8e002679ec818ad4dc1e818e.png"},{"id":105912312,"identity":"f1bf092c-4e3c-43d7-8104-d30bd479397e","added_by":"auto","created_at":"2026-04-01 10:59:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":99405,"visible":true,"origin":"","legend":"\u003cp\u003eThe Structural Model for Perception of AI use of Judges\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9275906/v1/cdd86a8aadb6807cabf37315.png"},{"id":106093322,"identity":"8cd16f52-343d-4671-b093-ec02a667ec1a","added_by":"auto","created_at":"2026-04-03 11:36:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2160135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9275906/v1/933b2534-a3c5-4167-8292-e15a0181480e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing Judicial Readiness for Artificial Intelligence Adoption: Functional, Normative, and Ethical Drivers from Bangladesh\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eJudicial institutions in technologically advanced nations have been incorporating artificial intelligence (AI) more and more into their fundamental operations, such as case administration, evidence evaluation, and, in certain situations, sentence assistance. To address systemic inefficiencies and mounting caseload pressures, nations including the US, China, and EU members have used AI-driven solutions (Choi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang \u0026amp; Kloza, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI has the potential to increase administrative effectiveness, procedural consistency, and transparency in the administration of justice as a tool for technological rationalization (Surden, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sourdin, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It commonly alleges that AI threatens judicial discretion, procedural fairness, accountability, explainability, and data ethics, even while technology may speed up adjudication and reduce some human errors (Carothers, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dicey, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These issues have an impact on judicial legitimacy, which depends on independence, reasoned judgment, and public trust in addition to efficiency. Thus, a major theme in current discussions about AI-assisted justice is the conflict between legal normativity and technological usefulness. AI usage in court systems is still relatively low in the Global South. In the context of Bangladesh, several initiatives are taken, such as Judicial Monitoring Dashboard (My Court App), while creating a platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.judiciary.gov.bd\u003c/span\u003e\u003c/span\u003e) to give information relating to judicial services, such as an inheritance calculator, judipay, e-filing, e-certified copy, etc. (Sourav, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Judicial digitalization has been hampered by structural issues such as insufficient infrastructure, institutional inertia, procedural conservatism, and shortages in technological capacity. Calls for change have increased due to persistent backlogs of more than four million cases, as well as perceptions of political interference and inefficiency (Rahman, 2020). AI integration is still mostly in the exploratory stage, despite recent e-judiciary initiatives showing a desire to embrace digital transformation (New Age, 2022). Importantly, there is a dearth of empirical studies on how judges view the use of AI, specifically concerning practicality, moral protections, institutional compatibility, and consequences for the rule of law. By empirically examining judicial perceptions of AI deployment in Bangladesh, with an emphasis on case management and evidence analysis as crucial technical interventions, this study fills this knowledge gap. Judicial readiness, which is based on a socio-legal framework of technological governance, is said to be influenced by both practical advantages and moral considerations. While ethical considerations include judicial autonomy, procedural fairness, accountability, and data protection, functional benefits include efficiency, transparency, uniformity, and interagency coordination. The main research issue is whether overall judicial readiness is influenced by perceived functional benefits of AI that outweigh ethical concerns. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to evaluate the structural relationships among six latent constructs: (B1) AI in Evidence Analysis; (B2) AI in Sentencing; (B3) AI in Case Management; (B4) Transparency, Accountability, and Rule-of-Law; (B5) AI in Investigation and Inter-Agency Collaboration; and (B6) Ethical Concerns. The study involved a quantitative, cross-sectional survey of 100 judicial officers. The dependent variable is judicial preparedness for adopting AI. Three main contributions are made by this work. First, it closes a significant empirical gap by offering unique factual data on judges\u0026apos; opinions of AI deployment in emerging nations. Second, by showing how judicial attitudes toward technological innovation are mediated by legal culture, institutional trust, and ethical norms, it advances socio-legal knowledge of AI governance. Thirdly, it provides a model-based analytical approach that connects empirical measures of institutional preparation with normative legal theory. Overall, the results help guide Bangladesh\u0026apos;s judicial modernization efforts and add to larger discussions about the morally sound and law-abiding use of AI in legal systems.\u003c/p\u003e"},{"header":"2. Conceptual Framework","content":"\u003cp\u003eThe conceptual framework of this study explores judicial readiness for AI deployment in Bangladesh, incorporating socio-legal theories of technological governance and the digital rule of law (Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sourdin, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun \u0026amp; Xiao, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Judicial preparedness is defined as a product of an evaluation process where projected functional benefits of AI are assessed against ethical protections and institutional conditions. Acceptance of AI is thus understood as a socio-institutional construct, mediated by legal culture, ethical awareness, and trust in governance, rather than a simply technological or efficiency-driven consequence. The conceptual framework comprises six dimensions: AI in Evidence Analysis, AI in Sentencing, AI in Case Management, Transparency, Accountability and Rule of Law, AI in Investigation and Inter-Agency Collaboration, and Ethical Concerns, which are considered factors that influence Judicial Readiness for AI Adoption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Literature Review and Hypothesis Development","content":"\u003cp\u003eThe literature on artificial intelligence (AI) in judicial systems indicates an increasing global engagement with algorithmic technology as a tool of judicial modernization. AI has been employed to help case management, evidence analysis, and procedural administration, aiming to promote efficiency, consistency, and access to justice. Yet, literature also highlights normative concerns, like judicial independence, accountability, transparency, and respect for the rule of law (Surden, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sourdin, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI adoption is thus regarded as a governance challenge at the nexus of law, ethics, and institutional architecture. Comparative evidence from advanced jurisdictions suggests that AI is most extensively employed in administrative and decision-support roles, including docket management, predictive analytics, and digital evidence processing (Choi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang \u0026amp; Kloza, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While these applications improve efficiency, questions exist around algorithmic opacity, bias, data quality, and the potential erosion of judicial discretion. Judicial acceptance of AI is therefore dependent upon rigorous legal safeguards, openness, and ethical oversight, ensuring AI complements rather than substitutes judicial reasoning. In Bangladesh, judicial digitalization has advanced through legal and institutional measures, including the Usage of Information and Communication Technology by Courts Act 2020, the National AI Strategy 2020, and the e-Judiciary Programme. Practical solutions such as the MyCourt App and digital case filing aim to increase procedural efficiency. However, empirical studies reveal that digitization alone has not appreciably decreased backlogs or boosted judicial performance, stressing the significance of judicial readiness and institutional trust in facilitating AI implementation. Building on this context, judicial readiness for AI deployment is characterized as affected by perceived functional benefits, ethical legitimacy, and compliance with judicial norms, including independence, discretion, procedural fairness, and accountability. The study investigates hypotheses across six dimensions: AI in evidence analysis, AI in sentencing, AI in case management, transparency and accountability, AI in investigation and inter-agency collaboration, and ethical problems.