Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent,Belgium

The rapid evolution of information and positioning technologies, and their increasing adoption in tourism management practices allows for new and challenging research avenues. This paper presents an empirical case study on the mining of association rules in tourist attraction visits, registered for 15 days by the Bluetooth tracking methodology. This way, this paper aims to be a methodological contribution to the field of spatiotemporal tourism behavior research by demonstrating the potential of ad-hoc sensing networks in the non-participatory measurement of small-scale movements. An extensive filtering procedure is followed by an exploratory analysis, analyzing the discovered associations for different visitor segments and additionally visualizing them in ‘visit pattern maps’. Despite the limited duration of the tracking period, we were able to discover interesting associations and further identified a tendency of visitors to rarely combine visits in the center with visits outside of the city center. We conclude by discussing both the potential of the employed methodology as well as its further issues.     

Vision (im)possible? The effects of in-store signage on customers’ visual attention

We used two eye-tracking field experiments to investigate the extent to which in-store signage is used during navigation and decision making, and how the viewing of signage influences customers’ visual attention and choice behavior. One hundred and seventy-five customers at a grocery store were exposed to signage stimuli while carrying out predefined shopping tasks. Experiment 1 shows that attention toward signage is affected by customers’ levels of store familiarity and in-store search stage (navigation vs. decision making). Experiment 2 demonstrates that signage has a considerable impact on the direction and magnitude of customers’ visual attention during decision making.     

Open Market Operation Effectiveness in China

This paper examines the effectiveness of target rate guidance in open market operation by the central bank in China (PBOC). We find that target rate change in open market operation is effective in adjusting the short-term Treasury rates. The target rate changing direction is more effective than the target rate changing level. There is no significant asymmetry in the effectiveness between the target rate increase and target rate decrease. We also document that the effectiveness of the target rate is conditional on liquidity operation of the same direction, especially when they both aim to loosen the monetary policy. Furthermore, consecutive operations with higher intensity appear to be more effective in adjusting the market interest rate.     

An experimental examination of target based conservation auctions

Conservation auctions are increasingly employed to increase the provision of Ecological Goods and Services (EG&S) for achieving environmental goals. Most applications of conservation auctions concern the efficient allocation of a fixed conservation budget. However an important and under-explored application of conservation auctions is in meeting environmental targets, either as part of regulatory compliance by industry or by governments in meeting specified policy objectives. This paper examines the ability of a conservation auction to meet an environmental target. We use experimental economics methods benchmarked to a wetlands restoration case study to examine bidder behavior and efficiency in both budget constrained and target constrained auctions when landowners with increasing marginal costs can offer increasing sized bundles of wetland acres. The target constrained auction is characterized by an all or nothing rule, where there is no payout if the target is not met. We find that rent seeking and average cost in the target constrained auction is less than in a budget constrained auction, but that efficiency eroded in repeated rounds suggesting learning effects as participants realize there is no budget cap. Even with learning effects the target based auction out-performs the budget based auction. Adding a reserve price to the target based auction significantly reduced rent seeking but also increased the probability that the target was not met. The results have important implications for auction design in real world settings.     

When the target may know better: Effects of experience and information asymmetries on value from mergers and acquisitions

Research summary : Extending research on the effect of experience on acquisition outcomes, we examine how the differential in previous M&A experience between the target and the acquirer affects the value they, respectively, obtain when the acquirer takes over the target. Drawing on literature about organizational learning, negotiation, and information economics, we theorize that the party with greater experience will be able to obtain more value. Furthermore, we theorize that the effect of differential M&A experience on value obtained is contingent on the level of information asymmetry the acquirer faces with respect to the target, specifically as a function of the target's product‐market scope and whether the deal is friendly. We test and find support for these predictions in a sample of 1,241 M&As over a 30‐year period. Managerial summary : Corporate strategy is about a firm's scope and development decisions and outcomes, but corporate strategizing is incomplete unless managers anticipate the moves of other economic actors. We demonstrate the importance of these points when it comes to learning to make acquisitions. Using an innovative research design and theory that enables comparison between acquirer and target gains, we show that whatever their firm's acquisition history and capabilities, acquisitive managers should mind the negotiation and other pitfalls that arise when target firms possess ample acquisition experience of their own. We also demonstrate that the effect of experience advantage, whereby the more experienced party benefits, depends on the target firm's scope and whether the deal is friendly—two dimensions that acquirers can and should take into account. Copyright © 2016 John Wiley & Sons, Ltd.     

