关于规避旅游项目重复建设的思考

近年来旅游业的快速发展与地方招商引资的驱动,使得方兴未艾的旅游项目建设存在或隐藏着重复建设问题。旅游产品同质化、结构的雷同,不仅引起区内无序竞争,也造成旅游资源、生态环境的严重破坏和社会资源的巨大浪费。地方政府与旅游行政管理机构应从旅游产业市场角度,对投资主体进行项目建设指导和运营管理,从行政政策、行业管理机制入手,通过宏观的和微观的措施对旅游项目进行有效控制和规避,才能保证旅游项目的合理化建设。     

河南省旅游市场产业转移面临的新问题及对策探讨

河南具有丰富的旅游资源,要实现在旅游业产业转移背景下的旅游业大发展,应当从产业部门整合、资源整合、编制规划、产品深层次开发和招商引资等方面着手。     

城市品牌塑造中合肥旅游网站的构架研究

合肥市是安徽省的省会城市,境内旅游景点众多,历史文化资源和自然资源丰富,旅游业发展前景广阔。然而合肥旅游业与众多旅游发展较好城市相对比,任有较大的差异。如合肥旅游产业开发不完善,其内容形式相对单调,旅游产品也相对匮乏。因此,合肥旅游业需从传统的旅游模式中脱离出来,改革创新,开发创新型服务项目,发展创新型旅游业。综上,我们选择架构合肥线上旅游网站这一创新型项目,将线上方便快捷的优势与线下真实可触摸的特点相结合,优势互补,在此基础上开发更多的服务功能和服务项目。     

青海特色旅游资源正在积极开发

去过青海旅游的国内外游客 ,对青藏高原上那蓝天、白云、绿草地和水天一色的青海湖、神秘的藏传佛教寺院以及世界上第一个退役的原子城基地等自然、人文景观 ,无不流连忘返 ,竖起拇指称赞青海是一个美丽的地方。近年来 ,青海省旅游业依托独特的资源优势得到了一定的发展。旅游业作为一种新兴产业 ,已经成为这个省新的经济增长点 ,大力开发旅游资源也成为全省实施资源转换的重要内容。青海省采取“谁投资、谁建设、谁受益”、“你发财、我发展”的原则 ,把旅游资源开发全面推向市场。目前 ,正积极准备一批招商引资项目 ,做好项目前期工作 ,为…     

旅游业的新增长点--浙江工农业旅游发展问题研究

产业旅游的崛起 ,既是旅游业自身发展的要求 ,也是相关产业实现其综合经济社会效益的需要。开发工业、农业旅游 ,有利于延伸高附加值的产业链 ,丰富旅游产品 ,改善供给结构 ,进而增强旅游业及关联产业的竞争力。为此 ,应以科学的发展观为指导 ,转变理念与思路 ,强化引导和培训 ,及时开发工农业旅游的新产品 ,着力培育旅游业的新增长点     

大型实景演艺产品开发模式初探

近年来,随着国内旅游业的快速发展和大众休闲娱乐意识的增强,加之国家提倡文化产业开发,重视非物质文化遗产的保护,各地为推动本地区旅游产业发展、展示地方独特历史文化和民俗风情,纷纷打造文化精品项目,以文化为主题的商业演艺与旅游产业迅速结合,旅游演艺产品应运而生,有力地推动了文化与旅游的互动发展。     

假日体育经济中体育旅游业的存在问题的分析

在界定假日经济、假日体育经济和体育旅游等相关概念的基础上,对假日体育经济和体育旅游业的关系进行辨析,并对体育旅游业的开发进行了探讨,提出抓住奥运契机,加快体育旅游业发展步伐;加强理论研究和体育旅游产业开发人才的培养力度;坚持可持续发展原则,打造地方特色体育旅游产品;营造良好的体育旅游业发展环境。     

假日体育经济中体育旅游业的开发对策研究

在界定假日经济、假日体育经济和体育旅游等相关概念的基础上,对假日体育经济和体育旅游业的关系进行辨析,并对体育旅游业的开发进行了探讨,提出抓住"后奥运热",加快体育旅游业发展步伐;加强理论研究和体育旅游产业开发人才的培养力度;坚持可持续发展原则,打造地方特色体育旅游产品;营造良好的体育旅游业发展环境。     

旅游业对地方经济社会发展的影响力研究——以无锡市为例

旅游业是高度综合性产业,与国民经济诸多行业相关联,旅游消费又具有分散性,使旅游业对地方经济的影响往往融合在其他物质生产部门之中。文章提出了旅游业对地方经济社会影响力的研究方式和计算方法,以2009年无锡旅游经济发展现状为实例,测算了无锡旅游全年平均影响系数、旅游产业的乘数效应。明确旅游在拉动地方经济社会发展中的影响力,以充分的数据证明旅游产业是地方经济社会发展所重点依靠的产业。     