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Global Integration of AI in Judicial Processes\u003c/h2\u003e \u003cp\u003eAI integration represents a radical shift in judicial systems, supporting case management, evidence analysis, sentencing, and procedural administration. These techniques are meant to promote efficiency, consistency, and transparency, but must be carefully placed inside normative frameworks to ensure judicial discretion and accountability (Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun \u0026amp; Xiao, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comparative studies reveal that adoption is context-dependent, impacted by legal tradition, institutional capability, procedural safeguards, and ethical monitoring. Common law jurisdictions emphasize administrative and decision-support applications, although civil law systems have sometimes dabbled with predictive analytics and standardized decision-making. Across contexts, transparency, explainability, accountability, and human monitoring are crucial to sustaining legitimacy. Against this context, judicial readiness for AI adoption is defined as a multidimensional entity, operationalized through six interconnected dimensions (B1\u0026ndash;B6), covering both functional and normative variables.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 AI in Evidence Analysis\u003c/h2\u003e \u003cp\u003eAI-assisted evidence analysis has emerged as a transformational tool in contemporary court processes, giving increased skills in data extraction, document inspection, pattern identification, and forensic analytics. These technologies are particularly beneficial in handling complicated digital evidence, boosting accuracy, consistency, and procedural efficiency, while keeping judicial oversight and discretion. By automating repetitive processes and facilitating quick analysis, AI assists courts in rendering better-informed and speedy rulings without supplanting human judgment. China exhibits the large-scale deployment of AI in judicial evidence evaluation. In over 1,500 criminal cases, methods such as the \u0026ldquo;206 System\u0026rdquo; in Shanghai have shown considerable reductions in trial duration and gains in transparency (The Star, 2020). In the United States, AI is extensively employed in e-discovery and digital evidence analysis, where machine learning tools assist judges and litigants in efficiently managing vast volumes of electronically stored information, enabling faster and more precise identification of relevant evidence (Remus \u0026amp; Levy, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Metrics such as citation counts, download trends, and adoption rates reflect increased academic and institutional interest, demonstrating that courts worldwide are gradually appreciating the importance of AI-assisted evidence tools (Kerdvibulvech, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These studies further reveal that judicial acceptability is increased when AI is viewed as a supportive, rather than a replacement, technology, boosting evidential evaluation while maintaining judicial autonomy. Collectively, these findings show a positive correlation between judicial impressions of AI-assisted evidence processing and institutional preparedness to incorporate AI in judicial procedures, and based on these studies, the first hypothesis is developed;\u003c/p\u003e \u003cp\u003eH1: Perception of judges on AI-assisted evidence analysis favorably affects judicial preparedness for AI deployment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 AI in Sentencing\u003c/h2\u003e \u003cp\u003eAI-assisted sentencing is inherently normatively sensitive, given its implications for judicial discretion, proportionality, and procedural fairness. Artificial Intelligence (AI) technology is increasingly being integrated into judicial systems to provide decision-support tools that enhance the efficiency, consistency, and accuracy of sentencing, while eliminating human prejudice (Pixelplex, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These systems do not replace judicial authority but instead give direction, predictive analytics, and document automation to inform court thinking. China and Estonia exhibit large-scale implementation of AI-assisted sentencing. In some jurisdictions, AI systems create suggestive sentencing ranges, indicate procedural abnormalities, and automate administrative documentation, while preserving the final decision-making power with human judges (Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hern, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, common law jurisdictions typically deploy AI for procedural and administrative support, rather than automated determination of penalties, suggesting a careful approach that respects judicial autonomy and due process.\u003c/p\u003e \u003cp\u003eEmpirical and socio-legal studies reveal that judicial readiness for AI adoption is largely influenced by perceptions of system reliability, fairness, and procedural integrity. Judges are more inclined to adopt AI-assisted sentencing when technology is regarded as boosting consistency, impartiality, and efficiency, without weakening discretion or respect to legal norms. Positive judicial impressions thus serve as a substantial mediator between technical availability and institutional acceptance, and the following hypothesis arises from it.\u003c/p\u003e \u003cp\u003eH2: Perceptions of Judges on AI-assisted sentencing favorably influence court readiness for AI adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 AI in Case Management\u003c/h2\u003e \u003cp\u003eAI- assisted case management has become an important instrument for boosting administrative efficiency inside judicial systems. By optimizing scheduling, docket control, and workflow monitoring, AI solutions speed regular operations, eliminate human error, and help courts to handle caseloads more effectively. Within the legal profession, AI-driven case management systems improve efficiency, productivity, accuracy, and client satisfaction by automating repetitive tasks, facilitating faster and more precise evidence analysis, and allowing legal professionals to dedicate greater attention to strategic and client-focused work (Access, n.d.).Empirical research from China, India, and Singapore reveals that AI-assisted case management contributes to reduced case backlogs, faster processing, and increased institutional performance (China Daily, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Times of India, 2023). Judges regard these instruments as supporting administrative aids, improving efficiency without encroaching upon adjudicative authority. This impression boosts institutional trust, strengthens confidence in technology integration, and positively affects judge preparation to deploy AI technologies across broader portions of court administration and the third hypothesis is formed on this premise.\u003c/p\u003e \u003cp\u003eH3: Perception of Judges on AI-assisted case management favorably influences court readiness for AI implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Transparency, Accountability, and Rule of Law\u003c/h2\u003e \u003cp\u003eThe institutional legitimacy of AI in judicial processes depends on the integration of robust governance structures that assure openness, accountability, and compliance with the rule of law. AI-driven predictive justice systems employ algorithmic analysis and Big Data to anticipate legal choices, thereby lowering ambiguity and boosting consistency in judicial rulings (Masum, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Practical experiences in China and Estonia indicate that explainable, auditable AI systems boost judicial oversight, promote procedural fairness, and enhance public confidence in algorithmically supported decisions (Ji, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, programs in Bangladesh, such as the e-Judiciary Programme, expressly emphasize accountable, transparent, and ethical use of AI in judicial processes. Scholars contend that while AI might offer efficiency improvements through automation, its integration must be carefully calibrated to maintain transparency, eliminate prejudice, safeguard due process, and uphold fairness (Tahura \u0026amp; Selvadurai, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Collectively, these findings imply that judicial perceptions of the transparency, accountability, and rule-of-law compliance of AI systems are significant predictors of institutional readiness for AI implementation, and the following hypothesis can be drawn on this basis.\u003c/p\u003e \u003cp\u003eH4: Court perceptions of openness, accountability, and rule-of-law compliance in AI systems favorably influence court readiness for AI implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 AI in Investigation and Inter-Agency Collaboration\u003c/h2\u003e \u003cp\u003eArtificial Intelligence (AI) has increasingly been applied to judicial investigations and inter-agency collaboration, enabling increased data integration, evidence verification, and coordination across numerous governmental and judicial institutions. By simplifying information flows between courts, law enforcement agencies, and administrative bodies, AI increases institutional efficiency, procedural coherence, and the trustworthiness of evidence management. Experiences in China and India highlight the practical benefits of AI in inter-agency operations. In many jurisdictions, AI technologies offer real-time information sharing, case tracking, and verification of complex evidence, thereby minimizing redundancies, alleviating delays, and boosting the overall effectiveness of the court process (China Daily, 2024; The Business Standard, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These data reveal that court preparedness to use AI is positively influenced when these technologies are regarded as boosting oversight and inter-agency efficiency, while protecting judicial independence and procedural integrity. Based on this analysis, the following hypothesis can be developed.