Purpose This study aims to examine the relationships between the perceived quality of services received by foreign trade companies from customs brokerage firms and customer satisfaction and loyalty. In addition, the research re-evaluated the dimensional structure of the service quality scale in a sector-specific manner. Design/methodology/approach In this research, which was conducted with data collected through a survey method from 404 participants working in foreign trade companies in Türkiye, the service quality (SERVQUAL) model was used to evaluate service quality, and the structural equation modeling (SEM) method was used to examine the links between customer satisfaction and loyalty. Findings The findings indicate that perceived service quality is positively associated with customer satisfaction and customer loyalty, with customer satisfaction representing a significant mediating pathway. Furthermore, the analysis results yielded a three-component representation consisting of Reliability, Interaction Quality, and Tangibles. Research limitations/implications During the adaptation process of the SERVQUAL scale, some items were found to not provide sufficient decomposition within the factor structure and were therefore removed from the analysis. This limits the extent to which conclusions can be drawn regarding the dimensional structure of SERVQUAL and highlights the need for further validation in customs brokerage settings. Originality/value This study extends service quality research to the under-examined customs brokerage context and provides empirical evidence on the relationships among perceived service quality, customer satisfaction, and customer loyalty while also illustrating context-specific patterns in the representation of service quality in a regulation-intensive, information-system-mediated setting.
Purpose The study examines the interaction between generative artificial intelligence (GAI)and digital task innovation by conceptualizing and hypothesizing GAI orchestration as a multifaceted variable comprising GAI integration, enablement and workflow alignment. Furthermore, the study distinguishes digital task innovation from broader concepts such as process innovation, digital innovation and product innovation. Design/methodology/approach Today, firms employ GAI technologies to enhance organizational performance, operational efficiency and productivity. Research has provided valuable insights into the role of generative technologies in product innovation; however, their impact on digital task innovation remains underexplored, particularly in relation to task redesign, workflow reconfiguration and human–GAI collaboration. The study examines the interaction between GAI and digital task innovation by conceptualizing and hypothesizing GAI orchestration as a multifaceted variable comprising GAI integration, enablement and workflow alignment. Furthermore, the study distinguishes digital task innovation from broader concepts such as process innovation, digital innovation and product innovation. Findings The findings of the study indicate that GAI orchestration is positively associated with GAI augmentation and negatively associated with GAI anxiety. GAI anxiety negatively influences digital task innovation. However, GAI augmentation noted as positive while mapping digital task innovation. Thus, the findings support the proposed dual-pathway framework. Originality/value The results highlight the pivotal role of organizations in dealing with GAI anxiety and upholding GAI augmentation for effective GAI orchestration that leads to digital task innovation. The findings also have implications for both research and practice, opening new avenues in the context of digital transformation. The study contributes by positioning GAI orchestration as a higher-order perceived organizational capability and by clarifying how GAI-enabled task innovation depends not only on technology integration but also on workflow alignment, employee enablement and augmentation-oriented use.
Purpose Drawing on a unified resource-orchestration model, this study examines how artificial intelligence (AI)-enabled inter-firm cooperation influences organizational resilience in manufacturing small and medium-sized enterprises (SMEs), through a mediated-moderated pathway involving digital service innovation strategy and leadership cognition. Design/methodology/approach A sequential mixed-methods design was employed: exploratory qualitative construct refinement followed by a time-lagged quantitative survey of 269 SME managers in Viet Nam, analyzed using partial least squares structural equation modeling (PLS-SEM) and the Johnson–Neyman technique. Findings AI-enabled inter-firm cooperation is positively associated with organizational resilience, a relationship positively mediated by an AI-driven digital service innovation strategy. Crucially, the executive's digital paradox mindset negatively moderates this link, revealing a novel cognitive substitution effect. Practical implications SME managers should invest in interoperable AI platforms for automated boundary-spanning data sharing. Furthermore, leaders must recognize that internal paradoxical cognition and external AI networks can functionally substitute for one another in building resilience. Originality/value The study extends an established AI-enablement construct by demonstrating a novel integrative mechanism through which such cooperation translates into organizational resilience. It enriches paradox theory and technology management by shifting focus from structural resource accumulation to cognitive resource substitution as a theorized boundary condition.
