Cognitive ability and human behavior in experimental ultimatum games

Recent researches have shed light on the effect of cognitive ability on economic decision-making. By measuring cognitive ability applying Raven's progressive matrix test, we obtain two significant results that this effect affects decision-making in two types of experimental ultimatum games. First, the higher the cognitive ability, the larger the amount a sender offers when the offer is smaller than or equal to the half split. Second, the higher the responders’ cognitive ability, the smaller the offer they accept, when they accept it or not with the strategy method. This study not only finds new factors that affect decision-making in experimental ultimatum games, but also provides more evidences that cognitive ability influences economic decision-making.     

基于技术轨道的制造企业服务化机理研究

基于技术轨道理论与制造企业服务化间的关系，认为技术轨道对制造企业服务化的影响机理包括3类：驱动机理、拉动机理和差异化机理。制造企业服务化转型内在机理是技术轨道驱动下投入服务化、企业边界变化以及用户需求、产出、政策拉动下与技术轨道的协同演化过程，且通过技术轨道形成服务化转型差异。制造企业对自身技术轨道的识别有利于确定发展所需主要技术来源及技术演化轨迹，把握实现服务化的关键机理。     

基于认知视角的创业机会发展过程研究

理性创业时代背景下，如何把握创业机会已成为创业者普遍关心的问题。选取34位创业者作为研究样本，运用文本挖掘和多案例匹配分析方法对创业机会发展过程进行分析、编码，探究创业行为背后的认知机制。研究发现，在机会发现或创造阶段，创业者通过反事实思维推动的创业警觉和创业拼凑两种途径形成创意，并经过新旧认知体系的排斥与趋同形成心智模型，以揭示创业机会发现或创造的认知机制；在机会评估阶段，创业者通过对风险—收益感觉与知觉的多轮反复交互，形成第一人称机会信念和第三人称机会评估，由此揭示创业机会评估的认知机制；在机会开发阶段，创业者形成机会开发内外两种机制，外在机制围绕“探索导向—商业模式创新—新企业成长”展开，内在机制围绕换位思考、效果推理等认知因素推动企业创新、资源获取展开。结论对丰富创业认知理论和管理实践具有重要意义。     

Application of graph theory to mining the similarity of travel trajectories

Travel mobility has attracted considerable attention from tourism scholars. Studies have extensively discussed discovering key (i.e., collective) movement patterns. Recently, the advancement of information technology has allowed tourism researchers to obtain detailed information regarding travel digital footprints. This study, which analyzes mobile sensor big data, proposes a data mining approach to measure the similarity of travel trajectories by performing a pair comparison of individual trajectory. This method considers the spatial and temporal dimensions of travel flow to help identify trajectory similarity across individual travelers. Considering graph theory, this research also applies graph-based spatiotemporal analytics to identify important insights from complex travel mobility networks. As a result, this study suggests an innovative approach to assess travel trajectory similarity, which can be regarded as a type of data-driven clustering method. This paper also demonstrates the applicability of network science in travel mobility.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Small seeds for grand challenges—Exploring disregarded seeds of change in a foresight process for RTI policy

The recent turn in research, technology and innovation (RTI) policy towards challenge-led strategies is posing new demands to Foresight methodology. RTI Foresight practitioners need to complement their well developed set of technology oriented methods with equally sophisticated approaches tackling societal aspects of innovation. In this paper we aim to make a contribution to this requirement. Building on user innovation theory we argue that demand oriented RTI Foresight needs to systematically integrate voices and hypotheses from the fringes of the innovation system. In order to develop a sound approach for this we set out from well established Foresight theory on “weak signals” and “cognitive biases”. Adopting a constructivist stance towards such signals leads us to the need to set up a socially robust, diverse discourse on “seeds of change hypotheses”. We then outline a practical implementation of such a discourse in the context of the recent Foresight process of the Federal Ministry of Education and Research (BMBF) in Germany. We describe and discuss the experience of this Foresight process and suggest avenues for further development of the approach.     

Determinants of holistic passenger experience in public transportation: Scale development and validation

Transportation literature is shifting its focus from passengers’ perception of transit quality to understanding passenger experience from a holistic perspective. Few studies have investigated passenger experience factors from a holistic perspective; however, they are explored independently. A holistic approach can help the transport service providers to understand the complete journey of the passenger. This study proposes the concept of holistic passenger experience (HPX) and identifies the determinants of HPX in the context of public transportation with specific reference to Indian Railways. The study develops a comprehensive scale that measures the influence of these determinants on HPX to address this gap. Following a systematic scale development process, this study identified and validated (content, construct, convergent, and discriminant validity) a seven factor structure scale that determines HPX. Of these seven factors, four factors (vehicle maintenance, comfort & security, off board service, and travel information) constitute the core services offered by the transport service provider. Interestingly these four factors can be effectively managed by the service provider. The other three factors (social environment, supporting services, and accessibility) cannot be entirely managed by the transport service provider effectively, as these factors fall beyond the scope of the service provider.Nomological validity results are explained by constructing a matrix (holistic passenger experience Matrix) which reveals that most of the constructs that are can be managed by the transport service provider can be mapped to the cognitive component of customer experience, whereas most of the factors which go beyond the scope of the transport service provider could be mapped to the emotional component of customer experience. The findings can assist the transport sector executives to identify the key determinants to deliver superior passenger experiences throughout the journey.     

How to win the consumer’s heart? Exploring appraisal determinants of consumer pre-consumption emotions

Emotions are key drivers of consumption in the tourism and hospitality sector. Therefore, advancing our understanding of the determinants of consumer emotions is critical for tourism and hospitality marketers to effectively influence consumer affect-driven consumption behavior. However, research on the determinants of pre-consumption emotions remains under-explored. This study aims to fill the research gap and proposes a theoretical framework to explain affect-driven behavior. Grounded on the cognitive appraisal theory of emotions, we hypothesize that marketing stimuli are personally appraised based on several dimensions such as novelty and goal relevance. These appraisal dimensions determine the elicitation of positive emotions, leading to behavioral intentions. A total of 655 participants were recruited for an empirical study to test this framework in the context of a tourism event experience. We use an experimental research design to examine consumer appraisals, emotions and behavioral intentions in four conditions: two marketing visual formats (poster vs. video) over two delivery channels (advertising vs. e-word-of-mouth). Research outcomes support the theoretical framework of consumer affect-driven behavior, suggesting that the elicitation of positive emotions beneficially guides consumer behavior in goal striving and novelty seeking. Based on this, emotion-focused marketing implications are recommended, including goal-based segmentation, goal-relevant content development and the use of eWOM as an emotion-enhancing message delivery channel.     

发明人角色识别及二元创新能力差异分析——社会资本视角的解释

发明人的创新活动嵌入在合作创新网络中。基于社会资本视角，从结构资本和认知资本两个维度辨识并定义4类发明人角色，包括“明星”、“纽带”、“平庸”和“活跃”发明人。以人工智能领域在德温特专利数据库（DII）授权的专利数据为样本，运用负二项模型，对4类发明人的二元创新能力进行实证对比分析。结果表明，不同角色发明人的二元创新能力显著不同。其中，“明星”发明人的二元创新能力最高，“平庸”发明人的二元创新能力最低，“活跃”发明人的二元创新能力高于“纽带”发明人，即认知资本对二元创新的影响大于结构资本。结论对提升发明人二元创新能力具有重要参考价值。