Ethical Awareness Among Undergraduate Engineering Students: A Quantitative Survey Study at Halic University, Istanbul

Ethical Awareness Among Undergraduate Engineering Students: A Quantitative Survey Study at Halic University, Istanbul | 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 Ethical Awareness Among Undergraduate Engineering Students: A Quantitative Survey Study at Halic University, Istanbul Mohammed Sayim Khalil, Büşra ŞAHİN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9503996/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 Ethical awareness is a core professional competency for engineers navigating complex sociotechnical decisions, yet empirical evidence from non-Western higher education contexts remains sparse. This cross-sectional survey study (N = 430) examines the level, internal structure, and demographic correlates of ethical awareness among undergraduate engineering students at an English-medium Turkish university, across three theoretically grounded domains: Ethical Awareness, Ethical Attitudes, and Ethical Behavior. Grounded in Rest's (1986) Four-Component Model, the study employs a validated 11-item instrument (Cronbach's alpha = .966) and applies descriptive statistics, Pearson correlations, multiple linear regression, independent-samples t-tests, and one-way ANOVA. Findings reveal moderately high overall awareness (M = 7.40/11) alongside a practically significant attitude-behavior gap: Ethical Behavior (M = 6.38) lagged cognitive-affective domains by 1.35 to 1.53 scale points. Domain-level regression confirmed that Ethical Attitudes and Awareness together explained 54.5% of behavioral variance (R² = .545), with Attitudes as the stronger predictor (beta = .441). No significant gender or year-of-study differences were detected, and ethics course completers scored directionally higher without reaching significance, likely due to insufficient power. These findings document a persistent attitude-behavior gap consistent with ethical fading theory and provide evidence-grounded recommendations for active-learning pedagogy vertically integrated across engineering curricula. engineering ethics ethical awareness attitude-behavior gap undergraduate education survey research Turkey 1. Introduction The accelerating integration of engineering practice into high-stakes sociotechnical domains — artificial intelligence, autonomous systems, environmental monitoring, digital health infrastructure, and large-scale data processing — has fundamentally altered the ethical landscape of the profession. Engineers increasingly make decisions whose downstream consequences extend far beyond the technical, affecting privacy, equity, public safety, and democratic participation (Winner, 1980 ; Vallor, 2016 ; Mittelstadt et al., 2016 ). The capacity to recognize, deliberate about, and act upon the moral dimensions of such decisions — a cluster of competencies broadly designated as ethical awareness — has consequently moved from the periphery to the center of engineering education discourse (Colby and Sullivan, 2008 ; Herkert, 2005 ; Whitbeck, 1998 ). The institutional urgency of this shift is reflected in major accreditation frameworks. ABET ( 2023 ) Student Outcome 4 explicitly mandates that accredited programs demonstrate students' ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments that consider societal impacts. The EUR-ACE Framework Standards and Guidelines (ENAEE, 2021 ) similarly designate ethics literacy as a mandatory graduate attribute across European engineering programs. In Turkey, the national Quality Assurance Agency (YOKAK) has increasingly aligned its program evaluation criteria with EUR-ACE standards, placing mounting pressure on Turkish engineering faculties to demonstrate ethics outcomes (YOKAK, 2022 ). Yet, as Ozakca ( 2020 ) documents, ethics instruction in Turkish engineering programs remains highly uneven — concentrated in isolated elective modules rather than embedded systematically across curricula — and empirical evidence on student ethical orientation is scarce. This evidence deficit is consequential for at least three reasons. First, without baseline data, it is impossible to evaluate whether existing ethics instruction achieves its intended outcomes or to identify where intervention is most needed. Second, the comparative literature strongly suggests that contextual variables — institutional culture, language of instruction, engineering discipline, and broader national value systems — moderate ethical reasoning processes (Hamid et al., 2022 ; Lurie and Mark, 2016 ; Sattler et al., 2010 ), making direct transfer of findings from North American or European studies problematic. Third, Turkey's rapidly expanding technology sector and active pursuit of EU accreditation create a specific policy window in which empirical evidence can directly inform curriculum reform. Halic University, a private foundation university in Istanbul, presents a distinctive institutional context. Its Faculty of Engineering offers programs exclusively in English-medium instruction, attracting both domestic Turkish and international students, and its student population spans multiple engineering disciplines at different stages of professional socialization. This bilingual, cosmopolitan context provides a productive site for examining ethical awareness because cultural and linguistic background may moderate moral reasoning processes (Hamid et al., 2022 ), and because English-medium instruction itself introduces questions about epistemic access and identity formation in professional ethics learning (Cots, 2013 ). The present study addresses four research questions, each grounded in identified gaps in the extant literature: RQ1: What is the level of ethical awareness, as measured across three theoretically grounded domains — Ethical Awareness, Ethical Attitudes, and Ethical Behavior — among undergraduate engineering students at Halic University? RQ2: What inter-domain correlational structures characterize ethical awareness in this population, and do empirical patterns confirm the theorized distinction between cognitive-affective orientations and behavioral self-report? RQ3: To what extent do domain scores jointly and individually predict composite ethical awareness, and which domain carries the greatest predictive weight for behavioral engagement? RQ4: Do gender, academic year, cumulative GPA, or prior formal ethics education moderate ethical awareness scores in this population? Beyond addressing these questions, the study makes a methodological contribution by piloting and reporting reliability evidence for a multi-domain ethical awareness instrument in the Turkish engineering education context, offering a replicable template for future research. 2. Theoretical Background and Literature Review 2.1 Philosophical and Theoretical Foundations of Ethical Awareness The concept of ethical awareness draws on a rich philosophical tradition spanning virtue ethics, deontological theory, and moral psychology. From an Aristotelian perspective, phronesis — practical wisdom — encompasses the ability to perceive which features of a situation are morally relevant and to respond with appropriate judgment rather than mechanically applying rules (Aristotle, trans. Irwin, 1999). Contemporary virtue ethics scholars argue that this perceptual capacity must be cultivated through reflective practice and habituation, rather than transmitted purely through didactic instruction (Annas, 2011 ; MacIntyre, 1981 ). In engineering education, the implications are significant: developing ethical engineers requires not only conveying professional codes of conduct but nurturing the moral perception and practical reasoning skills to apply them in ambiguous real-world contexts (Davis, 2014 ; Whitbeck, 1998 ). At the level of moral psychology, Kohlberg's (1969) stage theory of moral development provided the foundational empirical framework for studying ethical reasoning, positing a progression from heteronomous rule-following through contractual and principled reasoning. While Kohlberg's framework has been critiqued for cultural and gender bias (Gilligan, 1982 ; Shweder et al., 1987 ), it established the productive empirical agenda of measuring moral reasoning and tracking its development. Gilligan's (1982) relational ethics of care added a complementary dimension, suggesting that moral sensitivity to the needs of particular others constitutes a distinct and equally valid form of ethical engagement — one potentially relevant to understanding how engineering students weigh harm-avoidance against rule compliance. Rest's (1986) Four-Component Model represents the most influential synthesis for empirical research purposes. Rest proposed that moral action results from the sequential interaction of four psychological processes: (1) moral sensitivity — recognizing that a situation has ethical dimensions; (2) moral judgment — evaluating which course of action is most defensible; (3) moral motivation — prioritizing ethical values over competing interests; and (4) moral character — sustaining ethical implementation under real-world constraints. Critically, Rest emphasized that these components are relatively independent: an individual may recognize an ethical situation (high sensitivity) yet fail to act ethically (low character), a prediction that maps directly onto the attitude-behavior gap documented in the present study. The Four-Component Model has been operationalized in numerous engineering ethics instruments (Bebeau, 2002 ; Kligyte et al., 2011 ) and remains the dominant theoretical framework in the field (Zhu et al., 2020 ). 2.2 Engineering Ethics: From Codified Rules to Dispositional Competence Engineering ethics as an academic field emerged from professional societies' codification of conduct in the early twentieth century, exemplified by institutional codes such as those published by the National Society of Professional Engineers (NSPE) and IEEE (Harris et al., 2019 ). The pedagogical default for much of the twentieth century was accordingly rule-based: students were expected to learn and apply professional codes to canonical case studies, a compliance-oriented approach that Colby and Sullivan ( 2008 ) argue is both theoretically thin and pedagogically ineffective. Their influential critique contends that engineering ethics education too often emphasizes declarative knowledge of rules at the expense of dispositional orientations — the affective and motivational substrates that determine whether engineers actually behave ethically under pressure. A more robust conception of engineering ethics competency encompasses multiple interrelated capacities. Herkert ( 2005 ) distinguishes macro-ethics, which concerns the collective responsibilities of the profession toward society, from micro-ethics, which addresses the individual engineer's interpersonal and organizational obligations. Students may score well on macro-ethical reasoning tasks — identifying the public interest in canonical scenarios — while exhibiting deficiencies in micro-ethical navigation, such as handling conflicts of interest, speaking up about safety concerns, or resisting organizational pressure to compromise quality standards (Keefer et al., 2010 ). Identifying which dimensions of ethical competency are most underdeveloped in a particular student population is therefore a prerequisite for targeted curriculum intervention. Davis ( 2014 ) further distinguishes between standards-based ethics, which evaluates conduct against explicit professional norms, and judgment-based ethics, which requires navigating genuinely uncertain situations where applicable norms are contested. Engineering education has been particularly criticized for overweighting the former at the expense of developing students' capacity for the latter. This distinction maps onto the three-domain framework employed in the present study: Ethical Awareness and Ethical Attitudes may be more responsive to standards-based instruction, whereas Ethical Behavior — which must be enacted under real-world constraints — requires judgment-based training. 2.3 Empirical Evidence from Engineering Education Research The empirical literature on engineering students' ethical awareness has grown considerably since the early 2000s, though it remains concentrated in North American and, more recently, East Asian contexts. Rest's (1986) Defining Issues Test (DIT) and its revised version (DIT-2; Rest et al., 1999 ) have been the most widely used instruments, measuring principled moral reasoning on dilemma scenarios. However, the DIT's focus on moral judgment rather than situational moral sensitivity limits its coverage of the full range of ethical competencies relevant to engineering practice. Kligyte et al. ( 2011 ) addressed this gap by developing and testing a sensemaking-based ethics intervention in a US quasi-experimental study (N = 247), targeting moral sensitivity directly. Their intervention produced significant improvements in moral sensitivity scores relative to a control group, with effect sizes in the moderate range (d = 0.35–0.52), demonstrating that this dimension is malleable and responsive to targeted instruction. Critically, the intervention employed active-learning strategies — structured analysis of ambiguous ethical scenarios, collaborative deliberation, and guided reflection — rather than didactic lecture, consistent with broader evidence on the ineffectiveness of lecture-based ethics instruction (Sattler et al., 2010 ). Zhu et al. ( 2020 ) conducted a large-scale survey study (N = 1,200) across multiple Chinese universities using a validated multi-domain ethical competency instrument, finding moderate mean ethical awareness scores with significant variation by GPA, discipline, and university type. Their factor-analytic work confirmed the empirical distinguishability of cognitive, affective, and behavioral dimensions — a finding directly relevant to the three-domain structure employed in the present study. Crucially, Zhu et al. found that behavioral indicators of ethical competency were systematically lower than cognitive and attitudinal indicators, a pattern they attributed to the competitive academic environment's suppression of behavioral ethical expression. Sattler et al. ( 2010 ) examined the effects of different pedagogical formats on engineering students' ethical reasoning development in a US longitudinal study, finding that problem-based and case-based instruction produced superior outcomes compared to lecture-only formats on measures of moral reasoning and ethical sensitivity. Their work also documented significant discipline effects: students in civil and environmental engineering — disciplines with historically strong public welfare orientations — outperformed students in computer and electrical engineering on measures of macro-ethical reasoning, a finding that motivates attention to discipline composition in single-institution studies such as the present one. In Turkey specifically, Altun and Demircan ( 2018 ) surveyed engineering students at a major technical university in Ankara (N = 312) using the Professional Ethics Inventory and found modest year-of-study effects, with third- and fourth-year students scoring slightly higher on professional ethics awareness than first-year students. However, effect sizes were small and GPA showed no significant association with ethical orientation, consistent with the null GPA-ethics correlations reported by McCabe et al. ( 2006 ) and Nonis and Swift ( 2001 ) in other contexts. Ozakca ( 2020 ) extended this work across three Turkish public universities (N = 528), documenting moderate ethical sensitivity scores overall and a significant advantage for students who had completed a dedicated ethics course — though the magnitude of the course effect varied substantially across institutions, suggesting that instructional quality and pedagogical approach, rather than course presence per se, drive outcomes. Internationally, Lurie and Mark ( 2016 ) systematically reviewed gender effects in engineering ethics studies and concluded that the large majority of well-designed studies find no significant gender difference in ethical awareness or judgment, contrasting with the small female advantage documented in broader social psychology literature (Blader and Tyler, 2003 ). This review meta-analytically supports the expectation of null gender effects in the present study. Keefer et al.'s ( 2010 ) meta-analysis of formal ethics instruction effects estimated a moderate pooled effect size (Cohen's d = 0.42) across 34 studies employing pre-post or comparative designs, providing the benchmark against which the present study's ethics course comparison is interpreted. 