发展延边地区旅游业的思考

延边地区旅游业经历了曲折的发展过程,现已形成了一个配套服务、粗具规模的延边特色旅游产业,但与我国加入WTO后的新要求相比仍然存在一些问题。因此,文章提出以旅游项目建设为突破口,扩大旅游经济规模;开创新的宣传方式,开发旅游客源市场;提高旅游商品的档次,增加旅游收入;抓好旅游业队伍建设,提高服务质量等行之有效的对策措施,以促进延边旅游业的腾飞。     

滚筒洗衣机噪声分析

本文具体针对全自动滚筒洗衣机,研究其在脱水状态下以每分钟1200转运转时所产生噪声的成因,接着提出三个改进方案,然后重新研究改进方案产生的噪声大小,以此验证改进方案的有效性。本文采用CFD软件FLUENT对所有模型进行模拟仿真分析。因为是搅拌模型,所以具体计算时采用动参考系法（MRF）进行计算。通过研究发现加强筋结构会造成涡流气流,是导致噪声问题的主要成因。在各个改进方案中,填充外围第四和第五圈栅格的方案被验证为最佳方案,既起到减噪效果又能节省填充材料。     

The Use of Computer Vision to Analyze Brand-Related User Generated Image Content

With the increasing popularity of visual-oriented social media platforms, the prevalence of visual brand-related User Generated Content (UGC) have increased. Monitoring such content is important as this visual brand-related UGC can have a large influence on a brand's image and hence provides useful opportunities to observe brand performance (e.g., monitoring trends and consumer segments). The current research discusses the application of computer vision for marketing practitioners and researchers and examines the usability of three different pre-trained ready-to-use computer vision models (i.e., YOLOV2, Google Cloud Vision, and Clarifai) to analyze visual brand-related UGC automatically. A 3-step approach was adopted in which 1) a database of 21,738 Instagram pictures related to 24 different brands was constructed, 2) the images were processed by the three different computer vision models, and 3) a label evaluation procedure was conducted with a sample of the labels (object names) outputted by the models. The results of the label evaluation procedure are quantitatively assessed and complemented with four concrete examples of how the output of computer vision can be used to analyze visual brand-related UGC. Results show that computer vision can yield various marketing insights. Moreover, we found that the three tested computer vision models differ in applicability. Google Cloud Vision is more accurate in object detection, whereas Clarifai provides more useful labels to interpret the portrayal of a brand. YOLOV2 did not prove to be useful to analyze visual brand-related UGC. Results and implications of the findings for marketers and marketing scholars will be discussed.     

Exploring consumer knowledge structures using associative network analysis

This paper offers a new perspective on consumer knowledge analysis that combines Human Associative Memory (HAM) models from cognitive psychology with network analytic approaches in order to gain deeper insights into consumers” mental representations, such as brand images. An illustrative case study compares the associative networks of a manufacturer brand with a retail brand and is used to demonstrate the application and interpretation of various network measures. Network analysis is conducted on three levels: Node‐level analysis yields insights about salient brand image components that can be affected through short‐term marketing activities. Group‐level analysis is concerned with brand image dimensions that characterize a brand and can be strategically influenced in the medium term. Finally, network‐level analysis examines the network structure as a whole, drawing parallels to brand imagery, which needs to be managed over the long term. Management implications are derived and suggestions for further research are provided. © 2010 Wiley Periodicals, Inc.     

Modeling CLV: A test of competing models in the insurance industry

Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions.

A generalized framework for estimating customer lifetime value when customer lifetimes are not observed

Measuring customer lifetime value (CLV) in contexts where customer defections are not observed, i.e. noncontractual contexts, has been very challenging for firms. This paper proposes a flexible Markov Chain Monte Carlo (MCMC) based data augmentation framework for forecasting lifetimes and estimating customer lifetime value (CLV) in such contexts. The framework can be used to estimate many different types of CLV models—both existing and new. Models proposed so far for estimating CLV in noncontractual contexts have built-in stringent assumptions with respect to the underlying customer lifetime and purchase behavior. For example, two existing state-of-the-art models for lifetime value estimation in a noncontractual context are the Pareto/NBD and the BG/NBD models. Both of these models are based on fixed underlying assumptions about drivers of CLV that cannot be changed even in situations where the firm believes that these assumptions are violated. The proposed simulation framework—not being a model but an estimation framework—allows the user to use any of the commonly available statistical distributions for the drivers of CLV, and thus the multitude of models that can be estimated using the proposed framework (the Pareto/NBD and the BG/NBD models included) is limited only by the availability of statistical distributions. In addition, the proposed framework allows users to incorporate covariates and correlations across all the drivers of CLV in estimating lifetime values of customers.

