The rapid advancement of artificial intelligence technologies presents transformative potential alongside unprecedented cybersecurity challenges. Despite widespread industry concerns, empirical evidence establishing this relationship remains limited. Drawing upon Routine Activity Theory, this study examines the impact of artificial intelligence implementation on cybersecurity risks within the information technology sector, and employs an instrumental variable approach to establish causality and address endogeneity. We unpack the framework along three dimensions. First, using a large language model to classify disclosure contexts, we find that only substantive AI implementation, not pre-implementation signaling, raises cybersecurity risk. Second, the risk profile is heterogeneous. We find that frontier AI increases cybersecurity risk, while prior experience with fundamental AI attenuates this effect. Third, for realized breaches, general AI implementation increases externally hacked breaches but not overall breach likelihood, whereas frontier AI raises overall breach likelihood, external hacking, and accidental internal breaches. Malicious insider breaches show no such association. This study provides a causal architecture for understanding artificial intelligence-cybersecurity dynamics, highlighting the necessity for continuous strategic adaptation in the field of cybersecurity and underscoring the importance of striking a balance between artificial intelligence implementation and cybersecurity to ensure a secure and responsible digital ecosystem.
Digital product passports (DPPs) are a product-service innovation designed to increase transparency on product sustainability and support circular economy practices by providing consumers with lifecycle-wide information on materials, repairability, and end-of-life options. However, empirical evidence on which factors drive not only initial uptake but also regular use of DPPs is scarce. Building on the norm activation model (NAM) and UTAUT2, this paper investigates antecedents of consumers’ regular use intentions for DPPs and examines differences between pre-purchase and post-purchase scenarios. We conducted two scenario-based online surveys with German (n = 344) and Spanish (n = 195) consumers and analyzed the data using partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA), and a qualitative analysis of open-ended responses. Personal norms, performance expectancy, and anticipated habit emerge as main drivers of regular use intentions, with performance expectancy particularly relevant in the post-purchase phase. The NCA identifies anticipated habit as a necessary condition for regular use across scenarios and samples, whereas the necessity of effort expectancy and performance expectancy is scenario-specific; qualitative responses likewise highlight usability as a key prerequisite. Overall, regular use intentions are stronger in the post-purchase context. This study contributes to research by (1) providing the first theoretically grounded examination of consumers’ regular use intentions for DPPs across pre- and post-purchase stages, (2) showing that normative beliefs explain regular use intentions alongside technology acceptance constructs, and (3) identifying both sufficient and necessary conditions for regular DPP use. Our findings offer actionable implications for policymakers and practitioners advancing DPP implementation.
Immersive commerce is attracting strong managerial attention, yet many initiatives do not progress beyond pilots—especially in platform-dominated mobile markets. This study examines how Chinese e-commerce and digital marketing firms evaluate immersive marketing and metaverse-enabled commerce in super-app ecosystems. Using the technology–organization–environment (TOE) framework as a theory-informed organizing lens, we draw on 33 semi-structured interviews with senior decision-makers and analyze the data through a hybrid codebook thematic approach. The findings demonstrate that adoption is not best understood as a standalone technology decision. Rather, movement from experimentation to scale is conditioned by platform governance, monetization logics, and ecosystem access. Technological, organizational, and environmental drivers remain important, but their commercial effects are reweighted by four ecosystem conditions: super-app lock-in, platform governance and monetization norms, cultural acceptance of digital humans, and guanxi-enabled partner access. Across cases, immersive initiatives moved beyond pilots only when firms could align a credible return on investment pathway, workable mobile-first integration, and dependable platform or partner access. The paper contributes by reframing immersive commerce adoption as platform-mediated conditional scaling, by showing how China’s super-app context sharpens rather than replaces TOE logics, and by connecting Chinese-driven technologies to broader debates on platform ecosystems, governance, and electronic markets.
As generative AI (GenAI) becomes increasingly embedded in workplaces, understanding its impact on the future of work is important. Drawing on social cognitive theory and the technology-organization-environment framework, this study develops a conceptual framework examining the associations among perceived regulatory influence, perceived organizational support, perceived GenAI usefulness, perceived applied AI literacy, and managerial job satisfaction. Using survey data from 310 managers in China and structural equation modeling, we find that perceived usefulness of GenAI and perceived applied AI literacy are positively associated with managerial job satisfaction. The positive association between perceived usefulness and job satisfaction is stronger among managers reporting higher applied AI literacy. Perceived organizational support is positively associated with perceived GenAI usefulness, while perceived regulatory influence has a modest positive association with perceived organizational support. The study contributes to research on human-AI collaboration and digital work by linking managers’ evaluations of GenAI to their job satisfaction and identifying perceived operational and evaluative capabilities as a condition that strengthens this relationship.
Effective risk management is crucial yet challenging in modern projects because early warning signals often surface in informal, unstructured data sources and evolve as project contexts change. Since prevailing project risk management (PRM) practices largely depend on structured inputs and episodic control routines, they can miss these subtle signals, creating a temporal detection gap between risk emergence and formal identification. This paper introduces an LLM-based architecture for information extraction in PRM. Following a Design Science Research approach, we design, instantiate, and evaluate it, governed by two complementary flows: an Information Flow that continuously transforms heterogeneous project communication into structured, evidence-linked risk indicators through domain-specialized extraction and cross-source reconciliation, and an Extraction Control Flow that versions extraction behavior to remain adaptable, transparent, and auditable. We abstract four nascent design principles capturing transferable design knowledge for LLM-based information extraction in dynamic, context-rich domains.
