harvard case study emotion

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• 06 May 2024
• Research & Ideas

The Critical Minutes After a Virtual Meeting That Can Build Up or Tear Down Teams

Weak communication and misunderstandings during virtual meetings can give way to resentment and rifts when the cameras turn off. Research by Leslie Perlow probes the nuances of digital communication. She offers advice for improving remote teamwork.

• 21 Nov 2023

Employee Negativity Is Like Wildfire. Manage It Before It Spreads.

One overwhelmed person's gripes can quickly escalate into collective distress. Research by Amit Goldenberg gives managers reasons to pay close attention to teams' emotions. He offers advice to help groups reframe negative experiences.

• 30 Apr 2021

Why Anger Makes a Wrongly Accused Person Look Guilty

Too often, people rely on biases and hunches to judge complex situations. Research by Leslie John shows how easy it is to make the wrong call. Open for comment; 0 Comments.

• 08 Oct 2020

Keep Your Weary Workers Engaged and Motivated

Humans are motivated by four drives: acquire, bond, comprehend, and defend. Boris Groysberg and Robin Abrahams discuss how managers can use all four to keep employees engaged. Open for comment; 0 Comments.

• 28 Sep 2020

How Leaders Can Navigate Politicized Conversations and Inspire Collaboration

Francesca Gino discusses the psychology of conversation in politicized workplaces and how managers can improve their conversation styles to create high-quality collaboration. Open for comment; 0 Comments.

• 17 Aug 2020

What the Stockdale Paradox Tells Us About Crisis Leadership

The Stockdale Paradox and survival psychology contain wisdom for how leaders can manage the coronavirus crisis, according to Boris Groysberg and Robin Abrahams. Open for comment; 0 Comments.

• 20 Jul 2020
• Working Paper Summaries

The Pursuit of Passion Propagates Privilege

While graduating students are often exhorted to do work they love to do, those from lower socioeconomic backgrounds are less likely to feel that they are a fit for and have the skills to thrive in a job that calls for passion.

• 31 Mar 2020
• Cold Call Podcast

Controlling the Emotion of Negotiation

Leslie John discusses the importance of asking (and answering) the right questions when negotiating, particularly under emotional stress. Open for comment; 0 Comments.

• 25 Nov 2019

When Your Passion Works Against You

Passion is supposed to be the secret sauce that transforms average managers into dynamic leaders. The reality is more complicated, says Jon M. Jachimowicz. Open for comment; 0 Comments.

• 14 Mar 2018

Feeling Stressed? Try Sniffing Your Romantic Partner's Shirt

Attention business travelers: Reducing on-the-road stress might be as simple as tucking a loved one’s t-shirt into your suitcase, according to new research by Marlise Hofer, Hanne K. Collins, Ashley V. Whillans, and Frances S. Chen. Open for comment; 0 Comments.

• 02 Aug 2017
• What Do You Think?

Summing Up: Why Can’t Organizations Engage Their Employees?

Who is responsible for generating employee engagement? The employee, or the employee's managers? Readers of this month's James Heskett column have a lot to say on the issue. Open for comment; 0 Comments.

• 07 Jan 2015

The Quest for Better Layoffs

Professor Sandra Sucher wants to change the way business thinks about workforce reductions. "We want people to learn about the forces they unleash in the firm when they institute layoffs." Open for comment; 0 Comments.

• 30 Jun 2014
• Lessons from the Classroom

The Role of Emotions in Effective Negotiations

Andy Wasynczuk, a former negotiator for the New England Patriots, explores the sometimes intense role that emotions can play in negotiations. Closed for comment; 0 Comments.

• 23 Oct 2013

Overcoming Nervous Nelly

In situations from business negotiations to karaoke, Alison Wood Brooks explores the harmful effects of anxiety on performance—and how to combat them. Closed for comment; 0 Comments.

• 03 Jun 2013

The Power of Rituals in Life, Death, and Business

Experimental research by Michael I. Norton, Francesca Gino, and colleagues proves multiple benefits of using rituals. Not only do they have the power to alleviate grief, but they also serve to enhance the experience of consuming food—even something as mundane as a carrot. Closed for comment; 0 Comments.

• 13 Feb 2013

5 Weight Loss Tips From Behavioral Economists

Behavioral economists study what motivates people to buy, save, donate, and any other number of actions that build society. The following studies reveal proven methods of encouraging healthy eating and exercise. Open for comment; 0 Comments.

• 12 Oct 2011

Creating Online Ads We Want to Watch

The mere fact that an online video advertisement reaches a viewer's computer screen does not guarantee that the ad actually reaches the viewer. New experimental research by Thales S. Teixeira looks at how advertisers can effectively capture and keep viewers' attention by evoking certain emotional responses. Closed for comment; 0 Comments.

• 19 May 2011

Empathy: The Brand Equity of Retail

Retailers can offer great product selection and value, but those who lack empathy for their customers are at risk of losing them, says professor Ananth Raman. Closed for comment; 0 Comments.