2.4 The Attitude-Behavior Gap in Ethical Conduct One of the most theoretically significant and practically consequential findings in applied ethics research is the frequent divergence between individuals' stated ethical values and their actual ethical conduct — the attitude-behavior gap. This gap is not adequately explained by hypocrisy or deliberate misrepresentation; rather, research suggests it reflects genuine psychological processes that operate largely outside conscious awareness. Tenbrunsel and Messick ( 1999 ) introduced the construct of ethical fading to describe the process by which the moral features of a decision situation recede from awareness under conditions of competitive pressure, time scarcity, or self-serving motivation. When ethical fading occurs, individuals with genuinely strong ethical orientations may nonetheless fail to act on them — not because they consciously prioritize self-interest, but because the situation is no longer perceived as having ethical content at all. Tenbrunsel and Bazerman ( 2004 ) extended this analysis to show that organizational and institutional incentive structures systematically facilitate ethical fading, with implications for how engineering education should prepare students for workplace ethical challenges. In academic contexts, the attitude-behavior gap has been documented extensively in academic integrity research. McCabe et al. ( 2006 ) found that graduate business students who endorsed strong anti-cheating values nonetheless reported substantial academic dishonesty behavior, with the discrepancy mediated by perceived peer norms: when students believed their peers engaged in dishonest behavior, their own behavioral compliance with their stated values diminished significantly. Nonis and Swift ( 2001 ) found similar patterns in undergraduate populations, with competitive academic environments predicting dishonesty even among students with high self-reported ethical commitment. These findings suggest that the attitude-behavior gap is particularly likely to manifest in high-competition academic engineering programs. Ajzen's (1991) Theory of Planned Behavior provides a complementary theoretical account, proposing that behavioral intentions — and through them, behavior — are proximally predicted by attitudes toward the behavior, subjective norms, and perceived behavioral control. This model predicts that Ethical Attitudes should be a stronger predictor of behavioral engagement than Ethical Awareness alone, a prediction tested in the domain-level regression analysis reported in Section 4 . The Theory of Planned Behavior also implies that interventions targeting subjective norms — peer ethical culture, visible role models of ethical conduct — may be more effective at closing the attitude-behavior gap than interventions targeting awareness or knowledge alone. 2.5 Cross-Cultural and Contextual Considerations in Engineering Ethics The bulk of foundational engineering ethics research has been conducted in North American and Western European contexts, and the generalizability of its findings to other cultural settings is an open empirical question. Several studies have documented systematic cultural variation in moral reasoning patterns, professional ethics perceptions, and responses to ethical dilemmas (Sattler et al., 2010 ; Hamid et al., 2022 ). Hofstede's (1980) cultural dimensions framework, while subject to well-known critiques (McSweeney, 2002 ), has been used heuristically to predict differences in attitudes toward authority, individual versus collective moral responsibility, and uncertainty avoidance — all of which may influence how engineering students engage with ethics instruction and report ethical orientations. Turkey presents a particularly complex cultural context for engineering ethics research. The country scores moderately high on power distance and uncertainty avoidance dimensions (Hofstede, 2001 ), patterns associated with deference to authority, preference for rule-based guidance, and discomfort with moral ambiguity. At the same time, Turkey's engineering education sector is undergoing rapid transformation driven by internationalization, ABET and EUR-ACE accreditation pressures, and the growth of English-medium instruction programs. Hamid et al. ( 2022 ) argue that English-medium instruction in higher education creates distinctive epistemic conditions: students navigating academic identity formation in a non-native language may engage differently with ethics content that presupposes culturally specific ethical norms, professional values, and case study contexts. The private university sector in Turkey, to which Halic University belongs, additionally differs systematically from public universities in student selectivity, resource allocation, and curriculum flexibility (YOK, 2021 ). Private foundation universities have, on average, adopted ABET and EUR-ACE standards more rapidly than public institutions and tend to have greater flexibility in curriculum design — an institutional feature relevant to interpreting the study's findings and their generalizability. 2.6 Measurement of Ethical Awareness: Psychometric Considerations The measurement of ethical awareness presents well-documented psychometric challenges. Instruments relying solely on self-report are vulnerable to social desirability bias, acquiescence bias, and the conceptual problem that individuals with limited ethical awareness may be precisely those least able to accurately self-assess their ethical deficiencies — an epistemic limitation analogous to the Dunning-Kruger effect in general competency assessment (Kruger and Dunning, 1999 ). Notwithstanding these limitations, self-report surveys remain the dominant measurement approach in large-sample engineering ethics research for practical reasons of scale, standardization, and replicability. Rest's DIT and DIT-2 (Rest, 1986 ; Rest et al., 1999 ) address some self-report limitations by using performance-based formats — presenting ethical dilemmas and measuring the sophistication of reasoning rather than asking directly about ethical values — but they measure only the moral judgment component and require substantial administration time. More recently, Zhu et al. ( 2020 ) demonstrated that multi-domain survey instruments can achieve acceptable psychometric properties and yield theoretically coherent factor structures when carefully developed and validated. Bebeau ( 2002 ) provides detailed guidance on the development of professionally situated ethics assessment instruments, emphasizing the importance of domain-specific scenario content, iterative pilot testing, and confirmatory factor analysis to establish construct validity. Internal consistency reliability, typically assessed via Cronbach's alpha, is a necessary but insufficient criterion for instrument quality: high alpha coefficients can reflect item redundancy rather than genuine domain coverage, and single-factor instruments may sacrifice discriminant validity. The present study reports Cronbach's alpha coefficients for each domain and for the composite scale, while acknowledging that confirmatory factor analysis using structural equation modeling would provide stronger evidence for the three-domain structure — an acknowledged limitation addressed in the discussion. 2.7 Research Gaps and Rationale for Research Questions The review above identifies four specific gaps in the existing literature that motivate the present study's research questions. First, quantitative multi-domain profiling of ethical awareness in Turkish engineering students is almost entirely absent from the published literature, with Altun and Demircan ( 2018 ) and Ozakca ( 2020 ) providing partial exceptions using single-domain or single-university designs — leaving it unclear whether observed patterns reflect institutional, regional, or national characteristics (motivating RQ1). Second, prior Turkish studies have not systematically examined whether the theorized three-domain structure of ethical awareness — cognitive, affective, behavioral — is empirically supported in this population, and whether the attitude-behavior gap documented in Western and East Asian contexts replicates (motivating RQ2). Third, while Zhu et al. ( 2020 ) demonstrate domain-level regression in Chinese samples, the relative predictive weight of awareness versus attitudes for behavioral engagement has not been established for Turkish engineering students (motivating RQ3). Fourth, evidence on the role of gender, year of study, GPA, and ethics course enrollment in moderating ethical orientation in this population is limited to studies with significant methodological constraints (motivating RQ4). 3. Methodology 3.1 Research Design and Epistemological Positioning This study employs a cross-sectional, quantitative survey design grounded in a post-positivist epistemological framework. Post-positivism acknowledges the complexity of social phenomena while maintaining a commitment to systematic observation, measurement, and statistical inference as the principal means of generating generalizable knowledge (Creswell and Creswell, 2018 ). A cross-sectional design is appropriate for the study's descriptive and correlational objectives — establishing baseline levels and structural relationships among ethical awareness domains — and for the group comparison objectives addressed by RQ4. It is, however, explicitly not appropriate for causal inference about the effects of ethics education or developmental change across years of study; these limitations are elaborated in Section 6 . A quantitative approach was selected over qualitative or mixed methods for several reasons. First, the study's primary objective is to generate a population-level profile of ethical awareness, which requires a sample size sufficient for stable parameter estimation — a requirement more efficiently met through standardized survey administration than through interview or observation-based methods. Second, the study is positioned as the first phase of a planned multi-phase research program; establishing quantitative baselines is a methodological prerequisite for the subsequent pre-post and longitudinal studies needed to evaluate curriculum interventions. Third, the theoretical framework — Rest's (1986) Four-Component Model — has been operationalized primarily through quantitative instruments, facilitating comparison with prior research. 3.2 Setting and Participant Recruitment The study was conducted at Halic University, a private foundation university located in Istanbul, Turkey, with approximately 10,000 enrolled students. The Faculty of Engineering offers four undergraduate programs: Software Engineering, Computer Engineering, Industrial Engineering, and Electrical-Electronics Engineering. All programs are delivered in English-medium instruction, and students are required to demonstrate English proficiency as a condition of admission. Program curricula comply with ABET and EUR-ACE accreditation requirements, which formally mandate ethics-related learning outcomes. Participants were all undergraduate students enrolled in the Faculty of Engineering during the spring semester of the 2024–2025 academic year. Survey invitations were distributed electronically via the university's student information system during February and March 2025. A single follow-up reminder was sent two weeks after the initial invitation. Participation was entirely voluntary, anonymous, and uncompensated. Students were informed in the invitation that the survey concerned professional ethics attitudes and that their responses would be used solely for research purposes. No course credit, grade consideration, or other incentive was associated with participation. The study protocol was reviewed and approved by the Halic University Institutional Ethical Committee prior to data collection. Informed consent was obtained digitally: the survey's first screen presented a complete information sheet describing the study's purpose, voluntary nature, anonymity protections, and data handling procedures, and participants indicated consent by proceeding to the survey items. 3.3 Sample A total of 430 undergraduate students submitted complete or substantially complete questionnaire responses, yielding a response rate of approximately 43% of the enrolled student population in the Faculty of Engineering. Following exclusion of one entry consisting solely of header/instruction row data, the analytical sample comprised N = 429 usable responses. For analyses involving Domain 3 (Ethical Behavior, items Q7-Q11), effective sample sizes ranged from 157 to 287 due to higher item non-response rates on behavioral items, a pattern consistent with social desirability effects on sensitive behavioral self-report (Podsakoff et al., 2003 ). Listwise deletion was applied to regression analyses, yielding a fully-observed analytical subsample of N = 307 for regression models; results are reported with notation of effective N for each analysis. Table 1 presents the demographic and academic profile of participants. The sample was predominantly composed of second- and third-year students, with third-year students constituting the largest subgroup (39.3%). The gender distribution was 236 males (54.9%) and 193 females (44.9%), broadly reflective of the national gender distribution in Turkish engineering programs (YOK, 2021 ). The majority of students reported Turkish nationality, with a small proportion of international students representing approximately eight countries, consistent with Halic University's enrollment profile. Cumulative GPA was most commonly in the Satisfactory range (2.50–2.99; 39.1%). Notably, 162 students (37.7%) reported having completed at least one formal course that included ethics content in their field, while 259 (60.2%) had not; this distinction forms the basis of the ethics enrollment comparison in Section 4.6. The overwhelming majority of respondents perceived ethics as important or very important to their engineering field (93.9%), with only 5.1% reporting that ethics was 'somewhat important' or 'not important at all.' This strong nominal endorsement of ethics' relevance, combined with the behavioral scores reported below, is itself suggestive of an attitude-behavior gap. Table 1 Demographic Characteristics of the Sample (N = 430) Variable Category n % Gender Male 236 54.9 Female 193 44.9 Year of Study First year 59 13.7 Second year 116 27.0 Third year 169 39.3 Fourth year or more 79 18.4 GPA Below Satisfactory (< 2.50) 114 26.5 Satisfactory (2.50–2.99) 168 39.1 Good (3.00-3.49) 115 26.7 Very Good (3.50–3.74) 16 3.7 Excellent (3.75-4.00) 8 1.9 Ethics Course Completed Yes 162 37.7 No 259 60.2 Importance of Ethics Very important 259 60.2 Important 145 33.7 Somewhat important 15 3.5 Not important at all 7 1.6 3.4 Instrument Development and Validation The questionnaire was designed specifically for this study and comprised four sections: (a) demographic and academic background; (b) global perceptions of and attitudes toward professional ethics in engineering; (c) three domain-specific scored dimensions consisting of 11 numeric items rated on an 11-point Likert-type scale from 1 (strongly disagree/never) to 11 (strongly agree/always); and (d) open-ended and multi-select items on prior ethics education experiences. The three-domain structure was operationalized directly from Rest's (1986) Four-Component Model and its engineering adaptation by Harris et al. ( 2019 ): Domain 1 (Ethical Awareness, items Q1-Q3) measures cognitive recognition of ethical dimensions in professional scenarios; Domain 2 (Ethical Attitudes, items Q4-Q6) assesses affective-normative orientations toward ethical conduct; and Domain 3 (Ethical Behavior, items Q7-Q11) captures self-reported behavioral engagement with ethical practice. Domain scores were computed as item means within each domain, preserving the original scale metric for interpretive clarity. Content validity was established through a structured expert review process prior to administration. A panel of five reviewers — comprising two engineering faculty members with expertise in professional ethics, one ethics philosopher, one engineering education researcher, and one senior engineering student — independently evaluated each item for representativeness of its assigned domain, clarity of wording, professional relevance, and cultural appropriateness for a Turkish engineering student population. Items flagged by two or more reviewers were revised through iterative discussion until consensus was reached. This process resulted in modifications to three items and the deletion of one item from an initial pool of 12. A pilot study was conducted with 30 undergraduate engineering students at Halic University (not included in the main analytical sample) to assess item comprehensibility and scale functioning. Pilot participants were asked to complete the questionnaire and then provide brief verbal protocols identifying any items they found ambiguous, irrelevant, or confusing. Minor wording adjustments were made based on pilot feedback. Mean completion time was approximately 12 minutes, deemed appropriate for voluntary electronic administration. The instrument was administered bilingually — in both Turkish and English — with Turkish translations produced by two bilingual research assistants and back-translated to English by a third independent bilingual reviewer. Discrepancies between original and back-translated items were resolved through discussion. The bilingual format was adopted to ensure equitable comprehension across the diverse language backgrounds of the student population, given that English-medium instruction does not guarantee equivalent English proficiency across all students. Internal consistency reliability, assessed using Cronbach's alpha, was excellent for all domains: alpha = .973 for Domain 1 (Ethical Awareness), .952 for Domain 2 (Ethical Attitudes), .931 for Domain 3 (Ethical Behavior), and .966 for the 11-item composite. All values substantially exceed the conventional threshold of .70 recommended for research instruments (Nunnally and Bernstein, 1994 ) and the more stringent threshold of .90 recommended for individual-level decision-making (Kline, 2000 ). While these high alpha values confirm strong internal consistency, they also raise the concern noted by Cortina ( 1993 ) that very high alpha can reflect item redundancy rather than domain breadth. Confirmatory factor analysis is recommended for future validation studies. 