购买行为分析与新产品最优广告和价格策略

本文应用贝叶斯风险决策理论分析了消费者的新产品购买行为，导出消费者购买决策必需信息量的计算公式，并在购买集结模型基础上研究了新产品的动态最优广告和价格策略。     

基于供应链关系的数字产品最优版权保护决策

版权保护技术是阻击盗版的重要手段,然而数字产品企业对于版权保护技术的看法不尽相同。针对数字企业如何设定最优的版权保护水平,根据数字产品市场中两类潜在的消费者—伦理消费者和普通消费者,构建基于消费者搜索行为的供应链模型,研究制造商和零售商的最优版权保护决策。研究发现,企业设定的版权保护水平具有双重效应,一方面对于普通消费者而言存在主效应,即企业可以通过提高版权保护水平以此增加普通消费者的需求;另一方面,版权保护水平的提高又会对伦理消费者的搜索意愿产生消极的策略效应,从而导致伦理消费者的需求下降。此外,当伦理消费者规模较大时,制造商和零售商关于版权保护水平有不同的执行标准。     

一种主体行为可信度量模型

提出一种针对计算机信息系统的主体行为进行可信度量的模型,给出模型的一个实现框架。该模型可以对主体的行为进行可信度量,根据主体行为的可信度使不合法程序和代码无法执行、合法程序和代码无法执行未授权访问,使合法程序和代码的可疑行为受到严格控制,并能根据不同阈值来保证安全性和实用性的合理折中。     

一种考虑随机性的多主体创新资源配置仿真模型

针对二元创新资源配置难题,基于主体建模方法,构建考虑随机性的多主体创新资源配置仿真模型,并制定多主体交互中的企业行为规则和消费者行为规则.利用Netlogo软件开展模拟仿真,进行可靠性检验和灵敏度分析.研究发现,资源柔性过大不利于资源配置,降低企业利润.能力柔性的增强有利于提高资源配置效率,进而提升企业创新绩效.两种柔性对资源配置有效性并未表现出协同效应,企业应优先提升能力柔性.该仿真模型综合考虑了消费者和市场变化、竞争条件、策略调整等因素,更符合创新实际,可以为企业制定二元创新资源配置决策提供参考.     

基于信息生命周期的多主体交互模型及形式化

研究社会行为主体间信息交互模型优化问题,社会在应用多主体建模技术模拟人群社会行为的过程中,多主体间的信息交互模式决定着主体自组织和自学习的方式和效率。从现实社会中人与人之间的消息传播行为规律出发,分析主体间信息交互过程,以信息生命周期理论为基础建立多主体间信息交互模型,给出主体信息交互各个阶段的形式化方法和表述结果。利用新闻传播学中消息的5W1H定义来表达信息内容,采用时态逻辑描述单一主体信息需求的判定,应用多目π-演算形式化方法表述多主体获取和转发信息的行为,使用模糊集和Brooks方程分别表示信息过滤与信息吸收行为。进行分析的结果表明,多主体间信息交互模型的建立及其形式化描述,可为人群社会行为仿真模型的实现奠定了基础。     

中草药包装设计探索

中华民族悠久的历史,铸就了源远流长的中医中药文明史。中草药作为防病治病、滋补健身的特殊商品,历来就受到人们的喜爱。而日益紧张快速的生活节奏,使人们越来越注重自身的健康。以温和的中草药来调理身体越来越受到一些中青年消费者的欢迎,其市场需求也越来越大。但是中草药的包装却一直被人们忽视,跟不上时代与市场的需求。本文首先分析了目前市场上中草药包装存在的问题;然后根据消费者类型、消费动机,分析中草药市场的消费需求;最后根据消费需求提出中草药包装设计中应把握的原则。     

数据挖掘

一种透过数理模式来分析企业内储存的大量资料，以找出不同的客户或市场划分，分析出消费者喜好和行为的方法。     

网上零售业多产品配送服务定价研究

随着越来越多的网上零售商开始实施有条件的免运费策略, 如何确定免运费的条件和运费成为电商企业面临的重要问题。该问题抽象成为一个两阶段的博弈模型：首先消费者根据效用最大化的原则确定购买决策, 然后零售商在考虑消费者购买决策的基础上依据利润最大化原则设定物流定价策略。通过算法设计和算例分析, 得到免运费阈值设定在产品价格组合边界时, 零售商利润会发生跳跃。     

网上零售业多产品配送服务定价研究

随着越来越多的网上零售商开始实施有条件的免运费策略,如何确定免运费的条件和运费成为电商企业面临的重要问题。该问题抽象成为一个两阶段的博弈模型:首先消费者根据效用最大化的原则确定购买决策,然后零售商在考虑消费者购买决策的基础上依据利润最大化原则设定物流定价策略。通过算法设计和算例分析,得到免运费阈值设定在产品价格组合边界时,零售商利润会发生跳跃。     