• 20 Aug 2009

A Decision-Making Perspective to Negotiation: A Review of the Past and a Look into the Future

The art and science of negotiation has evolved greatly over the past three decades, thanks to advances in the social sciences in collaboration with other disciplines and in tandem with the practical application of new ideas. In this paper, HBS doctoral student Chia-Jung Tsay and professor Max H. Bazerman review the recent past and highlight promising trends for the future of negotiation research. In the early 1980s, Cambridge, Massachusetts, was a hot spot on the negotiations front, as scholars from different disciplines began interacting in the exploration of exciting new concepts. The field took a big leap forward with the creation of the Program on Negotiation, an interdisciplinary, multicollege research center based at Harvard University. At the same time, Roger Fisher and William Ury's popular book Getting to Yes (1981) had a pronounced impact on how practitioners think about negotiations. On a more scholarly front, a related, yet profoundly different change began with the publication of HBS professor emeritus Howard Raiffa's book The Art and Science of Negotiation (1982), which for years to come transformed how researchers would think about and conduct empirical research. Key concepts include: Even as it has transitioned from decision analysis to behavioral decision research to social psychology, the decision perspective to negotiation has remained central to practitioners and academics alike, offering both practical relevance and the foundation for exciting new lines of research. Some of the most recent directions being pursued are surprises that early contributors to the decision perspective could have never predicted, as negotiation scholars engage with other disciplines and draw insights from diverse fields ranging from philosophy to neurobiology. Such collaboration is a healthy sign for an ongoing line of negotiation research. Closed for comment; 0 Comments.

• 15 May 2007

I’ll Have the Ice Cream Soon and the Vegetables Later: Decreasing Impatience over Time in Online Grocery Orders

How do people’s preferences differ when they make choices for the near term versus the more distant future? Providing evidence from a field study of an online grocer, this research shows that people act as if they will be increasingly virtuous the further into the future they project. Researchers examined how the length of delay between when an online grocery order is completed and when it is delivered affects what consumers order. They find that consumers purchase more "should" (healthy) groceries such as vegetables and less "want" (unhealthy) groceries such as ice cream the greater the delay between order completion and order delivery. The results have implications for public policy, supply chain managers, and models of time discounting. Key concepts include: Consumers spend less and order a higher percentage of "should" items and a lower percentage of "want" items the further in advance of delivery they place a grocery order. Encouraging people to order their groceries up to 5 days in advance of consumption could influence the healthfulness of the foods that people consume. Similarly, asking students in schools to select their lunches up to a week in advance could considerably increase the healthfulness of the foods they elect to eat. Online and catalog retailers that offer a range of goods as well as different delivery options might be able to improve their demand forecasting by understanding these findings. Closed for comment; 0 Comments.

How do emotions respond to outcome values and influence choice?

• Open access
• Published: 10 July 2024

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• Aikaterini Grimani 1 ,
• Ayse Yemiscigil 2 ,
• Qing Wang 1 ,
• Georgi Kirilov 3 ,
• Laura Kudrna 4 &
• Ivo Vlaev 1  

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Emotions powerfully, predictably, and pervasively influence decision making. The risk-as-feelings hypothesis states that two kinds of emotions are important in decision-making, anticipatory emotions and anticipated emotions. We empirically investigated whether and how anticipatory and anticipated emotions may change as a function of outcome values and whether anticipatory or anticipated emotions may explain the influence of outcome values on risky choice. To study the effects of value on emotions and choice, we offered people hypothetical large amounts ($100, $200, $300, $400) and incentivized moderate amounts ($10, $20, $30, $40) as prospects in gambles over two consecutive studies. Using a representative sample from the US to ensure the generalizability of the findings, each participant in our two studies made choices in gain and loss domains. Overall, anticipatory and anticipated emotions responded very similarly to changes in value for the sure gains in both studies. The findings also indicated that both anticipatory and anticipated emotions explained the effects of the value on choice for the sure gain and sure losses, while both mediated the effect of framing on choice towards the sure and the gamble option. Although anticipatory emotions mediated a larger portion of the effect, anticipated emotions also show some mediation.

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Emotion Regulation and Economic Decision-Making

Differential effects of irrelevant emotional context on regret and rejoice: a behavioural economic investigation of decision making under risk, do affective states influence risk preferences.

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Introduction

Research in decision-making has predominantly adopted a consequentialist view; individuals are assumed to choose the course of action with the most beneficial expected outcome. In standard economic models, for instance, choices are conceived to be determined by the weighted probability of monetary outcomes (Loewenstein & Lerner, 2003 ; Von Neumann & Morgenstern, 1944 ).