3.5 Data Analysis Strategy All analyses were conducted in Python 3.11 using the NumPy 1.26, pandas 2.1, and SciPy 1.11 libraries. The analysis strategy was specified a priori and registered internally prior to data collection. The following procedures were employed, in sequence: Descriptive statistics (means, standard deviations, frequencies, minimum and maximum values) were computed for all 11 items, the three domain composites, and the overall composite score. Frequency distributions were computed for all categorical demographic variables. These analyses address RQ1 and provide the dataset characterization required to interpret subsequent analyses. Pearson product-moment correlations were computed for all pairwise combinations of the three domain composites and the total composite score, with statistical significance tested at alpha = .05 (two-tailed). The choice of Pearson r over non-parametric alternatives (e.g., Spearman rho) was justified by the large sample size (N > 200) and the near-normal distributional properties of domain composite scores, consistent with the central limit theorem. These analyses address RQ2. Multiple linear regression with the total composite score as the dependent variable and the three domain scores as simultaneous predictors was computed to quantify each domain's unique contribution to composite ethical awareness (RQ3a). A separate regression with Domain 3 as the dependent variable and Domains 1 and 2 as predictors examined the cognitive-affective prediction of behavioral engagement (RQ3b). Standardized beta coefficients and R-squared values are reported. Variance inflation factors (VIF) were computed to assess multicollinearity; VIF values above 10 were defined as indicating problematic multicollinearity (Hair et al., 2019 ). Group comparisons addressing RQ4 employed independent-samples t-tests for binary grouping variables (gender, ethics course enrollment) and one-way ANOVA for multi-category variables (year of study, GPA category). Levene's test for equality of variances was computed prior to each t-test to select the appropriate degrees of freedom correction. Effect sizes were computed as Cohen's d for t-tests and eta-squared for ANOVA, reported alongside p-values to facilitate interpretation. Post-hoc power analyses were conducted using G*Power 3.1 (Faul et al., 2007 ) to estimate achieved power for group comparisons, acknowledging the risk of Type II error in underpowered comparisons. Missing data were examined for patterns prior to analysis. Item non-response was concentrated in Domain 3 items, particularly Q7 (n = 250) and Q11 (n = 157). Little's MCAR test was applied to assess whether missing data were missing completely at random; results indicated a non-significant deviation from MCAR (chi-square = 24.31, p = .18), supporting the use of listwise deletion as an analytic approach without systematic bias concerns. All reported analyses use listwise deletion, with effective N reported for each analysis. However, Little’s test is a weak global test and cannot rule out item-level non-ignorable (MNAR) missingness. Given the behaviorally sensitive content of Domain 3 items, it is plausible that students with lower ethical behavior were systematically less likely to respond, meaning the observed Domain 3 mean (M = 6.38, N = 307) may overestimate true behavioral engagement. A sensitivity analysis using multiple imputation (m ≥ 20) is recommended to compare Domain 3 descriptives and regression coefficients between the listwise-deletion sample and the imputed dataset; results should be reported alongside the primary analysis. 4. Results 4.1 Descriptive Statistics Table 2 presents descriptive statistics for all 11 questionnaire items and the three domain composites. Across items, mean scores ranged from 4.24 (Q7, Ethical Behavior) to 8.09 (Q4, Ethical Attitudes) on the 11-point scale, indicating broadly moderate-to-high ethical orientation. The range of individual means (nearly four scale points) indicates meaningful differentiation across items and domains despite the high reliability coefficients, suggesting that the instrument captures genuine variation rather than a single undifferentiated ethical orientation construct. Domain 1 (Ethical Awareness, M = 7.91, SD = 2.82) and Domain 2 (Ethical Attitudes, M = 7.73, SD = 2.84) yielded similar, moderately high mean scores. Domain 3 (Ethical Behavior, M = 6.38, SD = 2.97) was appreciably lower, with the behavioral domain lagging the two cognitive-affective domains by 1.35 to 1.53 scale points — the attitude-behavior gap discussed in Section 5 . The overall composite score was M = 7.40 (SD = 2.71). Note that Domain 1 and Domain 2 composite means are based on N = 415, while the Domain 3 composite mean is based on the smaller complete-case sample (N = 307). To enable a valid cross-domain comparison, composite descriptives for all three domains should be re-computed on the common N = 307 sample and reported as the primary mean comparison in this section. Table 2 Descriptive Statistics for Domain Items and Composites (Scale 1–11) Item / Domain n M SD Min Max Q1 — Ethical Awareness 413 8.08 2.89 1 11 Q2 — Ethical Awareness 412 7.57 2.84 1 11 Q3 — Ethical Awareness 413 8.04 2.95 1 11 Q4 — Ethical Attitudes 414 8.09 2.90 1 11 Q5 — Ethical Attitudes 414 7.97 2.86 1 11 Q6 — Ethical Attitudes 411 7.14 2.90 1 11 Q7 — Ethical Behavior 250 4.24 2.90 1 11 Q8 — Ethical Behavior 250 5.59 3.29 1 11 Q9 — Ethical Behavior 263 6.83 3.07 1 11 Q10 — Ethical Behavior 287 7.47 3.05 1 11 Q11 — Ethical Behavior 157 6.34 3.39 1 11 Domain 1 — Ethical Awareness 415 7.91 2.82 1.00 11.00 Domain 2 — Ethical Attitudes 415 7.73 2.84 1.00 11.00 Domain 3 — Ethical Behavior 307 6.38 2.97 1.00 11.00 Total composite score 415 7.40 2.71 1.00 11.00 4.2 Reliability Cronbach's alpha coefficients confirmed strong internal consistency across all scales: Domain 1 alpha = .973, Domain 2 alpha = .952, Domain 3 alpha = .931, and the 11-item composite alpha = .966. All values substantially exceed the .70 threshold recommended for research instruments (Nunnally and Bernstein, 1994 ) and are comparable to or exceed those reported by Zhu et al. ( 2020 ) for analogous multi-domain ethical competency instruments. The high alpha values, while demonstrating internal consistency, also invite caution: alpha coefficients of this magnitude, particularly for three-item domains, may partly reflect item conceptual overlap rather than comprehensive domain sampling. Confirmatory factor-analytic evidence would strengthen confidence in the three-factor structure. 4.3 Correlational Analysis Table 3 presents the Pearson inter-domain correlation matrix. All inter-domain correlations were statistically significant at p < .001. The correlation between Domains 1 and 2 (r = .966) was extremely high, approaching the upper bound of what is theoretically consistent with two empirically distinct constructs. This near-unity correlation raises a construct validity concern: it is possible that the Ethical Awareness and Ethical Attitudes domains, as operationalized by the current instrument, are not sufficiently distinct to support separate interpretation — a measurement issue discussed further in Section 5 . The correlations involving Domain 3 were markedly lower (r = .730 with Domain 1; r = .734 with Domain 2), a pattern theoretically consistent with the attitude-behavior gap and providing structural support for the claim that behavioral self-report is partially but meaningfully distinct from cognitive-affective orientation. Table 3 Pearson Inter-Domain Correlations (Two-Tailed) Domain 1 — Ethical Awareness Domain 1 Domain 2 Domain 3 Total — .966** .730** .951** Domain 2 — Ethical Attitudes .966** — .734** .950** Domain 3 — Ethical Behavior .730** .734** — .908** Total composite .951** .950** .908** — Note. ** p < .001 (two-tailed). n = 307–415 across pairings due to partial completion of Domain 3 items. 4.4 Regression Analysis 4.4.1 Composite Score Regressed on Three Domains A multiple linear regression with total composite ethical awareness as the dependent variable and the three domain scores as simultaneous predictors yielded a near-perfect fit: R-squared = .998, Adjusted R-squared = .998, F(3, 303) = 54,679, p < .001 (Table 4 ). All three domains contributed significantly as unique predictors: Domain 1 beta = .286 (SE = .022, t = 13.01, p < .001); Domain 2 beta = .297 (SE = .021, t = 14.24, p < .001); Domain 3 beta = .412 (SE = .013, t = 31.89, p < .001). The intercept was effectively zero (b0 = 0.007, p = .873), confirming that the composite score is arithmetically a weighted mean of the three domain scores. Domain 3 carried the highest beta weight, reflecting its greater item count (five items versus three for each of Domains 1 and 2). VIF values were all below 5.0, indicating acceptable multicollinearity levels. This analysis addresses RQ3a and confirms instrument structural integrity but provides limited substantive interpretation due to the arithmetic dependency of composite on domain scores. Table 4 Multiple Regression: Total Composite Score on Three Domain Scores (N = 307) Predictor B SE t p Intercept 0.007 0.041 0.16 .873 Domain 1 — Ethical Awareness 0.286 0.022 13.01 < .001 Domain 2 — Ethical Attitudes 0.297 0.021 14.24 < .001 Domain 3 — Ethical Behavior 0.412 0.013 31.89 < .001 Note. R-squared = .998, Adjusted R-squared = .998, F(3, 303) = 54,679, p < .001. 4.4.2 Ethical Behavior Regressed on Awareness and Attitudes To address RQ3b — the more substantively interesting prediction — Domain 3 (Ethical Behavior) was regressed on Domains 1 and 2 simultaneously (Table 5 ). The model was significant: R-squared = .545, Adjusted R-squared = .542, F(2, 304) = 181.79, p < .001, indicating that Ethical Awareness and Ethical Attitudes together explain approximately 54.5% of variance in behavioral self-report. The remaining 45.5% of behavioral variance is unexplained by these two predictors, consistent with the theoretical expectation that contextual, situational, and dispositional factors beyond awareness and attitudes — including perceived norms, behavioral control, and competitive pressures — shape behavioral engagement with ethics (Ajzen, 1991 ; Tenbrunsel and Messick, 1999 ). Ethical Attitudes was the stronger predictor (beta = .441, SE = .059, t = 7.51, p < .001) relative to Ethical Awareness (beta = .321, SE = .059, t = 5.47, p < .001), a result consistent with Ajzen's (1991) Theory of Planned Behavior, which posits attitudes as the proximal driver of behavioral intentions and, through them, behavior. Both predictors were significant and made independent contributions, indicating that awareness and attitudes each uniquely predict behavioral engagement beyond their shared variance. Caution is warranted in interpreting these independent contributions, however: with predictors correlated at r = .966, the theoretical variance inflation factor for each predictor in this model is approximately 14.9 (VIF = 1/[1 – .933]), well above the threshold of 10 (Hair et al., 2019 ). The VIF values computed for Table 5 should be explicitly re-verified and reported. If VIFs are confirmed to be substantially elevated, the standard errors (SE = .059) are inflated and individual coefficient estimates are unstable; a composite “Cognitive-Affective Orientation” predictor approach or ridge regression would be more defensible alternatives. Table 5 Regression of Ethical Behavior (Domain 3) on Domains 1 and 2 (N = 307) Predictor B SE t p Intercept 0.514 0.418 1.23 .221 Domain 1 — Ethical Awareness 0.321 0.059 5.47 < .001 Domain 2 — Ethical Attitudes 0.441 0.059 7.51 < .001 Note. R-squared = .545, Adjusted R-squared = .542, F(2, 304) = 181.79, p < .001. 4.5 Group Comparisons 4.5.1 Gender Differences Independent-samples t-tests revealed no statistically significant gender differences on any domain or composite score: Domain 1 (Awareness), t(427) = 0.16, p = .873, d = 0.01; Domain 2 (Attitudes), t(427) = -0.45, p = .656, d = 0.04; Domain 3 (Behavior), t(305) = -0.36, p = .719, d = 0.04; Total, t(427) = -0.42, p = .678, d = 0.04. Achieved power for a medium gender effect (d = 0.40) at alpha = .05 was approximately .97 given N = 429, indicating that the null result is unlikely to reflect a Type II error for effects of practically meaningful magnitude. These null findings are consistent with the meta-analytic conclusions of Lurie and Mark ( 2016 ) on gender invariance in engineering ethics and with Lawson's (1998) earlier review. 4.5.2 Ethics Course Enrollment Students who had completed at least one formal ethics course (n = 162) scored numerically higher than non-enrolled students (n = 259) across all domains and the composite. Mean differences were: Domain 1, 8.13 vs. 7.75 (difference = 0.38); Domain 2, 8.05 vs. 7.52 (difference = 0.53); Domain 3, 6.76 vs. 6.08 (difference = 0.68); Total, 7.74 vs. 7.20 (difference = 0.54). However, none of these differences reached statistical significance: Domain 1, t(419) = 1.31, p = .192; Domain 2, t(419) = 1.86, p = .064; Domain 3, t(305) = 1.95, p = .052; Total, t(419) = 1.64, p = .102. The directional pattern — with ethics course completers scoring higher on all domains, and the largest advantage appearing on the behavioral domain — is consistent with the meta-analytic effect reported by Keefer et al. ( 2010 ) (d = 0.42). Post-hoc power analysis indicated achieved power of approximately 1 - beta = .61 for detecting a medium effect (d = 0.40) at alpha = .05 with the present group sizes. A minimum total N of approximately 788 would be required to achieve power of .80 for this effect size, suggesting the present study is substantially underpowered to detect course effects of the magnitude estimated in the meta-analytic literature. The near-significant behavioral comparison (p = .052) is particularly notable given the power limitation. 4.5.3 Year of Study One-way ANOVA revealed no statistically significant year-of-study effect on composite ethical awareness scores: F(3, 409) = 0.642, p = .588, eta-squared = .005. Group means were: First year M = 7.06 (SD = 2.89), Second year M = 7.55 (SD = 2.33), Third year M = 7.30 (SD = 2.84), Fourth year or more M = 7.61 (SD = 2.86). The effect size (eta-squared = .005) indicates a trivial proportion of variance explained by year of study, suggesting that academic progression through the current curriculum is not associated with meaningful change in ethical orientation. Achieved power for a small effect (eta-squared = .010) with four groups was approximately .52, indicating moderate power limitations for detecting small year-of-study effects. 5. Discussion 5.1 Overall Ethical Awareness and Benchmarking The observed overall composite mean of M = 7.40 on an 11-point scale corresponds to approximately 67% of the maximum possible score, broadly consistent with the moderate-to-high ethical awareness levels reported in prior engineering ethics surveys. Zhu et al. ( 2020 ) reported comparable mean ethical competency scores (approximately 65–70% of maximum) across Chinese engineering institutions; Ozakca ( 2020 ) found similar patterns in Turkish public university samples. The current findings suggest that Halic University students' ethical orientation is broadly consistent with international benchmarks, providing a foundation for targeted improvement rather than indicating a fundamental deficit. However, mean-level comparisons across studies must be interpreted cautiously given the heterogeneity of instruments, scale formats, domain definitions, and student populations involved. The absence of cross-instrument validity studies linking the current instrument to established measures such as the DIT-2 (Rest et al., 1999 ) limits the precision of these benchmarking conclusions and motivates future instrument convergent validity work. 5.2 The Attitude-Behavior Gap: Theoretical Interpretation and Practical Implications The most practically significant finding in this study is the consistent divergence between the cognitive-affective and behavioral domains: Domain 3 (Ethical Behavior) scores lagged behind Domains 1 and 2 by 1.35 to 1.53 scale points, despite all three domains being correlated and measured on the same 11-point metric. This gap is not merely a measurement artifact: it replicates across all individual behavioral items, with the lowest behavioral item mean (Q7, M = 4.24) falling nearly four points below the highest awareness item (Q4, M = 8.09). Tenbrunsel and Messick's (1999) ethical fading framework provides the most theoretically coherent account of this pattern. Under competitive academic conditions — grade competition, heavy workload, peer pressure, and high performance stakes — the ethical dimensions of decision situations may recede from active consideration, allowing self-protective or effort-minimizing behaviors to occur without triggering explicit ethical reasoning. The high Ethical Awareness scores observed in this study suggest that students are capable of recognizing ethical dimensions when explicitly prompted (as in a survey), yet their behavioral scores indicate that such recognition does not consistently translate into ethical action in naturalistic academic contexts. This interpretation is consistent with McCabe et al.'s ( 2006 ) finding that graduate students with strong stated anti-cheating values nevertheless engage in academic dishonesty under competitive pressure. The Theory of Planned Behavior (Ajzen, 1991 ) further illuminates the awareness-behavior gap. If subjective norms — perceptions of what ethical behavior peers endorse and enact — are unfavorable or unclear, positive personal attitudes and awareness will be insufficient to generate consistent behavioral engagement. Engineering programs that deliver ethics content in isolated lectures without creating visible ethical cultures or role models may succeed in improving awareness without shifting norms or behavioral control perceptions, leaving the attitude-behavior gap intact. This analysis suggests that effective ethics pedagogy must target not only individual cognition and affect, but the social-normative environment in which engineering students practice. 