The Impact of CRM on Firm- andRelationship-Level Performance in Distributed Networks

This paper develops and empirically tests a model to evaluate a manufacturer\u27s strategy which provides customer relationship management (CRM) technology to its exclusive retailers. The impact of the strategy on manufacturer-retailer relationship quality is also examined. The research objectives are (1) to identify and test factors that promote active implementation of CRM technology among small retail organizations; (2) to determine whether our expanded concept of CRM implementation that integrates customer information management activities and relationship marketing activities explains CRM performance better; and (3) to investigate whether a manufacturer\u27s support contributes to manufacturer-retailer relationship quality. Statistical analysis shows that the model provides an adequate fit to the data. The retailer\u27s perception of the importance of customer information, manufacturer support, and trade area competitiveness significantly impacts the intensity of CRM implementation by small retailers. CRM implementation intensity positively influences the performance outcomes of CRM, which in turn greatly improves the quality of the manufacturer-retailer relationship. Different from our expectation, supporting retailers with CRM technology did not directly impact the manufacturer-retailer relationship quality. The ease of use of the CRM system also did not influence CRM implementation intensity significantly. The implications of these results and their importance for successful CRM implementation are discussed

Neuronal ensemble decoding using a dynamical maximum entropy model

As advances in neurotechnology allow us to access the ensemble activity of multiple neurons simultaneously, many neurophysiologic studies have investigated how to decode neuronal ensemble activity. Neuronal ensemble activity from different brain regions exhibits a variety of characteristics, requiring substantially different decoding approaches. Among various models, a maximum entropy decoder is known to exploit not only individual firing activity but also interactions between neurons, extracting information more accurately for the cases with persistent neuronal activity and/or low-frequency firing activity. However, it does not consider temporal changes in neuronal states and therefore would be susceptible to poor performance for nonstationary neuronal information processing. To address this issue, we develop a novel decoder that extends a maximum entropy decoder to take time-varying neural information into account. This decoder blends a dynamical system model of neural networks into the maximum entropy model to better suit for nonstationary circumstances. From two simulation studies, we demonstrate that the proposed dynamic maximum entropy decoder could cope well with time-varying information, which the conventional maximum entropy decoder could not achieve. The results suggest that the proposed decoder may be able to infer neural information more effectively as it exploits dynamical properties of underlying neural networks.open0

Image Captioning with Very Scarce Supervised Data: Adversarial Semi-Supervised Learning Approach

Constructing an organized dataset comprised of a large number of images and several captions for each image is a laborious task, which requires vast human effort. On the other hand, collecting a large number of images and sentences separately may be immensely easier. In this paper, we develop a novel data-efficient semi-supervised framework for training an image captioning model. We leverage massive unpaired image and caption data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples via Generative Adversarial Networks to learn the joint distribution of image and caption. To evaluate, we construct scarcely-paired COCO dataset, a modified version of MS COCO caption dataset. The empirical results show the effectiveness of our method compared to several strong baselines, especially when the amount of the paired samples are scarce.Comment: EMNLP 2019. Project page : https://sites.google.com/view/emnlp19scarcecaptio

Visual content analysis of visitors’ engagement with an instagrammable exhibition

This study aims to show how a museum exhibition designed to encourage visitors to take pictures can affect visitors’ behavior. We analyzed visitors’ engagement with the Yumi's Cell Special Exhibition (hereafter Yumi) held in South Korea, employing computer vision for the analysis of visitors’ Instagram pictures. Our research questions are: What types of pictures do visitors post on Instagram during or after their visit? And can Yumi’s instagrammable features make visitors interact more with the exhibition? We also formulated two corresponding hypotheses: Visitors are primarily interested in taking selfies in the instagrammable environment; and visitors struck more active poses when taking pictures in an instagrammable exhibition than in a traditional art exhibition. Through the image analysis of Instagram posts of the exhibition, we found many pictures of people, but the proportion of selfies was relatively limited. This suggests that visitors were more interested in interacting with the exhibition rather than taking selfies. This has also been confirmed by the pose analysis, which showed that the participatory feature of the exhibition encouraged visitors to take photos in active poses, interacting, mimicking, and performing. The framework presented and the findings offer insights about how to design exhibitions to increase visitors’ participation