Tversky and Kahneman ( 1981 ) suggested that the selection between varying gains and losses reflects an individual’s emotional response to these alternatives, with losses evoking stronger negative emotions than equivalent gains evoke positive emotions. More recent studies that integrated emotions into these models have also remained consequential in their approach (Cheng et al., 2022 ). These studies have showed that the emotions people expect to feel as a consequence of their decisions, the so-called anticipated emotions (Bagozzi et al., 2003 ; Loewenstein & Lerner, 2003 ; Mellers et al., 1999 ), play a significant role in decision-making (Bell, 1982 , 1985 ; Dorison et al., 2020 ; Duxbury et al., 2020 ; George & Dane, 2016 ; Hillebrandt & Barclay, 2017 ; Mellers et al., 1997 ; Rutledge et al., 2014 ; Zaleskiewicz & Traczyk, 2020 ). Regret Theory, developed by Loomes and Sugden ( 1982 ) suggests that people make decisions not just based on the outcomes but also on the regret they anticipate feeling if they make the wrong choice. This theory highlights the powerful role of anticipated regret in driving individuals toward options that minimize potential future regret, even if those options involve more risk. In addition, Psychological Expected Utility Theory provides a comprehensive framework, incorporating both anticipatory and anticipated emotions into the evaluation of choices, explaining how these emotions mediate the framing effect (Caplin & Leahy, 2001 ). The goal of this study is to add to our understanding of ways that emotions influence decision-making by considering how people feel in the moment about the possible outcomes of their choices and not only how they expect to feel in the future because of their decisions. According to Simon ( 1983 ), “ in order to have anything like a complete theory of human rationality , we have to understand what role emotion plays in it. ” (Simon, 1983 , p. 29).

To fully understand emotions and decision-making, we must move beyond consequentialist frameworks. Consequentialist frameworks allow us to uncover crucial processes underlying decision-making, however, they do not capture all the ways emotions influence decisions. Existing literature (Loewenstein & Lerner, 2003 ; Loewenstein et al., 2001 ) highlights that decisions would also be impacted by “anticipatory emotions” which are immediate emotions that arise while contemplating about the decision alternatives. Both anticipatory and anticipated emotions are about the decisions in hand, strongly and routinely shaping decision making, so they are together called “integral emotions” (Damasio & Sutherland, 1994 ; Dorison et al., 2020 ; Duxbury et al., 2020 ; George & Dane, 2016 ; Hillebrandt & Barclay, 2017 ; Lerner et al., 2015 ; Zaleskiewicz & Traczyk, 2020 ). For example, a person who feels anxious about the potential outcome of a risky choice may choose a safer option rather than a potentially more lucrative option. In particular, a few philosophers pioneered the idea that integral emotion could be a beneficial guide, as, for example, anticipation of regret provides a reason to avoid excessive risk-taking (George & Dane, 2016 ; Lerner et al., 2015 ). However, integral emotions can also bias decision making. For example, one may feel afraid to fly and decide to drive instead, even though flying on a plane is overwhelmingly safer than driving a car (Gigerenzer, 2004 ).

Yet, while anticipated emotions are judgments about some future experiential state that will happen as a consequence of the decision (e.g., anticipated joy or regret), anticipatory emotions are actually experienced in the moment as people are contemplating decision alternatives (e.g., hope or fear), so these are conceptually and empirically distinct in terms of uncertainty, range and phenomenology of the emotion (Baumgartner et al., 2008 ; Loewenstein & Lerner, 2003 ). A few studies have compared anticipatory and anticipated emotions and found that anticipated emotions have a stronger effect in avoiding negative outcomes of the millennium transition than anticipatory emotions (Baumgartner et al., 2008 ; Xu & Guo, 2019 ). However, due to a limited number of studies, we do not know whether anticipatory emotions or anticipated emotions have different effects in the decision-making process and whether they have different levels of impacts on various kinds of behaviours.

It is important to understand the relationship between anticipatory and anticipated emotions on decision-making because these relationships underlie theories of behaviour change and influence. Such theories contribute to shaping behaviour in ways that may generate more health and wellbeing. Prominent approaches to behaviour change include the behaviour change wheel, which focusses on people’s capabilities, opportunities, and motivations to change; emotions are an aspect of motivation (Michie et al., 2011 ). Emotions respond to outcomes in ways that are not consistent or proportional to the outcome, as people react more strongly to losses than gains (Kahneman & Tversky, 1979 ; Dolan et al., 2012 ; Vlaev et al., 2019 ). Understanding the interplay of emotions with decisions about losses can significantly improve behavioural interventions. Overall, the purpose this study is to examine if anticipatory and anticipated emotions have differential relationships with the decision-making process according to outcome values.

Current studies have examined whether anticipatory emotions have predictive power in financial decision-making above and beyond anticipated emotions. Schlösser et al. ( 2013 ) asked samples of students “How do you feel right now about choosing alternative X?” before they made a choice between a $5 and a 50% chance to win $10 with a coin flip (with the roll of a die in a second study). Results showed anticipatory emotions predicted choice independent of the anticipated emotions (“How would you feel when the decision for alternative X leads to consequence Y?”) and the subjective probability attached to outcomes. This was the first empirical study to demonstrate the unique predictive ability of anticipatory emotions in decision-making under risk, but since the outcomes were held constant, it didn’t offer an understanding of how the predictive power of emotions holds under different outcome values and alternative framings of the outcomes as gain and loss.

