Information Technology and Management最新文献

As the gamers market has a positive outlook in the post-pandemic period, the revenue of online games is expected to increase. User motivations and the pleasure-arousal-dominance (PAD) model are adopted in this study to explain why individuals continue to play a particular online game. User motivations (achievement, immersion, and social components) are utilized as antecedents of the PAD model, where achievement includes advancement and mechanics; immersion includes role-playing; and social includes socializing and relationship. Social influence and sunk costs act as moderators to examine the relationships between pleasure and players’ proactive stickiness. A total of 801 valid responses from online game players were collected and used for data analysis. The results revealed causation relationships among advancement, mechanics, escapism to dominance, and energetic arousal. Socializing influenced dominance with the relationship affecting energetic arousal. Furthermore, dominance influenced energetic arousal, and both affected pleasure, leading to the effect of proactive stickiness. Social influence and sunk costs were also proven to have moderating effects. It is suggested that gaming companies can utilize the proposed motivations to design the games in order to stimulate gamers’ emotional states and further increase their online game proactive stickiness.

Companies generate operational reports to measure business performance and evaluate discrepancies between actual outcomes and forecasts. Analysts comment on these reports to explain the causes of deviations. In this paper, we propose a machine learning-based framework to predict the commentaries from the operational data generated by a company. We use time series classification to predict labels for the existing commentaries, and compare various machine learning models for the prediction task including XGBoost, long short term memory networks and fully convolutional networks (FCN). Classification models are trained on three datasets and their performance is evaluated in terms of accuracy and F1-score. We consider AI interpretability as an additional component in our framework to better explain the predictions to the decision makers. Our numerical study shows that FCN architecture provides higher classification performance, and Class Activation Maps and SHAP interpretability methods provide intuitive explanations for the model predictions. We find that the proposed framework that is enabled by machine learning-based methods offers new avenues to leverage management information systems for providing insights to the managers on key financial issues including sales forecasting and inventory management.

While digitalization has become inevitable for firms of every size, a limited number of studies to date aimed to investigate the impact of digital capabilities and digital transformation on the organizational performance of small businesses. Drawing on the dynamic capabilities view, the current study analyzes the conditions under which the dynamic digital capability of a small and medium-sized enterprise (SME) would lead to higher performance. In this study, a unique fuzzy-set qualitative comparative analysis methodology was used for analyzing the data collected from 136 SMEs for investigating the IT utilization, human capital, digital maturity, and digitalization strategy antecedents of dynamic digital capability. The results reveal that two particular configurations of dynamic digital capability are identified as the main digitalization influencers of organizational performance in SMEs. To the best of our knowledge, this study presents the first empirical findings to the literature about dynamic digital capability and organizational performance relationships in SMEs through the utilization of configurational analysis methodology. Theoretically, the study addresses an acknowledged need for a holistic approach to uncover the underlying mechanisms of dynamic digital capability formation and digital transformation in small firms, with their impact on firm performance. The findings also present vital practical implications for business owners, policy-makers, and bodies responsible for SMEs, by providing new insights about the combination of factors that drive high performance, particularly at times of turbulence, in these units.

This paper presents the findings of a systematic literature review aimed at elucidating the key anchors, strategies, methods, and techniques pertinent to trust management (TM) in cybersecurity. Drawing upon a meticulous analysis of 106 scholarly papers published between 2001 and 2024, the review offers a comprehensive overview of TM in cybersecurity practices in diverse cyber contexts. The study identifies seven foundational anchors crucial for effective TM frameworks: authentication, authorization, access control, privacy protection, monitoring and auditing, encryption and cryptography, risk management, and iterative and interactive trust processes. Additionally, ten overarching strategies emerge from the synthesis of literature, encompassing identity and access management, role-based access control, least privilege principle, digital certificates or public key infrastructure, security policies and procedures, encryption and data protection, continuous monitoring and risk assessment, vendor and third-party risk management, compliance management and continuous collaboration. Furthermore, the review delineates several methods instrumental in TM processes, and various techniques augmenting these methods were also identified, ranging from trust scoring algorithms and trust aggregation mechanisms to trust reasoning engines and trust-aware routing protocols. The synthesis of literature not only elucidates the multifaceted nature of TM in cybersecurity presented in a framework but also underscores the evolving strategies and technologies employed to establish and maintain trust in dynamic digital ecosystems. By providing a comprehensive overview of anchors, strategies, methods, and techniques in TM in cybersecurity. This review offers valuable insights for practitioners, researchers, and policymakers engaged in enhancing trustworthiness and resilience in contemporary cyber environments.