\u003c/p\u003e \u003cp\u003eH5: Court perceptions of AI-assisted inquiry and inter-agency collaboration positively influence court readiness for AI implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6 Ethical Concerns\u003c/h2\u003e \u003cp\u003eEthical readiness constitutes a fundamental facet of judicial AI adoption, encompassing values like justice, bias mitigation, openness, data protection, human oversight, and gradual implementation. The introduction of rigorous ethical protections boosts the legitimacy and acceptance of AI systems within judicial institutions, increasing the impact of functional benefits\u0026mdash;such as efficiency, accuracy, and administrative support\u0026mdash;on overall judicial readiness (Masum, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).AI is most effectively integrated as a complementary decision-support tool, rather than a substitute for human judgment, ensuring that ethical, legal, and procedural norms are upheld. In this context, Judges are more willing to employ AI-assisted systems when they are confident that these technologies are morally calibrated, responsible, and procedurally sound, therefore lowering risks of prejudice, injustice, or loss of due process and this leads to the following hypothesis.\u003c/p\u003e \u003cp\u003eH6: Judicial perceptions of ethical protections and readiness positively moderate the association between functional benefits of AI and judicial readiness for AI deployment.\u003c/p\u003e \u003cp\u003eThe literature highlights that judicial readiness for AI adoption depends on both functional utility and normative validity. Globally, AI promotes efficiency, uniformity, and transparency, while ethical governance, accountability, and adherence to the rule of law are vital for confidence and legitimacy. This study operationalizes six hypotheses reflecting these dimensions, giving an experimentally tested framework to evaluate how judges\u0026rsquo; perceptions influence AI deployment inside Bangladesh\u0026rsquo;s judiciary. The subsequent PLS-SEM analysis examines these correlations, delivering theoretical and practical insights for AI-enabled judicial modernization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThis study adopts a quantitative, cross-sectional survey approach to empirically assess judicial attitudes on artificial intelligence (AI) adoption across Bangladesh\u0026rsquo;s judiciary. It focuses on AI\u0026rsquo;s potential contributions to evidence analysis, sentencing, case management, investigative collaboration, transparency, and ethical governance. By combining socio-legal inquiry with empirical modeling, the methodology assesses how functional benefits and normative legitimacy interact in building judicial readiness, coinciding with contemporary ideas on the digital rule of law and technological governance.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Sampling and Representativeness\u003c/h2\u003e\n\u003cp\u003eThe target population includes about 1,600 judicial personnel across Bangladesh\u0026rsquo;s subordinate and superior courts. Through official communication with the Judicial Administration Training Institute (JATI) and follow-ups, 100 judges engaged voluntarily, giving a 6.25% response rate. The sample covers numerous judicial tiers, such as district courts, metropolitan magistrates\u0026rsquo; courts, and higher forums. A stratified purposive sampling strategy maintained variation in court level, jurisdiction, and judicial experience. Despite its modest size, the sample is sufficient for Partial Least Squares Structural Equation Modeling (PLS-SEM), which supports smaller samples and intricate path architectures under non-normal data conditions. Bootstrapping with 5,000 resamples boosts inferential reliability. Early\u0026ndash;late response comparisons and demographic balance checks were done to mitigate potential biases. The results are seen as suggestive, presenting empirically grounded insights into factors impacting judicial readiness for AI deployment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Instrument Development\u003c/h2\u003e\n\u003cp\u003eBased on an exhaustive assessment of worldwide literature on the integration of artificial intelligence into judicial systems, a structured survey instrument was carefully constructed and contextualized for the Bangladeshi judiciary. The questionnaire was informed by seminal works from scholars such as Remus and Levy (2016), Sourdin (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Sun and Xiao (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), as well as policy guidelines from the European Commission (2021), ensuring its alignment with global discourse while remaining sensitive to local institutional realities. The instrument was arranged into three coherent portions, each aiming to capture various characteristics of judicial vision and readiness. The first segment focused on the Demographic Profile of respondents, obtaining crucial background information such as judge rank, years of tenure, and past exposure to or expertise with digital court management systems. This data was designed to contextualize responses and discover potential relationships between judge seniority, technological familiarity, and opinions regarding AI deployment.\u003c/p\u003e\n\u003cp\u003eThe core of the questionnaire comprised 30 Likert-scale items, systematically divided over six latent variables developed from the study\u0026rsquo;s conceptual framework.\u003c/p\u003e\n\u003cp\u003eThese structures were:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eAI in Evidence Analysis \u0026mdash; examining perceptions of AI\u0026rsquo;s utility in analyzing digital evidence, document analysis, and forensic data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAI in Sentencing - examining viewpoints on the use of algorithmic technologies in sentencing recommendations and consistency.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAI in Case Management \u0026ndash; evaluating opinions on AI-driven scheduling, backlog reduction, and process automation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransparency, Accountability, and Rule of Law - assessing concerns relating to explainability, judicial scrutiny, and adherence to legal norms.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAI in Investigation and Inter-Agency Collaboration \u0026mdash; evaluating openness to AI-facilitated coordination between courts, law enforcement, and other government entities.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEthical Concerns and Readiness \u0026mdash; exploring apprehensions around prejudice, data privacy, human dignity, and institutional preparedness for ethical AI governance.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll items featured a six-point Likert scale, ranging from Strongly Disagree (1) to Strongly Agree (5), purposely excluding a neutral midpoint to encourage deliberate stance-taking by respondents. This scaling strategy was used to enhance measurement clarity and eliminate ambivalence in responses. Finally, the instrument contained a section of Open-ended Questions, allowing participants to expound qualitatively on institutional constraints, ethical concerns, training needs, and the practical viability of AI-driven judicial changes in Bangladesh. These comments were meant to provide nuanced insights beyond quantitative measurements, capturing the perspectives and reflections of judicial officers in their own words.\u003c/p\u003e\n\u003cp\u003eCollectively, these constructs operationalize the study\u0026rsquo;s central hypothesis: that judicial readiness for AI adoption is not merely a function of technological access, but is fundamentally shaped by the perceived functional benefits of AI applications in judicial work, balanced against ethical preparedness and institutional confidence in maintaining transparency, accountability, and the rule of law. The instrument thus acts as both a diagnostic tool and a foundational resource for policy creation targeted at responsible and context-sensitive AI integration in the Bangladeshi judicial system.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Data Collection and Analysis\u003c/h2\u003e\n\u003cp\u003eData were collected over six weeks in November and December 2025 using safe online platforms and formal correspondence. Initial screening and coding were performed in SPSS to address missing values, assess normalcy, and create descriptive statistics. Subsequent analyses were undertaken in SmartPLS 4, permitting simultaneous estimation of measurement and structural models within the PLS-SEM framework.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4 Measurement Model Assessment\u003c/h2\u003e\n\u003cp\u003eThe measurement model was carefully validated to confirm the reliability and validity of the built latent variables, in accordance with accepted psychometric standards for structural equation modelling. This review entailed the use of three major criteria to confirm that the conceptions were both theoretically sound and empirically robust.\u003c/p\u003e\n\u003cp\u003eFirst, internal consistency reliability was assessed using two complementary indicators: Cronbach\u0026rsquo;s Alpha and Composite Reliability (CR). Both metrics were calculated for each latent construct to verify that the items inside each dimension consistently measured the same underlying notion. Acceptable thresholds were maintained, with values exceeding the specified minimum of 0.70, validating the scale's dependability and the coherence of the theoretical notions.\u003c/p\u003e\n\u003cp\u003eSecond, convergent validity was examined by the Average Variance Extracted (AVE). This measure analyzes the extent to which items within a construct converge or share a significant proportion of variance. An AVE value larger than 0.50 was retained as the benchmark, showing that more than half of the variance in the observed variables was accounted for by the latent construct, thereby proving appropriate convergent validity and validating the soundness of the measurement model.