Channel selection in e-commerce age: a strategic analysis of co-op advertising models

Purpose: The purpose of this paper is to develop and compare two co-op advertising models: advertising model under traditional channel and co-op advertising model under dual channel, to select optimal channel structure to sell products for manufacturer and to derive optimal co-op advertising strategies for the manufacturer and the retailer. Design/methodology/approach: Stackelberg game theoretical is used to develop two co-op advertising models: co-op advertising model under traditional channel and co-op advertising model under dual channel. Then we compare the two models to select optimal channel structure to sell products for manufacturer and to derive optimal co-op advertising strategies for the manufacturer and the retailer. Furthermore, we analyze the impact of product web-fit on these optimal strategies and illustrate by some numeral examples. Based on our results, we provide some significant theories and managerial insights, and derive some probable paths of future research. Findings: We provide a framework for researching optimal co-op advertising strategies in a two-level supply chain considering different marketing channel structures. First, we discuss the traditional channel co-op adverting model and the dual channel co-op advertising model based on Stackelberg game theoretical, and we derive optimal co-op advertising strategies. Next, comparisons of these two channel structures are discussed and we find that the manufacturer always benefits from dual channel. But the retailer not always benefits from dual channel structure, and dual channel structure is better than retail channel with certain conditions. Also, the optimal co-op advertising strategies for the manufacturer and the retailer are obtained. Research limitations/implications: First, we focus on the aforementioned two channel structures; a further comparison with other channel structures can be investigated. Second, we ignore some factors that influence the demand of product, such as service and price. We can do some researches from the point of these factors. Third, how demand uncertainty affects the channel selection and co-op advertising strategy is another interesting research item. Practical implications: The manufacturer and the retailer know that the impact of co-op adverting on the demands of traditional channel and direct channel, both would like to choose reasonable strategies to improve the channel coordination. Therefore, it would be best if business managers conduct market survey before they start their co-op advertising campaign. Originality/value: Two new co-op advertising models in E-commerce age are developed, and the impact of product web-fit on these optimal strategies are analyzed and illustrate by some numeral examples. In addition, optimal channel structure in E-commerce age are selected for manufacturer and the retailerPeer Reviewe

A Network Resource Allocation Recommendation Method with An Improved Similarity Measure

Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias

Dynamic Stochastic Multi-Criteria Decision Making Method Based on Prospect Theory and Conjoint Analysis

A method based on prospect theory and conjoint analysis is proposed for dynamic stochastic multi-criteria decision making problems, in which the information about criteria weight is unknown and criteria values follow some kinds of distributions. Decision-maker’s attitude towards risk is introduced into this paper. First, data is collected by investigation and criteria weights are derived by conjoint analysis. The prospect values of each alternative in different periods are calculated according to distribution function. Then, index distribution decides time sequence weight, and overall prospect values of each alternative are obtained and ranked by aggregating prospect values in different periods. Finally, an example of choosing the best product illustrates the feasibility and effectiveness of this method

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From Capture to Display: A Survey on Volumetric Video

Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services poses significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.Comment: Submitte

Preparation and electrical properties of Mn silicides by reaction of MnCl2 and Si powder

AbstractMn-silicides have been synthesized by reaction between Si powder and MnCl2 vapour. The growth temperature varied from 400 °C to 500 °C. The resultant silicides powders were characterized by X-ray diffraction (XRD). By powder reaction, the silicidation temperature can be decreased to 400 °C. The dominant growth of HMS was obtained at the heating temperature of 500 °C for 36 h. The phase evolution was discussed and compared with bulk reaction. The electrical properties of Mn silicides tablets were measured

RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accurate results on the two subsets of textureless regions and large depth variation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2204.0132

Self-Consistent Density Estimation in the Presence of Errors-in-Variables

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