With the growing attention to information security (infosec) in safeguarding data assets, more and more firms voluntarily disclose their infosec practices to cater to the market. Such disclosures not only affect the trust of investors but also have significant impacts on hackers’ behaviors. Previous studies empirically discussed how infosec disclosure affects firm value, but didn’t consider the authenticity of such disclosure. This study develops a game-theoretic model involving one firm and a strategic hacker, aiming to analyze how overstatement in infosec disclosure affects both firms and hackers. We find that when the firm discloses infosec practice, compared with the non-disclosure situation, the threat faced by the firm will decrease but the breach probability will increase, while the hacker’s effort and expected benefit will increase. When the firm overstates infosec practices, compared with honest disclosure, the threat and the breach probability will decrease, while the hacker’s effort and expected benefit will decrease. Moreover, we find that when the firm overstates the infosec practice, it increases other firms’ expected costs, resulting in a negative spillover effect in the industry.
The detection of material weaknesses in internal control (MWIC) of companies is essential since it provides early warning for various financial risks. Currently machine learning methods have been extensively adopted in this field. However, previous studies have not fully captured the relationships among different modalities, and neglected the consistency and complementary information. To address these challenges, we propose a Consistency and Complementary information-aware Multi-modal Deep Learning method (namely, CCMDL), which effectively captures the consistency and complementary relationships among different modalities. CCMDL comprises three main components, namely, the multi-modal feature extraction, the multi-modal feature enhancement and multi-modal feature fusion. Initially, multi-modal feature extraction module extracts valuable features and transforms them into deep representations. Subsequently, multi-modal feature enhancement module captures both the consistency and complementary information between these modalities, leveraging the joint effect of multi-modalities for MWIC detection. Finally, multi-modal feature fusion module introduces a novel pooling mechanism to detect MWIC. Through the experimental results on the real-world dataset, the proposed CCMDL outperforms the benchmark method in detecting MWIC. Our method enhances the effectiveness of detecting MWIC and provides robust decision-making support for regulators and investors.
After the sudden disaster occurred, the damage to communication infrastructure and road capacity can lead to an explosive increase in demand for emergency supplies, and the demands for different types of emergency supplies can vary depending on the period. Due to the poor communication between the affected area and the external environment, the demand generation pattern will be unpredictable. To address this challenge, we propose a novel anticipatory routing, acceptance, and postponement policy for the Multi-period Dynamic Vehicle Routing Problem with Stochastic Requests (MDVRPSR). We first provide a detailed description of the unique features of MDVRPSR, followed by the formulation of a mathematical model using Markov Decision Process (MDP). Our model is designed to respond to stochastic requests within a multi-period dynamic framework, offering a comprehensive perspective on the decision state, reward setting, transition process, and objective function. Subsequently, we employ Deep Reinforcement Learning (DRL) to optimize the routing policy. Experiments demonstrate that the DRL-based policy improves the effectiveness of emergency supplies distribution in dynamic and uncertain scenarios.
With the rapid growth of short video platforms, covert advertising has emerged as a key strategy for driving consumer behavior in e-commerce. However, existing research has primarily focused on explicit advertising and its effects on consumer attitudes, with limited exploration of how covert advertising in short video e-commerce influences impulse buying through psychological mechanisms like perceived entertainment and trust. To address this gap, this study investigates the impact of covert advertising features—creativity, content-congruity, and personalization—on consumers’ impulse buying intentions, based on the Stimulus-Organism-Response theory. Data were collected via a survey of active short video platform users and analyzed using structural equation modeling. This study extends the applicability of the Stimulus-Organism-Response framework in digital marketing contexts and offers practical insights for optimizing short video advertising design to enhance consumer engagement and conversion rates.
To investigate the pricing and power battery warranty decisions of new energy vehicle enterprises when the consumers have different perceptions of the battery warranty service, we consider a supply chain system in which a battery warranty service is provided by a new energy vehicle manufacturer. We first derive the optimal decisions of the new energy vehicle enterprise using the Nash equilibrium. Then, we conduct the sensitivity analysis on some critical parameters and compare the optimal profits of the new energy vehicle enterprise in different scenarios. The results show that the consumers’ sensitivity coefficient to the battery warranty service and the scaling parameter for battery failure distribution are related to the decision making of new energy vehicle enterprises. Based on the above study, we provide theoretical support for new energy vehicle enterprises to make more appropriate and profitable decisions.
