A 25-year bibliometric retrospective of Information Technology and Management (ITM) characterizes publication growth, citation structure, knowledge foundations, and thematic evolution. A Scopus corpus of 398 citable documents (articles, reviews, and conference papers) for 2000–2024 underpins all ranked performance tables and keyword analyses, complemented by WoS (2007–2024) for science-mapping networks in VOSviewer. The workflow follows a SPAR-4-SLR design and combines productivity and impact indicators with co-citation (journals and documents), bibliographic coupling (documents, authors, institutions, and countries), and keyword co-occurrence analyses implemented via VOSviewer and bibliometrix/biblioshiny, alongside SciVal topic-cluster signals for 2015–2024. Results show stepwise changes in annual output, with a pronounced expansion around 2011–2012 and subsequent stabilization. Citation distributions are highly skewed, with early cohorts containing the strongest outliers and the highest citation density, while recent cohorts exhibit tighter dispersion consistent with shorter citation windows. ITM’s citation canon is anchored in trust, privacy, technology adoption, and IT value creation, while high-velocity recent contributions align with payments, logistics, analytics, and domain-specific IS applications. WoS co-citation structures place ITM at the intersection of core IS journals, decision analytics, enterprise/industrial systems, and behavioral/marketing outlets. Country and institutional patterns indicate a persistent US hub and a marked post-2012 rise in China, reinforcing a dense US–China collaboration axis. SciVal topic clusters highlight strong recent alignment with globally prominent fronts in digital consumer behavior and digital innovation, with particularly high field-weighted impact in recommendation-oriented and selected human-centered analytics clusters. Collectively, the findings firmly position ITM as a mature, internationally integrated journal with durable socio-technical pillars and evolving methodological and topical breadth.
Advanced technologies like Artificial Intelligence (AI) have shown great potential in achieving Sustainable Development Goals (SDGs), yet, little is known on how they influence the key factors that drive firm SDG achievement. This study conducts a systematic literature review and synthesizes scholarly articles, identifying nine theoretical concepts and presenting testable propositions. It argues that the extent of AI implementation strengthens both the internal and external factors driving SDG achievement, ultimately enhancing firm’s environmental and financial performance. The study also identifies key theories and perspectives used in extant literature and proposes multiple research questions to guide future research. Our findings hold implications for practitioners and scholars, demonstrating how organizations can effectively leverage AI for SDG achievement while also benefitting from superior environmental and financial performance.
Internet of Things (IoT) services have developed rapidly in recent years. However, existing research has not yet proposed a mature method for quantitatively evaluating IoT services, nor has it correctly considered the impact of cooperation and competition among IoT service providers on IoT service pricing strategies. This paper constructs a duopolistic Cournot competition model comprising data providers, service providers, and consumers. IoT service providers can utilize machine learning to train models that generate user-oriented IoT services, then market and monetize them, thereby generating profits. This paper introduces a logistic-curve-based service quality function, using the amount of data employed for machine learning as the principal metric of IoT service quality. We then describe the competitive behavior between IoT service providers in a Cournot duopoly market and derive the IoT service pricing and machine learning training data procurement strategies that maximize profits for both parties. Subsequently, we explore the possibility of bundling IoT services at discounted prices and categorize the scenarios into four cases based on the relationship between service quality and pricing. We calculate the pricing of the IoT service bundle and the optimal data quantities for both parties to maximize their profits. Finally, we incorporate a price fluctuation factor to account for market uncertainty, refining the profit function model to better reflect actual market dynamics.
Accurately identifying core technologies is essential for driving technological progress, informing strategic decision-making, and enhancing industrial competitiveness. Existing methods, ranging from expert assessments and quantitative analyses to machine learning techniques, are often hampered by limited adaptability, shallow semantic understanding, and poor time efficiency. To overcome these limitations, we introduce a novel and universally applicable framework that integrates continuous pre-training with multi-task curriculum learning to significantly improve the ability of large language models (LLMs) to identify core technologies and their associated International Patent Classification (IPC) codes across diverse domains. By incorporating extensive, multi-source domain knowledge within a unified continuous pre-training and fine-tuning pipeline, our approach achieves markedly superior semantic comprehension and identification accuracy relative to conventional methods, offering a scalable and effective solution for real-time core technology identification across a wide range of technological domains.
