Purpose Existing studies have mainly examined the effects of IT or R&D investment on firm outcomes, but few have systematically assessed how these investments relate to corporate credit risk. This study aims to investigate how project-level IT investment and R&D investment, individually and interactively, influence corporate credit risk. Design/methodology/approach This study constructs a panel dataset from the CSMAR database, covering 1,446 firm year observations between 2012 and 2024, and employs fixed effects regression models to examine the direct and interactive effects of project level IT and R&D investments on corporate credit risk, incorporating lagged variables to capture delayed realizations of investment effects. This study also considers heterogeneity across IT system types and R&D level. Findings Project level IT investment and R&D investment are each significantly associated with lower corporate credit risk. Moreover, the interaction between IT and R&D is significantly associated with firms’ credit risk, with this association being more pronounced among R&D intensive firms. In addition, firms that deploy multiple types of information systems exhibit stronger IT R&D interaction associations with credit risk mitigation than firms relying on single system deployments. Research limitations/implications While this study provides valuable insights, several limitations warrant consideration. First, our measure of project level IT investment focuses on core enterprise systems and may not capture the full scope of firms' technology spending. Second, the reliance on manual text screening supported by “Jieba” segmentation constrained the sample size. Third, the analysis is based on publicly listed firms in China, which may limit the generalizability of the findings to other institutional contexts. Finally, although lagged specifications and instrumental variables are employed, endogeneity concerns cannot be fully ruled out. Practical implications From a managerial perspective, this study provides three implications. First, firms should recognize project level IT investments as more than operational expenditure: they directly improve credit standing and can lower financing costs. Second, managers should avoid short term cutbacks in R&D, since sustained innovation spending contributes to long run credit resilience. Third, our findings suggest that aligning IT and R&D strategically, and considering interactions across different IT systems, may provide added benefits in reducing credit risk, though managers should approach portfolio strategies with careful evaluation of costs and integration challenges. Originality/value This study contributes to the literature by offering nuanced evidence on the interplay between project level IT investment, R&D investment, and credit risk: firstly, this paper operationalizes project level IT investment using granular data on information system construction to better assess its impact on credit risk. Second, this research provides novel empirical evidence on the interaction between IT and R&D investments in mitigating credit risk. Third, this work uncovers heterogeneity effects across IT system types and R&D intensities.

Purpose This study investigates how digital sustainability leadership drives new revenue models in emerging market small medium-sized enterprises (SMEs), examining the mediating role of digital knowledge exploitation and moderating effects of radical green innovation. Design/methodology/approach Employing a sequential mixed-methods approach with qualitative interviews for theory development, followed by a multi-stage time-lagged quantitative study with data from 245 SMEs analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), PROCESS macro and fuzzy-set qualitative comparative analysis (fsQCA) methodologies. Findings This study finds that digital sustainability leadership positively affects new revenue models both directly and through digital knowledge exploitation as a critical mediating mechanism. Radical green innovation serves as a significant moderator, positively moderating the digital knowledge exploitation pathway. Practical implications The study provides managers with strategic guidance for developing complementary digital and sustainability capabilities to achieve innovative revenue positioning in environmentally conscious business landscapes, highlighting the nuanced role of green innovation in shaping digital transformation pathways for emerging market SMEs. Originality/value These findings extend theory by elucidating how sustainability-focused leadership maximizes digital capabilities for revenue innovation in resource-constrained environments and identifying specific mediation mechanisms through which sustainability initiatives translate into business model innovation.

Purpose This study investigates the phenomenon of algorithm aversion in AI systems. We examine whether AI agents exhibit preferences or aversions to algorithm-generated advice and explores how this aligns with or diverges from human behaviour in similar decision-making contexts. Design/methodology/approach The research replicates three seminal studies on algorithm aversion using GPT models as participants. Scenarios span forecasting tasks, advice-weighting experiments and healthcare recommendations. Findings GPT models exhibit algorithm aversion, preferring human advice over algorithmic inputs. However, the mechanisms driving this behaviour differ from humans. While humans resist algorithmic advice due to biases like uniqueness neglect, GPT models show a broader aversion rooted in performance comparisons. Model version and temperature also influence these preferences. Originality/value This study extends the understanding of algorithm aversion beyond human contexts to AI systems. By systematically comparing human and GPT behaviours, it highlights differences in how aversion manifests, providing insights for designing AI systems that integrate human and algorithmic inputs effectively.

