Business, Economics and Management

Abstract: This working paper develops a methodological approach for integrating mathematical optimization with a digital twin environment in the analysis of first-mile milk collection systems. The approach combines a mixed-integer linear programming (MILP) model for network design with a digital representation that enables the evaluation of system behavior under changing operating conditions. The optimization model determines the baseline configuration, including the location of collection points, capacity allocation, and producer assignments. This configuration is then embedded into the digital twin, where its performance is examined under a representative perturbation scenario involving a 20% reduction in milk supply. The analysis shows that the baseline configuration, while efficient under nominal conditions, is sensitive to variations in supply, leading to reduced utilization and higher unit costs. Allowing limited operational adjustments within the fixed network structure improves performance, although economic indicators do not fully return to baseline levels. The results also reveal uneven effects across performance dimensions, indicating the presence of trade-offs between economic, operational, and environmental outcomes. The contribution of this study lies in connecting optimization-based design with a digital evaluation environment that enables the assessment of network configurations beyond their initial formulation. The approach provides a structured way to examine how a given design responds to changing conditions without requiring immediate structural modifications. The analysis is illustrative and intended to demonstrate the integration mechanism. Future work will extend this approach through systematic scenario design, quantitative validation, and the incorporation of real-time data.

Abstract: This study examines the role of women executives in shaping crisis management strategies and firm outcomes during the COVID-19 pandemic, focusing on small and medium-sized enterprises (SMEs) in Türkiye. Drawing on survey data from 207 SMEs across 12 sectors in Istanbul and using nonparametric tests and regression models controlling for firm age, size, and sector, we analyze whether the presence of women in executive positions influenced firm resilience and strategic responses under crisis conditions. The findings reveal that firms with women executives demonstrated significantly stronger cash flow sustainability and cost management outcomes. However, no significant differences were observed in revenue generation or overall performance. In terms of strategy, women executives were more likely to adopt cost-control measures such as operational cost reduction, telecommuting, and staff expense adjustments. At the same time, they were more likely to pursue ambidextrous strategies combining cost control with revenue gene-ration rather than relying solely on defensive approaches. Despite this balanced strategic orientation, only the cost-related dimension translated into measurable outcomes. This indicates an intention–outcome gap, where revenue-generating efforts did not yield significant advantages under severe crisis conditions. The results suggest that women’s leadership advantages during crisis may be domain-specific, emerging primarily in areas of internal organizational control rather than market-dependent outcomes.

Abstract: Sustaining high performance in private healthcare requires that internal organizational processes actively reinforce the values employees carry into their daily work. This study examines how organizational communication and internal branding shape employee commitment, positioning commitment as a foundational condition for sustainable organizational performance in health services. Drawing on quantitative data from 247 healthcare professionals, a structured questionnaire validated through reliability analysis and Confirmatory Factor Analysis was used to assess the direct and indirect pathways between these constructs. Multiple regression analysis confirmed that both organizational communication and internal branding are significant positive predictors of employee commitment, with communication carrying a marginally stronger direct effect. Mediation analysis further revealed that internal branding mediates the relationship between organizational communication and employee commitment, accounting for approximately 29 percent of the total effect. These findings suggest that coherent communication structures and well-anchored internal branding practices function as complementary mechanisms that, together, support the workforce stability and internal alignment that sustainable service delivery requires. The study concludes by proposing an integrated managerial framework that brings these two strategic levers into closer coordination, with the aim of advancing long-term organizational sustainability in the private healthcare sector.

Abstract: This article examines the robust determinants of firm-level innovation in developing countries to understand why firms in these economies exhibit different propensities to innovate. The hypotheses are empirically tested using a representative sample of 10,500 small, medium, and large firms across the manufacturing, retail, and other service sectors in 14 developing countries. The study employs Bayesian Model Averaging to overcome the issue of model uncertainty. We linked firms’ propensity to innovate to five broad determinants: firm characteristics, innovation efforts, access to finance, business environment and technological capabilities. The results reveal that most of the conventional determinants of innovation identified by the existing literature are not as important as previously thought. Furthermore, our analysis presents new evidence that only variables capturing innovation efforts and technological capabilities at the firm- and country-levels can be classified as robust determinants of innovation. These findings link to the firm's knowledge-based view, emphasising that technological capabilities and innovation efforts are forms of knowledge management practices that can augment firms’ absorptive capacity and organisational memory.