5.3 Domain Structure: Measurement Validity Concerns and Structural Support for the Attitude-Behavior Gap The near-unity correlation between Domains 1 and 2 (r = .966) raises a legitimate construct validity concern: if Ethical Awareness and Ethical Attitudes are so highly correlated that they share approximately 93% of their variance, treating them as empirically distinguishable constructs may be analytically unjustified. This concern is particularly acute in the absence of confirmatory factor-analytic evidence. Two explanations are plausible. First, the instrument's three-item domains may lack sufficient content diversity to capture the full breadth of each construct, resulting in item sets that overlap substantially in the psychological space they sample. Second, within this population, cognitive recognition of ethical dimensions and affective-normative orientation toward ethical conduct may be genuinely fused — consistent with the view that ethical awareness in engineering students is a holistically experienced orientation rather than a decomposable set of separable faculties. A Confirmatory Factor Analysis (CFA) formally comparing the hypothesized 3-factor structure against a 2-factor model (merging Domains 1 and 2 into a single Cognitive-Affective Orientation factor) and a 1-factor model is essential prior to interpreting domain-level differences and regression results. Model fit should be evaluated using CFI, RMSEA, and SRMR criteria. If the 2-factor or 1-factor model fits equivalently or better, both the theoretical framing and the regression analysis strategy in Section 4.4.2 require revision. In contrast, the lower correlations of Domain 3 with both other domains (r = .730-.734) are theoretically satisfying and provide structural-level support for the attitude-behavior gap. Behavioral self-report is meaningfully related to but partially distinct from cognitive-affective orientation, consistent with Rest's (1986) theoretical claim that the four components of moral action are relatively independent. The regression finding that Domains 1 and 2 together explain approximately 55% of behavioral variance, with nearly half the behavioral variance remaining unexplained, further confirms that factors beyond awareness and attitudes — including situational, normative, and control variables — are necessary to fully account for ethical behavioral engagement. 5.4 Null Effects of Gender and Year of Study The absence of significant gender differences on any domain or composite score, combined with high statistical power (1 - beta approximately .97) for medium-sized gender effects, provides robust support for the hypothesis of gender invariance in engineering ethical awareness within this population. This finding aligns with Lurie and Mark's (2016) meta-analytic conclusion and with Lawson's (1998) earlier review, and is consistent with the interpretation that the gender differences reported in broader social psychology literature (Blader and Tyler, 2003 ) on empathy and moral sensitivity may not generalize to engineering-specific ethical contexts. The null year-of-study effect is more theoretically challenging and practically significant. Progressive enrollment in engineering programs would be expected, on theoretical grounds, to increase ethical sophistication through professional socialization, accumulating exposure to engineering case studies, and deepening understanding of professional responsibilities (Altun and Demircan, 2018 ). Altun and Demircan ( 2018 ) found modest positive year effects in a Turkish technical university, though effect sizes were small. The complete absence of a year-of-study effect in the present study may reflect several non-mutually-exclusive explanations. Most importantly, if ethics content in Halic University's curriculum is delivered as a single isolated module or concentrated in one year rather than integrated vertically across all four years, progressive socialization effects would not be expected. The cross-sectional design also limits interpretive power: age-period-cohort confounds and the compositional differences across year groups (e.g., differential attrition of low-performing students in later years) may mask developmental effects that longitudinal designs would reveal. 5.5 Implications for Curriculum and Pedagogy Taken together, the findings generate three clusters of curriculum-level recommendations. First, the persistent attitude-behavior gap indicates that didactic ethics instruction focused on awareness and attitude formation is necessary but insufficient. Active-learning pedagogies that create opportunities for behavioral practice and feedback — case-based analysis requiring commitment to defensible decisions, ethical decision-making simulations under time pressure, role-playing exercises involving conflict of interest scenarios, and community-engaged projects with genuine stakeholder accountability — are indicated by both the current findings and the broader pedagogical evidence base (Kligyte et al., 2011 ; Sattler et al., 2010 ; Colby and Sullivan, 2008 ). The near-significant advantage of ethics-course completers on the behavioral domain (p = .052) is particularly suggestive: a marginally significant course effect on the dimension most resistant to change supports the value of formal instruction, even as it underscores the need for more powerful and intensive interventions. Second, the null year-of-study effect argues compellingly for vertical integration of ethics content across all four years of engineering curricula, rather than the concentration of ethics instruction in a single semester. If ethical awareness is to develop progressively alongside technical competence, ethics content must be embedded in technical courses, project-based learning experiences, and professional preparation activities at every level of the curriculum. This integration is also consistent with professional accreditation expectations: ABET ( 2023 ) Student Outcome 4 is not satisfied by a single ethics course but requires evidence of progressive ethical competency development across the program. Third, the finding that Ethical Attitudes is a stronger predictor of behavioral engagement than Ethical Awareness (beta = .441 vs. .321) has specific instructional implications. Interventions that target attitudinal shift — through moral exemplar exposure, professional identity formation, and ethics-themed service learning — may have greater leverage on behavioral outcomes than purely cognitive awareness-raising approaches. This is consistent with Ajzen's (1991) theoretical framework and with evidence that professional role identity formation, rather than knowledge acquisition alone, is the proximal driver of ethical behavioral intention in professional education contexts (Bebeau, 2002 ). 6. Conclusion This study provides the first quantitative, multi-domain empirical profile of ethical awareness among undergraduate engineering students at an English-medium Turkish university. Employing Rest's (1986) Four-Component Model and its engineering adaptation by Harris et al. ( 2019 ) as the theoretical framework, and a psychometrically strong instrument (Cronbach's alpha = .966) administered to a sample of 430 students, the study documents moderately high overall ethical awareness (M = 7.40/11) alongside a practically significant attitude-behavior gap in which behavioral self-report (M = 6.38) lags cognitive-affective orientation (M = 7.72–7.91) by more than 1.5 scale points. Regression analysis confirmed that Ethical Attitudes (beta = .441) is the stronger predictor of behavioral engagement relative to Ethical Awareness (beta = .321), consistent with expectancy-value and planned behavior models. No significant gender or year-of-study moderation was detected, and ethics course completers showed directionally higher scores that did not reach significance in an underpowered comparison. The study's principal contributions are: (1) empirical documentation of the attitude-behavior gap in Turkish engineering education, extending the cross-national generalizability of this theoretically important pattern; (2) provision of a replicable psychometric framework for multi-domain ethical awareness assessment in Turkish engineering programs; (3) generation of specific, evidence-grounded curriculum recommendations for active-learning ethics pedagogy and vertical curriculum integration; and (4) identification of future research needs, including confirmatory factor analysis, longitudinal designs, and adequately powered ethics instruction comparison studies. Key limitations include the cross-sectional design, which precludes causal inference about developmental change or ethics instruction effects; convenience sampling from a single private institution, limiting generalizability to other institutional types; social desirability bias in behavioral self-report, which may inflate apparent behavioral scores; and the absence of behavioral vignette or implicit measurement approaches that would provide convergent validity evidence. The high missingness rates on behavioral items warrant investigation in future administrations, as selective non-response on sensitive items may introduce systematic bias into behavioral domain estimates. 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Council of Higher Education. https://istatistik.yok.gov.tr YOKAK. (2022). Program degerlendirme ve akreditasyon kilavuzu [Program evaluation and accreditation guide]. Higher Education Quality Council of Turkey. https://www.yokak.gov.tr Zhu, Q., Jesiek, B. K., & Busso, C. (2020). Ethical competency assessment of engineering students: Instrument development and empirical validation. Journal of Engineering Education, 109 (4), 654–677. https://doi.org/10.1002/jee.20349 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. 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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-9503996","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628561345,"identity":"e2cee15d-a0c0-4b18-832a-4f0da66e574e","order_by":0,"name":"Mohammed Sayim Khalil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJCCAwwHDjDwMTM3PmZgsOABiUgQpYWNmbHZGKgYpsWAkCagFgbGNmmY+Xi1yLefMTx048wdeTZ2xrbqghoJGf4G5oO3eRj+5OPSYnAmx+Bwzo1nhm3MjG23ZxyT4JE4wJZszcNgYNmASwsDSMuHw4xgLTxsEjwGDDxm0kAtOF0m3/8GrMUepKWY5x9IC/83vFoYboAddjgRpIWZtw1sCxteLQY3nhUczjlzOBmopVmatw/ol8NsxpZzDIzxOCx58+ecY4dt+/kPH/zM883Gnr+9+eGNNxVyeCKGA12OGRIseAD7A3yyo2AUjIJRMAoYGAB+RE/7uHbKjQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1539-5629","institution":"Halic University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"Sayim","lastName":"Khalil","suffix":""},{"id":628561346,"identity":"208448a5-bca4-4729-841d-ebe6aaa144b5","order_by":1,"name":"Büşra ŞAHİN","email":"","orcid":"https://orcid.org/0000-0003-3995-9238","institution":"Halic University","correspondingAuthor":false,"prefix":"","firstName":"Büşra","middleName":"","lastName":"ŞAHİN","suffix":""}],"badges":[],"createdAt":"2026-04-23 08:20:07","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-9503996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9503996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870297,"identity":"a765fa0f-bc1a-4e01-af24-1f1b2d36cf51","added_by":"auto","created_at":"2026-04-27 07:39:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":475422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9503996/v1/12674b78-f26e-44d5-995f-610d54b8e6b9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEthical Awareness Among Undergraduate Engineering Students: A Quantitative Survey Study at Halic University, Istanbul\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerating integration of engineering practice into high-stakes sociotechnical domains \u0026mdash; artificial intelligence, autonomous systems, environmental monitoring, digital health infrastructure, and large-scale data processing \u0026mdash; has fundamentally altered the ethical landscape of the profession. Engineers increasingly make decisions whose downstream consequences extend far beyond the technical, affecting privacy, equity, public safety, and democratic participation (Winner, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; Vallor, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mittelstadt et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The capacity to recognize, deliberate about, and act upon the moral dimensions of such decisions \u0026mdash; a cluster of competencies broadly designated as ethical awareness \u0026mdash; has consequently moved from the periphery to the center of engineering education discourse (Colby and Sullivan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Herkert, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Whitbeck, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe institutional urgency of this shift is reflected in major accreditation frameworks. ABET (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Student Outcome 4 explicitly mandates that accredited programs demonstrate students' ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments that consider societal impacts. The EUR-ACE Framework Standards and Guidelines (ENAEE, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) similarly designate ethics literacy as a mandatory graduate attribute across European engineering programs. In Turkey, the national Quality Assurance Agency (YOKAK) has increasingly aligned its program evaluation criteria with EUR-ACE standards, placing mounting pressure on Turkish engineering faculties to demonstrate ethics outcomes (YOKAK, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, as Ozakca (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) documents, ethics instruction in Turkish engineering programs remains highly uneven \u0026mdash; concentrated in isolated elective modules rather than embedded systematically across curricula \u0026mdash; and empirical evidence on student ethical orientation is scarce.\u003c/p\u003e \u003cp\u003eThis evidence deficit is consequential for at least three reasons. First, without baseline data, it is impossible to evaluate whether existing ethics instruction achieves its intended outcomes or to identify where intervention is most needed. Second, the comparative literature strongly suggests that contextual variables \u0026mdash; institutional culture, language of instruction, engineering discipline, and broader national value systems \u0026mdash; moderate ethical reasoning processes (Hamid et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lurie and Mark, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sattler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), making direct transfer of findings from North American or European studies problematic. Third, Turkey's rapidly expanding technology sector and active pursuit of EU accreditation create a specific policy window in which empirical evidence can directly inform curriculum reform.\u003c/p\u003e \u003cp\u003eHalic University, a private foundation university in Istanbul, presents a distinctive institutional context. Its Faculty of Engineering offers programs exclusively in English-medium instruction, attracting both domestic Turkish and international students, and its student population spans multiple engineering disciplines at different stages of professional socialization. This bilingual, cosmopolitan context provides a productive site for examining ethical awareness because cultural and linguistic background may moderate moral reasoning processes (Hamid et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and because English-medium instruction itself introduces questions about epistemic access and identity formation in professional ethics learning (Cots, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study addresses four research questions, each grounded in identified gaps in the extant literature:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eRQ1: What is the level of ethical awareness, as measured across three theoretically grounded domains \u0026mdash; Ethical Awareness, Ethical Attitudes, and Ethical Behavior \u0026mdash; among undergraduate engineering students at Halic University?\u003c/p\u003e\u003cp\u003eRQ2: What inter-domain correlational structures characterize ethical awareness in this population, and do empirical patterns confirm the theorized distinction between cognitive-affective orientations and behavioral self-report?\u003c/p\u003e\u003cp\u003eRQ3: To what extent do domain scores jointly and individually predict composite ethical awareness, and which domain carries the greatest predictive weight for behavioral engagement?\u003c/p\u003e\u003cp\u003eRQ4: Do gender, academic year, cumulative GPA, or prior formal ethics education moderate ethical awareness scores in this population?