This paper explores the critical role of trust in logistics collaboration within the context of the Physical Internet (PI). As logistics concepts evolve towards greater cooperation and integration, establishing trust becomes a fundamental challenge that obstructs information sharing and operational efficiency. This paper aims to: 1) provide a comprehensive overview of trust development in the context of PI in the industry, 2) propose a two-stage trust-building framework with selected factors for PI’s context, and 3) suggest the necessary actions to be undertaken. We review existing literature on trust, highlighting its importance in promoting collaboration among stakeholders and the innovative mechanisms that can be implemented to build trust in logistics systems. Furthermore, we define trust in logistics settings, assess logistics companies’ perceptions of PI across various roles through a survey, and identify necessary initiatives to enhance PI and automated data-sharing in the future. Our findings confirm the distinction between initial trust and developing trust, the reinforcing effects of collaboration on trust building, and the varying understanding of PI and automated data sharing in the industry nowadays. We also emphasise the importance of an interconnected, data-driven logistics framework and propose avenues for future research and practical application toward achieving substantial trust in logistics operations.
While artificial intelligence (AI) adoption is increasingly critical, firms often exhibit a divergence between symbolic adoption and substantive action. This study investigates this opportunistic gap, conceptualized as AI washing. Drawing on the resource-based view, we examine how different types of organizational slack influence AI washing based on a large sample of Chinese listed firms. The empirical results reveal an asymmetric effect. Unabsorbed slack provides high resource flexibility that effectively suppresses AI washing. Conversely, absorbed slack induces resource rigidity, which is associated with an inverted U-shaped relationship between absorbed slack and AI washing. Furthermore, boundary conditions such as asset specificity, as well as financial constraints significantly moderate these relationships. By shifting the research focus toward the organizational drivers of AI washing, this study offers critical insights into bridging the gap between symbolic adoption and substantive action.
Competitive firms may share their digital resource to improve their efficiency in the industries of banks, insurance and hospitals. Their decisions about information security are important, but fail to receive enough attention. This paper examines two competitive firms’ information security investment and security information sharing through building a game-theoretic model in which these firms interact with one strategic hacker who launches cyber-attacks against them. We can obtain the following interesting findings: (a) when the efficiency of security information sharing remains low, resource sharing may hurt the firms because the security investment is restricted; (b) the firms may benefit from security competition that would urge security investment to effectively defend against cyber-attacks; (c) the firms may suffer from government intervention to raise their security awareness in that security information sharing is inhibited. We further discuss how the equilibrium outcomes of the firms and the hacker change when security decisions of the firms are made in a centralized way and made prior to cyber-attacks of the hacker respectively.
In recent years, technological change through the use of artificial intelligence (AI) has been an inevitable strategy for information technology (IT) artifacts to better meet individualized needs and provide smart services. However, AI-driven technological change differs from previous technological changes because it has been an emerging, disruptive technological change that aims to make accepted IT smart. It may bring significant sociotechnical changes in existing IT artifacts that require user adaptation to such technological changes, which may hinder the success of the technological change strategy. This paper investigates how IT identity inspires user adaptation to AI-driven technological change in existing IT artifacts in terms of three dimensions: emotional energy, relatedness, and dependence. Drawing on the commitment to technological change (CTC) theory, we found that two dimensions of CTC (affective CTC and continuous CTC) could explain the underlying mechanism between IT identity and user adaptation in existing IT artifacts. Emotional energy, relatedness, and dependence have different effects on the two dimensions of CTC, leading to different influences on user adaptation. The findings can provide new insights for service providers of IT artifacts to integrate AI artifacts into IoT-based smart services.
High loan default rates pose a significant challenge to the sustainability of microfinance institutions (MFIs). While digital interventions offer promising solutions, their effectiveness, particularly in designing text message reminders, remains underexplored in this context. This study investigates the impact of text message reminders, designed based on Construal Level Theory (CLT), on loan repayment behavior in microfinance. Through a randomized 2 × 2 factorial field experiment involving 767 active borrowers of Addis Ababa Credit and Saving Institution (ADCSI), Ethiopia’s third-largest MFI, we manipulated two key dimensions of psychological distance: temporal distance (reminders sent 1 day vs. 10 days before the due date) and informational distance (messages with vs. without loan amount disclosure). Our findings reveal that text message reminders significantly increase loan repayment rates (25.5
This study aims to discuss the impact of FinTech on corporate financialization and its governance measures, revealing the dominant motive for over-financialization among Chinese enterprises. It finds that the primary motivation behind financialization among Chinese enterprises is the pursuit of profits rather than risk aversion. FinTech exacerbates this motivation by improving financial investment returns, thus enhancing the level of corporate financialization. Furthermore, FinTech exacerbates the degree of corporate over-financialization, significantly boosting the financial investment levels of enterprises that excessively hold financial investment portfolios. The external and internal governance of enterprises can be potential solutions to mitigate FinTech-induced corporate financialization. The impact of FinTech-induced corporate financialization can be alleviated through institutional investor ownership and high-quality external audits. Also, executive compensation incentives and high information transparency can reduce the financialization effects caused by FinTech.