Abstract: Purpose: This research intends to explore the relationships between digital risk management practices and the successful implementation of innovative banking services with moderation effect of digital capabilities and moderation effect of digital culture. Methodology Approach: In this study, the data was gathered using a quantitative approach and the cross-sectional survey method with responses from participants who were chosen as the unit of analysis of being investigated for the study. Islamic finance institutions in Jordan were used as the unit of analysis in this study. Responses of different Islamic finance institutions were surveyed in a structured manner to collect data. The current study then used a structural equation modeling using SmartPLS to investigate the relationship between the variables. Findings: The results show that utilizing digital risk management advanced analytics artificial intelligence and automated compliance systems is essential to fostering innovation while upholding Shariah compliance. The study also shows that efficient digital risk management boosts users confidence increases service effectiveness and facilitates the launch of cutting-edge Shariah-compliant products. The findings reveal meditation and moderation significant effect of digital capabilities and digital culture respectively among between digital risk management and innovative banking services. Originality: By investigating digital risk management in the particular context of Islamic innovative banking services, this study provides novel insight. In contrast to earlier research that focuses on innovation in Islamic finance or digital risk management in conventional banking independently this paper examines how digital risk management frameworks impact the creation governance and sustainability of innovative banking services that adhere to Shariah.

Abstract:

The accelerating integration of artificial intelligence into societal structures risks reducing human existence to quantifiable data points, algorithmic classifications, and performative metrics—a phenomenon we term algorithmic reductionism. This paper introduces the Alrohaimi Canonical Model, a unified mathematical and cognitive framework designed to diagnose, simulate, and guide civilizational transformation while safeguarding human meaning and agency. Unlike linear economic or technological indices, the Alrohaimi Index () models transformation as a non-linear, meaning-driven process:

where:

· (Composite Cognition) integrates four civilizational inputs: Environment (), Memory (), Systems (), and Human ().

· represents Qualitative Time (density of transformative events multiplied by linear time).

· is Effective Latency (base response time reduced by resilience ).

· is Meaning (Belief × Awareness), acting as an exponential amplifier of transformation.

The model operationalizes abstract philosophical concepts into measurable indicators through three validated questionnaires (Leadership, Organizational, Individual) that assess cognitive balance, awareness, and the meaning gap. It introduces four cognitive profiles (Balanced, Reductionist Efficiency, Disempowered Awareness, Superficial Stability) and automatic strategic recommendations. A real-time interactive dashboard allows users to simulate “what-if” scenarios, compare societies or periods, and export diagnostic reports. We demonstrate the model’s application to protect human integrity in the algorithmic era: by quantifying the risk of reductionism (), policymakers and organizational leaders can design targeted interventions—raising awareness, shortening institutional latency, enhancing resilience, and recentering meaning—to ensure that AI serves human flourishing rather than reducing it to statistical noise. The Alrohaimi Index offers a actionable bridge between complexity theory, Islamic civilizational thought (Ibn Khaldun), existential psychology (Frankl), and contemporary AI ethics, providing a robust decision-support tool for sustainable civilizational transformation.

Abstract: The concept of Industry 5.0 is gaining wide recognition both in academia and in business practice. On the other hand, the assessment of maturity using the maturity model for the implementation of the 5.0 concept is characterized by a much lower degree of recognition. In the face of the increasing complexity of global supply chains and the need to ensure the efficient flow of materials, the authors postulate a model of maturity for the implementation of the Industry 5.0 concept, which will become a key subject of scientific research, and the solutions developed within it will be gradually implemented by companies striving to increase their competitiveness. This study presents the conceptual framework of the maturity model for the implementation of the Industry 5.0 concept in logistics industry enterprises, the aim of which is to enable organizations to assess the current level of advancement in the implementation of the elements of the Industry 5.0 concept and to develop a strategic improvement action plan. This model was created on the basis of a review of the literature on the subject, covering both industry issues and existing models of organizational maturity. Its structure has been designed and empirically verified in such a way as to form the foundation for the development of a set of practical guidelines supporting the logistics transformation towards the concept of Industry 5.0.