In an additional study, Young et al. ( 2019 ) have researched the comparative role of anticipatory and anticipated emotions when outcomes are framed as gain and loss. They endowed students with money ($25 to $100) and asked each participant to make choices between a sure (riskless) gain/loss of money and a gamble option where the expected outcome is equivalent to the sure money (e.g., 80% chance of keeping all and 20% chance of losing all). In within-subjects’ conditions, the sure option was framed as a gain or a loss. In line with earlier studies (Cheung & Mikels, 2011 ; Stark et al., 2017 ), the authors find that framing affected anticipatory emotions and these emotions in turn mediated the effect of framing on choice. Anticipated emotions, on the other hand, didn’t play a significant role in explaining the relationship between framing and choice.

Existing literature provides suggestive evidence that anticipatory emotions have a unique predictive power in risky decision-making (Baumgartner et al., 2008 ; Schlösser et al., ( 2013 ). However, it is not yet clear what gives rise to anticipatory emotions. Baumgartner et al. ( 2008 ) described anticipatory emotions as a direct product of risk and raise the possibility that these emotions would arise in response to the probabilities of the events whereas anticipated emotions would be more closely linked to outcomes as they correspond to a time where the risk is resolved and outcomes are experienced. Similar to this, Schlösser et al. ( 2013 ) found that changes in objective risk affect anticipatory emotions. Another important component of decisions, outcome values, have not yet received empirical attention as a determinant of anticipatory emotions. Loewenstein and Lerner ( 2003 ) stressed that outcomes (as opposed to risk) would be important determinant of anticipatory emotions in comparison to anticipated emotions. The study also refers to Damasio and Sutherland ( 1994 ), who proposed that emotions arise in response to the mental images of an outcome. Indeed, studies have shown that anticipated emotions change as a function of outcome values (Charpentier, 2016 ; Charpentier et al., 2016 ). However, no study, to our knowledge, have examined the influence of outcome values on anticipatory emotions.

Present research

In the current study, we empirically investigated whether and how anticipatory and anticipated emotions may change as a function of outcome values and whether anticipatory or anticipated emotions may explain the influence of outcome values on risky choice. While there is no standard definition, the term “risky choice” in the current experiment is empirically defined as a choice between two options, a guaranteed “sure option” and a risky “gamble option, exemplified by Prospect-Theory (Kahneman & Tversky, 1979 ). Risky-choice behaviour has garnered significant attention from researchers in various fields, particularly in behavioural psychology and economics, where individuals must select between a certain option and an uncertain one (Cheng et al., 2022 ). It is important to emphasize the differentiation between decision-making under uncertainty and decision-making under risk within psychology and related fields (De Groot & Thurik, 2018 ). Neglecting to make this distinction appropriately could potentially result in researchers drawing misleading or inaccurate conclusions. In economics, the differentiation between uncertainty and risk initially proposed by Knight ( 1921 ). Under the concept of risk, the outcome is unknown, but the probability distribution governing that outcome is known. In contrast, uncertainty is characterized by both an unknown outcome and an unknown probability distribution. In both scenarios, preferences are established based on the probability distributions of outcomes. For risk, these probabilities are considered objective, while for uncertainty, they are subjective. Furthermore, behavioural economics literature underscores a prevailing aversion to uncertainty as compared to risky choices, a phenomenon often termed “ambiguity aversion.” Individuals tend to favour known probabilities over unknown ones, even when the known probability is low, and the unknown probability could potentially result in a guaranteed win, as demonstrated by Ellsberg ( 1961 ). The psychological literature also supports the empirical distinction between uncertainty and risk. For instance, Buckert et al. ( 2014 ) illustrated that the cortisol response to stress influences decision-making under risk but not under uncertainty.

To study the effects of value on emotions and choice, we offered people hypothetical large amounts ($100, $200, $300, and $400) and incentivized moderate amounts ($10, $20, $30, $40) as prospects in gambles over two consecutive studies. We conducted the two studies to ensure our results were incentive-compatible and assess any differences according to monetary amount. The sure option was calculated using a utility function that accounts for different levels of risk preferences. Given prior evidence that stressed the importance of anticipatory emotions in gain and loss domain (Cheung & Mikels, 2011 ; Stark et al., 2017 ; Young et al., 2019 ), we had each participant in our study to make choices under gain and loss domains. Finally, prior studies employed only student samples (Davis et al., 2009 ; Kocher et al., 2014 ; Schlösser et al., 2013 ), whereas we used a nationally representative sample to test the generalizability of the findings. Both studies were approved by the University of Warwick’s ethics committee.

Study 1 – hypothetical large choices

Participants.

In total, 311 adults from the US took part in the study. The recruitment of research participants was conducted by Qualtrics. Participants were screened based on age, gender and education to maintain representativeness of the population. Gender was equally split in our sample. Age breakdown was 37% for ages 55+, 18% for ages between 45 and 54, 17% for ages between 35 and 44, 17% for ages between 25 and 34, and 10% for ages between 18 and 24 (see Fig.  1 ). The highest levels of education completed were less than high school for 2% of the sample, high school for 43%, bachelors for 38%, Master’s degree for 13% and doctoral degree for 4% (see Fig.  2 ).