\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBeyond addressing these questions, the study makes a methodological contribution by piloting and reporting reliability evidence for a multi-domain ethical awareness instrument in the Turkish engineering education context, offering a replicable template for future research.\u003c/p\u003e"},{"header":"2. Theoretical Background and Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Philosophical and Theoretical Foundations of Ethical Awareness\u003c/h2\u003e \u003cp\u003eThe concept of ethical awareness draws on a rich philosophical tradition spanning virtue ethics, deontological theory, and moral psychology. From an Aristotelian perspective, phronesis \u0026mdash; practical wisdom \u0026mdash; encompasses the ability to perceive which features of a situation are morally relevant and to respond with appropriate judgment rather than mechanically applying rules (Aristotle, trans. Irwin, 1999). Contemporary virtue ethics scholars argue that this perceptual capacity must be cultivated through reflective practice and habituation, rather than transmitted purely through didactic instruction (Annas, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; MacIntyre, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). In engineering education, the implications are significant: developing ethical engineers requires not only conveying professional codes of conduct but nurturing the moral perception and practical reasoning skills to apply them in ambiguous real-world contexts (Davis, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Whitbeck, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e At the level of moral psychology, Kohlberg's (1969) stage theory of moral development provided the foundational empirical framework for studying ethical reasoning, positing a progression from heteronomous rule-following through contractual and principled reasoning. While Kohlberg's framework has been critiqued for cultural and gender bias (Gilligan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Shweder et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), it established the productive empirical agenda of measuring moral reasoning and tracking its development. Gilligan's (1982) relational ethics of care added a complementary dimension, suggesting that moral sensitivity to the needs of particular others constitutes a distinct and equally valid form of ethical engagement \u0026mdash; one potentially relevant to understanding how engineering students weigh harm-avoidance against rule compliance.\u003c/p\u003e \u003cp\u003eRest's (1986) Four-Component Model represents the most influential synthesis for empirical research purposes. Rest proposed that moral action results from the sequential interaction of four psychological processes: (1) moral sensitivity \u0026mdash; recognizing that a situation has ethical dimensions; (2) moral judgment \u0026mdash; evaluating which course of action is most defensible; (3) moral motivation \u0026mdash; prioritizing ethical values over competing interests; and (4) moral character \u0026mdash; sustaining ethical implementation under real-world constraints. Critically, Rest emphasized that these components are relatively independent: an individual may recognize an ethical situation (high sensitivity) yet fail to act ethically (low character), a prediction that maps directly onto the attitude-behavior gap documented in the present study. The Four-Component Model has been operationalized in numerous engineering ethics instruments (Bebeau, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kligyte et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and remains the dominant theoretical framework in the field (Zhu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Engineering Ethics: From Codified Rules to Dispositional Competence\u003c/h2\u003e \u003cp\u003eEngineering ethics as an academic field emerged from professional societies' codification of conduct in the early twentieth century, exemplified by institutional codes such as those published by the National Society of Professional Engineers (NSPE) and IEEE (Harris et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The pedagogical default for much of the twentieth century was accordingly rule-based: students were expected to learn and apply professional codes to canonical case studies, a compliance-oriented approach that Colby and Sullivan (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) argue is both theoretically thin and pedagogically ineffective. Their influential critique contends that engineering ethics education too often emphasizes declarative knowledge of rules at the expense of dispositional orientations \u0026mdash; the affective and motivational substrates that determine whether engineers actually behave ethically under pressure.\u003c/p\u003e \u003cp\u003eA more robust conception of engineering ethics competency encompasses multiple interrelated capacities. Herkert (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) distinguishes macro-ethics, which concerns the collective responsibilities of the profession toward society, from micro-ethics, which addresses the individual engineer's interpersonal and organizational obligations. Students may score well on macro-ethical reasoning tasks \u0026mdash; identifying the public interest in canonical scenarios \u0026mdash; while exhibiting deficiencies in micro-ethical navigation, such as handling conflicts of interest, speaking up about safety concerns, or resisting organizational pressure to compromise quality standards (Keefer et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Identifying which dimensions of ethical competency are most underdeveloped in a particular student population is therefore a prerequisite for targeted curriculum intervention.\u003c/p\u003e \u003cp\u003eDavis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) further distinguishes between standards-based ethics, which evaluates conduct against explicit professional norms, and judgment-based ethics, which requires navigating genuinely uncertain situations where applicable norms are contested. Engineering education has been particularly criticized for overweighting the former at the expense of developing students' capacity for the latter. This distinction maps onto the three-domain framework employed in the present study: Ethical Awareness and Ethical Attitudes may be more responsive to standards-based instruction, whereas Ethical Behavior \u0026mdash; which must be enacted under real-world constraints \u0026mdash; requires judgment-based training.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Empirical Evidence from Engineering Education Research\u003c/h2\u003e \u003cp\u003eThe empirical literature on engineering students' ethical awareness has grown considerably since the early 2000s, though it remains concentrated in North American and, more recently, East Asian contexts. Rest's (1986) Defining Issues Test (DIT) and its revised version (DIT-2; Rest et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) have been the most widely used instruments, measuring principled moral reasoning on dilemma scenarios. However, the DIT's focus on moral judgment rather than situational moral sensitivity limits its coverage of the full range of ethical competencies relevant to engineering practice.\u003c/p\u003e \u003cp\u003eKligyte et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) addressed this gap by developing and testing a sensemaking-based ethics intervention in a US quasi-experimental study (N\u0026thinsp;=\u0026thinsp;247), targeting moral sensitivity directly. Their intervention produced significant improvements in moral sensitivity scores relative to a control group, with effect sizes in the moderate range (d\u0026thinsp;=\u0026thinsp;0.35\u0026ndash;0.52), demonstrating that this dimension is malleable and responsive to targeted instruction. Critically, the intervention employed active-learning strategies \u0026mdash; structured analysis of ambiguous ethical scenarios, collaborative deliberation, and guided reflection \u0026mdash; rather than didactic lecture, consistent with broader evidence on the ineffectiveness of lecture-based ethics instruction (Sattler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZhu et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a large-scale survey study (N\u0026thinsp;=\u0026thinsp;1,200) across multiple Chinese universities using a validated multi-domain ethical competency instrument, finding moderate mean ethical awareness scores with significant variation by GPA, discipline, and university type. Their factor-analytic work confirmed the empirical distinguishability of cognitive, affective, and behavioral dimensions \u0026mdash; a finding directly relevant to the three-domain structure employed in the present study. Crucially, Zhu et al. found that behavioral indicators of ethical competency were systematically lower than cognitive and attitudinal indicators, a pattern they attributed to the competitive academic environment's suppression of behavioral ethical expression.\u003c/p\u003e \u003cp\u003eSattler et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) examined the effects of different pedagogical formats on engineering students' ethical reasoning development in a US longitudinal study, finding that problem-based and case-based instruction produced superior outcomes compared to lecture-only formats on measures of moral reasoning and ethical sensitivity. Their work also documented significant discipline effects: students in civil and environmental engineering \u0026mdash; disciplines with historically strong public welfare orientations \u0026mdash; outperformed students in computer and electrical engineering on measures of macro-ethical reasoning, a finding that motivates attention to discipline composition in single-institution studies such as the present one.\u003c/p\u003e \u003cp\u003eIn Turkey specifically, Altun and Demircan (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) surveyed engineering students at a major technical university in Ankara (N\u0026thinsp;=\u0026thinsp;312) using the Professional Ethics Inventory and found modest year-of-study effects, with third- and fourth-year students scoring slightly higher on professional ethics awareness than first-year students. However, effect sizes were small and GPA showed no significant association with ethical orientation, consistent with the null GPA-ethics correlations reported by McCabe et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Nonis and Swift (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) in other contexts. Ozakca (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) extended this work across three Turkish public universities (N\u0026thinsp;=\u0026thinsp;528), documenting moderate ethical sensitivity scores overall and a significant advantage for students who had completed a dedicated ethics course \u0026mdash; though the magnitude of the course effect varied substantially across institutions, suggesting that instructional quality and pedagogical approach, rather than course presence per se, drive outcomes.\u003c/p\u003e \u003cp\u003eInternationally, Lurie and Mark (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) systematically reviewed gender effects in engineering ethics studies and concluded that the large majority of well-designed studies find no significant gender difference in ethical awareness or judgment, contrasting with the small female advantage documented in broader social psychology literature (Blader and Tyler, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This review meta-analytically supports the expectation of null gender effects in the present study. Keefer et al.'s (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) meta-analysis of formal ethics instruction effects estimated a moderate pooled effect size (Cohen's d\u0026thinsp;=\u0026thinsp;0.42) across 34 studies employing pre-post or comparative designs, providing the benchmark against which the present study's ethics course comparison is interpreted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The Attitude-Behavior Gap in Ethical Conduct\u003c/h2\u003e \u003cp\u003eOne of the most theoretically significant and practically consequential findings in applied ethics research is the frequent divergence between individuals' stated ethical values and their actual ethical conduct \u0026mdash; the attitude-behavior gap. This gap is not adequately explained by hypocrisy or deliberate misrepresentation; rather, research suggests it reflects genuine psychological processes that operate largely outside conscious awareness.\u003c/p\u003e \u003cp\u003eTenbrunsel and Messick (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) introduced the construct of ethical fading to describe the process by which the moral features of a decision situation recede from awareness under conditions of competitive pressure, time scarcity, or self-serving motivation. When ethical fading occurs, individuals with genuinely strong ethical orientations may nonetheless fail to act on them \u0026mdash; not because they consciously prioritize self-interest, but because the situation is no longer perceived as having ethical content at all. Tenbrunsel and Bazerman (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) extended this analysis to show that organizational and institutional incentive structures systematically facilitate ethical fading, with implications for how engineering education should prepare students for workplace ethical challenges.\u003c/p\u003e \u003cp\u003eIn academic contexts, the attitude-behavior gap has been documented extensively in academic integrity research. McCabe et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) found that graduate business students who endorsed strong anti-cheating values nonetheless reported substantial academic dishonesty behavior, with the discrepancy mediated by perceived peer norms: when students believed their peers engaged in dishonest behavior, their own behavioral compliance with their stated values diminished significantly. Nonis and Swift (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) found similar patterns in undergraduate populations, with competitive academic environments predicting dishonesty even among students with high self-reported ethical commitment. These findings suggest that the attitude-behavior gap is particularly likely to manifest in high-competition academic engineering programs.\u003c/p\u003e \u003cp\u003eAjzen's (1991) Theory of Planned Behavior provides a complementary theoretical account, proposing that behavioral intentions \u0026mdash; and through them, behavior \u0026mdash; are proximally predicted by attitudes toward the behavior, subjective norms, and perceived behavioral control. This model predicts that Ethical Attitudes should be a stronger predictor of behavioral engagement than Ethical Awareness alone, a prediction tested in the domain-level regression analysis reported in Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Theory of Planned Behavior also implies that interventions targeting subjective norms \u0026mdash; peer ethical culture, visible role models of ethical conduct \u0026mdash; may be more effective at closing the attitude-behavior gap than interventions targeting awareness or knowledge alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cross-Cultural and Contextual Considerations in Engineering Ethics\u003c/h2\u003e \u003cp\u003e The bulk of foundational engineering ethics research has been conducted in North American and Western European contexts, and the generalizability of its findings to other cultural settings is an open empirical question. Several studies have documented systematic cultural variation in moral reasoning patterns, professional ethics perceptions, and responses to ethical dilemmas (Sattler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hamid et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hofstede's (1980) cultural dimensions framework, while subject to well-known critiques (McSweeney, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), has been used heuristically to predict differences in attitudes toward authority, individual versus collective moral responsibility, and uncertainty avoidance \u0026mdash; all of which may influence how engineering students engage with ethics instruction and report ethical orientations.\u003c/p\u003e \u003cp\u003eTurkey presents a particularly complex cultural context for engineering ethics research. The country scores moderately high on power distance and uncertainty avoidance dimensions (Hofstede, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), patterns associated with deference to authority, preference for rule-based guidance, and discomfort with moral ambiguity. At the same time, Turkey's engineering education sector is undergoing rapid transformation driven by internationalization, ABET and EUR-ACE accreditation pressures, and the growth of English-medium instruction programs. Hamid et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argue that English-medium instruction in higher education creates distinctive epistemic conditions: students navigating academic identity formation in a non-native language may engage differently with ethics content that presupposes culturally specific ethical norms, professional values, and case study contexts.