消费者口碑传播行为及交互影响的模拟仿真研究

基于元胞自动机模型对消费者正面口碑、负面口碑和中立口碑传播行为之间的影响作用和动态演变进行了模拟仿真,讨论了在不同消费者初始状态、行为保持性、行为传播性和实施不同政策力度条件下,消费者口碑传播行为演化的趋势和状态。模拟结果得到如下结论：a）口碑传播网络中消费者的初始状态对系统演化的走向起重要作用,要重视对现有消费者构成的调查和研究;b）行为传播力度增大,对口碑传播的影响增大,能够引起口碑系统的连锁反应,系统构成发生很大改变;c）消费者自身口碑态度的保持性对口碑系统的演化有重要影响;d）对正面口碑的鼓励政策能够在很大程度上提高消费者的正面传播,从而提高整个系统的消费者忠诚度。研究能够帮助企业正确理解消费者口碑行为及相互影响作用,制定针对性的营销与售后管理策略,从而有效控制和引导消费者口碑行为。     

Agent-based diffusion model for an automobile market with fuzzy TOPSIS-based product adoption process

This paper focuses on the product diffusion in a competitive automobile market. Since purchasing a car is costly, the consumers in the market tend to behave like rational decision makers. They naturally compare the attributes of cars (e.g., brand preference, fuel economy, safety, comfort) and make overall decisions. In this paper, we propose an agent-based (AB) diffusion model consisting of tens of thousands of interacting agents. In the model, an agent represents a consumer and bases its multi-attribute decision-making on fuzzy TOPSIS. The decision-making process integrates three purchasing forces: expert’s product information provided by mass media, subjective weights on product attributes assigned by individual consumers, and social influence (i.e., information delivered from a consumer’s neighbors who have already adopted products). The AB model executes the agents and observes the collective behavior. In this sense, the model can assist in the analysis of the complex market dynamics. We conducted an empirical study to verify the performance of the AB model.     

The Market for Video on Demand

In spite of predictions to the contrary, video on demand service has yet to become a significant channel for distribution of audiovisual entertainment to consumers’ homes. In this paper, we examine the market for video on demand service under the assumption that quality of service is a threshold factor that consumers take into consideration when making their purchasing decision. Given this characteristic of consumer behavior, we model the joint demand for video on demand service and broadband network access. We then proceed to characterize the optimal solution to monopolist and duopolist content providers’ price and quantity decisions in this market. The comparative statics results are extended in a two-period game theoretic framework that addresses the timing issue of service adoption.     

A model to evaluate the effects of price fairness perception in online hotel booking

Research on consumer and market behavior related to prices has increased remarkably in recent years. Researchers have paid special attention to the effects of price perception in consumer purchasing processes. In this paper a model of antecedents and consequences of consumer price fairness perception in an online hotel booking setting is proposed. The results show that consumers use reference prices and are guided by their familiarity with online hotel bookings in determining price fairness. Moreover, when consumers perceive prices as fair, they show more confidence in the decisions made and are more satisfied with the price. However, there is no direct influence on loyalty, although this relationship appears indirectly through satisfaction with the price and confidence in the decision.     

Stock market trading rule discovery using two-layer bias decision tree

This study uses the daily stock prices of Microsoft, Intel, and IBM to assess stock market purchasing opportunities with simple technical indicators. This study used a two-layer bias decision tree. The methodology used in this study differs from that used in other studies in two respects. First, this study modified the decision model into the bias decision model to reduce the classification error. Second, this study used the two-layer bias decision tree to improve purchasing accuracy. The empirical results of this study not only improve purchasing accuracy and investment returns, but also have the advantages of fast learning speed, robustness, simplicity, stability, and generality.     

Understanding online consumers’ purchase intentions: a contribution from social network theory

There is a dynamic and interconnected international setting shaped by the power of the Internet and social media. To gain more consumers, understand their behaviours and needs, and maintain closest relationships with them, businesses should understand how consumers behave in social media and how they vary in their purchase intentions. In the scope of the study, we integrate the social network theory and the theory of planned behaviour to analyse online consumers’ purchase intentions and to investigate their structural positions by analysing their friendships in social networks. We target Twitter users to conduct analysis due to Twitter's popularity in use, market penetration, and opportunity to work with open-source data. This study contributes to a better theoretical understanding of online consumers’ purchase intentions by integrating multiple theoretical perspectives. It expands the literature by considering both online consumers’ friendship network in Twitter and their individual online purchasing intentions. The study also guides e-marketers to design proper strategies for potential and current consumers and target the right sets of people in the social networks.     