\u003c/p\u003e\n\u003cp\u003eFinally, discriminant validity was assessed using the Heterotrait-Monotrait ratio (HTMT) of correlations. This advanced criterion evaluates whether theoretically distinct conceptions are truly experimentally distinguished. An HTMT value below the cautious threshold of 0.85 was used, ensuring that each construct recorded a unique dimension of judicial perception toward AI adoption, without unnecessary overlap with other constructs in the model.\u003c/p\u003e\n\u003cp\u003eTogether, these analytical steps\u0026mdash;evaluating internal consistency, convergent validity, and discriminant validity\u0026mdash;provided a full validation of the measurement model. This comprehensive assessment guarantees that the latent constructs are not only statistically trustworthy but also conceptually different, so building a solid empirical foundation for subsequent hypothesis testing and structural analysis within the study\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4.5 Structural Model Evaluation\u003c/h2\u003e\n\u003cp\u003eThe structural model examined hypothesized associations (H1\u0026ndash;H6) using path coefficients, t-statistics, and p-values obtained by bootstrapping with 5,000 resamples. Explanatory power was assessed by the coefficient of determination (R\u0026sup2;), and effect sizes (f\u0026sup2;) evaluated the substantive influence of each predictor. Predictive relevance (Q\u0026sup2;) was examined using the blindfolding process. The moderating influence of Ethical Concerns and Readiness was explored by introducing interaction terms between ethical protections and each functional component, analyzing how ethical preparedness shapes the link between AI functionality and judicial readiness.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Empirical Analysis","content":"\u003cp\u003eThis study uses Partial Least Squares Structural Equation Modeling (PLS-SEM), a robust and commonly used technique for evaluating causal links across latent constructs obtained via survey data. PLS-SEM is particularly useful for exploratory research using complicated models. Both reflective measurement model assessment and structural model evaluation were accomplished within this paradigm.\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s independent variables contained six latent constructs:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCMD: AI in Case Management and Disposal of Non-Cognizable Offences.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEA: AI in Evidence Analysis.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTAR: Transparency, Accountability, and Rule of Law.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSENT: AI in Sentencing.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eINV\u0026amp;C: AI in Investigation and Inter-Agency Collaboration.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eETH: Ethical Concerns and Readiness.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe dependent variable was Judicial Readiness for AI Adoption. Functional constructs (CMD, EA, TAR, SENT, INV\u0026amp;C) were modeled as direct predictors, while ETH served as a moderator impacting the strength of functional factors on Judicial Readiness. To detect any common method bias coming from self-reported survey data, diagnostic tests were undertaken in SPSS before the PLS-SEM analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e5.1 Demographic Profile of the Respondents\u003c/h2\u003e\n\u003cp\u003eOne hundred judicial officers took part. The composition of the judiciary was mirrored in the gender distribution, which was 80.3% male (n\u0026thinsp;=\u0026thinsp;80), 18.2% female (n\u0026thinsp;=\u0026thinsp;18), and 1.5% (n\u0026thinsp;=\u0026thinsp;2) \"prefer not to say.\" The age distribution showed that the majority were in the mid-career range: 63.6% were between the ages of 35 and 45, 25.8% were under 35, 7.6% were between the ages of 46 and 55, and 1.5% were over the age of 56. In terms of professional readiness, 19.7% (n\u0026thinsp;=\u0026thinsp;15) had some training in legal technology or artificial intelligence, but 80.3% (n\u0026thinsp;=\u0026thinsp;85) had no formal training. Digital court systems, such as e-filing, virtual hearings, and digital case management platforms, were at least somewhat familiar to all of the respondents. The magnitude of the judicial workload varied greatly among jurisdictions, with pending cases ranging from 170 to 11,000 and annual case disposals from 100 to 4,000. Table\u0026nbsp;1 presents the demographic data of the judges.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSub-category\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (n)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercentage (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrefer not to say\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u0026ndash;45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u0026ndash;55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56+\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraining in AI / Legal Technology\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCase Load (Pending)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLowest pending cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170 cases\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest pending cases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,000 cases\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTypical range (majority of respondents)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026mdash;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e800\u0026ndash;3,200 cases\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2 Reflective Measurement Model Results\u0026mdash;Reliability and Validity\u003c/h2\u003e\n\u003cp\u003eReliability was evaluated by looking at the objects' outer loadings. According to Chin (1998), loadings greater than 0.707 signify sufficient dependability. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e confirms satisfactory item validity by demonstrating that all constructs satisfy this requirement.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOuter loadings of the measurement model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOuter Loadings\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI in Evidence Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.759\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.792\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE A1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI in Sentencing\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSentencing 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.751\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSentencing 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.735\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSentencing 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSentencing 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.582\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI in Case Management and Disposal of Non-Cognizable Offences\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMD 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.893\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMD 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.816\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMD 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.875\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCMD 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTransparency, Accountability, and Rule of Law\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT R A 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT R A 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.839\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT R A 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT R A 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.838\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI in Investigation and Inter-Agency Collaboration\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.847\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.863\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.824\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Concerns and Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.786\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eJudicial Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.776\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.665\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.529\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.795\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.797\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJudicial Readiness 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.649\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\u003eAverage Variance Extracted (AVE) and Composite Reliability (CR) were used to evaluate convergent validity (Hair et al., 2010). Except for SENT, all constructs had CR values greater than 0.70 and AVE values greater than 0.50, showing acceptable reliability and convergent validity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eConstruct reliability and validity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCronbach's alpha\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComposite reliability (rho_a)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.898\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.901\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.929\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.766\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.807\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.811\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.872\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.694\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.823\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.825\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.657\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.876\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.505\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.767\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.747\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.500\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.857\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.860\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.903\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.700\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\u003eHTMT, cross-loadings, and Fornell-Larcker (1981) approaches were used to evaluate discriminant validity. Items loaded higher on their intended constructs, as proven by cross-loadings (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) and the Fornell-Larcker criterion (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). With the exception of CMD\u0026ndash;Readiness (0.934) and CMD\u0026ndash;SENT (0.988), most HTMT values were below 0.90. The absence of 1 in bootstrapped confidence intervals supported empirical distinctiveness. Since these constructs reflect interconnected elements of judicial decision-making, elevated HTMT scores were theoretically warranted.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiscriminant Validity: Employing the Fornell-Larcker criterion\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE A\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEthical\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReadiness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSentencing\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT A R\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.875\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.486\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.795\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.810\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.727\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.552\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.527\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.588\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.685\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.836\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\u003eThe second method of assessing discriminant validity is to compare the \"cross-loadings\" between variables. Cross-loadings were calculated to evaluate the items' equal loading on their targeted constructs and other constructs (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The intended construct must have a greater loading than other constructs in order for cross-validated items to be included in the final dataset. The current study met this condition, guaranteeing that the analysis contained cross-validated items.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCross-loadings\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE A\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEthical\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReadiness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSentencing\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT A R\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.893\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.345\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.433\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.599\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.733\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.668\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e0.485\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.816\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.496\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.325\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.648\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.531\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.875\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.429\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.722\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.538\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.448\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.663\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.693\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.508\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.433\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.449\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.544\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.434\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.449\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.416\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.534\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.565\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.786\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.220\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 1(21)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.585\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.417\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.278\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.362\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.291\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.136\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.654\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.847\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.441\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.559\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.608\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.262\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.863\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.597\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.502\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.531\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.194\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.513\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.555\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.698\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.610\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.364\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.664\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.614\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.562\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.497\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.485\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.446\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.553\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.634\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.361\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.564\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.665\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.440\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.398\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.529\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.415\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.520\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.493\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.795\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.441\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 6\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.613\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.488\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.458\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.587\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.481\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness 7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.598\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.186\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.649\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.523\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.574\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.532\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.633\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.575\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.331\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.261\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.269\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.299\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R 1\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.524\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.507\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.566\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.464\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R 2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.592\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.074\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.489\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.492\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.839\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R 3\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.473\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.487\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R 4\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.497\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.606\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.622\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.477\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.838\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\u003eThe Heterotrait\u0026ndash;Monotrait (HTMT) ratio was used to further evaluate discriminant validity (Henseler, Ringle, \u0026amp; Sarstedt, 2015). All of the HTMT ratios are below the suggested cut-off of 0.90, as seen in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Adequate discriminant validity is supported by bootstrapped confidence intervals, which verified that none of the HTMT estimations include 1.