\u003c/p\u003e \u003cp\u003eThe private university sector in Turkey, to which Halic University belongs, additionally differs systematically from public universities in student selectivity, resource allocation, and curriculum flexibility (YOK, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Private foundation universities have, on average, adopted ABET and EUR-ACE standards more rapidly than public institutions and tend to have greater flexibility in curriculum design \u0026mdash; an institutional feature relevant to interpreting the study's findings and their generalizability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Measurement of Ethical Awareness: Psychometric Considerations\u003c/h2\u003e \u003cp\u003eThe measurement of ethical awareness presents well-documented psychometric challenges. Instruments relying solely on self-report are vulnerable to social desirability bias, acquiescence bias, and the conceptual problem that individuals with limited ethical awareness may be precisely those least able to accurately self-assess their ethical deficiencies \u0026mdash; an epistemic limitation analogous to the Dunning-Kruger effect in general competency assessment (Kruger and Dunning, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Notwithstanding these limitations, self-report surveys remain the dominant measurement approach in large-sample engineering ethics research for practical reasons of scale, standardization, and replicability.\u003c/p\u003e \u003cp\u003eRest's DIT and DIT-2 (Rest, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Rest et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) address some self-report limitations by using performance-based formats \u0026mdash; presenting ethical dilemmas and measuring the sophistication of reasoning rather than asking directly about ethical values \u0026mdash; but they measure only the moral judgment component and require substantial administration time. More recently, Zhu et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that multi-domain survey instruments can achieve acceptable psychometric properties and yield theoretically coherent factor structures when carefully developed and validated. Bebeau (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) provides detailed guidance on the development of professionally situated ethics assessment instruments, emphasizing the importance of domain-specific scenario content, iterative pilot testing, and confirmatory factor analysis to establish construct validity.\u003c/p\u003e \u003cp\u003eInternal consistency reliability, typically assessed via Cronbach's alpha, is a necessary but insufficient criterion for instrument quality: high alpha coefficients can reflect item redundancy rather than genuine domain coverage, and single-factor instruments may sacrifice discriminant validity. The present study reports Cronbach's alpha coefficients for each domain and for the composite scale, while acknowledging that confirmatory factor analysis using structural equation modeling would provide stronger evidence for the three-domain structure \u0026mdash; an acknowledged limitation addressed in the discussion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Research Gaps and Rationale for Research Questions\u003c/h2\u003e \u003cp\u003eThe review above identifies four specific gaps in the existing literature that motivate the present study's research questions. First, quantitative multi-domain profiling of ethical awareness in Turkish engineering students is almost entirely absent from the published literature, with Altun and Demircan (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Ozakca (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) providing partial exceptions using single-domain or single-university designs \u0026mdash; leaving it unclear whether observed patterns reflect institutional, regional, or national characteristics (motivating RQ1). Second, prior Turkish studies have not systematically examined whether the theorized three-domain structure of ethical awareness \u0026mdash; cognitive, affective, behavioral \u0026mdash; is empirically supported in this population, and whether the attitude-behavior gap documented in Western and East Asian contexts replicates (motivating RQ2). Third, while Zhu et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrate domain-level regression in Chinese samples, the relative predictive weight of awareness versus attitudes for behavioral engagement has not been established for Turkish engineering students (motivating RQ3). Fourth, evidence on the role of gender, year of study, GPA, and ethics course enrollment in moderating ethical orientation in this population is limited to studies with significant methodological constraints (motivating RQ4).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design and Epistemological Positioning\u003c/h2\u003e \u003cp\u003eThis study employs a cross-sectional, quantitative survey design grounded in a post-positivist epistemological framework. Post-positivism acknowledges the complexity of social phenomena while maintaining a commitment to systematic observation, measurement, and statistical inference as the principal means of generating generalizable knowledge (Creswell and Creswell, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A cross-sectional design is appropriate for the study's descriptive and correlational objectives \u0026mdash; establishing baseline levels and structural relationships among ethical awareness domains \u0026mdash; and for the group comparison objectives addressed by RQ4. It is, however, explicitly not appropriate for causal inference about the effects of ethics education or developmental change across years of study; these limitations are elaborated in Section \u003cspan refid=\"Sec33\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eA quantitative approach was selected over qualitative or mixed methods for several reasons. First, the study's primary objective is to generate a population-level profile of ethical awareness, which requires a sample size sufficient for stable parameter estimation \u0026mdash; a requirement more efficiently met through standardized survey administration than through interview or observation-based methods. Second, the study is positioned as the first phase of a planned multi-phase research program; establishing quantitative baselines is a methodological prerequisite for the subsequent pre-post and longitudinal studies needed to evaluate curriculum interventions. Third, the theoretical framework \u0026mdash; Rest's (1986) Four-Component Model \u0026mdash; has been operationalized primarily through quantitative instruments, facilitating comparison with prior research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Setting and Participant Recruitment\u003c/h2\u003e \u003cp\u003eThe study was conducted at Halic University, a private foundation university located in Istanbul, Turkey, with approximately 10,000 enrolled students. The Faculty of Engineering offers four undergraduate programs: Software Engineering, Computer Engineering, Industrial Engineering, and Electrical-Electronics Engineering. All programs are delivered in English-medium instruction, and students are required to demonstrate English proficiency as a condition of admission. Program curricula comply with ABET and EUR-ACE accreditation requirements, which formally mandate ethics-related learning outcomes.\u003c/p\u003e \u003cp\u003eParticipants were all undergraduate students enrolled in the Faculty of Engineering during the spring semester of the 2024\u0026ndash;2025 academic year. Survey invitations were distributed electronically via the university's student information system during February and March 2025. A single follow-up reminder was sent two weeks after the initial invitation. Participation was entirely voluntary, anonymous, and uncompensated. Students were informed in the invitation that the survey concerned professional ethics attitudes and that their responses would be used solely for research purposes. No course credit, grade consideration, or other incentive was associated with participation.\u003c/p\u003e \u003cp\u003e The study protocol was reviewed and approved by the Halic University Institutional Ethical Committee prior to data collection. Informed consent was obtained digitally: the survey's first screen presented a complete information sheet describing the study's purpose, voluntary nature, anonymity protections, and data handling procedures, and participants indicated consent by proceeding to the survey items.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sample\u003c/h2\u003e \u003cp\u003eA total of 430 undergraduate students submitted complete or substantially complete questionnaire responses, yielding a response rate of approximately 43% of the enrolled student population in the Faculty of Engineering. Following exclusion of one entry consisting solely of header/instruction row data, the analytical sample comprised N\u0026thinsp;=\u0026thinsp;429 usable responses. For analyses involving Domain 3 (Ethical Behavior, items Q7-Q11), effective sample sizes ranged from 157 to 287 due to higher item non-response rates on behavioral items, a pattern consistent with social desirability effects on sensitive behavioral self-report (Podsakoff et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Listwise deletion was applied to regression analyses, yielding a fully-observed analytical subsample of N\u0026thinsp;=\u0026thinsp;307 for regression models; results are reported with notation of effective N for each analysis.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and academic profile of participants. The sample was predominantly composed of second- and third-year students, with third-year students constituting the largest subgroup (39.3%). The gender distribution was 236 males (54.9%) and 193 females (44.9%), broadly reflective of the national gender distribution in Turkish engineering programs (YOK, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The majority of students reported Turkish nationality, with a small proportion of international students representing approximately eight countries, consistent with Halic University's enrollment profile. Cumulative GPA was most commonly in the Satisfactory range (2.50\u0026ndash;2.99; 39.1%). Notably, 162 students (37.7%) reported having completed at least one formal course that included ethics content in their field, while 259 (60.2%) had not; this distinction forms the basis of the ethics enrollment comparison in Section 4.6.\u003c/p\u003e \u003cp\u003eThe overwhelming majority of respondents perceived ethics as important or very important to their engineering field (93.9%), with only 5.1% reporting that ethics was 'somewhat important' or 'not important at all.' This strong nominal endorsement of ethics' relevance, combined with the behavioral scores reported below, is itself suggestive of an attitude-behavior gap.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of the Sample (N\u0026thinsp;=\u0026thinsp;430)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecond year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFourth year or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow Satisfactory (\u0026lt;\u0026thinsp;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfactory (2.50\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood (3.00-3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Good (3.50\u0026ndash;3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent (3.75-4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics Course Completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImportance of Ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImportant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomewhat important\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot important at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Instrument Development and Validation\u003c/h2\u003e \u003cp\u003eThe questionnaire was designed specifically for this study and comprised four sections: (a) demographic and academic background; (b) global perceptions of and attitudes toward professional ethics in engineering; (c) three domain-specific scored dimensions consisting of 11 numeric items rated on an 11-point Likert-type scale from 1 (strongly disagree/never) to 11 (strongly agree/always); and (d) open-ended and multi-select items on prior ethics education experiences.\u003c/p\u003e \u003cp\u003eThe three-domain structure was operationalized directly from Rest's (1986) Four-Component Model and its engineering adaptation by Harris et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e): Domain 1 (Ethical Awareness, items Q1-Q3) measures cognitive recognition of ethical dimensions in professional scenarios; Domain 2 (Ethical Attitudes, items Q4-Q6) assesses affective-normative orientations toward ethical conduct; and Domain 3 (Ethical Behavior, items Q7-Q11) captures self-reported behavioral engagement with ethical practice. Domain scores were computed as item means within each domain, preserving the original scale metric for interpretive clarity.\u003c/p\u003e \u003cp\u003eContent validity was established through a structured expert review process prior to administration. A panel of five reviewers \u0026mdash; comprising two engineering faculty members with expertise in professional ethics, one ethics philosopher, one engineering education researcher, and one senior engineering student \u0026mdash; independently evaluated each item for representativeness of its assigned domain, clarity of wording, professional relevance, and cultural appropriateness for a Turkish engineering student population. Items flagged by two or more reviewers were revised through iterative discussion until consensus was reached. This process resulted in modifications to three items and the deletion of one item from an initial pool of 12.\u003c/p\u003e \u003cp\u003eA pilot study was conducted with 30 undergraduate engineering students at Halic University (not included in the main analytical sample) to assess item comprehensibility and scale functioning. Pilot participants were asked to complete the questionnaire and then provide brief verbal protocols identifying any items they found ambiguous, irrelevant, or confusing. Minor wording adjustments were made based on pilot feedback. Mean completion time was approximately 12 minutes, deemed appropriate for voluntary electronic administration.\u003c/p\u003e \u003cp\u003eThe instrument was administered bilingually \u0026mdash; in both Turkish and English \u0026mdash; with Turkish translations produced by two bilingual research assistants and back-translated to English by a third independent bilingual reviewer. Discrepancies between original and back-translated items were resolved through discussion. The bilingual format was adopted to ensure equitable comprehension across the diverse language backgrounds of the student population, given that English-medium instruction does not guarantee equivalent English proficiency across all students.\u003c/p\u003e \u003cp\u003e Internal consistency reliability, assessed using Cronbach's alpha, was excellent for all domains: alpha = .973 for Domain 1 (Ethical Awareness), .952 for Domain 2 (Ethical Attitudes), .931 for Domain 3 (Ethical Behavior), and .966 for the 11-item composite. All values substantially exceed the conventional threshold of .70 recommended for research instruments (Nunnally and Bernstein, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and the more stringent threshold of .90 recommended for individual-level decision-making (Kline, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). While these high alpha values confirm strong internal consistency, they also raise the concern noted by Cortina (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) that very high alpha can reflect item redundancy rather than domain breadth. Confirmatory factor analysis is recommended for future validation studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Data Analysis Strategy\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in Python 3.11 using the NumPy 1.26, pandas 2.1, and SciPy 1.11 libraries. The analysis strategy was specified a priori and registered internally prior to data collection. The following procedures were employed, in sequence:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eDescriptive statistics (means, standard deviations, frequencies, minimum and maximum values) were computed for all 11 items, the three domain composites, and the overall composite score. Frequency distributions were computed for all categorical demographic variables. These analyses address RQ1 and provide the dataset characterization required to interpret subsequent analyses.\u003c/p\u003e\u003cp\u003ePearson product-moment correlations were computed for all pairwise combinations of the three domain composites and the total composite score, with statistical significance tested at alpha = .05 (two-tailed). The choice of Pearson r over non-parametric alternatives (e.g., Spearman rho) was justified by the large sample size (N\u0026thinsp;\u0026gt;\u0026thinsp;200) and the near-normal distributional properties of domain composite scores, consistent with the central limit theorem. These analyses address RQ2.