战略性新兴产业集群创新网络形成机制研究——基于灰靶双边匹配的决策方法

战略性新兴产业的发展对国民经济和社会发展具有重要的战略意义，目前中国战略性新兴产业仍面临着“高端产业，低端技术”的发展困境，如何突破产业关键技术促进产业健康、可持续发展已成为亟待解决的问题。战略性新兴产业集群创新网络的形成是突破产业关键技术、共性技术及前瞻性技术的核心环节，中国正在兴起的产业技术研究院、航空高技术产业园等跨组织知识集成平台为培育和发展战略性新兴产业集群创新网络提供了重要的支撑平台。本文依托跨组织知识集成平台探究了战略性新兴产业集群创新网络的形成机制，构建了创新主体间的灰靶双边匹配模型，并采用平衡计分法建立了以创新能力为重要衡量标准的综合评价指标体系，最后通过案例进行了分析，证明了其有效性和实用性，可为促进战略性新兴产业集群创新网络的形成提供理论指导。     

Upgrading peripheral wind sectors

The implementation of traditional policies does not consider institutional singularities in peripheral sectors. The main aim of this paper is to analyse the interaction among local capabilities and multinational agents to upgrade a peripheral wind sector. The case study conducted in the peripheral region of Galicia (Spain) emphasises the challenges concerning policy design and implementation in this kind of regions. The theoretical foundations are based on the systemic and evolutionary approach of the innovation system and the global value chain perspective. This combination enables to analyse how local contexts could shape governance patterns within value chains in peripheral regions. Likewise, the target approach is also applied concerning the design and implementation of a policy programme and its timing. Empirical evidences underline the complementarity of market size and foreign direct investment policies, as well as the diversification towards green transitions through enhancing local capabilities.     

目标价格改革试点对棉花市场的影响分析

[目的]通过分析棉花目标价格改革试点工作实施前后3年内(2011~2016年)棉花生产和需求变化情况,研究新疆目标价格改革对棉花市场的影响。[方法]运用3年滑动平均法,比较分析了棉花目标价格改革试点工作前后我国棉花产业的市场变化。[结果]自2014年棉花目标价格改革试点工作全面启动以来,生产方面:全国棉花种植结构日趋合理,新疆棉花种植优势明显,棉花市场价格形成机制初步建立,棉农利益得到了保护;市场需求影响方面:棉花价格逐步回归市场,国内外棉价差显著缩小,棉纺织企业经营状况趋于好转,棉花产业正在转型升级,但棉花市场整体需求不旺。[结论]试点工作取得了阶段性成效,但同时在信息化进程、棉花产业发展模式、生产力提升等方面暴露出一些问题亟待解决;下一步应进一步完善相关政策体系、加快棉花产业升级、加强科技创新,并推进信息化建设。     

采用航迹特征的智能机型识别

针对空战目标识别中机型自动识别比较困难的问题，提出了采用航迹特征的智能目标识别方法。利用卷积神经网络（Convolutional Neural Network，CNN）分层学习特征的能力，训练CNN算法模型自动地从航迹数据中学习有用的特征并分类。利用沿海实地采集的15个类别的飞机航迹数据，经一系列数据预处理后作为智能识别算法的训练和测试数据，在验证实验中描述了算法网络的相关配置,对比了CNN与其他分类器的识别结果。实验结果表明，CNN具有很好的识别性能。     

一种一维距离像的识别算法

随着高分辨率雷达的发展,一维距离像识别已成为雷达目标识别的重要方法之一.为了消除一维距离像的平移,将一维距离像变换到频域,提取其频谱信息作为识别特征,并依据Fisher判据,将高维特征空间数据降到一维空间,提高了算法的实时性.仿真结果表明这是一种方便有效的识别方法.