Abstract: Artificial Intelligence (AI) is increasingly central to modern system engineering and service operations, enabling real-time decision-support in cyber-physical and data-intensive environments. This study develops an Extended Deep Neural Network–Logistic Regression (EDNN–LR) hybrid framework as a scalable AI solution for predictive optimisation within Industry 4.0 decision systems. The model integrates the nonlinear learning capability of deep neural networks with the interpretability and convergence stability of logistic regression, thereby enhancing transparency, robustness, and computational efficiency in engineering applications characterised by uncertainty and behavioural variability. The proposed framework is validated using a publicly available financial–cyber dataset comprising over 4.44 million records from CoinMarketCap (2013–2025), representing a dynamic cyber-physical decision environment analogous to complex industrial ecosystems. Implemented in MATLAB R2024a and TensorFlow 2.17, the model achieves rapid convergence by epoch 142 and 98 % classification accuracy (AUC = 0.846, MSE = 0.79, recall = 90.6 %) on selected high-liquidity assets. These results confirm the framework’s ability to model nonlinear dependencies and adapt to stochastic disturbances typical of service-oriented and engineering-operation contexts. Beyond predictive precision, the EDNN–LR framework provides explainable probabilistic outputs that can be directly incorporated into decision variables such as resource allocation, demand forecasting, and dynamic scheduling under real-time constraints. Its hybrid design reduces computational cost, enhances interpretability, and enables cross-domain adaptability—from financial risk management to logistics, supply-chain coordination, and energy-system optimisation. By bridging deep learning, system engineering, and behavioural decision analytics, this study contributes a generalised AI-driven architecture for intelligent and transparent decision-support across Industry 4.0 service and production ecosystems.

Abstract: The paper explores how Green digital technologies (GDTs) - ERP systems, cloud, IoT, artificial intelligence, and big data analytics can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). On the basis of institutional isomorphism theory, we examine the relationship between the coercive, mimetic and normative institutional pressures and adoption of green technology interaction on the sustainability reporting practices. On the basis of panel data of 30 ASE-listed industrial companies during the period of 20202024 (N = 146 firm-year observations), we use pooled OLS and random-effects frameworks characterized by strong clustering of standard errors. Findings show that Green Digital Technology Index has a positive and significant agreement with the ESG disclosure scores (0.019; 0.024; 2.486, p value 0.019; 2.507, p value 0.024), with adopting firms having an average score of 1.73 higher. Its impact has been the most significant to the environmental aspect ( = 3.460, 0.074) = 0.074. Although institutional pressures fail to modulate the GDT-disclosure relationship, mediation analysis shows that institutional pressure is also a powerful predictor of GDT adoption (0.098, p 0.100), indicating that institutional forces play the role through technology adoption. The quality of disclosure has a negative relationship with CEO duality ( -4.863, p < 0.001). The results validate the assumption that the green digital technologies are a transmission mechanism where institutional pressures are converted to an enhancement of sustainability disclosure in the emerging markets.

Abstract: Strategic knowledge disclosure is becoming more and more common and important in enterprise innovation management. Nowadays enterprises are in the global open innovation network with many strategic alliances and supply chain partners. The behavior and relationship factors of external important stakeholders are of great significance to strategic knowledge disclosure. Based on transaction cost economics theory and the circular model of strategic knowledge disclosure process, this study takes manufacturing listed enterprises from 2010 to 2019as the research object, and uses their published papers in scientific journals to represent strategic knowledge disclosure behavior. Finally, this study empirically examines the influence of supplier’s dependence on major customers (i.e. customer concentration) on supplier’s strategic knowledge disclosure. The customer concentration of suppliers has a significant negative impact on the implementation of strategic knowledge disclosure. High customer concentration will inhibit the total amount of strategic knowledge disclosure by suppliers. When the internal R&amp;D expenditure of suppliers is further taken into account in the model, high customer concentration has a more obvious inhibitory effect on the intensity of suppliers' strategic knowledge disclosure. In order to analyze the selection mechanism of strategic knowledge disclosure based on the perspective of external important stakeholders, and provide a feasible path for the strategic management innovation knowledge of enterprises under the specific relationship network.

Abstract: In this paper, the different emerging metaverse technologies are identified and a comprehensive understanding of the various technologies that can empower supply chains in various parts of the world is provided. It also presents a structure that shows how each of these classified technologies would work towards a robust and sustainable system of supply chain. Moreover, the study uses the fuzzy TOPSIS method to determine the most significant metaverse technology that can significantly enhance the resilience and sustainability of the supply chain networks across the world. The basic aim of this research is to arm the organizations with the latest technology in the metaverse, which enables them to develop a future-proof supply chain network capable of surviving in this ever-evolving world.