Visual depiction of the participants age breakdown

Visual depiction of the highest levels of education

An online survey was designed, in which participants made choices between a sure thing and a risky option (p chance of x). Each pair of options was presented as two pie charts. The two regions of the pie chart represented the risky bet indicating the two probabilities for gain versus nothing, respectively (see Fig.  3 ). Such gambles are widely used to measure risk aversion in most laboratory settings (Charness et al., 2013 ).

Visual depiction of the risky bet indicating the two probabilities for gain versus nothing, respectively

The risky option was constructed by crossing four levels of probability (0.2, 0.4, 0.6, and 0.8) and four levels of prospect ($100, $200, $300, $400) to create 16 choices. The sure amount was generated using a function with four levels of power γ (gamma) (0.35, 0.50, 0.65, 0.80) as they were observed in previous studies (Vlaev et al., 2010 ). A person with power γ would be indifferent between the sure thing and the risk. In particular, we used the following equation:

y = x p 1/γ , (1)

where y is the sure amount and the prospect is a “p chance of x.” γ describes the curvature of a hypothetical power law utility function, u(x) = xγ. Gamma is equal to one for a risk-neutral person. Smaller values of γ denote greater risk aversion. It is selected for its theoretical underpinning in Constant Relative Risk Aversion (CRRA), a fundamental concept in economic decision-making under uncertainty. CRRA utility functions assume that individuals’ risk preferences remain proportional to their wealth levels, providing a structured framework to analyze risk-taking behaviour across different economic contexts. Empirical studies frequently support the validity of CRRA functions in explaining how individuals balance risks and rewards, particularly in financial decision-making. The parameter 𝛾 determines the curvature of the utility function, influencing the sensitivity of individuals to changes in wealth or outcomes. This mathematical form allows researchers to simulate and predict behaviour in uncertain environments, offering insights into how risk preferences shape economic outcomes.

Four levels of γ were used (0.35, 0.50, 0.65, and 0.80) so that participants in the middle of the risk-aversion continuum will choose a mixture of sure amounts and risky prospects. As might be expected, very risk-averse individuals will choose only the sure amounts and very risk-seeking persons would choose only the prospects. Levels of γ were randomly assigned to gambles with the constraint that each level of γ occurred once for each amount and once for each probability. A set of 64 gambles (4 × 4 × 4) would be needed to map the whole surface of possible combinations between the four levels of probability, prospect amount, and γ. However, asking 64 questions to each person would induce a significant respondent burden so we randomly assigned the combinations to 4 groups that constitute 16 combinations each. Participants in our study were randomly allocated to these 4 groups. We made sure that all four levels of γ paired with every monetary amount and probability. We also used four different orders of the four γ levels across the 16 gambles.

The same 16 gambles framed as gains and losses were presented to the participants. Participants saw gain and loss blocks in a random order. “Gain” gambles asked the participants to make imaginary choices between a sure gain and an option that gave a chance to gain another amount, and “loss” gambles involved imaginary choices between a sure loss and an option that gave a chance to lose another amount.

After each decision, participants answered questions in blocks related to anticipatory emotions and anticipated emotions and the order of presentation for these blocks were randomized. The anticipatory emotions block consisted of 2 questions: “How do you feel right now about choosing the sure option?” and “How do you feel right now about choosing the gamble?”. Anticipated emotions block consisted of 3 questions: “How would you feel if you have chosen the “sure option” and received (or lost) $50?” “How you would you feel if you have chosen the “gamble” and got $0?” and “How you would you feel if you have chosen the gamble and won (or lost) $10?” After each emotion question, respondents rated first the valence of their feelings from − 4 to 4 and the intensity of their feelings from 1 to 7. Lastly, the participants were instructed to answer as they would answer if they were making these decisions for real. The lotteries are presented in Supplemental Materials.

The effects of outcome values on emotions

A within-person fixed effects linear regression model was used for estimating each effect. This model enables us to compare within-person emotion ratings as a function of values. When we are estimating the effects of sure dollars on emotions to the sure option, we control for gamble dollars and risk, and when estimating the effects of gamble dollars on the emotions to the gamble option, we control for sure dollars and risk. Errors are clustered at the individual level to account for the correlations in errors for repeated observations per person.

• Anticipatory emotions

Sure money: A 1% increase in sure dollars is associated with a 0.48 decrease in the valence ratings of anticipatory emotions towards the sure option in the loss domain ( p  < 0.001), and a 0.57 increase in the gain domain ( p  < 0.001). Footnote 1 Controlling for anticipated emotions towards the sure option, the effect becomes 0.12 ( p  < 0.001) and 0.20 ( p  < 0.001) respectively (around three quarters and two thirds smaller respectively). For intensity ratings, the effect is not significant in the loss domain (b = 0.01, p  = 0.619) and point to a 0.07 ( p  = 0.002) increase in the gain domain (see Fig.  4 ).