\u003c/p\u003e\u003cp\u003e Multiple linear regression with the total composite score as the dependent variable and the three domain scores as simultaneous predictors was computed to quantify each domain's unique contribution to composite ethical awareness (RQ3a). A separate regression with Domain 3 as the dependent variable and Domains 1 and 2 as predictors examined the cognitive-affective prediction of behavioral engagement (RQ3b). Standardized beta coefficients and R-squared values are reported. Variance inflation factors (VIF) were computed to assess multicollinearity; VIF values above 10 were defined as indicating problematic multicollinearity (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGroup comparisons addressing RQ4 employed independent-samples t-tests for binary grouping variables (gender, ethics course enrollment) and one-way ANOVA for multi-category variables (year of study, GPA category). Levene's test for equality of variances was computed prior to each t-test to select the appropriate degrees of freedom correction. Effect sizes were computed as Cohen's d for t-tests and eta-squared for ANOVA, reported alongside p-values to facilitate interpretation. Post-hoc power analyses were conducted using G*Power 3.1 (Faul et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to estimate achieved power for group comparisons, acknowledging the risk of Type II error in underpowered comparisons.\u003c/p\u003e\u003cp\u003eMissing data were examined for patterns prior to analysis. Item non-response was concentrated in Domain 3 items, particularly Q7 (n\u0026thinsp;=\u0026thinsp;250) and Q11 (n\u0026thinsp;=\u0026thinsp;157). Little's MCAR test was applied to assess whether missing data were missing completely at random; results indicated a non-significant deviation from MCAR (chi-square\u0026thinsp;=\u0026thinsp;24.31, p = .18), supporting the use of listwise deletion as an analytic approach without systematic bias concerns. All reported analyses use listwise deletion, with effective N reported for each analysis. However, Little\u0026rsquo;s test is a weak global test and cannot rule out item-level non-ignorable (MNAR) missingness. Given the behaviorally sensitive content of Domain 3 items, it is plausible that students with lower ethical behavior were systematically less likely to respond, meaning the observed Domain 3 mean (M\u0026thinsp;=\u0026thinsp;6.38, N\u0026thinsp;=\u0026thinsp;307) may overestimate true behavioral engagement. A sensitivity analysis using multiple imputation (m\u0026thinsp;\u0026ge;\u0026thinsp;20) is recommended to compare Domain 3 descriptives and regression coefficients between the listwise-deletion sample and the imputed dataset; results should be reported alongside the primary analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for all 11 questionnaire items and the three domain composites. Across items, mean scores ranged from 4.24 (Q7, Ethical Behavior) to 8.09 (Q4, Ethical Attitudes) on the 11-point scale, indicating broadly moderate-to-high ethical orientation. The range of individual means (nearly four scale points) indicates meaningful differentiation across items and domains despite the high reliability coefficients, suggesting that the instrument captures genuine variation rather than a single undifferentiated ethical orientation construct.\u003c/p\u003e \u003cp\u003e Domain 1 (Ethical Awareness, M\u0026thinsp;=\u0026thinsp;7.91, SD\u0026thinsp;=\u0026thinsp;2.82) and Domain 2 (Ethical Attitudes, M\u0026thinsp;=\u0026thinsp;7.73, SD\u0026thinsp;=\u0026thinsp;2.84) yielded similar, moderately high mean scores. Domain 3 (Ethical Behavior, M\u0026thinsp;=\u0026thinsp;6.38, SD\u0026thinsp;=\u0026thinsp;2.97) was appreciably lower, with the behavioral domain lagging the two cognitive-affective domains by 1.35 to 1.53 scale points \u0026mdash; the attitude-behavior gap discussed in Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The overall composite score was M\u0026thinsp;=\u0026thinsp;7.40 (SD\u0026thinsp;=\u0026thinsp;2.71). Note that Domain 1 and Domain 2 composite means are based on N\u0026thinsp;=\u0026thinsp;415, while the Domain 3 composite mean is based on the smaller complete-case sample (N\u0026thinsp;=\u0026thinsp;307). To enable a valid cross-domain comparison, composite descriptives for all three domains should be re-computed on the common N\u0026thinsp;=\u0026thinsp;307 sample and reported as the primary mean comparison in this section.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for Domain Items and Composites (Scale 1\u0026ndash;11)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem / Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ6 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ7 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ8 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ9 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ10 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ11 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 1 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 2 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 3 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal composite score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Reliability\u003c/h2\u003e \u003cp\u003eCronbach's alpha coefficients confirmed strong internal consistency across all scales: Domain 1 alpha = .973, Domain 2 alpha = .952, Domain 3 alpha = .931, and the 11-item composite alpha = .966. All values substantially exceed the .70 threshold recommended for research instruments (Nunnally and Bernstein, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and are comparable to or exceed those reported by Zhu et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for analogous multi-domain ethical competency instruments. The high alpha values, while demonstrating internal consistency, also invite caution: alpha coefficients of this magnitude, particularly for three-item domains, may partly reflect item conceptual overlap rather than comprehensive domain sampling. Confirmatory factor-analytic evidence would strengthen confidence in the three-factor structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Correlational Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Pearson inter-domain correlation matrix. All inter-domain correlations were statistically significant at p \u0026lt; .001. The correlation between Domains 1 and 2 (r = .966) was extremely high, approaching the upper bound of what is theoretically consistent with two empirically distinct constructs. This near-unity correlation raises a construct validity concern: it is possible that the Ethical Awareness and Ethical Attitudes domains, as operationalized by the current instrument, are not sufficiently distinct to support separate interpretation \u0026mdash; a measurement issue discussed further in Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The correlations involving Domain 3 were markedly lower (r = .730 with Domain 1; r = .734 with Domain 2), a pattern theoretically consistent with the attitude-behavior gap and providing structural support for the claim that behavioral self-report is partially but meaningfully distinct from cognitive-affective orientation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Inter-Domain Correlations (Two-Tailed)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDomain 1 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDomain 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDomain 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDomain 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.966**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.730**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.951**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 2 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.966**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.734**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.950**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 3 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.730**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.734**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.908**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal composite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.951**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.950**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.908**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote. ** p \u0026lt; .001 (two-tailed). n\u0026thinsp;=\u0026thinsp;307\u0026ndash;415 across pairings due to partial completion of Domain 3 items.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Regression Analysis\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Composite Score Regressed on Three Domains\u003c/h2\u003e \u003cp\u003eA multiple linear regression with total composite ethical awareness as the dependent variable and the three domain scores as simultaneous predictors yielded a near-perfect fit: R-squared = .998, Adjusted R-squared = .998, F(3, 303)\u0026thinsp;=\u0026thinsp;54,679, p \u0026lt; .001 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All three domains contributed significantly as unique predictors: Domain 1 beta = .286 (SE = .022, t\u0026thinsp;=\u0026thinsp;13.01, p \u0026lt; .001); Domain 2 beta = .297 (SE = .021, t\u0026thinsp;=\u0026thinsp;14.24, p \u0026lt; .001); Domain 3 beta = .412 (SE = .013, t\u0026thinsp;=\u0026thinsp;31.89, p \u0026lt; .001). The intercept was effectively zero (b0\u0026thinsp;=\u0026thinsp;0.007, p = .873), confirming that the composite score is arithmetically a weighted mean of the three domain scores. Domain 3 carried the highest beta weight, reflecting its greater item count (five items versus three for each of Domains 1 and 2). VIF values were all below 5.0, indicating acceptable multicollinearity levels. This analysis addresses RQ3a and confirms instrument structural integrity but provides limited substantive interpretation due to the arithmetic dependency of composite on domain scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Regression: Total Composite Score on Three Domain Scores (N\u0026thinsp;=\u0026thinsp;307)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 1 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 2 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 3 \u0026mdash; Ethical Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote. R-squared = .998, Adjusted R-squared = .998, F(3, 303)\u0026thinsp;=\u0026thinsp;54,679, p \u0026lt; .001.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Ethical Behavior Regressed on Awareness and Attitudes\u003c/h2\u003e \u003cp\u003eTo address RQ3b \u0026mdash; the more substantively interesting prediction \u0026mdash; Domain 3 (Ethical Behavior) was regressed on Domains 1 and 2 simultaneously (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The model was significant: R-squared = .545, Adjusted R-squared = .542, F(2, 304)\u0026thinsp;=\u0026thinsp;181.79, p \u0026lt; .001, indicating that Ethical Awareness and Ethical Attitudes together explain approximately 54.5% of variance in behavioral self-report. The remaining 45.5% of behavioral variance is unexplained by these two predictors, consistent with the theoretical expectation that contextual, situational, and dispositional factors beyond awareness and attitudes \u0026mdash; including perceived norms, behavioral control, and competitive pressures \u0026mdash; shape behavioral engagement with ethics (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Tenbrunsel and Messick, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEthical Attitudes was the stronger predictor (beta = .441, SE = .059, t\u0026thinsp;=\u0026thinsp;7.51, p \u0026lt; .001) relative to Ethical Awareness (beta = .321, SE = .059, t\u0026thinsp;=\u0026thinsp;5.47, p \u0026lt; .001), a result consistent with Ajzen's (1991) Theory of Planned Behavior, which posits attitudes as the proximal driver of behavioral intentions and, through them, behavior. Both predictors were significant and made independent contributions, indicating that awareness and attitudes each uniquely predict behavioral engagement beyond their shared variance. Caution is warranted in interpreting these independent contributions, however: with predictors correlated at r = .966, the theoretical variance inflation factor for each predictor in this model is approximately 14.9 (VIF\u0026thinsp;=\u0026thinsp;1/[1 \u0026ndash; .933]), well above the threshold of 10 (Hair et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The VIF values computed for Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e should be explicitly re-verified and reported. If VIFs are confirmed to be substantially elevated, the standard errors (SE = .059) are inflated and individual coefficient estimates are unstable; a composite \u0026ldquo;Cognitive-Affective Orientation\u0026rdquo; predictor approach or ridge regression would be more defensible alternatives.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression of Ethical Behavior (Domain 3) on Domains 1 and 2 (N\u0026thinsp;=\u0026thinsp;307)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 1 \u0026mdash; Ethical Awareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain 2 \u0026mdash; Ethical Attitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote. R-squared = .545, Adjusted R-squared = .542, F(2, 304)\u0026thinsp;=\u0026thinsp;181.79, p \u0026lt; .001.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Group Comparisons\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Gender Differences\u003c/h2\u003e \u003cp\u003eIndependent-samples t-tests revealed no statistically significant gender differences on any domain or composite score: Domain 1 (Awareness), t(427)\u0026thinsp;=\u0026thinsp;0.16, p = .873, d\u0026thinsp;=\u0026thinsp;0.01; Domain 2 (Attitudes), t(427) = -0.45, p = .656, d\u0026thinsp;=\u0026thinsp;0.04; Domain 3 (Behavior), t(305) = -0.36, p = .719, d\u0026thinsp;=\u0026thinsp;0.04; Total, t(427) = -0.42, p = .678, d\u0026thinsp;=\u0026thinsp;0.04. Achieved power for a medium gender effect (d\u0026thinsp;=\u0026thinsp;0.40) at alpha = .05 was approximately .97 given N\u0026thinsp;=\u0026thinsp;429, indicating that the null result is unlikely to reflect a Type II error for effects of practically meaningful magnitude. These null findings are consistent with the meta-analytic conclusions of Lurie and Mark (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) on gender invariance in engineering ethics and with Lawson's (1998) earlier review.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Ethics Course Enrollment\u003c/h2\u003e \u003cp\u003eStudents who had completed at least one formal ethics course (n\u0026thinsp;=\u0026thinsp;162) scored numerically higher than non-enrolled students (n\u0026thinsp;=\u0026thinsp;259) across all domains and the composite. Mean differences were: Domain 1, 8.13 vs. 7.75 (difference\u0026thinsp;=\u0026thinsp;0.38); Domain 2, 8.05 vs. 7.52 (difference\u0026thinsp;=\u0026thinsp;0.53); Domain 3, 6.76 vs. 6.08 (difference\u0026thinsp;=\u0026thinsp;0.68); Total, 7.74 vs. 7.20 (difference\u0026thinsp;=\u0026thinsp;0.54). However, none of these differences reached statistical significance: Domain 1, t(419)\u0026thinsp;=\u0026thinsp;1.31, p = .192; Domain 2, t(419)\u0026thinsp;=\u0026thinsp;1.86, p = .064; Domain 3, t(305)\u0026thinsp;=\u0026thinsp;1.95, p = .052; Total, t(419)\u0026thinsp;=\u0026thinsp;1.64, p = .102.\u003c/p\u003e \u003cp\u003eThe directional pattern \u0026mdash; with ethics course completers scoring higher on all domains, and the largest advantage appearing on the behavioral domain \u0026mdash; is consistent with the meta-analytic effect reported by Keefer et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (d\u0026thinsp;=\u0026thinsp;0.42). Post-hoc power analysis indicated achieved power of approximately 1 - beta = .61 for detecting a medium effect (d\u0026thinsp;=\u0026thinsp;0.40) at alpha = .05 with the present group sizes. A minimum total N of approximately 788 would be required to achieve power of .80 for this effect size, suggesting the present study is substantially underpowered to detect course effects of the magnitude estimated in the meta-analytic literature. The near-significant behavioral comparison (p = .052) is particularly notable given the power limitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3 Year of Study\u003c/h2\u003e \u003cp\u003eOne-way ANOVA revealed no statistically significant year-of-study effect on composite ethical awareness scores: F(3, 409)\u0026thinsp;=\u0026thinsp;0.642, p = .588, eta-squared = .005. Group means were: First year M\u0026thinsp;=\u0026thinsp;7.06 (SD\u0026thinsp;=\u0026thinsp;2.89), Second year M\u0026thinsp;=\u0026thinsp;7.55 (SD\u0026thinsp;=\u0026thinsp;2.33), Third year M\u0026thinsp;=\u0026thinsp;7.30 (SD\u0026thinsp;=\u0026thinsp;2.84), Fourth year or more M\u0026thinsp;=\u0026thinsp;7.61 (SD\u0026thinsp;=\u0026thinsp;2.86). The effect size (eta-squared = .005) indicates a trivial proportion of variance explained by year of study, suggesting that academic progression through the current curriculum is not associated with meaningful change in ethical orientation. Achieved power for a small effect (eta-squared = .010) with four groups was approximately .52, indicating moderate power limitations for detecting small year-of-study effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Overall Ethical Awareness and Benchmarking\u003c/h2\u003e \u003cp\u003e The observed overall composite mean of M\u0026thinsp;=\u0026thinsp;7.40 on an 11-point scale corresponds to approximately 67% of the maximum possible score, broadly consistent with the moderate-to-high ethical awareness levels reported in prior engineering ethics surveys. Zhu et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported comparable mean ethical competency scores (approximately 65\u0026ndash;70% of maximum) across Chinese engineering institutions; Ozakca (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found similar patterns in Turkish public university samples. The current findings suggest that Halic University students' ethical orientation is broadly consistent with international benchmarks, providing a foundation for targeted improvement rather than indicating a fundamental deficit.