Abstract: Digital transformation is a must to remain competitive in today’s advanced technological environment. This study examines how digitalization influences the performance of SMEs in Albania, focusing on digital adoption, skills, training, and institutional support. Using survey data from SMEs and quantitative analysis, the results show that adopting digital tools helps businesses expand their markets and improve performance. Findings reveal that Albanian SMEs recognize the importance of digitalization, but many use technology in fragmented ways, associated with barriers such as high costs and limited digital skills. Institutional support appears weak, while current training programs may lack practical value.

Abstract: Background: The hospitality industry faces the critical challenge of transitioning toward sustainability without compromising profitability. While circular economy principles offer a theoretical framework for sustainable development, a significant gap persists between theory and integrated operational models that deliver verifiable business outcomes. Methods: This study employed a mixed-methods approach combining systematic literature review, expert validation with 9 industry practitioners, and longitudinal case study implementation across four hotel properties representing different market segments (luxury resort, urban boutique, beach resort chain, and rural eco-lodge). Data collection spanned 24 months, tracking performance metrics across six categories. Results: Implementation of the Hospitality 360° framework yielded substantial improvements: energy consumption decreased by 28.1%, water usage by 22.0%, waste diversion rates increased by 204.0% (from 25% to 76%), local procurement expenditure rose by 42.9%, employee turnover decreased by 48.6%, and guest satisfaction regarding sustainability initiatives improved by 26.5%. Financial analysis revealed an average payback period of 2.8 years and a 6.5% RevPAR premium compared to competitive sets. Conclusions: The Hospitality 360° model successfully operationalises circular economy principles, transforming sustainability from a cost center into a driver of competitive advantage. The framework provides a replicable roadmap for practitioners seeking to build resilient and profitable hotel enterprises in the conscious travel market.

Abstract: Dominant paradigms across biology, artificial intelligence, and cognitive science define intelligence through its observable expressions—gene activation, computational output, and decision-making behavior. While this perspective has enabled significant advances, it systematically overlooks a fundamental architectural dimension: the preservation of structured potential in non-executing states.Across biological systems, large portions of the genome remain transcriptionally inactive yet structurally conserved, suggesting the existence of preserved functional capacity beyond immediate expression. In artificial intelligence, knowledge is encoded within high-dimensional latent spaces that guide outputs without continuous activation. In human systems, cognition depends on layers of unexpressed interpretation and perceptual structure that shape decision-making beyond observable behavior.Here, we propose that this shared phenomenon reflects a universal principle, which we define as latency: the structured preservation of encoded potential in a non-executing state with conditional accessibility across time. Within this framework, intelligence is not solely a function of execution, but of the dynamic balance between preservation and activation. We introduce the concept of a latency spectrum, in which elements vary in depth, stability, and activation cost, providing a graded architecture of temporal accessibility rather than a binary distinction between active and inactive states.We further identify a critical failure mode—the Meaning Gap—which arises when the velocity of system output exceeds the depth of latent structure. This misalignment manifests as incoherent outputs in artificial intelligence, dysregulated activation in biological systems, and loss of interpretive coherence in human decision-making environments.Extending this framework, we introduce Cognitive Sovereignty as the capacity of individuals and institutions to interpret, contextualize, and assume authorship over decisions in increasingly automated environments. We argue that this capacity depends fundamentally on the preservation of cognitive latency. As intelligent systems accelerate decision cycles, the compression of latency risks reducing interpretive depth, undermining autonomy, and destabilizing system coherence.By integrating genomic memory, computational latent representations, and human cognitive frameworks, this study advances a unified theory in which latency emerges as the hidden architecture of intelligence. This perspective reframes intelligence from a purely kinetic phenomenon to a temporally structured system of preserved potential, with implications for biological theory, artificial intelligence design, and the governance of complex sociotechnical systems.We conclude that the central challenge is no longer the acceleration of intelligence, but the preservation and regulation of latency itself. Designing latency-aware systems will be essential for sustaining meaning, coherence, and human agency in the age of intelligent machines.