Visual depiction of the effects of outcome values on emotions: Anticipatory emotions - Sure Money

Gamble money: A one unit increase in gamble dollars is associated with a 0.19 ( p  < 0.001) decrease in the valence ratings of anticipatory emotions towards the gamble option in the loss domain, and a 0.12 ( p  < 0.001) increase in the gain domain. Controlling for anticipated emotions, the effect becomes 0.16 ( p  < 0.001) and 0.10 ( p  = 0.001) respectively. For intensity ratings, the effect is 0.06 in the loss domain ( p  = 0.01) and 0.04 in the gain domain ( p  = 0.030) (see Fig.  5 ).

Visual depiction of the effects of outcome values on emotions: Anticipatory emotions - Gamble Money

• Anticipated emotions

Sure money: A 1% increase in sure dollars is associated with a 0.56 decrease in the valence ratings of anticipated emotions towards the sure option in the loss domain ( p  < 0.001), and a 0.55 increase in the gain domain ( p  < 0.001). Controlling for anticipatory emotions, the effect becomes 0.22 ( p  < 0.001) and 0.22 ( p  < 0.001) respectively (around one fifth and two thirds smaller respectively). For intensity ratings, the effect is not significant in the loss domain (b = 0.03, p  = 0.32) and point to a 0.15 increase in the gain domain ( p  < 0.001) (see Fig.  6 ).

Visual depiction of the effects of outcome values on emotions: Anticipated emotions - Sure Money

Gamble money. A one unit increase in gamble dollars (which equals a $100 change) is associated with a 0.13 ( p  < 0.001) decrease in the valence ratings of anticipated emotions towards the gamble option in the loss domain, and a 0.07 ( p  < 0.001) decrease in the gain domain. Controlling for anticipatory emotions, the effect becomes insignificant for loss domain (b = -0.03, p  = 0.304) and remains 0.07 ( p  < 0.001) in the gain domain. For intensity ratings, the effect is a 0.09 increase in both gain and loss domain ( p  < 0.001) (see Fig.  7 ).

Visual depiction of the effects of outcome values on emotions: Anticipated emotions - Gamble Money

Emotions as a mediator of outcome values’ effects on choice

For mediation analyses, we first demeaned the variables in order to retrieve estimates identical to a fixed-effects model in a regression analysis. We did this in order to be able to employ a multiple mediation analysis (Preacher & Hayes, 2008 ) using the “sureg” command in Stata. We controlled for risk (probability) and monetary values in all models. In estimating the mediating effects on choice, the model was a within-person fixed effects linear probability model (LPM) where dependent variable measures the probability of choosing the risky gamble (0 ≤  p  ≤ 1) and was coded 1 if the choice was the risky gamble and 0 if the choice was the sure amount. Note that the estimated effects and significance levels are practically the same in linear probability models as in a logistic regression. However, the ease of interpreting the coefficients is considerably easier with the LPM (Hellevik, 2009 ). For ease of interpreting the results and for being able to conduct the mediation analyses, we split the data to gain and loss for these analyses instead of using interaction terms for each predictor and covariate.

Initial analyses confirmed that in the gain domain, (log) sure dollars (b = -0.16, p  < 0.001), gamble dollars (b = 0.04, p  < 0.001) and risk (b = 0.37, p  < 0.001) were all significant predictors of risky choice. In the loss domain, (log) sure dollars (b = 0.06, p  < 0.001), gamble dollars (b = -0.04, p  < 0.001) and risk (b = -0.17, p  < 0.001) were all significant predictors of risky choice (see Fig.  8 ).

Visual depiction of the emotions as a mediator of outcome values’ effects on choice

Sure money. In multiple mediation models, we tested the mediation effects of all emotions related to the sure option simultaneously. Results revealed that, in the gain domain, anticipatory emotions towards the sure option mediated the effects of sure dollars on choice (Indirect effect: -0.04, p  < 0.001). In the loss domain, anticipatory emotions towards the sure option mediated the effects of sure dollars on choice (Indirect effect: 0.02, p  < 0.001).

Gamble money. In the gain domain, anticipatory emotions towards the gamble option mediated the effects of gamble dollars on choice (Indirect effect: 0.008, p  < 0.001). In the loss domain, anticipatory emotions towards the gamble option mediated the effects of gamble dollars on choice (Indirect effect: -0.01, p  < 0.001) (see Fig.  9 ).

Sure money. In the gain domain, anticipated emotions towards the sure also mediated the effects of gamble dollars on choice (Indirect effect: -0.02, p  < 0.001). In the loss domain, anticipated emotions towards the sure also mediated the effects of gamble dollars on choice (Indirect effect: 0.01, p  < 0.001).

Gamble money. In the gain domain, anticipated emotions towards winning the gamble didn’t significantly mediate the effects of gamble dollars on choice (indirect effect: 0.00, p  = 0.600), and mediation for anticipated emotions towards losing the gamble was not statistically either (indirect effect: -0.0007, p  = 0.069). In loss, anticipated emotions towards winning the gamble (and losing the prospect amount) didn’t significantly mediate the effects of gamble dollars on choice (indirect effect: -0.0006, p  = 0.352), and anticipated emotions towards losing the gamble (and not losing the prospect amount) was only not significant either (indirect effect: 0.0007, p  = 0.089) (see Fig.  10 ).