Online Q A communities have gained traction for their role in disseminating high-value information in the form of question-and-answer. While prior research has examined factors influencing answer helpfulness, the role of consistency between questions and answers remains underexplored. Based on the Heuristic-Systematic Model, this study explores how sentiment and thematic consistency function as heuristic and systematic cues respectively to shape answer helpfulness. Using a dataset of 50,525 answers from Zhihu.com, we find that thematic consistency is positively related to answer helpfulness, whereas sentiment consistency is negatively related to it. Additionally, based on identity theory, we examine how social and relational identity moderate these effects. This study extends prior research by integrating content consistency and identity perspectives to explain content evaluation in Q A platforms. It differentiates cognitive routes via the Heuristic-Systematic Model and reveals that identity dimensions significantly shape users’ processing of different consistency cues. These offer valuable insights for platform managers and content creators, highlighting strategies to increase perceived answer helpfulness in online Q A communities.
User-generated content (UGC), such as health-related answers, is critical in online health communities, and financial incentives are a practical tool designed to motivate such content generation behaviors. Although the effect of financial incentives on content quality has been extensively investigated, the empirical findings remain inconsistent, suggesting a need for further study. Drawing on cognitive evaluation theory and the effects of social pressure, we develop a conceptual framework to examine the impacts of financial incentives on answer quality across three dimensions, as measured by linguistic features. We use an archival dataset from a popular online health Question-and-Answer (Q A) community and construct question-answer pairs to conduct empirical analysis. Our findings reveal that financial incentives exert different effects on the three dimensions of answer quality: they increase information value and source credibility but reduce socio-emotional support. These results contribute to a more nuanced theoretical understanding of how financial incentives influence answer quality and offer an explanation for the inconsistent findings of prior research. Furthermore, our findings provide practical guidance for the design of financial incentive schemes.
This study proposes a novel multi-stage artificial intelligence (AI) methodology to predict the key drivers of integrated reporting (IR) quality (IRQ). Regarding the violation of the normality assumption in traditional methods, this study integrates clustering (K-means++), prediction (random forest, decision tree, extreme gradient boosting), and model explanation (Shapley additive explanations) techniques of AI in a consecutive way. It brings a methodological novelty, while providing more consistent results over traditional methods. Using a sample of 260 integrated reports published in 2019 and considering each pillar of the IR framework, namely, Fundamental Concepts (FC), Guiding Principles (GP), and Content Elements (CE), the model achieves strong predictive performance, with Random Forest Regressor explaining 67
The growing consumer focus on information security has shifted firm competition from price alone to a dual focus on price and security. A firm’s security efforts indirectly affect competitor demand through two opposing channels: a negative competitive effect and a positive cross effect. The cross effect arises when the firm’s efforts enhance overall industry security and is modulated by their investment overlap. However, the interplay of these effects remains underexplored in existing literature. This paper examines the strategic interaction on both product price and security efforts between two competitive firms when these effects are taken into account. We find that, faced with the competitor’s increased efforts, firms could charge a higher price when the overlap degree is under a threshold and vice versa. Moreover, the effects of cross and overlap are opposite on firms’ decisions and expected payoffs. If a firm fails to recognize the impact of the cross and overlap effect explicitly, it may underinvest or overinvest in security efforts, leading to a loss in its payoff. To address the distortion decision problem, we propose a cooperation mechanism, which rewards or refunds the firm from its competitor based on the security efforts it exerts. We find that firms will achieve the social security efforts under the mechanism when setting the reward rate appropriately, and especially, the reward rate is positive (negative) when the overlap degree is relatively low (high). Last, we extend the model to an asymmetric case to make our model more general.
As firms increasingly rely on interconnected information systems to carry out business activities, both firms’ security investments and hackers’ attack strategies are influenced by security externality. Meanwhile, while the government often imposes mandatory standards that require firms to meet minimum security investment thresholds, a firm’s true security investment may not be fully observable due to varying degrees of technological transparency. In this study, we develop a game-theoretic model with two firms and a strategic hacker to examine their interaction under both security externality and partially or fully verifiable regimes. First, our findings indicate that the positive externality reduces firms’ incentives for security investment, while the negative externality enhances their incentives. Next, with the tightening of mandatory standards, both firms will raise investments for the fully verifiable regime. However, regarding the partially verifiable regime, verifiable firms keep increasing security investments, whereas non-verifiable firms’ investments are motivated exclusively by the security externality. Finally, our study shows that excessively stringent mandatory standards do not necessarily enhance the aggregate information security performance of firms.