Optimal pricing of online products based on customer anchoring-adjustment psychology

Online product pricing is an important factor that affects the purchase of customers and the earnings of e-commerce platform enterprises. It has an increasingly prominent influence on consumers’ purchasing decisions. To study the influence of customers’ anchoring psychology and product pricing on customers’ purchasing behaviors, this paper proposes a pricing model that considers online consumers to be anchored by the product price and sales. The cognitive bias of online consumers is described by a utility function based on an anchoring-adjustment heuristic, and consumer choice behavior and an online product pricing strategy are studied with a multinomial logit model. Then, it studies the influence of the anchoring point and anchoring degree on optimal pricing, sales and profit. In particular, when consumers are anchored only by sales of goods, the optimal solution is monotonic with respect to the anchoring degree. However, when consumers are anchored by both price and sales, both factors affect consumer behaviors and produce more complex results. This paper provides a flexible pricing mechanism for platform enterprises, and it can provide a theoretical basis and decision support for dynamic pricing of platform enterprises based on historical consumption data.     

基于Jess推理的网络消费者非理性购买预测

随着电子商务的高速发展,网络购物越来越经济便捷,相比于传统的网下购物,更多的消费者选择网购,这就使得非理性购买行为大量涌现,研究网络消费者非理性行为势在必行。然而,学者们大多关注传统购物环境下的非理性购买行为,并且研究内容比较琐碎,缺乏系统的框架。考虑到很多消费者选择通过在线评论表达对购买行为的情感和观点,因此首先利用情感计算和文本挖掘技术,在充分挖掘语义资源的基础上,借助模糊数学理论构建模糊语义模型,同时,以内、外部诱导因素为前件,以消费者非理性购买程度作后件,建立了模糊推理模型;然后,针对消费者非理性购买行为和过程,通过protégé建立本体,梳理各个因素之间的联系,构建知识库;最后,利用Jess构建模糊推理事实库与规则库,通过Jess推理机获取消费者非理性购买程度。     

Optimum design of electronic communities as economic entities

Electronic communities can be designed to organize consumers, to pool their purchasing power, and to guide their purchasing decisions. Such commercial electronic communities have the potential to facilitate the creation of novel marketplaces, and even radically change the buyer-seller interaction, as physical communities did throughout the history. Commercial electronic communities are groups of consumers that participate in the marketplace as a single unit. In addition to bargaining power gained from such bundling, such communities can expand markets by reducing market uncertainty, and they have the potential to drastically reduce consumers’ transaction costs, by facilitating group transactions and bulk purchasing. Communities are characterized by their size, their pricing strategy, and their membership characteristics. Analytical models and numeric analysis is utilized to compute the optimum size of a community for given market characteristics. Two major community pricing strategies are analyzed to improve the community design, and the conditions are derived where one dominates the other. Finally, market segmentation techniques are introduced to control the membership characteristics of the community to further improve the design. Levent V. Orman is a professor of Information Systems at Cornell University, Graduate School of Management. He received a Ph.D. degree from Northwestern University. He has taught courses and written articles on electronic commerce, database management, decision support systems, and expert systems. His recent articles appeared in Journal of Information Technology and Management, Journal of MIS, Acta Informatica, and IEEE Transactions on Knowledge and Data Engineering. He is the associate editor of the Journal of Database Management, and serves on the editorial board of the Journal of Information Technology and Management.     

An electricity market game between consumers, retailers and network operators

We consider a simple game-theoretical model in which an electricity retailer and a network owner offer incentives to consumers to shift load from a peak period to an off-peak period. Using a simple example, we compare the market outcomes from collusion with those from the equilibrium of a non-cooperative game and examine the behaviour in this game when it is repeated in a situation in which agents have imperfect information.     

Optimal decision making in multi-product dual sourcing procurement with demand forecast updating

In this paper, we will investigate a buyer's decision making problem in procuring multiple products, each treated as a newsvendor, from two markets. The contract market has a long lead time, a fixed wholesale price and resource constraints. While the spot market has an instant lead time and a highly volatile price. The purchasing decision at the spot market can be made near the beginning of the selling season to take the advantage of the most recent demand forecast. The buyer needs to determine the purchasing quantity for each product at the two markets to maximize the expected profit by trading off between the resource availability, demand uncertainty and price variability. The procurement decision making is modeled as a bi-level programming problem under both a single resource constraint and under multiple resource constraints. We show that this bi-level programming problem can be formulated as a single-level concave programming problem. We then develop a sequential algorithm which solves for a linear approximation of the concave programming problem in each iteration. This algorithm can be used to solve a real world problem with up to thousands of kinds of products, and is found to be highly efficient and effective.