Game-theoretic properties of Final-Offer Arbitration

Final-Offer Arbitration (FOA) is a dispute settlement procedure in which an arbitrator chooses one side's final position as the resolution. Game-theoretic models of FOA in two-sided interest disputes are reviewed, especially models of the disputants' final offer choices under uncertainty about the arbitrator's preferences. The extent to which the Brams-Merrill Theorem (1986) reveals optimal strategic behavior under FOA, and the implications for efficiency and equity, are assessed. Analysis of a model not satisfying the hypotheses of the Theorem suggests that, for some arbitrators, FOA can have an undesirable tendency. Another game model is used to address the question of how disputants' differential risk-aversion is reflected in their strategic behavior, and in the fairness of FOA outcomes. This calculation clarifies some apparently contradictory empirical evidence about FOA.     

Investigation of the ordering behavior of a retailer in the revenue sharing and buyback contracts considering round number bias

This study investigates the ordering behavior of a boundedly rational retailer who purchases a product from a rational supplier under a revenue sharing or buyback contract. We develop a behavioral model considering random error, learning effect and anchoring bias using a logit choice framework which also includes a new behavioral paradigm known as round number bias. Experimental data and theoretical analysis are employed to demonstrate the differences between retailer order quantity and related optimal decisions using the suggested behavioral model. We found that round numbers are enticing anchors in retailer ordering decisions. The findings further reveal that the adaptive learning model better explains and predicts the retailer's ordering behavior than the time trend learning model. Moreover, the results indicate that the prediction accuracy of the model is higher for revenue sharing compared to buyback contracts.     

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Consumption-related emotions over time: Fit between prediction and experience

Consumers base many decisions on affective forecasts, i.e., predictions about future emotions. These predictions, however, are susceptible to systematic biases. The present paper examines the accuracy of predicting emotional change in the consumer context. The goals of the study were to examine whether predictions of a change in consumption-related emotions fit actual experienced change and whether original predictions are remembered correctly. A two-wave longitudinal survey with 86 consumers in Austria and the UK showed that consumers who had bought shoes overestimated the decrease of positive consumption-related emotions over time. Results indicate that the intuitive theory of adaptation is related to the prediction bias. Moreover, participants misremembered original predictions as more consistent with actual experienced emotions. This hindsight bias hinders consumers from learning from previous prediction errors.     

How does perception on green environment across generations affect consumer behaviour? A neural network process

This paper examines how the perception on green environment can affect consumer behaviour. It tests whether consumer behaviour responds to environmental change. This study captures the interaction between green environment perception and behaviour. A neural network analysis is used to address this challenge to model such a behaviour. A total of 462 interviews were conducted and analysed using neural network process. A Scaled Conjugate Gradient with random data division was used in training and the performance was calculated using mean square error. The result reveals that perception–behaviour gap exists across generations on green environment. It indicates that perception on green producing, green living, green transport and green product is an important factor in determining the behaviour on green consumption. The neural network accurately recognized green behaviour up to nearly 82% with MSE = 0.09 at epoch 31, which is a high level of recognition accuracy. This study underlines the existence of perception–behaviour gap on green environment. However, these are only identified to people with low‐to‐medium level of green behaviours (level of correct only 44.5% and 30%). People with high level of green behaviour in fact show consistency between their perceptions and behaviours (level of correct 96.4%). This result shows that people tend to have higher perceptions on green environment, but their behaviour reflects otherwise.     

基于行为规律的搜索资源分配新算法

针对具有行为规律的目标搜索问题,提出一种搜索资源分配算法。该方法以目标在各搜索区域的概率分布为基础,利用最优搜索理论分配区域搜索时长;以目标在各搜索区域的行为规律为基础,利用包络检测等方法决定区域开始搜索的时刻。针对网站关键词搜索的实验显示,根据目标行为规律在时间上相关程度的不同,本算法相对于最优搜索算法的性能提升在15%~50%之间,在对大量信息源进行信息搜索时具有应用价值。     

A deep hybrid learning model for customer repurchase behavior

Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies.     

Green cooperation in last-mile logistics and consumer loyalty: An empirical analysis of a theoretical framework

Based on the contingency, transaction, and trust theories, this paper explored how the green cooperation between supplier and customer in last-mile logistics contributes to consumers’ sustainable consumption behaviors and trust and transaction cost's contingency role. We collected survey data from 453 users of green shopping platforms in South Korea (e.g., Homepick and Smart Locker) and analyzed the data using structural equation modeling. The results suggest that green cooperation with companies positively affects consumers’ sustainable consumption behaviors and trust, and transaction costs negatively moderate such relationships. In addition, customer trust positively affects sustainable consumption behavior and loyalty. We also found that improved sustainable consumption behavior can form loyalty, but transaction cost decreases its positive impact. This paper evaluates the last-mile delivery through green cooperation and enriches the literature on contingency, transaction cost, and trust theories.     