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHeterotrait\u0026ndash;Monotrait ratio (HTMT)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCMD\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE A\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEthical\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInv \u0026amp; C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReadiness\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSentencing\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT A R\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.571\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.641\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.687\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.441\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReadiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.734\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.709\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.636\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.788\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.756\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.511\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.671\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.758\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3 Assessment of the Structural Model Results\u003c/h2\u003e\n\u003cp\u003eInner VIF values, which range from 1.359 to 3.526 and are all below the crucial threshold of 5.0, were used to analyze predictor collinearity. This suggests that multicollinearity is not a significant concern. 500 resamples were bootstrapped to test for path significance. Judicial Readiness is strongly influenced by CMD (\u0026beta;\u0026thinsp;=\u0026thinsp;0.324, t\u0026thinsp;=\u0026thinsp;3.320, p\u0026thinsp;=\u0026thinsp;0.001), ETH (\u0026beta;\u0026thinsp;=\u0026thinsp;0.240, t\u0026thinsp;=\u0026thinsp;3.405, p\u0026thinsp;=\u0026thinsp;0.001), and TAR (\u0026beta;\u0026thinsp;=\u0026thinsp;0.238, t\u0026thinsp;=\u0026thinsp;2.809, p\u0026thinsp;=\u0026thinsp;0.005), according to the data (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). There was no statistical significance for SENT, INV\u0026amp;C, or EA. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the structural model of AI use by judges.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe Results of the Structural Model\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHypothesis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecoefficient\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard deviation (STDEV)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT statistics (|O/STDEV|)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP values\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.324\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.320\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.978\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.071\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.405\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.128\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.172\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.088\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.163\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.809\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e5.4 Structural Path Coefficients, R\u0026sup2;, f\u0026sup2;, and Predictive Relevance\u003c/h2\u003e\n\u003cp\u003eAccording to Cohen (2013), the model has a modest explanatory power, accounting for 42% of the variance in judicial readiness (R2\u0026thinsp;=\u0026thinsp;0.42). According to effect sizes (f2), TAR, ETH, and CMD appear to have minor to moderate effects. Adequate predictive capacity is confirmed by predictive relevance (Q\u0026sup2; \u0026gt; 0). Additionally examined were the control factors of location, education, gender, and age. Judicial Readiness was strongly impacted by geography (\u0026beta;\u0026thinsp;=\u0026thinsp;0.171, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), education (\u0026beta;\u0026thinsp;=\u0026thinsp;0.071, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and gender (\u0026beta;\u0026thinsp;=\u0026thinsp;0.106, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that demographic factors are important.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eVIF Values for Structural Paths\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCMD -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVIF\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3.526\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eE A -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.098\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthical -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.359\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInv \u0026amp; C -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.690\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSentencing -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.459\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eT A R -\u0026gt; Readiness\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.310\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"6. Analysis and Findings","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Finding 1: AI-Assisted Evidence Analysis and Judicial Readiness\u003c/h2\u003e \u003cp\u003eThe study demonstrates that judicial views of AI-assisted evidence analysis (EA) exert a beneficial influence on court preparedness for AI adoption (β\u0026thinsp;=\u0026thinsp;0.096), suggesting convergence between quantitative PLS-SEM results and qualitative insights. Judges consider AI as a tool that can improve the speed and accuracy of evidence review (EA1; Mean\u0026thinsp;=\u0026thinsp;3.369), reduce human error (EA2; Mean\u0026thinsp;=\u0026thinsp;3.692), and enhance judgment quality in complicated cases (EA3; Mean\u0026thinsp;=\u0026thinsp;3.492). The strongest recommendation was for judicial access to AI tools for examining evidence (EA4; Mean\u0026thinsp;=\u0026thinsp;4.000), demonstrating institutional openness toward integrating AI inside ordinary judicial operations.\u003c/p\u003e \u003cp\u003eInterview data reflect these findings, with judges stressing AI\u0026rsquo;s capacity to help analytical clarity in cases requiring massive digital records, forensic datasets, and electronic conversations. Critically, AI was portrayed as a decision-support mechanism rather than a substitute for judicial discretion. This accords with prevalent jurisprudential norms that defend adjudicative autonomy, indicating that acceptance is predicated upon AI operating as an augmentative rather than intrusive device. The conclusion highlights that AI readiness is function-specific, stronger when it enhances procedural efficiency without encroaching on basic judicial judgment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Finding 2: AI-Assisted Sentencing and Judicial Readiness\u003c/h2\u003e \u003cp\u003eJudicial readiness was not substantially impacted by perceptions of AI-assisted sentencing (β\u0026thinsp;=\u0026thinsp;0.123, t\u0026thinsp;=\u0026thinsp;1.396, p\u0026thinsp;=\u0026thinsp;0.163). According to descriptive statistics, judges have cautious attitudes. There is moderate agreement on other measures (Means\u0026thinsp;=\u0026thinsp;3.185\u0026ndash;3.262) and strong agreement only on AI's capacity to improve sentencing uniformity (Sentencing 2; Mean\u0026thinsp;=\u0026thinsp;4.108). These findings imply that judges distinguish between adjudicative responsibility and operational support. Sentencing is still firmly within the human realm as a field that calls for moral reasoning, contextual judgment, and subjective assessment. Despite the recognized technical advantages, AI-assisted sentencing does not determine overall preparedness, which reflects the judiciary's normative commitment to moral accountability and procedural fairness. This illustrates professional boundaries where technology enhances rather than replaces human judgment, supporting a function-specific model of AI acceptance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Finding 3: AI-Assisted Case Management and Judicial Readiness\u003c/h2\u003e \u003cp\u003eJudicial impressions of AI in case management and disposition of non-cognizable offences (CMD) considerably boost readiness (β\u0026thinsp;=\u0026thinsp;0.324, t\u0026thinsp;=\u0026thinsp;3.320, p\u0026thinsp;=\u0026thinsp;0.001). High mean ratings across CMD indicators (3.738\u0026ndash;4.123) demonstrate broad unanimity on AI\u0026rsquo;s potential to improve administrative efficiency, workflow optimization, and quick disposal of routine cases. Qualitative statistics reinforce this view, emphasizing AI\u0026rsquo;s involvement in backlog reduction, procedural tracking, and clerical workload alleviation, freeing judges to concentrate on substantive legal thinking. Unlike sentencing, case management responsibilities are non-discretionary, making them particularly susceptible to automation. This places CMD as a crucial entry point for AI deployment in court systems, indicating that acceptance is strongest where efficiency improvements correspond with professional norms and