Understanding users’ continuance intention toward Artificial Intelligence-Generated Content (AIGC) tools is crucial, given the increasing market competition and the significant economic and societal benefits associated with these technologies. Existing research on factors influencing continuance intention remains limited, especially from the perspective of technical features and perceived value. This study addresses this gap by developing a conceptual model based on the Stimulus-Organism-Response (SOR) model and perceived value theory. It hypothesizes that technical features (Creative Quality, Interaction Quality, Aesthetic Quality and Empathy) directly and indirectly influence continuance intention through perceived value (Perceived Emotional Value and Perceived Functional Value), and to further examine the relationships between empathy and other technical characteristic dimensions (Creative Quality and Interaction Quality). Using data from 476 Chinese users of text-based AIGC tools engaged in creative activities, Partial Least Squares Structural Equation Modeling (PLS-SEM) tests the conceptual model. The findings reveal that Creative Quality, Aesthetic Quality, Perceived Emotional Value, and Perceived Functional Value significantly influence continuance intention. Furthermore, Perceived Emotional Value mediates the impact of Creative Quality and Aesthetic Quality, while Perceived Functional Value mediates the effects of Creative Quality, Interaction Quality, and Aesthetic Quality on continuance intention. Empathy exerts a significant positive effect on enhancing the Creative Quality and Interaction Quality of AIGC tools. This study provides novel insights into mechanisms driving continuance intention from the perspective of technical features, offering strategic recommendations for AIGC tool developers to foster users’ continued engagement.
Artificial intelligence plays a critical role in automating jobs. Early application of artificial intelligence primarily relies on expert systems to automate routine jobs that follow predetermined rules. Recent advancement in machine learning (ML) has ushered in a new and fundamentally different mechanism for job automation, in which rules are derived by ML models from data and then applied to automation. In this research, we extend existing studies on job automation and argue for the necessity of explicitly incorporating both mechanisms of job automation: task routineness and ML model appropriateness. We demonstrate that expert systems are mainly used for automating routine jobs, whereas model appropriateness is more effective for automating nonroutine jobs. Furthermore, the consequence of error exhibits an inverted-U shaped relationship with job automation, and this relationship varies across different types of jobs. Our study sheds new light on the automation-augmentation paradox and offers both theoretical insights and practical guidance for managers making AI-enabled automation decisions.
Existing research on live video recommendation primarily focuses on predicting the user’s next click on a live video, neglecting the significance of user engagement. This paper proposes an algorithm based on Expectation Confirmation Theory (ECT) to predict user engagement by modeling user expectation and perceived experience. User expectation is determined by the historical experience of similar live videos, while the perceived experience depends on whether the target video aligns with the user’s evolving content preferences. To effectively model the evolution of user preferences, this paper employs a multi-head causal self-attention mechanism to capture user preferences and uses historical engagement sequences to control the mask matrix, capturing users’ dynamic preferences. Finally, this paper integrates user expectation and perceived experience to predict engagement for each target video. To evaluate the performance of the proposed recommendation algorithm, experiments are conducted on a real-world live video dataset based on user’s viewing behavior. The results demonstrate that the proposed algorithm outperforms baselines in both predicting user engagement and Top-N recommendation tasks. Moreover, this paper conducts several experiments to validate the robustness of the proposed recommendation algorithm and finally empirically tested the impact of experience and perceived experience on user engagement.
With the rapid growth of music streaming platforms, effective music auto-tagging has become crucial for Music Information Retrieval (MIR) and recommendation. However, existing approaches face significant limitations: single-modality methods, which use only audio or text features, fail to capture the rich semantic diversity of music tags, while current multimodal approaches overlook critical interactive relationships beyond music content. Moreover, most studies ignore the co-occurrence dependencies among tags, which are essential for multi-label prediction. To address these challenges, we propose MuCoGraph, a novel multimodal graph-based hybrid learning framework for music auto-tagging. Our approach integrates multiple data modalities—lyrics, user comments, and audio spectrograms—with three heterogeneous graph neural networks: preference graphs that capture artist-listener interactions, group graphs that model content similarities, and tag co-occurrence graphs that learn label dependencies. The framework employs hierarchical co-attention mechanisms that enable cross-modal feature enhancement, allowing graph-based features to strengthen textual and audio representations through mutual learning. Experiments were conducted on a real-world dataset, which we integrated from multiple online platforms, demonstrating that MuCoGraph outperforms all the compared baseline methods in music auto-tagging. Notably, MuCoGraph achieves the most substantial improvements in top-12 recommendations, with relative gains of 12