Under the background of environmental protection, the government of China has issued a series of environmental regulations. More and more enterprises are using technological innovation to improve production quality and reduce environmental pollution. This makes the government and entrepreneurs pay more attention to strategic emerging industries. A strategic emerging industry is characterized by advanced technology, minimal resource consumption, and significant growth potential, and the number of enterprises related to strategic emerging industries also increases. Therefore, the impact of the intensity level of environmental regulation on technological innovation in strategic emerging industries has also become particularly important and of great research significance. In this paper, the panel data of 161 listed companies in China's industries from 2018 to 2021 are used to explore the influence of environmental regulations on their technological innovation by applying the Generalized Moment Method (GMM). The result confirms that the intensity level of environmental regulation and enterprise characteristics positively promote the technological innovation of strategic emerging industries.

The adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided.

Our paper analyzes the current state of research on artificial intelligence (AI) adoption from a production perspective. We represent a holistic view on the topic which is necessary to get a first understanding of AI in a production-context and to build a comprehensive view on the different dimensions as well as factors influencing its adoption. We review the scientific literature published between 2010 and May 2024 to analyze the current state of research on AI in production. Following a systematic approach to select relevant studies, our literature review is based on a sample of articles that contribute to production-specific AI adoption. Our results reveal that the topic has been emerging within the last years and that AI adoption research in production is to date still in an early stage. We are able to systematize and explain 35 factors with a significant role for AI adoption in production and classify the results in a framework. Based on the factor analysis, we establish a future research agenda that serves as a basis for future research and addresses open questions. Our paper provides an overview of the current state of the research on the adoption of AI in a production-specific context, which forms a basis for further studies as well as a starting point for a better understanding of the implementation of AI in practice.

Collaborative Filtering remains the most widely used recommendation algorithm due to its simplicity and effectiveness. However, most studies addressing the trade-off between accuracy and diversity in collaborative filtering recommendation algorithms focus solely on optimizing the recommendation list, often neglecting users’ diverse demands for recommendation results. We propose a new user-based Two-Stage collaborative filtering method for Neighborhood Selection (TSNS) that considers both the similarity between users and the dissimilarity between neighbors in the neighborhood selection phase. Firstly, we define the user’s preference value for the attributes of evaluated items and determine the range and ranking of user preferences. Then, we construct a preference heterogeneity model to evaluate preference differences among users and obtain a preference heterogeneity matrix based on the range and ranking of preferences. Finally, to effectively ensure recommendation accuracy and diversity, we adopt a two-stage neighborhood selection method to identify a group of neighbors that are internally dissimilar but similar to target users. Deep representation learning methods can also be incorporated into this framework to calculate user similarity in the first stage. Experimental results on two datasets show that our proposed method outperforms the benchmark method, including those using deep learning, in terms of comprehensive performance. Our approach offers new insights into improving the accuracy and diversity of personalized recommendations.

Despite extensive attention that researchers and practitioners have paid to supply chain digitalization, our understanding of how to leverage supply chain digitalization for superior supply chain performance remains limited. By adopting the theory of information processing, this research explores how supply chain digitalization affects supply chain performance through supply chain agility and how such relationships are moderated by environmental uncertainty. Using data collected from 143 companies in China, the current study finds the significant mediating role of supply chain agility and the moderating role of environmental uncertainty (environmental dynamism, munificence, and complexity). Furthermore, the mediating effect of supply chain agility on the relationship between supply chain digitalization and supply chain performance can be enhanced under high environmental dynamism and complexity. The research enhances the understanding of supply chain digitalization and provides managerial insights into how to leverage supply chain digitalization for improving supply chain performance.

This paper studies the Bundled Task Assignment Problem in Mobile Crowdsensing (BTAMC), a significant extension of the traditional Task Assignment Problem in Mobile Crowdsensing (TAMC). Unlike TAMC, BTAMC introduces a more realistic scenario where requesters present bundles of two tasks to the platform, giving the platform the flexibility to accept both tasks, accept one, or reject both. This added complexity reflects the multifaceted nature of task assignment in mobile crowdsensing. To address the challenges inherent in BTAMC, we examine two pricing strategies—discount and premium pricing—available to platform operators for pricing task bundles. Additionally, we delve into the critical issue of task quality, emphasizing the quality of workers assigned to each task. This is achieved by ensuring that the overall quality of workers assigned to each task consistently meets a predefined quality threshold, which, in turn, offers a more favorable outcome for all task requesters. The paper presents an integer programming formulation for the BTAMC. This formulation serves as the foundation for a Lagrangean-based solution approach, which has proven to be remarkably effective. Notably, it provides near-optimal solutions even for instances considerably larger than those traditionally encountered in the literature. These contributions offer valuable insights for platform operators and stakeholders in the mobile crowdsensing domain, presenting opportunities to augment profitability and enhance system performance.