do not threaten judicial independence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Finding 4: Transparency, Accountability, and Rule-of-Law Compliance (TAR)\u003c/h2\u003e \u003cp\u003eThe readiness of AI systems is positively and statistically significantly impacted by judicial opinions of TAR compliance (β\u0026thinsp;=\u0026thinsp;0.238, t\u0026thinsp;=\u0026thinsp;2.809, p\u0026thinsp;=\u0026thinsp;0.005). As prerequisites for adoption, judges repeatedly underlined the importance of responsibility, transparency, and conformity to legal standards (Means\u0026thinsp;=\u0026thinsp;3.492\u0026ndash;3.754). According to qualitative findings, institutional trust is crucial; for AI outputs to be accepted by judges, they must be comprehensible, accountable, and compliant with the law. \"Black-box\" AI models have been found to threaten public trust and procedural justice. According to these findings, adoption is significantly facilitated by governance-oriented elements such as verifiable accountability, ethical and legal compliance, and procedural clarity. As a result, judicial preparedness is firmly rooted in institutional and moral validity rather than being exclusively a consequence of technological capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.5 Finding 5: AI-Assisted Investigation and Inter-Agency Collaboration (INV\u0026amp;C)\u003c/h2\u003e \u003cp\u003eReadiness for AI use was not substantially impacted by judges' positive ratings of AI-assisted investigative and inter-agency collaboration capabilities (Means\u0026thinsp;=\u0026thinsp;3.831\u0026ndash;4.200) (β\u0026thinsp;=\u0026thinsp;0.128, t\u0026thinsp;=\u0026thinsp;1.366, p\u0026thinsp;=\u0026thinsp;0.172). This implies that judges uphold distinct institutional boundaries: executive-stage duties like coordination and inquiry are seen as separate from judicial duties. The direct effect of AI on readiness is constrained by issues with the contestability, procedural integrity, and evidential reliability of AI-generated outputs. Judges underlined the judiciary's cautious attitude to tasks that overlap with prosecutorial or administrative authorities by emphasizing that AI in investigative contexts does not transfer into higher adoption willingness without transparency and verifiability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.6 Finding 6: Ethical Safeguards and Judicial Readiness (ETH)\u003c/h2\u003e \u003cp\u003eOverall preparedness of judges for AI use is strongly influenced by judicial views on ethical protections (β\u0026thinsp;=\u0026thinsp;0.240, t\u0026thinsp;=\u0026thinsp;3.405, p\u0026thinsp;=\u0026thinsp;0.001). High mean scores (4.385\u0026ndash;4.569) show that justice, accountability, transparency, and bias mitigation are strongly supported as prerequisites for the adoption of AI. Research indicates that judicial receptivity requires ethical protections. The lack of normative guarantees leads to doubts about judicial independence and the defense of fundamental rights, even in cases when AI provides technical efficiency. Thus, ethical readiness acts as a major facilitator of adoption across domains and modifies opinions regarding the validity of AI's functional advantages. The broader idea that legitimacy supports technology acceptance in public governance is reflected in this study, which emphasizes that the judiciary's adoption of AI is ethically contingent. All of the results point to a model of judicial preparedness for AI adoption that is function-specific, governance-sensitive, and morally grounded. Applications like evidence analysis and case management that improve operational effectiveness and decision support without compromising discretionary authority are the most prepared. On the other hand, even in cases when technical advantages are present, topics that are thought to be fundamental to adjudicative judgment\u0026mdash;like punishment or investigatory oversight\u0026mdash;evoke cautious involvement. The analysis emphasizes how crucial it is to include AI into frameworks that are open, accountable, and compliant with the law, all while maintaining strong ethical standards. These results imply that a balanced approach is needed for the judiciary to deploy AI effectively: focusing on high-acceptance functions, maintaining judicial autonomy, and integrating governance and ethical standards into the technology's use. This strategy is consistent with the larger body of jurisprudential discussion on responsible technological governance, procedural justice, and the rule of law in developing nations.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Discussion and Conclusion","content":"\u003cp\u003eThis paper presents a detailed evaluation of judicial readiness for AI deployment across Bangladesh\u0026rsquo;s judiciary, combining empirical survey data with qualitative insights from judicial interviews. The analysis underlines the connection between technological functionality, ethical governance, and rule-of-law compliance, revealing insights of both theoretical and practical value for AI integration in judicial systems.\u003c/p\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n\u003ch2\u003e7.1 Function-Specific Patterns of AI Adoption\u003c/h2\u003e\n\u003cp\u003eResults show that judicial adoption of AI is rather function-specific. Judges' recognition of AI as a tool to optimize workflows, reduce clerical burdens, and improve evidentiary review is reflected in their positive perceptions of AI applications that support decision-making and enhance administrative efficiency, such as evidence analysis (EA; \u0026beta;\u0026thinsp;=\u0026thinsp;0.096) and case management (CMD; \u0026beta;\u0026thinsp;=\u0026thinsp;0.324, t\u0026thinsp;=\u0026thinsp;3.320, p\u0026thinsp;=\u0026thinsp;0.001). Judges emphasize AI as a decision-support tool that maintains judicial discretion while enhancing analytical clarity, especially in instances involving extensive records or intricate forensic evidence, according to qualitative interviews. Conversely, inter-agency investigative functions (Inv \u0026amp; C; \u0026beta;\u0026thinsp;=\u0026thinsp;0.128, p\u0026thinsp;=\u0026thinsp;0.172) and AI-assisted sentencing (Sentencing; \u0026beta;\u0026thinsp;=\u0026thinsp;0.123, p\u0026thinsp;=\u0026thinsp;0.163) generated cautious or non-significant answers. These findings highlight the continued significance of contextual judgment and judicial discretion in adjudicative procedures. The institutional and normative limits of AI acceptance were highlighted by judges' perceptions of AI interventions in these areas as potentially invasive. By showing that perceived utility depends on both technological performance and the nature of judicial functions, this task-specific adoption pattern expands on current technology adoption models like the Technology Acceptance Model (TAM) and Task-Technology Fit (TTF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n\u003ch2\u003e7.2 Ethical and Governance Considerations\u003c/h2\u003e\n\u003cp\u003eCritical factors of judicial preparation were shown to be normative governance and ethical protections (ETH; \u0026beta;\u0026thinsp;=\u0026thinsp;0.240, t\u0026thinsp;=\u0026thinsp;3.405, p\u0026thinsp;=\u0026thinsp;0.001), highlighting the fact that institutional legitimacy and ethical preparedness are fundamental to AI deployment rather than incidental. On measures of fairness, accountability, transparency, and bias mitigation, judges expressed high agreement (Means\u0026thinsp;=\u0026thinsp;4.385\u0026ndash;4.569), suggesting that normative protections are necessary for fostering trust in AI systems. Qualitative findings also show that ethical frameworks serve as a lens through which functional benefits are evaluated, highlighting the significance of auditability, traceable outputs, and \"ethics-by-design\" in preserving judicial independence. Rule-of-law alignment, accountability, and transparency all had a similar impact on readiness (TAR; \u0026beta;\u0026thinsp;=\u0026thinsp;0.238, t\u0026thinsp;=\u0026thinsp;2.809, p\u0026thinsp;=\u0026thinsp;0.005), demonstrating the importance of governance-oriented design for AI adoption. Collectively, these results expand socio-legal research on technology integration in crucial public institutions by indicating that ethical and governance factors influence the legitimacy of AI adoption across functional domains.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\n\u003ch2\u003e7.3 Theoretical and Practical Implications\u003c/h2\u003e\n\u003cp\u003eThe study adds to the body of literature in several ways:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTask-Specific Legitimacy: The perceived suitability of AI in relation to task sensitivity determines judicial preparedness. Adjudicative discretion is still safeguarded, but administrative and supportive duties are widely accepted. This provides empirical support for the theoretical claim that, in situations involving power and accountability, institutional legitimacy moderates the adoption of technology.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIntegration of Functionality, Ethics, and Governance: The study presents a socio-technical framework for judicial AI adoption by integrating normative protections, institutional trust, and functional performance (AI capabilities). It shows that adoption is influenced by a combination of legal compliance, ethical readiness, and technological usefulness.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContext-Specific Insights: Using a developing-country judiciary as its setting, the study shows how readiness is influenced by judge training, caseload demands, and demographic characteristics. It also offers a context-sensitive model for the adoption of AI in developing legal systems.