An investigation on research and development cost reduction and channel strategies in competing supply chains

With the intensification of market competition, the competition form of firms is evolving from the competition among firms to the competition among supply chains. This paper considers a market with two competing supply chains consisting of one supplier and one manufacturer. The two supply chains compete on products’ quantities and research and development (R&D) level when the two manufacturers conduct technological innovation. This paper analyses the supply chain competition in three scenarios: two decentralized supply chains (DD), one decentralized supply chain and one centralized supply chain (DC) and two centralized supply chains (CC). The results indicate that the production quantity, the R&D level and the total profit of the integrated supply chain in DC scenario are the largest, CC scenario comes second, those of the DD scenario come third and those of the decentralized supply chain in DC scenario are the smallest. CC strategy is the supply chain system’s Nash equilibrium, which is good for the both supply chains, and there is no prisoner's dilemma

Do User Reviews Matter? Empirical Evidence on the Role of User Involvement in App Performance

The extant literature often presumed that user involvement was positively associated with software performance. In the context of mobile applications (apps), user reviews were collected to enlighten app developers on improvement of app quality through identifying bugs or suggesting new features. However, the value of user reviews varied a great deal due to their unmanageable volume and content irrelevance. In this study, over 40,000 user reviews with 50 apps were analyzed to empirically examine the association between customer led improvement and the revenues from the apps. Our findings indicated that customer led improvement produced significant increase in quarterly revenues. Greater growth in revenues was also observed if the developers responded to the user reviews faster. These results showed empirical support for the value of co-creation of apps with users, as customers could contribute to continuous improvement of the apps by providing experienced-based solutions

Examining the Impact of User Reviews On Mobile Applications Development

User reviews were often collected to enlighten mobile applications (apps) developers on areas for improvement and novel features. However, users might not always possess the required technical expertise to make commercially feasible suggestions. The value of user reviews also varied due to their unmanageable volume and content irrelevance. In our study, over 40,000 user reviews with 50 apps would be analyzed using Python coding and regression analysis to examine the impacts of innovation and improvement led by users on apps performance in terms of revenues and user ratings. The developers’ lead time in responding to user reviews would be included as a moderator to investigate whether apps performance would be enhanced if developers respond faster. Our study should represent one of the first few attempts in offering empirical confirmation of the value of co-creation of apps with users

Understanding the Effect of Tie Strength on Continuance Intention of Second-Generation Mobile Instant Messaging Services

Facilitated by the widespread adoption of smartphones, applications (apps) on smartphones such as WeChat and WhatsApp have seen rapid and explosive growth. These apps are generally referred to as second-generation mobile instant messaging (SMIM) services. Unlike first-generation mobile instant messaging (FMIM) services (e.g. Short Message Service), SMIM services typically support multimedia contents and are embedded within social networks, which may have a bearing on the post-adoption behaviour of users in particular. However, prior studies on the post-adoption usage of SMIM services have a limited understanding of the effects of social network. Network tie strength, as a configuration of social network, has an important impact on users in SMIM services. In order to explore the effects of social network on users’ continued usage intention in SMIM services, we propose and empirically test an integrated model by identifying the antecedents such as tie strength, satisfaction, and perceived critical mass. This study contributes to existing IS post-adoption literature by understanding and capturing the role of social network (i.e. tie strength) in SMIM services. Implications for theory and practice are discussed

Dual Adversarial Resilience for Collaborating Robust Underwater Image Enhancement and Perception

Due to the uneven scattering and absorption of different light wavelengths in aquatic environments, underwater images suffer from low visibility and clear color deviations. With the advancement of autonomous underwater vehicles, extensive research has been conducted on learning-based underwater enhancement algorithms. These works can generate visually pleasing enhanced images and mitigate the adverse effects of degraded images on subsequent perception tasks. However, learning-based methods are susceptible to the inherent fragility of adversarial attacks, causing significant disruption in results. In this work, we introduce a collaborative adversarial resilience network, dubbed CARNet, for underwater image enhancement and subsequent detection tasks. Concretely, we first introduce an invertible network with strong perturbation-perceptual abilities to isolate attacks from underwater images, preventing interference with image enhancement and perceptual tasks. Furthermore, we propose a synchronized attack training strategy with both visual-driven and perception-driven attacks enabling the network to discern and remove various types of attacks. Additionally, we incorporate an attack pattern discriminator to heighten the robustness of the network against different attacks. Extensive experiments demonstrate that the proposed method outputs visually appealing enhancement images and perform averagely 6.71% higher detection mAP than state-of-the-art methods.Comment: 9 pages, 9 figure

WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond

Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.Comment: 10 pages, 13 figure

Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration

Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception. In practice, manual calibration-based registration is the most widely used process, and it is regularly calibrated to maintain accuracy, which is time-consuming and labor-intensive. To cope with these problems, we propose a scene-adaptive infrared and visible image registration. Specifically, in regard of the discrepancy between multi-modality images, an invertible translation process is developed to establish a modality-invariant domain, which comprehensively embraces the feature intensity and distribution of both infrared and visible modalities. We employ homography to simulate the deformation between different planes and develop a hierarchical framework to rectify the deformation inferred from the proposed latent representation in a coarse-to-fine manner. For that, the advanced perception ability coupled with the residual estimation conducive to the regression of sparse offsets, and the alternate correlation search facilitates a more accurate correspondence matching. Moreover, we propose the first ground truth available misaligned infrared and visible image dataset, involving three synthetic sets and one real-world set. Extensive experiments validate the effectiveness of the proposed method against the state-of-the-arts, advancing the subsequent applications.Comment: 10 pages, 11 figure