Abstract: As artificial intelligence increasingly mediates decision-making across organizational and societal contexts, a critical challenge emerges concerning the preservation of human cognitive coherence under system-driven conditions. While prior research has emphasized trust, fairness, and transparency, it provides limited explanation of whether individuals remain cognitively aligned with decisions generated through algorithmic processes. This paper introduces perceptual integrity as a construct capturing the extent to which individuals maintain coherence, interpretability, and authorship over their decisions in human–AI interaction. Building on a proposed theory of conscious leadership, cognition is conceptualized as an emergent interaction among environment, memory, systems, and the human agent, where decision quality depends on the balance among these forces. An experimental study (N = 602) was conducted to examine the impact of decision structure on perceptual integrity and its relationship with trust. Participants were assigned to either an algorithmic imposition condition or an interpretive autonomy condition. Results indicate that algorithmic imposition reduces perceptual integrity compared to interpretive autonomy (t(600) = 4.21, p < 0.001, d = 0.38). Furthermore, perceptual integrity significantly predicts trust (β = 0.36, p < 0.001) and partially mediates the relationship between decision condition and trust (indirect effect = 0.17, 95% CI [0.09, 0.27]). These findings suggest that trust in AI-assisted decisions is shaped not only by system performance but also by the degree to which individuals remain cognitively engaged in the decision process. By introducing perceptual integrity as a measurable manifestation of cognitive balance, this study contributes to human–AI research and offers a new perspective on leadership as the management of cognitive conditions rather than behavioral outcomes.

Abstract: Purpose: This article explains why early-stage ventures frequently display intense activity yet fail to achieve commercialization in institutionally complex environments. It argues that the problem is not simply resource scarcity or weak infrastructure, but whether product, market, and institutional validation become aligned over time. Design/methodology/approach: The study adopts a longitudinal, abductive, multi-venture design based on 15 early-stage technology ventures operating across African markets over a 12-month period. Drawing on milestone plans, quarterly progress reports, budget allocation records, and advisory or engagement records, the analysis traces how validation processes unfold, interact, and diverge across venture trajectories. Findings: Three recurrent outcome regimes emerge: commercialization, artificial progression, and stagnation. Commercialization occurs when product, market, and institutional validation advance in a coordinated and mutually reinforcing sequence. Artificial progression arises when ventures generate credible activity and visible advancement in one or more domains, yet fail to convert this momentum into commercialization because validation remains cross-domain misaligned. Stagnation occurs when ventures do not accumulate sufficient validation to build cumulative legitimacy. Across cases, sequencing capability, the ability to order validation efforts so that gains in one domain unlock gains in others, appears to be a critical differentiator. Originality/value: The article contributes by reframing venture progress as an alignment-dependent accomplishment rather than an activity count, theorizing artificial progression as a distinct structural condition, and introducing Institutionally Mediated Market Formation (IMMF) as a process-based explanation of commercialization under institutional complexity. The study extends entrepreneurship, legitimacy, and ecosystem research by showing that visible activity is an unreliable proxy for progress unless it becomes commercially convertible through cross-domain validation alignment.

Abstract: Speaking up is widely regarded as a critical driver of team learning and performance, yet research typically examines promotive and prohibitive voice as separate predictors rather than as a collective behavioral system. We introduce Voice Modality Divergence (VMD), a team-level composition construct capturing the extent to which teams collectively differentiate and balance two distinct voice modalities: promotive voice oriented toward improvement and prohibitive voice oriented toward harm prevention. Drawing on ambidexterity, information integration, and team learning theories, we argue that VMD enhances team performance by enabling teams to integrate complementary improvement. At the same time, we theorize that the same voice landscape can generate a divisive social structure. We conceptualize Voice-Based Faultline Strength (VFS) as a behavioral segmentation that emerges when high levels of both promotive and prohibitive voice cluster within one subgroup while low levels of both cluster within another, creating rigid subgroup boundaries and fractured communication. Building on faultline and social categorization theories, we argue that stronger VFS directly undermines team performance by restricting cross-subgroup exchange, intensifying misattributions, and fragmenting psychological safety. Using data from intact work teams (N = 41 teams), results support both hypotheses: VMD is positively associated with team performance, whereas VFS is negatively associated with team performance, above and beyond average voice levels and team controls. This study advances the voice literature by shifting attention from the frequency of speaking up to the structural configuration of voice modalities within teams, highlighting that teams benefit from balanced voice patterns but suffer when voice becomes behaviorally segregated into subgroups.