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePractically speaking, the results guide implementation and policy strategies:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003ePhased Deployment: AI should be introduced initially in high-acceptance domains (case management, evidence analysis) to build confidence before expanding to sensitive adjudicative areas (sentencing, investigation).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGovernance and Ethics Safeguards: To promote legitimacy and trust, it is crucial to establish audit procedures, ethics-by-design, and open AI workflows.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCapacity Building: AI literacy, ethical comprehension, and useful abilities for using AI tools should all be developed in judicial training programs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInfrastructure and Multi-Stakeholder Engagement: Adoption, procedural justice, and institutional legitimacy will all be improved by sufficient technology platforms and collaborative design procedures involving judges, court employees, attorneys, and legislators.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\n\u003ch2\u003e7.4 Limitations and Future Research Scope\u003c/h2\u003e\n\u003cp\u003eDespite its contributions, the study contains shortcomings that suggest paths for further research:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eLimited Global Literature: Scarce empirical research on judicial AI impedes cross-national contextualization. Future comparative research could investigate adoption, ethics, and governance practices across jurisdictions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eContextual Focus: The findings are peculiar to Bangladesh, and caution is needed in generalizing conclusions to other legal systems. Replication in varied institutional settings is warranted.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStakeholder Scope: The study focuses on judges\u0026rsquo; perceptions. Multi-stakeholder viewpoints, including lawyers, court workers, and technology vendors, could offer a more thorough knowledge of adoption dynamics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLongitudinal and Impact Studies: Future studies might track court readiness and AI adoption over time, and assess real-world effectiveness, procedural fairness, and ethical outcomes.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/div\u003e"},{"header":"8. Conclusion","content":"\u003cp\u003eThis study shows that judicial preparedness for the use of artificial intelligence (AI) is function-specific, founded in ethics, and cognizant of governance issues. According to empirical data from Bangladesh, the successful integration of AI in court systems hinges on compatibility with judicial discretion, procedural fairness, and institutional legitimacy under the rule of law, rather than just technological prowess or efficiency advantages. In the areas of case management and evidence analysis, where AI is seen as improving productivity, analytical clarity, and administrative efficacy without interfering with fundamental adjudicative duties, judges show the greatest preparedness for AI applications. On the other hand, judicial preparedness is not greatly impacted by AI-assisted sentencing or investigative cooperation, which reflects enduring worries about personalized justice, accountability, and the indispensable role of human judgment in legally delicate areas. These results highlight how judges make a clear distinction between AI as a tool for decision-making and AI as a possible replacement for judicial authority. One of the key enabling conditions influencing judicial approval is the presence of ethical protections. The normative lenses of accountability, explainability, transparency, and human oversight are used to assess the functional benefits. Therefore, trust in governance structures that guarantee legal observance, procedural integrity, and the defense of fundamental rights is essential to judicial preparedness. By showing how functionality, ethical legitimacy, and institutional trust interact to influence AI adoption in courts, the work theoretically improves socio-legal and technological governance scholarship. Practically speaking, the results encourage a gradual, function-sensitive approach to AI adoption, giving low-risk administrative and analytical applications priority, as well as consistent investment in digital infrastructure, judicial training, and ethics-by-design procedures. Notwithstanding sample and contextual constraints, the study offers empirically supported insights into the adoption of judicial AI in developing nations and identifies avenues for responsible, rule-of-law-compliant judicial modernization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe corresponding author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the established ethical standards and with the informed consent of the individual participants, this research was conducted. The participants joined in this data collection process voluntarily and\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with established ethical standards and was approved by the relevant institutional review body of the authors\u0026rsquo; affiliated institution. Informed consent was obtained from all individual participants prior to their participation in the study. Participation was voluntary, and respondents were informed of their right to withdraw at any stage without any consequences. All data were collected and analyzed anonymously to ensure confidentiality.\u003c/p\u003e\n\u003ch2\u003eAI-Assisted Writing\u003c/h2\u003e\n\u003cp\u003eLarge language models were used solely to support language polishing and structural refinement during the preparation of this manuscript. AI tools were not used for conceptual development, framework design, metric formulation, threshold determination, data interpretation, or substantive analysis. All theoretical choices, analytical judgments, and interpretive conclusions are the sole responsibility of the author.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArthanari A, Raj SS, Vignesh R (2025) A narrative review in application of artificial intelligence in forensic science: Enhancing accuracy in crime scene analysis and evidence interpretation. 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UNESCO. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unesco.org/en/articles/ai-and-judiciary-balancing-innovation-integrity\u003c/span\u003e\u003cspan address=\"https://www.unesco.org/en/articles/ai-and-judiciary-balancing-innovation-integrity\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e accessed 6 February 2026\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Malaya","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":"Artificial Intelligence, Judiciary, Judicial Readiness, Case Management, Transparency, Ethics, Bangladesh, PLS-SEM","lastPublishedDoi":"10.21203/rs.3.rs-9275906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9275906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobally, Artificial Intelligence (AI) is being used consistently as a means of improving procedural transparency, uniformity, and efficiency in conventional judicial processes designed by substantive and procedural laws of the countries. With an emphasis on six areas—evidence analysis, sentencing, case management, transparency and accountability, investigation and interagency coordination, and ethical safeguards—this study empirically investigates the judicial preparedness for AI adoption in the judicial process in Bangladesh. The study examines how views of judges regarding AI use are influenced by functional benefits and ethical readiness using Partial Least Squares Structural Equation Modeling (PLS-SEM) and a quantitative, cross-sectional survey of 100 judicial officers from various court tiers. The results show that while AI-assisted sentencing and investigative cooperation have non-significant effects on court readiness, views of AI-assisted case management, ethical protections, transparency, and rule-of-law compliance have a substantial impact. The significance of ethical governance, human oversight, and institutional trust is further highlighted by qualitative interviews, which show that judges largely see AI as a decision-support tool rather than a replacement for judicial discretion. By incorporating socio-legal and technological governance viewpoints into a model of judicial AI adoption, this paper makes a theoretical contribution. It also offers empirical support by presenting data from emerging nations. From a practical standpoint, the findings help judicial administrators and lawmakers create AI interventions that improve judicial efficiency, preserve procedural integrity, and protect public trust. The small sample size, Bangladeshi emphasis, and absence of other stakeholders like attorneys and court employees are among the limitations, which point to areas for further study on the deployment of AI in judicial and institutional settings.\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Assessing Judicial Readiness for Artificial Intelligence Adoption: Functional, Normative, and Ethical Drivers from Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 10:17:49","doi":"10.21203/rs.3.rs-9275906/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":"090caeac-6d22-4772-90d9-d6593d2502c3","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65445372,"name":"Artificial Intelligence and Machine Learning"},{"id":65445373,"name":"Criminal Law"}],"tags":[],"updatedAt":"2026-04-01T10:17:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 10:17:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9275906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9275906","identity":"rs-9275906","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}