This dissertation study examines how organizations in emerging economies, operating under persistent resource constraints, achieve integrated operational reliability and innovation. Integrating ambidexterity, emerging-economy, organizational action and learning, and pragmatist perspectives, it uses a longitudinal qualitative case study of a digital infrastructure firm over 15 years (2008–2023), guided by a Departure-Based Pragmatist Action Framework that traces breakdowns, departures from established practice, and evaluation and adjustment. It aims to explain ambidexterity under constraint as an emergent, practice-based accomplishment and to offer practitioners a process-oriented approach to reliability–innovation tensions.

Drawing on Organizational Information Processing Theory, this study examines how hospital interoperability capability influences EHR meaningful use maturity and how that maturity affects patient engagement and advanced analytics. Using structural equation modeling with mediation analysis on 1,374 hospitals from the 2023 AHA Healthcare IT Database, all five hypotheses were supported, with meaningful use maturity fully mediating the effects of interoperability on patient engagement and analytics. A suppression effect reveals an infrastructure paradox: interoperability alone is not enough without organizational capacity, informing how hospital leaders and policymakers sequence digital health investments.

This work-in-progress paper examines how an AI-based assistant can be embedded within established continuous improvement processes to enhance value stream analysis in an automotive supplier environment. Drawing on a revelatory single-case study of a German automotive supplier that deployed an AI-Assistant in collaboration with a specialised German start-up, the research investigates the coupling of artificial intelligence with the 8D problem-solving methodology. Using a qualitative research design grounded in grounded theory principles, data were collected from multiple sources across the manufacturing operation and analysed through open, selective, and theoretical coding. Preliminary findings indicate that embedding the AI-Assistant into the 8D cycle produces substantial operational gains, including reductions in order lead time of at least 50 percent (reaching up to 90 percent in selected areas), an increase of at least 80 percent in the value creation rate for materials subjected to completed 8D measures, and meaningful reductions in manual scheduling effort, scrap, and work-in-progress. Beyond these quantifiable results, the integration yielded qualitative benefits in transparency, systematic problem identification, master data quality, and root-cause analysis speed. The paper contributes empirical evidence to the Industry 4.0 literature by demonstrating that AI technologies can augment rather than displace established lean methodologies, thereby addressing a critical gap identified in prior research on integrated AI-enabled manufacturing optimisation systems.

This paper presents findings from a qualitative empirical study examining how senior leaders make strategic decisions under conditions of time pressure and uncertainty. The central research question is: what is the role of intuition in the strategic decision-making process when decisions are made in high-velocity environments? Drawing on 42 semi-structured interviews with CEOs, board directors, founders, and executive directors, conducted using the Critical Incident Technique, the study generates detailed, experience-proximate accounts of real strategic decision episodes. The findings reveal that intuition operates not as a discrete cognitive choice but as an embedded orienting mechanism that shapes early issue framing, option consideration, and the interpretation of emerging information. Rational analysis, while dominant as the language of organisational legitimacy, was frequently applied in compressed, selective, and confirmatory ways, serving to justify decisions already directionally established rather than to evaluate alternatives independently. Decision processes were further shaped by emotional activation, social authority structures, and organisational approval dynamics. The study identifies seven major empirical findings and nine specific process vulnerabilities through which senior decision-making routinely breaks down. These provide the evidential foundation for a structured decision-support tool currently under development. The findings carry direct practical implications for leadership development, executive education, and organisational decision governance.

This extended abstract reframes the deployment of smart meters (advanced metering infrastructure) by U.S. electric utilities as a governance and trust challenge rather than a purely technological adoption issue. Drawing on Organizational Justice Theory, Trust Theory and Consumer Socialization Theory, it proposes a justice–trust–acceptance model in which perceptions of informational and procedural justice shape trust in utilities, which in turn drives customer acceptance of smart meter deployment. The study outlines a quantitative cross-sectional survey of residential utility customers, to be analysed with factor analysis and structural equation modelling including mediation and moderation tests, and discusses anticipated findings and implications for utility communication, deployment governance and stakeholder engagement.