绿色消费驱动下的绿色营销策略及其启示

绿色消费是绿色营销产生和发展的时代基础和思想基础,绿色营销能有效引导和促进绿色消费。文章基于绿色消费和绿色营销的互动发展机制,从消费者感知效力、利他主义、自由主义和环境保护意识四个细分变量分析了绿色消费行为,揭示了其间隐含的营销策略启示。     

基于遗传算法优化混沌神经网络的股票指数预测

为提高BP神经网络预测模型对混沌时间序列的预测准确性,提出一种基于遗传算法优化BP神经网络的改进混沌时间序列预测方法。本文采用时间序列输入输出参数数量构造BP神经网络拓扑结构,利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型求得最优解,将该预测方法应用到上证综合指数的时间序列进行有效性验证,结果表明了该方法对上证综合指数具有更好的非线性拟合能力和更高的预测准确性。     

社会规范对绿色消费的影响及作用机制

绿色消费是我国推进生态文明建设所倡导的消费行为,本文将社会规范划分为描述性规范、动态描述性规范和命令性规范,探讨了社会规范对绿色消费的影响及作用机制。通过两个实验研究发现,描述性规范、动态描述性规范、命令性规范对绿色消费的促进作用显著,三者对绿色消费的促进作用无显著差异;描述性规范、动态描述性规范会激活消费者的个人规范,个人规范在描述性规范、动态描述性规范影响绿色消费过程中发挥部分中介作用;命令性规范会引发消费者的后果认知,进而激活个人规范,后果认知与个人规范在命令性规范影响绿色消费过程中发挥完全中介作用。因此,可以发挥描述性规范、动态描述性规范对绿色消费的引导示范作用和命令性规范对绿色消费的监督作用,激活绿色消费的个人规范水平,更为有效地促进绿色消费。     

浅析我国绿色消费存在的问题及发展绿色消费的对策

绿色消费是 2 0世纪后半叶兴起的一种可持续性消费观念和消费行为。随着人类社会经济可持续发展战略的推进 ,绿色消费作为一种全新的消费理念 ,已经成为 2 1世纪全球消费模式的呼声和消费者的共同追求 ,是温饱问题解决以后人们更加重视自己的生存质量的结果。绿色消费是一种适度的消费 ,既不奢侈又不吝啬 ;又是一种崇尚自然 ,保护环境的消费。既然这样 ,就应该大力发展绿色消费。从绿色消费的内涵与特征出发 ,对我国绿色消费存在的问题及发展绿色消费的对策进行了分析。     

The examination of the relationship between experiential value and price fairness in consumers’ dining experience

Consumers enjoy sharing meaningful consumption experiences with others. The purpose of this study is to better understand this behavior by identifying the specific aspects of experiential value that motivate word of mouth behavior. To these ends, this study tests a model that proposes relationships among four types of experiential value (aesthetics, escapism, service excellence, and efficiency), satisfaction, price fairness, and word of mouth. The results indicate that, among the four types of experiential value, service excellence and efficiency are the most important determinants of post-consumption word of mouth. Accordingly, this research contributes a better understanding of experiential value and its effects on motivating favorable post consumption word of mouth promotion. Managerial implications providing a holistic perspective of consumer cognition, emotion, and motivation are provided.     

Understanding consumers’ moral consciousness

The issue driving this paper is ‘Why don’t people, in their consumer role, have a well developed moral conscience?’ To address this compelling question, the paper explores the moral consciousness of consumption behaviour (or lack thereof). The first part of the paper provides brief overviews of: (1) moral consciousness applied to consumption, (2) the essence of morality and ethics, (3) four facets of the field of ethics, (4) two moral development models, and (5) the affective domain of learning. The intent is to prepare the reader for a discussion of an approach to understanding the moral consciousness of consumption that integrates particular concepts drawn from the theory of ethics and morality with the moral development models and the affective domain of learning.     

Detecting gender item bias and differential manifest response behavior: A Rasch-based solution

Although gender is a salient variable in consumer research, researchers largely overlook whether, and how, it influences consumer response to indicators measuring latent variables. The authors therefore extend the framework of measurement equivalence assessment to the largely overlooked issue of differential item response behavior between men and women. This paper demonstrates the efficacy of using item response theory to investigate the presence of gender item bias. This methodological approach affords researchers the means of objectively disentangling actual gender differences and gender bias. Ignoring the possibility of gender item bias has the potential to bias means and thereby compromise any substantive gender-based mean comparisons. The authors conclude with solutions to address gender item bias both pre and post survey construction.     