Purpose Disaster immunity capability has been demonstrated to benefit firms during supply chain disruptions. However, research on how supply chain digitalization provides data to assist firms in achieving immunity capability is lacking. Supply chain digitalization focuses on the application of digital technologies (e.g. the Internet of Things (IoT), artificial intelligence (AI) and blockchain) to optimize supply chain operations. This paper aims to answer the research question: How can supply chain digitalization contribute to organizational disaster immunity capability? Design/methodology/approach We propose a research model based on dynamic capabilities theory to address the research question by exploring relationships between supply chain digitalization, process integration, innovation speed and disaster immunity capabilities. Two empirical studies, a survey and a vignette-based experiment, provide empirical support using data collected from manufacturing and service companies to test the proposed model. Findings The results demonstrate that supply chain digitalization significantly enhances disaster immunity capability through the mediating effects of supply chain process integration and innovation speed. Originality/value This study contributes to existing literature by introducing supply chain process integration and innovation speed as mediating variables that explain how digitalization leads to disaster immunity capability. This paper provides practical, actionable insights for organizations to prepare for future disasters and make informed decisions.

Purpose Although data element market development (DEMD) has been shown to influence enterprises’ supply chain network configurations (SCNCs), their causal relationship and impact mechanism have not been extensively explored in the existing literature. This study integrates theoretical analysis and empirical evidence to investigate the impact of DEMD on SCNCs from a holistic supply chain perspective and a unilateral viewpoint. Design/methodology/approach This study uses 2009–2023 data on Chinese publicly listed enterprises and adopts a difference-in-differences model to evaluate the influence of DEMD on SCNCs. Additionally, an interaction term model is constructed to analyze the underlying mechanisms. Findings The study results reveal that DEMD promotes diversification in SCNCs, thereby exerting a similar impact on supplier configuration (SC) and customer configuration (CC). Such an effect is further amplified by various factors, such as enterprises’ pricing power, the extent of government subsidies, and information asymmetry level. This impact also demonstrates heterogeneity across enterprises, depending on their competitive advantages, development stages and market environments. Originality/value This study innovatively analyzes the causal effects of DEMD on SC, CC and SCNC from the unilateral and holistic supply chain perspectives. The heterogeneity in response mechanisms driven by data elements (DEs) and the differentiated impacts across various scenarios are also investigated. The findings offer practical insights to help Chinese enterprises effectively leverage DEs, thereby enabling them to adjust and optimize their collaborative relationships with suppliers and customers.

Purpose Cross-border e-commerce has rapidly grown, with many companies adopting omni-channel strategies to unify online and offline shopping experiences. However, in multi-echelon distribution networks, firms face high inventory pressure due to cost differences between domestic and overseas warehouses, logistics uncertainty and demand fluctuations. Unpredictable replenishment cycles often lead to overstock or stockout. Thus, optimizing inventory allocation to adapt to dynamic demand while balancing cost and uncertainty is a critical challenge for cross-border e-commerce companies. Design/methodology/approach This study presents a cross-border multi-echelon inventory optimization model to address challenges in cross-border e-commerce supply chains. The model is solved using the Adaptive Genetic Basin Hopping (GABH) algorithm, which accounts for uncertainties in logistics, dynamic demand fluctuations and cost differences across distribution nodes. This approach efficiently balances inventory levels, demand and costs, providing an effective inventory management strategy. Findings The comparison with the SAGA algorithm shows that, under the same parameters, the GABH algorithm uses less computation time and achieves a lower optimal cost, demonstrating higher computational efficiency. Sensitivity analysis reveals that demand fluctuations, replenishment cycles and inventory configuration significantly impact costs. Increasing safety stock reduces stockout risks and replenishment costs, although it raises holding costs, still ultimately lowers overall costs. Originality/value The study shows that the GABH algorithm improves inventory optimization by reducing costs and boosting computational efficiency. It identifies how demand volatility, replenishment frequency and safety stock affect outcomes, offering practical guidance for responsive, cost-effective strategies in complex cross-border supply chains and supporting strategic inventory planning under uncertainty.

Purpose Visually impaired users face significant digital exclusion due to inaccessible interfaces, perpetuating barriers in education, employment and social participation. While assistive interfacing technology (AIT) is pivotal for accessibility, its design often misaligns with the capabilities of the visually impaired, culminating in high abandonment rates. This study reconceptualizes AIT design through affordance theory to bridge the gap between functional features and effective usage. Design/methodology/approach Based on a systematic literature review, we applied affordance theory to synthesize empirical insights regarding operational input and informational output of AIT. Design principles were derived from diverse prescriptions of AIT in past studies to reveal how operational input and informational output should be designed to maximize accessibility. Findings Focal principles in terms of operational input include context-aware modality selection, minimalist reusable actions and automated action sequencing to reduce cognitive load. Likewise, focal principles of informational output include multimodal redundancy, proactive granular disclosure and precision-calibrated feedback. Essentially, sensory compensation is essential in designing both operational input and informational output of AIT. We pioneer an affordance-centered framework that explicitly connects designer features of AIT with the capabilities of visually impaired users. Originality/value We refine theory by foregrounding nonvisual perception dynamics and delivering theoretically grounded insights to guide disability-centered human–computer interaction. Practitioners would benefit from our work by gleaning actionable principles that can be incorporated into the design of AIT to maximize accessibility to digital resources for the visually impaired.