Emotions as a mediator of gain and loss domain effect on choice

We find that the same individual is more risk-seeking when making choices in the loss domain than gain (b = 0.05, p  = 0.001). We then tested whether emotions towards the sure option could explain the effect of framing on choice using a multiple mediation model where anticipatory and anticipated emotions’ mediation effects could be examined simultaneously. The loss condition increases risk-seeking indirectly via anticipatory emotions towards the sure option (indirect effect: 0.11, p  < 0.001) as well as anticipated emotions towards the sure option (indirect effect: 0.04, p  < 0.001).

Emotions towards the gamble, however, yielded an opposite influence on risk-taking. Anticipatory emotions towards the gamble was lower in loss domain than gain, which in turn, decreased risk taking (indirect effect: -0.09, p  < 0.001). The results were the same for anticipated emotions towards winning the gamble (and receiving the prospect) (indirect effect: -0.05, p  < 0.001). Anticipated emotions towards losing the gamble did not have significant mediation effects (indirect effect: 0.02, p  = 0.147).

Study 2 – incentivized moderate choices

In Study 1, we looked at hypothetical large choices. In Study 2, we looked at incentivized moderate choices. Before commencing the experiment, participants were briefed on the payments for the follow-up experiment. Each participant faced a two-step process where a random question from both the gain and loss domains was selected, which had the potential to be played out for real. Another random selection determined whether the participant’s choices would be enacted from the gain or loss domain. In cases where participants chose the gamble over the sure payoff, a random number was generated based on given probabilities to determine the final outcome. If the gain domain was chosen, participants received the amount they selected in the randomly chosen question from the gain domain. In the event that a choice from the loss domain was randomly selected to be played out for real, participants were compensated with the amount they would have earned in the gain domain minus the loss amount. If the resulting difference was below 0, participants received no payment. Participants were explicitly informed that in the loss domain, they would experience a deduction from the money they could have earned in the gain domain. The purpose of study to is to investigate if our results are incentive-compatible and assess any differences according to monetary amount.

In total, 268 adults from the US took part in the study and the recruitment was conducted by Qualtrics. The sampling procedure involved screening based on age, gender and education to maintain representativeness of the population. Gender was equally split. Age breakdown was 36% for ages 55 or above, 22% for ages between 45 and 54, 16% for ages between 35 and 44, 17% for ages between 25 and 34, and 9% for ages between 18 and 24 (see Fig.  11 ).

The highest levels of education completed were less than high school for 1% of the sample, high school for 42%, bachelors for 39%, Master’s degree for 15% and doctoral degree for 3% (see Fig.  12 ).

Design and procedure

The design and the procedure were identical with the following slight differences. The risky option was constructed by crossing four levels of probability (0.2, 0.4, 0.6, and 0.8) and four levels of prospect ($10, $20, $30, $40) to create 16 choices. The participants were notified that one of their choices will be played out for real. We made payments to the participants accordingly. Given resource constraints, we only asked about valence and not intensity in the second study.

Sure money: A 1% increase in sure dollars is associated with a 0.32 decrease in the valence ratings of anticipatory emotions towards the sure option in the loss domain ( p  < 0.001), and a 0.30 increase in the gain domain ( p  < 0.001). Controlling for anticipated emotions towards the sure option, the effect becomes 0.13 ( p  < 0.001) and 0.08 ( p  < 0.001) respectively (around two thirds and three quarters smaller respectively) (see Fig.  13 ).

Visual depiction of the effects of outcome values on emotions

Gamble money. A one unit increase in gamble dollars (which equals a $10 change) is associated with a 0.15 ( p  < 0.001) decrease in the valence ratings of anticipatory emotions towards the gamble option in the loss domain, and controlling for anticipated emotions, the effect becomes 0.09 ( p  < 0.001). There was no statistically significant effect in the gain domain (see Fig.  14 ).

Sure money. A 1% increase in sure dollars is associated with a 0.30 decrease in the valence ratings of anticipated emotions towards the sure option in the loss domain ( p  < 0.001), and a 0.31 increase in the gain domain ( p  < 0.001). Controlling for anticipatory emotions, the effect becomes 0.11 ( p  < 0.001) and 0.11 ( p  < 0.001) respectively (around two thirds smaller in each case).

Gamble money. A one unit increase in gamble dollars (which equals a $10 change) is associated with a 0.28 ( p  < 0.001) decrease in the valence ratings of anticipated emotions towards the gamble option in the loss domain, and a 0.09 ( p  < 0.001) decrease in the gain domain. Controlling for anticipatory emotions, the effect becomes 0.22 in the loss domain ( p  < 0.001) and remains 0.09 ( p  < 0.001) in the gain domain (see Fig.  15 ).

