表层扮演对员工离职倾向的影响：情绪耗竭的中介作用与组织支持的调节效应

随着消费经济的兴起与服务业的发展,企业服务性岗位员工因情绪劳动引发的负面效应越来越受到理论界和实践界的关注。文章基于资源保护理论与组织支持理论,以通讯公司客服人员为研究对象,探讨表层扮演对离职倾向的影响机制,重点分析情绪耗竭的中介作用与组织支持的调节效应。研究发现：表层扮演会影响员工的离职倾向;情绪耗竭在表层扮演与离职倾向的正向关系间具有完全中介作用;组织支持感和主管支持感对表层扮演和情绪耗竭的关系具有调节效应。研究结论丰富和发展了情绪劳动的理论模型,并提出企业可以采取相应的干预措施缓解员工由于表层扮演带来的情绪耗竭和离职倾向问题。     

管理理论的发展与行为控制模式的演变

从泰罗的科学管理到当代的人本管理．尽管各种管理理论各有其研究的侧重点和内容,但其本质上都有一个共同的目的,就是要解决如何对人的行为进行控制的问题。本文试图通过对古典管理理论、行为科学理论以及人本管理思想等管理理论的分析,说明行为控制模式的演变过程,以及行为控制模式对管理理论的形成和发展的影响作用。     

美国公司丑闻发生机理的行为金融学解释

用行为金融学的非有效市场理论等分析美国公司丑闻发生的机理,可以看出:非有效资本市场中存在的股票溢价现象,不仅是公司丑闻发生的必要外部环境,而且更造成了公司经营目标的异化;市场迎合理论和市场时机理论共同作用于非有效资本市场,大大增加了公司管理者非理性化恶性转变的可能.行为金融学理论对美国公司丑闻的分析,为我们认识资本市场存在的问题提供了一个新视角,是建设和完善我国资本市场的重要借鉴.     

证券市场内幕交易的行为动机研究

2007年中国资本市场进入了全流通时代,全流通下内幕交易可能会比股权分置时更加严重,如何有效防范大股东和机构投资者利用信息优势、资金优势、控制权优势进行内幕交易,是全流通下内幕交易监管防范面临的主要挑战。本文从内幕交易的行为动机及其影响因素展开研究,借鉴Becker(1968)"犯罪经济学"的研究框架及行为金融学的相关理论,选取了相应影响因素的度量指标,架构了内幕交易行为动机模型。然后基于问卷调查的模拟数据,运用结构方程模型对影响内幕交易行为动机模型中各个变量之间的逻辑关系和内在影响机制进行了实证研究,得出富有价值的研究结论,并据此给出相应的政策建议。     

Focusing effect and the poverty trap

I build a dynamic consumption-savings model in which agents׳ choices are distorted by the focusing effect: agents overweight the utility of goods in which their options differ more. I show that the consumption-savings choice depends both on the marginal return on savings and on the total return on savings, implying that the incentive to save may increase with the initial level of wealth. As a consequence, a salience-based poverty trap may exist when the marginal return on savings is sufficiently high and sufficiently flat. I also consider the case of a perfect credit market and show that a poverty trap may emerge when the salience of consumption is bounded above. I discuss policy implications. In particular, imposing upon an agent a punishment for decreasing savings below a threshold leads to a higher level of savings, even when the threshold triggering the punishment is not binding     

Why do insiders trade? Evidence based on unique data on Swedish insiders

In this paper, we examine if corporate insiders have other motives for trading besides exploitation of private information. Our results show that insiders’ portfolio re-balancing objectives, tax considerations and behavioral biases play the most important role in their trading decisions. We also find that insiders who have allocated a great (small) proportion of their wealth to insider stock sell more (less) before bad news earnings disclosures. Finally, insider selling is informative for future returns among those insiders who have the greatest proportion of wealth allocated to insider stocks.     

Enhancing Singapore travel agencies' customer loyalty: an empirical investigation of customers' behavioural intentions and zones of tolerance

The services marketing literature focuses on the measurement of service quality and its impact and consequences on businesses. Set in the context of the travel retail agency in Singapore, this study focuses on the relationship between service quality and favourable behavioural intentions. The findings revealed that improving service quality can increase favourable behavioural intentions. However, service improvements that exceed customers' minimum‐service threshold and those that exceed customers' desired‐service level were found to have mixed effect on behavioural intentions. Hence travel agencies must carefully determine the behavioural intentions they wish to foster amongst their various customers and develop service quality strategies in line with the findings of this study. Copyright © 2007 John Wiley & Sons, Ltd.     

An application of protection motivation theory to understand the influence of fear-appeal media on stated donations for coral reef restoration

This study applied a Protection Motivation Theory (PMT) framework and Extended Parallel Process Model to explore respondents’ emotional and behavioral responses to video treatments that messaged varied outcomes for coral reef health and pro-environmental behavioral adaption. Four video treatments manipulating fear-appeal messaging with higher or lower proportions of content focused on threats to coral reefs and reassurance about coral reef restoration were presented to respondents (n = 1636). Messaging with equal proportions of threat and reassurance stimulated higher arousal, which had the greatest impact on stated behavioral intent to donate for coral reef restoration. Negative-affectivity, driven by higher proportions of threat in video messaging, was also related to higher stated donations. Sub-group comparisons revealed respondents who had taken vacations in coral reef destinations were significantly more aroused by the videos and stated higher donations than those who had not. PMT antecedents threat-appraisal, coping-appraisal, and response-costs dynamically influenced emotional responses to fear-appeal videos.     

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal $do(.)$ operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.