The growing complexity and vulnerability of supply chains (SCM) have elevated logistics management to a matter of strategic and corporate governance relevance. Yet, although the literature suggests that boards of directors should play a key role in overseeing supply chain resilience and sustainability, a significant gap persists in the empirical evidence regarding how such oversight becomes visible in corporate governance documents. This study addresses this gap through the analysis of public corporate documents from 10 Mexican firms with high operational relevance, all included in the S&P/BMV IPC index. The study adopts an exploratory empirical design based on qualitative content analysis of a corpus of 29 official documents, including annual reports, sustainability reports, and investor presentations. Drawing on Resource Dependence Theory and Institutional Theory, the study employs a theoretically informed coding matrix to identify patterns of SCM oversight visibility. The findings suggest a phenomenon of "Logistical Blindness," characterized by a decoupling between strategic narrative and the documentary visibility of operational oversight. While SCM is presented as a pillar of growth, resilience, or sustainability, its public treatment in governance documents appears concentrated in silos of ethical compliance, sustainability, and risk management, with scarce presence of operational flow metrics. The study further identifies sectoral differences: consumer-sector firms exhibit greater institutional visibility of SCM, whereas infrastructure firms display a deeper documentary invisibility. These findings contribute to the corporate governance and supply chain management literature by showing that SCM gains public visibility primarily through legitimacy, compliance, and risk frameworks, while its operational dimension remains comparatively underrepresented. As a practical implication, the study proposes integrating flow indicators into the governance agenda to strengthen strategic resilience in the context of nearshoring.

This DBA study investigates which factors explain the apparent slowdown of the sustainable investment market in Spain, Portugal and Andorra, including misalignments between investors, borrowers and regulators and the effects of recent EU and national regulation. Drawing on stakeholder, agency, legitimacy, institutional and signalling theories, it adopts a mixed-methods design combining time-series analysis of ESG fund flows and issuance with Qualitative Comparative Analysis of practitioner interviews and regulatory documents. Preliminary insights from the literature point to interacting regulatory, market microstructure and investor behaviour factors.

This paper examines the Doctor of Business Administration (DBA) as a dialectical process of professional and scholarly transformation. Drawing on Benson's (1977) dialectical theory, it explores how practitioner-researchers experience and integrate multiple tensions—identity (professional–academic), activity (working–studying), knowledge (practical–theoretical), growth (learning–doing), and argument (verbal–written)—throughout their doctoral journey. A qualitative systematic literature review of 33 peer-reviewed studies was conducted following Snyder's (2019, 2024) methodological framework. Using interpretive analysis, thematic coding, and AI assessment, the review identifies how these tensions function as generative forces that enable reflection, synthesis, and identity reconstruction. Rather than viewing tension as a barrier, the DBA is conceptualized as a process where professional experience and scholarly inquiry coalesce, producing ‘Mode 2’ knowledge that bridges theory and practice (Gibbons et al., 1994). The findings reveal that successful DBA candidates employ critical reflexivity to transform opposing demands into opportunities for integrated understanding and scholarly contribution (Farrell, Oerton, & Plant, 2018; Yourston & Bown, 2024). As a result, this paper contributes to the literature by framing the DBA as a living dialectical system in which tension fuels development, enabling professionals to become reflexive practitioner-researchers who unite action and reflection in the pursuit of meaningful organizational and academic impact.

This study assesses the weak-form informational efficiency of the Lithium market, a critical mineral resource essential for the global energy transition, using the Global X Lithium & Battery Tech ETF (LIT) daily returns from 2010 to 2025. We apply the robust Multifractal Detrended Fluctuation Analysis (MF-DFA) to detect long-range correlation and multifractality in this complex, non-stationary time series. Our results reveal a multifractal market structure and deviation from the random walk, indicating greater inefficiency compared to major global stock indices, similar to volatile markets. The generalized Hurst exponent confirms this multifractal structure, exhibiting asymmetric scaling behavior where small price fluctuations show persistent long-range correlation, while large price fluctuations exhibit anti-persistence, reflecting a mean-reverting mechanism. Through analysis of shuffled series, we confirm that the multifractality originates predominantly from temporal dependence (long-range correlation) rather than the heavy-tailed distribution of returns. This deviation from the Efficient Market Hypothesis underscores the need for reforms to enhance transparency and information flow in the Lithium market, fostering optimal capital allocation crucial for sustaining the global energy transition.

This poster asks how algorithmic explainability and transparency affect customers’ perceptions of the fairness of surge prices on ride-hailing platforms. Grounded in dual entitlement theory, it hypothesizes that explanations mitigate perceived unfairness and that algorithmic transparency positively moderates this relationship. A scenario-based survey experiment with US ride-hailing consumers recruited via Prolific or MTurk, analysed with structural equation modeling, is planned to inform interface design that enhances perceived fairness and trust.