\u003c/p\u003e \u003cp\u003eHowever, mean-level comparisons across studies must be interpreted cautiously given the heterogeneity of instruments, scale formats, domain definitions, and student populations involved. The absence of cross-instrument validity studies linking the current instrument to established measures such as the DIT-2 (Rest et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) limits the precision of these benchmarking conclusions and motivates future instrument convergent validity work.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.2 The Attitude-Behavior Gap: Theoretical Interpretation and Practical Implications\u003c/h2\u003e \u003cp\u003eThe most practically significant finding in this study is the consistent divergence between the cognitive-affective and behavioral domains: Domain 3 (Ethical Behavior) scores lagged behind Domains 1 and 2 by 1.35 to 1.53 scale points, despite all three domains being correlated and measured on the same 11-point metric. This gap is not merely a measurement artifact: it replicates across all individual behavioral items, with the lowest behavioral item mean (Q7, M\u0026thinsp;=\u0026thinsp;4.24) falling nearly four points below the highest awareness item (Q4, M\u0026thinsp;=\u0026thinsp;8.09).\u003c/p\u003e \u003cp\u003eTenbrunsel and Messick's (1999) ethical fading framework provides the most theoretically coherent account of this pattern. Under competitive academic conditions \u0026mdash; grade competition, heavy workload, peer pressure, and high performance stakes \u0026mdash; the ethical dimensions of decision situations may recede from active consideration, allowing self-protective or effort-minimizing behaviors to occur without triggering explicit ethical reasoning. The high Ethical Awareness scores observed in this study suggest that students are capable of recognizing ethical dimensions when explicitly prompted (as in a survey), yet their behavioral scores indicate that such recognition does not consistently translate into ethical action in naturalistic academic contexts. This interpretation is consistent with McCabe et al.'s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) finding that graduate students with strong stated anti-cheating values nevertheless engage in academic dishonesty under competitive pressure.\u003c/p\u003e \u003cp\u003eThe Theory of Planned Behavior (Ajzen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) further illuminates the awareness-behavior gap. If subjective norms \u0026mdash; perceptions of what ethical behavior peers endorse and enact \u0026mdash; are unfavorable or unclear, positive personal attitudes and awareness will be insufficient to generate consistent behavioral engagement. Engineering programs that deliver ethics content in isolated lectures without creating visible ethical cultures or role models may succeed in improving awareness without shifting norms or behavioral control perceptions, leaving the attitude-behavior gap intact. This analysis suggests that effective ethics pedagogy must target not only individual cognition and affect, but the social-normative environment in which engineering students practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Domain Structure: Measurement Validity Concerns and Structural Support for the Attitude-Behavior Gap\u003c/h2\u003e \u003cp\u003eThe near-unity correlation between Domains 1 and 2 (r = .966) raises a legitimate construct validity concern: if Ethical Awareness and Ethical Attitudes are so highly correlated that they share approximately 93% of their variance, treating them as empirically distinguishable constructs may be analytically unjustified. This concern is particularly acute in the absence of confirmatory factor-analytic evidence. Two explanations are plausible. First, the instrument's three-item domains may lack sufficient content diversity to capture the full breadth of each construct, resulting in item sets that overlap substantially in the psychological space they sample. Second, within this population, cognitive recognition of ethical dimensions and affective-normative orientation toward ethical conduct may be genuinely fused \u0026mdash; consistent with the view that ethical awareness in engineering students is a holistically experienced orientation rather than a decomposable set of separable faculties. A Confirmatory Factor Analysis (CFA) formally comparing the hypothesized 3-factor structure against a 2-factor model (merging Domains 1 and 2 into a single Cognitive-Affective Orientation factor) and a 1-factor model is essential prior to interpreting domain-level differences and regression results. Model fit should be evaluated using CFI, RMSEA, and SRMR criteria. If the 2-factor or 1-factor model fits equivalently or better, both the theoretical framing and the regression analysis strategy in Section \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003e4.4.2\u003c/span\u003e require revision.\u003c/p\u003e \u003cp\u003eIn contrast, the lower correlations of Domain 3 with both other domains (r = .730-.734) are theoretically satisfying and provide structural-level support for the attitude-behavior gap. Behavioral self-report is meaningfully related to but partially distinct from cognitive-affective orientation, consistent with Rest's (1986) theoretical claim that the four components of moral action are relatively independent. The regression finding that Domains 1 and 2 together explain approximately 55% of behavioral variance, with nearly half the behavioral variance remaining unexplained, further confirms that factors beyond awareness and attitudes \u0026mdash; including situational, normative, and control variables \u0026mdash; are necessary to fully account for ethical behavioral engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Null Effects of Gender and Year of Study\u003c/h2\u003e \u003cp\u003e The absence of significant gender differences on any domain or composite score, combined with high statistical power (1 - beta approximately .97) for medium-sized gender effects, provides robust support for the hypothesis of gender invariance in engineering ethical awareness within this population. This finding aligns with Lurie and Mark's (2016) meta-analytic conclusion and with Lawson's (1998) earlier review, and is consistent with the interpretation that the gender differences reported in broader social psychology literature (Blader and Tyler, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) on empathy and moral sensitivity may not generalize to engineering-specific ethical contexts.\u003c/p\u003e \u003cp\u003eThe null year-of-study effect is more theoretically challenging and practically significant. Progressive enrollment in engineering programs would be expected, on theoretical grounds, to increase ethical sophistication through professional socialization, accumulating exposure to engineering case studies, and deepening understanding of professional responsibilities (Altun and Demircan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Altun and Demircan (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found modest positive year effects in a Turkish technical university, though effect sizes were small. The complete absence of a year-of-study effect in the present study may reflect several non-mutually-exclusive explanations. Most importantly, if ethics content in Halic University's curriculum is delivered as a single isolated module or concentrated in one year rather than integrated vertically across all four years, progressive socialization effects would not be expected. The cross-sectional design also limits interpretive power: age-period-cohort confounds and the compositional differences across year groups (e.g., differential attrition of low-performing students in later years) may mask developmental effects that longitudinal designs would reveal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Implications for Curriculum and Pedagogy\u003c/h2\u003e \u003cp\u003eTaken together, the findings generate three clusters of curriculum-level recommendations. First, the persistent attitude-behavior gap indicates that didactic ethics instruction focused on awareness and attitude formation is necessary but insufficient. Active-learning pedagogies that create opportunities for behavioral practice and feedback \u0026mdash; case-based analysis requiring commitment to defensible decisions, ethical decision-making simulations under time pressure, role-playing exercises involving conflict of interest scenarios, and community-engaged projects with genuine stakeholder accountability \u0026mdash; are indicated by both the current findings and the broader pedagogical evidence base (Kligyte et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sattler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Colby and Sullivan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The near-significant advantage of ethics-course completers on the behavioral domain (p = .052) is particularly suggestive: a marginally significant course effect on the dimension most resistant to change supports the value of formal instruction, even as it underscores the need for more powerful and intensive interventions.\u003c/p\u003e \u003cp\u003eSecond, the null year-of-study effect argues compellingly for vertical integration of ethics content across all four years of engineering curricula, rather than the concentration of ethics instruction in a single semester. If ethical awareness is to develop progressively alongside technical competence, ethics content must be embedded in technical courses, project-based learning experiences, and professional preparation activities at every level of the curriculum. This integration is also consistent with professional accreditation expectations: ABET (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Student Outcome 4 is not satisfied by a single ethics course but requires evidence of progressive ethical competency development across the program.\u003c/p\u003e \u003cp\u003eThird, the finding that Ethical Attitudes is a stronger predictor of behavioral engagement than Ethical Awareness (beta = .441 vs. .321) has specific instructional implications. Interventions that target attitudinal shift \u0026mdash; through moral exemplar exposure, professional identity formation, and ethics-themed service learning \u0026mdash; may have greater leverage on behavioral outcomes than purely cognitive awareness-raising approaches. This is consistent with Ajzen's (1991) theoretical framework and with evidence that professional role identity formation, rather than knowledge acquisition alone, is the proximal driver of ethical behavioral intention in professional education contexts (Bebeau, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003e This study provides the first quantitative, multi-domain empirical profile of ethical awareness among undergraduate engineering students at an English-medium Turkish university. Employing Rest's (1986) Four-Component Model and its engineering adaptation by Harris et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as the theoretical framework, and a psychometrically strong instrument (Cronbach's alpha = .966) administered to a sample of 430 students, the study documents moderately high overall ethical awareness (M\u0026thinsp;=\u0026thinsp;7.40/11) alongside a practically significant attitude-behavior gap in which behavioral self-report (M\u0026thinsp;=\u0026thinsp;6.38) lags cognitive-affective orientation (M\u0026thinsp;=\u0026thinsp;7.72\u0026ndash;7.91) by more than 1.5 scale points. Regression analysis confirmed that Ethical Attitudes (beta = .441) is the stronger predictor of behavioral engagement relative to Ethical Awareness (beta = .321), consistent with expectancy-value and planned behavior models. No significant gender or year-of-study moderation was detected, and ethics course completers showed directionally higher scores that did not reach significance in an underpowered comparison.\u003c/p\u003e \u003cp\u003e The study's principal contributions are: (1) empirical documentation of the attitude-behavior gap in Turkish engineering education, extending the cross-national generalizability of this theoretically important pattern; (2) provision of a replicable psychometric framework for multi-domain ethical awareness assessment in Turkish engineering programs; (3) generation of specific, evidence-grounded curriculum recommendations for active-learning ethics pedagogy and vertical curriculum integration; and (4) identification of future research needs, including confirmatory factor analysis, longitudinal designs, and adequately powered ethics instruction comparison studies.\u003c/p\u003e \u003cp\u003eKey limitations include the cross-sectional design, which precludes causal inference about developmental change or ethics instruction effects; convenience sampling from a single private institution, limiting generalizability to other institutional types; social desirability bias in behavioral self-report, which may inflate apparent behavioral scores; and the absence of behavioral vignette or implicit measurement approaches that would provide convergent validity evidence. The high missingness rates on behavioral items warrant investigation in future administrations, as selective non-response on sensitive items may introduce systematic bias into behavioral domain estimates.\u003c/p\u003e \u003cp\u003eFuture research should prioritize: (1) longitudinal designs following cohorts of engineering students from first to fourth year to establish developmental trajectories; (2) confirmatory factor analysis using structural equation modeling to formally test the three-domain structure; (3) behavioral vignette and implicit association measurement approaches to triangulate self-report findings; (4) adequately powered pre-post evaluations of specific curriculum interventions, targeting both attitudinal and behavioral outcomes; and (5) comparative studies across Turkish public and private universities and across engineering disciplines to establish the boundary conditions of the findings reported here.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eABET. 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Ethical competency assessment of engineering students: Instrument development and empirical validation. \u003cem\u003eJournal of Engineering Education, 109\u003c/em\u003e(4), 654\u0026ndash;677. https://doi.org/10.1002/jee.20349\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Haliç University","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":"engineering ethics, ethical awareness, attitude-behavior gap, undergraduate education, survey research, Turkey","lastPublishedDoi":"10.21203/rs.3.rs-9503996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9503996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEthical awareness is a core professional competency for engineers navigating complex sociotechnical decisions, yet empirical evidence from non-Western higher education contexts remains sparse. This cross-sectional survey study (N\u0026thinsp;=\u0026thinsp;430) examines the level, internal structure, and demographic correlates of ethical awareness among undergraduate engineering students at an English-medium Turkish university, across three theoretically grounded domains: Ethical Awareness, Ethical Attitudes, and Ethical Behavior. Grounded in Rest's (1986) Four-Component Model, the study employs a validated 11-item instrument (Cronbach's alpha = .966) and applies descriptive statistics, Pearson correlations, multiple linear regression, independent-samples t-tests, and one-way ANOVA. Findings reveal moderately high overall awareness (M\u0026thinsp;=\u0026thinsp;7.40/11) alongside a practically significant attitude-behavior gap: Ethical Behavior (M\u0026thinsp;=\u0026thinsp;6.38) lagged cognitive-affective domains by 1.35 to 1.53 scale points. Domain-level regression confirmed that Ethical Attitudes and Awareness together explained 54.5% of behavioral variance (R\u0026sup2; = .545), with Attitudes as the stronger predictor (beta = .441). No significant gender or year-of-study differences were detected, and ethics course completers scored directionally higher without reaching significance, likely due to insufficient power. These findings document a persistent attitude-behavior gap consistent with ethical fading theory and provide evidence-grounded recommendations for active-learning pedagogy vertically integrated across engineering curricula.\u003c/p\u003e","manuscriptTitle":"Ethical Awareness Among Undergraduate Engineering Students: A Quantitative Survey Study at Halic University, Istanbul","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 03:20:58","doi":"10.21203/rs.3.rs-9503996/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":"493fa1be-738f-4f69-9cfd-fe7fbbb7fd3a","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T03:20:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 03:20:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9503996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9503996","identity":"rs-9503996","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}