Purpose Depressive tendency, a precursor to major depressive disorder (MDD), is frequently undetected by existing early-warning systems. Traditional methods, based on univariate metrics or subjective inventories, neglect the multidimensional interactions of biomarkers essential for accurate diagnosis. This study aims to enhance the accuracy of depressive tendency detection, thereby contributing to the reduction of MDD incidence and alleviation of its societal burden through early warning and exploratory intervention use. Design/methodology/approach This study proposed a multidimensional model for detecting depressive tendency, integrating emotional, behavioral, cognitive, physiological and temporal features into a contextual modeling framework. The model combines bidirectional long short-term memory with an attention mechanism to improve detection accuracy and robustness. It has also been integrated into a virtual psychological intervention platform to support user interaction and exploratory intervention use. Findings The proposed model effectively captures complex manifestations of depressive tendency, including emotional fluctuations, behavioral dynamics and physiological patterns, particularly in social media data. It outperforms existing models in detection accuracy. An exploratory study within a virtual psychological intervention platform demonstrates the model’s practical utility for early detection and intervention. Originality/value This study integrates multidimensional psychological and physiological signals into a contextual modeling framework, overcoming limitations of traditional MDD detection methods. The proposed model enhances early detection accuracy and generalizes well across social media datasets. Its practical utility in virtual intervention use highlights its potential for proactive and personalized mental health support.

Purpose The purpose of this paper is to present a comprehensive analysis of the enablers pertaining to synchromodal logistics, emphasizing the identification of key decision criteria and the determination of their relative importance. Design/methodology/approach This study is rooted in the Natural Resource-Based View, Dynamic Capability and Social Well-Being theories, which provide essential frameworks for analyzing the diverse and interrelated factors shaping synchromodal logistics. The research involves an extensive literature review and consultations with experts from academia and industry to identify 11 enablers of synchromodal logistics. Neutrosophic interpretive structural modeling and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement analysis are employed to analyze these enablers, leveraging the advantages of neutrosophic sets for handling ambiguous, unpredictable and indeterminate information. Findings The analysis identified “Electronic Data Exchange” and “Legal Political Framework” as crucial focal points for the successful implementation of synchromodal logistics. These elements were found to be essential for achieving the necessary synchronization and flexibility within transportation networks. Research limitations/implications The study's reliance on expert consultations and literature review may introduce subjectivity and may not encompass all potential enablers of synchromodal logistics. Future research could expand on these findings by incorporating a broader range of expert opinions and empirical data. Practical implications The findings provide valuable insights for stakeholders in the logistics industry, highlighting the importance of focusing on “electronic data exchange” and “legal political frameworks” to enhance the efficiency and effectiveness of synchromodal logistics. Originality/value This paper contributes to the existing body of knowledge by introducing a nuanced approach using neutrosophic sets to handle uncertain and vague data in the analysis of synchromodal logistics enablers, surpassing the limitations of fuzzy sets and offering greater flexibility in dealing with indeterminate information.

Purpose This research employs innovative deep learning techniques to perform sentiment analysis on airline customer feedback, systematically investigating service quality dimensions and customer satisfaction determinants through the theoretical lens of the SERVQUAL framework. Design/methodology/approach We analyzed airline reviews from TripAdvisor and Skytrax from July 2014 to July 2023 using SERVQUAL theory. Coarse-grained clustering revealed themes in tangibles and reliability, while fine-grained sentiment analysis, using our FusionBERT model, assessed responsiveness, assurance and empathy for deeper service quality insights. Findings Our approach outperformed baselines, offering a nuanced analysis of airline customer satisfaction using the SERVQUAL theory. Coarse-grained sentiment analysis assesses tangibles and reliability (facilities, service consistency), while fine-grained analysis evaluates responsiveness, assurance, and empathy (staff promptness, professionalism and care). This dual-layered method enables comprehensive service quality evaluation, helping airlines identify strengths and improvement areas. Originality/value This research provides actionable management solutions and recommendations for airline managers.