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Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Icing on the cake or gilding the lily? The impact of high-modified model images on purchase intention

Marketers had high expectations for modified model images in E-commerce, however, the social movements against photoshopped models suggested the high-modified model images may be harmful to consumers which leads to a contradictory phenomenon. So far, the link between high-modified model image and consumers' behavior is not clearly understood. Drawing on a selective accessibility model (SAM), this study investigates the influence of high-modified model images on consumers' purchase intention by introducing the concept of misrepresentation and identifying consumer trust as a mediator. Based on five experiments, we demonstrate that over-modified model images decrease consumers’ perceived authenticity referring to “misrepresentation”, and further lower their purchase intention. By comparing the level of misrepresentation, the results suggest that high-modified model images decrease purchase intention. Moreover, this negative relationship between misrepresentation of modified model images and purchase intention is mediated by consumer trust. Hence, this study makes a step toward explaining how high-modified model images impacts the behaviors of consumers, shedding new light on the application of image modification in online advertising.     

Can users embed their user experience in user-generated images? Evidence from JD.com

Nowadays, massive user-generated images (UGIs) are posted online to convey users' experiences with specific brands or products. Thus, this visual information is precious, as it conveys users' actual and subjective feelings about brands and products. Because of the unprecedented quantity of images and the heterogeneity of their content, it is quite challenging for brand marketers and retailers to probe into subjective user experience in large-scale UGIs. To address this gap, this study aims to identify the connection between user experience and different image semantic features (i.e. centrality and richness) by using deep learning models. By employing objective data (8963 images) from JD.com and using deep learning algorithms (faster R-CNN), we found that users with positive user experience prefer to generate high-centrality and high-richness pictures. Our study enriches the relevant literature and provides valuable practical implications for brand marketers and e-commerce retailers. Based on findings of this work, relevant stakeholders can understand their users’ experience better from objective UGIs and devise corresponding recommendation and service strategies.     

基于深度学习的远程视频监控异常图像检测

针对复杂场景下远程视频监控图像异常检测困难、传统算法功能单一(仅针对某种特定场景或某种异常图像进行检测)等问题,提出一种基于深度学习的全自动远程视频异常图像检测方法。首先采用Xavier方法对自行设计的卷积神经网络(Convolutional Neural Network,CNN)的参数进行初始化,然后将标准化后的视频差分图送入CNN的输入层,通过特征提取及下采样,最后在CNN的输出层获得远程视频异常图像检测结果。实验结果表明,该方法可以对远程视频监控中突然出现遮挡、模糊和场景切换等多种异常同时进行实时在线检测,准确率可达88.75〖WT《Times New Roman》〗%〖WTBZ〗。     

超紧凑CCD彩色电视摄像机

具有代表世界最小摄象机头之特征,并使用了CCD(Charge-Coupled Device)图象传感器的彩色电视摄象机系统,已由东芝公司研制出来.姆指大小的摄象机头(直径17.5mm,长53mm)与一个小型宽视场角的镜头及CCD图象传感器相结合.光学图象由图象传感器传感而转换成电信号并由电缆传输到摄象机控制单元.然后它们被转换成NTSC视频信号,因此,图象能在电视荧光屏上显示或由VCR(Vidio Cassette Recorder)记录.这个新系统能在广泛的领域中使用.     

What's Wrong with Computer-Generated Images of Perfection in Advertising?

Advertisers often use computers to create fantastic images. Generally, these are perfectly harmless images that are used for comic or dramatic effect. Sometimes, however, they are problematic human images that I call computer-generated images of perfection. Advertisers create these images by using computer technology to remove unwanted traits from models or to generate entire human bodies. They are images that portray ideal human beauty, bodies, or looks. In this paper, I argue that the use of such images is unethical. I begin by explaining the common objections against advertising and by demonstrating how critics might argue that those objections apply to computer-generated images of perfection. Along the way, I demonstrate an ethically significant difference between computer-generated images of perfection and the images in ordinary ads. I argue that although critics might use this fact to apply the common objections to the use of computer-generated images of perfection, the objections fail. Finally, I argue that despite surviving the common objections, the use of computer-generated images of perfection is subject to an ethical objection that is based on aesthetic considerations. Advertisers are ethically obligated to avoid certain aesthetic results that are produced by computer-generated images of perfection.