Consumer learning, switching costs, and heterogeneity: A structural examination

This paper develops and estimates a model of forward-looking consumer learning with switching costs using household level scanner data from a frequently purchased product category. This is novel because current models of consumer purchase behavior assume that only one of these types of dynamics is present, not both at the same time. My model estimates support the presence of both learning and switching costs in this product category. The estimates show that before consuming new products, consumers are unsure of their tastes for them, and subsequently learn their tastes by purchase and consumption of new products. Switching costs are large, comprising roughly 30 percent of the cost of a medium sized package of the product. Additionally, the model incorporates very rich individual level unobserved heterogeneity in price sensitivities, tastes, and switching costs, and the amount by which consumers learn. To show that my model produces different implications than a model with learning or switching costs only, I estimate two more specifications, one without each type of dynamics, and simulate counterfactuals that are of interest to managers and policymakers. I find that intertemporal elasticities are underestimated when either type of dynamics is left out, by as much as 90%. Informative advertising is also affected by the presence of switching costs, although the direction of the bias is not signed. Leaving out dynamics also has a large impact on long-term elasticities, which are used by antitrust policymakers to evaluate the impact of mergers. When learning is ignored, cross elasticities are underestimated by as much as 45%. When switching costs are ignored, both own and cross elasticities are underestimated.     

How do electronic word of mouth practices contribute to mobile banking adoption?

Mobile banking (m-banking) is the fastest growing and most cost-effective channel for delivering banking services. Electronic word of mouth (eWOM) plays a crucial role in the success of e-commerce. Therefore, the main purpose of this study is to investigate a comprehensive moderated mediated mechanism for enhancing m-banking adoption behavior through positive eWOM triggers using the elaboration likelihood model (ELM). Argument Quality, valence, consistency and volume were considered eWOM triggers, and the conceptual model also included initial trust in m-banking as a mediator and consumer involvement in m-banking as a moderator. A total of 1153 useable surveys completed by the Indian users of social networking websites were used for analysis. The findings showed that of the triggers, argument quality, valence, and consistency enhance intention to adopt m-banking. These effects were mediated by initial trust in m-banking. The mediation effects of initial trust between valence – Intention varied between high and low consumer involvement in m-banking. These findings make several contributions to the literature on marketing communication, particularly to eWOM research and ELM theory. Practically, this study provides several recommendations to banks about how to use positive eWOM for motivating consumers to adopt m-banking.     

Does green morality lead to collaborative consumption behavior toward online collaborative redistribution platforms? Evidence from emerging markets shows the asymmetric roles of pro-environmental self-identity and green personal norms

The collaborative consumption toward online collaborative redistribution platforms can potentially ease sustainability problems. Although it has attracted the attention from academics and practitioners, research on its determinants and influencing mechanism is limited. This study develops an integrated theoretical framework to explore the impact of green morality and platform quality as potentially essential yet previously unexamined antecedents of collaborative consumption behavior toward online collaborative redistribution platforms. A questionnaire survey was conducted in a crucial emerging market, and consumer responses were assessed using a structural equation model. The results reveal that the green morality motive (i.e., pro-environment self-identity and green personal norms) and platform quality are determinants of collaborative consumption behavior. Second, platform quality moderate the relationship between green personal norms and attitude. Accordingly, the asymmetric roles of pro-environmental self-identity and green personal norms as green morality components are revealed. Third, attitude and intention play mediating roles in the relationship between collaborative consumption actions and antecedents. Finally, implications for both research and practice are discussed.     

Direct observation of search behavior in the purchase of two nondurable products

This study examines external information search for nondurable products using a relatively unobtrusive measurement technique in an effort to minimize the demand bias inherent in many previous search studies. An attempt is made to explain differences in search behavior using five groups of predictive variables: decision task, shopping lifestyles, general shopping behavior, personality, and demographics. The findings support the notion of very limited search in the purchase of common, inexpensive, low involvement goods. Variables related to the decision task appear to be the best predictors of external information search behavior.     

基于智慧旅游的三亚旅游业发展策略研究

互联网和大数据时代的到来,引领着旅游者的消费方式和消费习惯不断发生转变,倒逼城市适应消费者的需求、行为偏好和习惯来推动旅游业的发展。三亚市拥有得天独厚的旅游资源,但现有智慧旅游建设尚存在开发效能不足、推广力度欠缺、监督监管水平低下、数据采集方式不规范、信息更新不及时等问题,从而导致其准确性和实时性无法辨认等。基于此,本文提出通过政府提供制度保障,坚持“人本”服务理念,挖掘旅游者个性化需求,坚持以市场为导向,引导社会力量参与建设,实现数据信息资源整合、高新技术融合以及现代商业模式创新等途径,进而推动三亚旅游业实现数字化、个性化、智慧化发展。