Abstract: Recent advances in artificial intelligence have significantly enhanced decision efficiency, yet they have also introduced a less examined challenge: the transformation of human cognition within system-driven environments. While prior research has primarily focused on trust, fairness, and transparency, limited attention has been given to the cognitive structure underlying decision coherence in human–AI interaction.This paper introduces a proposed theory of conscious leadership that conceptualizes cognition as an emergent interaction among environment, memory, systems, and the human agent. Within this framework, cognitive balance is defined as the equilibrium among these forces, and perceptual integrity is positioned as its measurable manifestation, reflecting the extent to which individuals maintain coherence and authorship over their decisions when interacting with intelligent systems.We hypothesize that cognitive balance positively predicts perceptual integrity, which in turn influences trust in AI-assisted decisions, and that awareness—defined as the individual’s conscious recognition of system influence—moderates this relationship. An experimental study (N = 602) was conducted to test these propositions. Results indicate that perceptual integrity significantly predicts trust (β = 0.48, p < 0.001) and mediates the relationship between decision mode and trust (indirect effect = 0.42, 95% CI [0.31, 0.54]). Furthermore, awareness moderates the effect of system-driven imposition on perceptual integrity (β = 0.23, p < 0.01), such that higher awareness reduces the negative impact of algorithmic enforcement.These findings extend leadership theory by shifting the focus from behavioral control to the management of cognitive balance and contribute to human–AI research by introducing perceptual integrity and awareness as foundational constructs for preserving human coherence in increasingly automated environments.

Abstract: Background: Why promising health technology ventures stall between product development and real-world deployment remains insufficiently explained in entrepreneurship research. Market-centric models emphasize experimentation, customer discovery, and product-market fit, but these mechanisms are less explanatory where access to users, clinical settings, and procurement channels is institutionally mediated. Methods: This study develops a process explanation of venture progression in regulated health innovation settings. Drawing on institutional theory, innovation systems research, and process views of entrepreneurship, it uses a longitudinal comparative case design to analyze early-stage health technology ventures in Rwanda over an 18-month observation window. The empirical material comprises venture milestone plans, periodic progress reports, observational notes, and documented interactions with regulatory, clinical, and institutional actors. Results: Venture progression was not explained primarily by technical capability, entrepreneurial effort, or early market interest. Rather, outcomes varied according to whether ventures achieved coordinated validation across interdependent regulatory, clinical, and institutional domains. Three recurrent pathway configurations were identified: sequential alignment, associated with forward progression; temporally constrained alignment, associated with delayed progression despite coherent sequencing; and fragmented progression, associated with stagnation. The findings show that legitimacy in regulated health innovation settings is multi-domain, threshold-based, and time-dependent. Conclusion: The study advances the concept of coordinated validation under temporal constraint to explain how ventures move, or fail to move, from development to deployment when market entry depends on synchronized institutional approval. It contributes to entrepreneurship and innovation theory by reframing venture progression in regulated environments as a coordination problem rather than a pure capability problem. Rwanda serves as a revealing case because its comparatively coordinated health system makes the underlying synchronization problem especially visible.

Abstract: Contemporary intelligent systems increasingly participate in shaping, rather than merely supporting, human decision-making processes. While these systems enhance efficiency and predictive performance, they also introduce a critical but underexamined challenge: decisions may remain technically valid while becoming difficult for human actors to interpret and internalize. This misalignment is conceptualized in this study as the meaning gap, defined as the divergence between system output and human interpretive understanding.This paper proposes a human-centered governance framework that positions cognitive sovereignty—the capacity of individuals and institutions to interpret, contextualize, and assume responsibility for decisions—as a necessary condition for sustainable sociotechnical systems. The framework is structured around ten interdependent principles that collectively redefine governance as a cognitive architecture embedded within system design.Drawing on interdisciplinary literature in human–AI interaction, interpretability, and decision science, the study introduces latency as a conceptual construct describing temporal misalignment between system output and human interpretive readiness. This construct provides an integrative lens for understanding phenomena such as delayed comprehension, reduced accountability, and unstable trust in algorithmically mediated environments.Rather than treating interpretability as an auxiliary feature, the proposed framework positions it as a core system function. The paper further outlines potential pathways for operationalizing the meaning gap through measurable indicators, including time-to-comprehension, decision override frequency, and confidence misalignment.While existing frameworks have called for interpretability, few have proposed measurable indicators of cognitive alignment. This paper contributes preliminary metrics—time-to-comprehension, decision override frequency, and confidence misalignment—that operationalize the meaning gap for empirical testing.