Purpose Artificial intelligence (AI) is transforming human resource management (HRM), yet the cognitive and psychological factors associated with human resource (HR) professionals’ intentions to use AI remain incompletely understood. Drawing on the Technology Acceptance Model (TAM) and appraisal perspectives, this study examines the relative associations of Cognitive Evaluation, AI-Use Anxiety, AI-Related Job Insecurity, organizational AI use across HR functions, and AI Knowledge with behavioral intention to use AI. It also explores whether AI Knowledge moderates these relationships. Design/methodology/approach Survey data were collected from 285 HR professionals. Confirmatory factor analyses assessed the measurement structure of the study constructs, followed by hierarchical moderated regression analyses examining their unique associations with behavioral intention and the exploratory moderating role of AI Knowledge. Findings Cognitive Evaluation, reflecting perceived usefulness and ease of use, showed the strongest unique association with behavioral intention to use AI. AI-Use Anxiety demonstrated a smaller negative association, whereas AI-Related Job Insecurity, AI Use Across HR Functions, and AI Knowledge did not explain significant unique variance once the competing predictors were considered simultaneously. The comprehensive model explained 66.8% of the variance in behavioral intention. The exploratory interaction analyses provided no clear evidence that AI Knowledge systematically moderated the relationships examined. Practical implications Organizations implementing AI in HR should address both employees’ evaluations of the technology and apprehension surrounding its use. Implementation strategies should demonstrate the practical usefulness and usability of AI through role-specific examples, hands-on training, and opportunities to experiment with AI tools in realistic HR contexts. Organizations should also recognize AI-use anxiety as a distinct potential barrier by providing accessible support, low-risk opportunities for familiarization, and clear communication throughout implementation. These strategies may help create conditions that support employees’ willingness to use AI. Originality/value This study extends research on AI use intentions by examining cognitive, emotional, knowledge-related, and organizational-exposure factors simultaneously rather than in isolation. The findings show that HR professionals’ intentions to use AI are most strongly associated with evaluations of its usefulness and usability, while AI-use anxiety represents a smaller but distinct negative correlate. By distinguishing AI-use anxiety from AI-related job insecurity and individual AI knowledge from organizational AI exposure, the study provides a more differentiated account of the factors associated with HR professionals’ intentions to use AI.
Purpose This study develops and empirically validates a Corporate Ethical and Responsible AI (CERAi) lifecycle framework. The framework comprises three interdependent stages: design and development, testing and training, and deployment and usage. The study aims to move the research beyond abstract principles and policy analyses toward empirically grounded, lifecycle-integrated artificial intelligence AI governance theory. It positions ethical AI and responsible AI governance as the two constitutive components of the CERAi construct, and AI trustworthiness as a conceptually and empirically distinct downstream outcome anchored in stage-specific organizational governance mechanisms. The CERAi framework is scoped broadly to encompass governance of both conventional/traditional machine learning models and autonomous AI agents operating within organizational settings. Design/methodology/approach We administered a large-scale survey to 500 attendees at a national AI conference in the United States in early 2026, yielding 143 useable responses from AI professionals across healthcare, financial services, manufacturing, retail and energy sectors. We developed and validated multi-item scales for each CERAi lifecycle stage using established ethical AI and AI governance frameworks. Structural equation modeling was employed to assess the measurement model and to test both direct path hypotheses and mediation hypotheses. Findings Both CERAIi design and development and CERAi deployment and usage have significant positive direct effects on AI trustworthiness, whereas CERAi testing and training do not. Our results also indicate a significant serial mediation pathway in which CERAi deployment and usage enhance AI trustworthiness through testing and training, followed by design and development. Conversely, testing and training alone do not mediate the relationship between deployment and usage and AI trustworthiness, indicating that design-stage ethics serve as an intervening (mediating) mechanism through which governance activity enacted at later lifecycle stages is associated with perceived AI trustworthiness. This is consistent with testing and training's association with trustworthiness operating indirectly, mediated by the ethical architecture established during the design and development stage. Originality/value Findings of this study demonstrate that AI trustworthiness reflects differentiated perceived salience across AI lifecycle stages, whereby stakeholders appear to weight visible, deployment-stage governance more heavily than upstream design and testing stage governance in forming trustworthiness judgments, rather than reflecting a definitive validation that governance effects accumulate uniformly across all stages in isolation. This study makes a distinctive contribution by providing empirical evidence for a governance-sequencing mechanism, whereby design-stage ethics function as a mediating pathway shaping the effectiveness of governance activity at later lifecycle stages. To the best of our knowledge, such a mediated, stage-sequential mechanism has not previously been demonstrated using primary data within a comprehensive, three-stage CERAi lifecycle framework linking organizational ethical and responsible governance practices to AI trustworthiness outcomes.