Initial analyses confirmed that in the gain domain, (log) sure dollars (b = -0.03, p  = 0.031) and risk (b = 0.21, p  < 0.001) were significant predictors of risky choice. However, gamble dollars (b = -0.003, p  = 0.659) were not significant predictors of risky choice. In the loss domain, (log) sure dollars (b = 0.04, p  < 0.001), gamble dollars (b = -0.02, p  < 0.001) and risk (b = -0.11, p  = 0.012) were all significant predictors of risky choice (see Fig.  16 ).

Visual depiction of the effects emotions as a mediator of outcome values effects on choice

Sure money. In the gain domain, anticipatory emotions towards the sure option mediated the effects of sure dollars on choice (indirect effect: -0.02, p  < 0.001). In the loss domain, anticipatory emotions towards the sure option mediated the effects of sure dollars on choice (indirect effect: 0.01, p  < 0.001).

Gamble money. In the gain domain, anticipatory emotions towards the gamble option didn’t mediate the effects of gamble dollars on choice (Indirect effect: -0.002, p  = 0.132). In the loss domain, anticipatory emotions towards the gamble option mediated the effects of gamble dollars on choice (indirect effect: -0.01, p  < 0.001) (see Fig.  17 ).

Sure money. In the gain domain, anticipated emotions towards the sure also mediated the effects of gamble dollars on choice (indirect effect: -0.01, p  < 0.001). In the loss domain, anticipated emotions towards the sure also mediated the effects of gamble dollars on choice (indirect effect: 0.01, p  < 0.001) (see Fig.  18 ).

Gamble money. In the gain domain, anticipated emotions towards winning the gamble didn’t significantly mediate the effects of gamble dollars on choice (indirect effect: 0.00, p  = 0.959), and anticipated emotions towards losing the gamble did not mediate the effects of gamble dollars on choice (indirect effect: -0.0007, p  = 0.064). In loss, anticipated emotions towards winning the gamble (and losing the prospect amount) mediated the effects of gamble dollars on choice (indirect effect: -0.004, p  = 0.006), and anticipated emotions towards losing the gamble (and not losing the prospect amount) was not a significant mediator (indirect effect: 0.0007, p  = 0.980).

We found that the same individual is more risk-seeking when making choices in the loss domain than gain (b = 0.03, p  = 0.001). The loss condition increases risk-seeking indirectly via anticipatory emotions towards the sure option (indirect effect: 0.07, p  < 0.001) as well as anticipated emotions towards the sure option (indirect effect: 0.05, p  < 0.001).

Emotions towards the gamble, however, yielded an opposite influence on risk-taking. Anticipatory emotions towards the gamble was lower in loss domain than gain, which in turn, decreased risk taking (indirect effect: -0.08, p  < 0.001). The results were the same for anticipated emotions towards winning the gamble (and receiving the prospect) (indirect effect: -0.03, p  < 0.001). Anticipated emotions towards losing the gamble didn’t have significant mediation effects (indirect effect: 0.01, p  = 0.147) (see Fig.  19 ).

General discussion

Anticipatory emotions responded consistently to outcome values and the effects were in the expected direction. In general, higher values increased valence in gain domain and decreased valence in the loss domain. In Study 2 though, anticipatory emotions for the gamble in the loss domain didn’t change with value. For the gamble option, the effects were larger in the loss domain than gain in both studies. This finding suggests that anticipatory emotions respond more to risky losses (vs. gain): the fear from the possibility of losing loom greater than the excitement for the possibility of winning. For the sure option, however, the effects seemed larger in the gain domain than loss but only by a small margin in Study 2.

Anticipated emotions also increased with value in the gain domain, and decreased with value in the loss domain. We don’t observe any domain-dependent differences in anticipated emotions for the sure option in either studies. For the gamble, on the other hand, anticipated emotions changed more with value in the loss compared to gain, especially when the gambles were incentivized. This contradicts the findings in prior research where loss aversion was not reflected in anticipated emotion ratings but only in the way these emotions were weighed in decision-making (Charpentier, 2016 ; Charpentier et al., 2016 ). This could be due to methodological reasons. According to Charpentier et al. ( 2016 ), who studied the loss aversion, the payments were much lower than the current study. Our results are not consistent with Harinck et al. ( 2007 ) and Yechiam et al. ( 2014 ). These studies found a positivity bias for anticipated emotions, whereas our results are consistent with gain-loss neutrality. In our results, loss aversion also does not appear to be due to an asymmetry in feeling, inconsistent with other studies on high (though not small) hypothetical amounts (McGraw et al., 2010 ). Alternative explanations should be considered. As our results show an asymmetry between gains and losses for the risky but not safe choices, it is possible that risk and not loss aversion explain the results – and future research could seek to disentangle these.

Overall, anticipatory and anticipated emotions responded very similarly to changes in value for the sure gains in both studies (1 and 2). We can thus learn that, generally, the valence results for the non-incentivized amounts are likely to extend to incentive compatible scenarios. The responses were very similar for sure losses in the second study too, although anticipated emotions changed more with sure losses in the first non-incentivized study. It may thus be that that this result on anticipated emotions lacks ecological validity. In the incentivized study, anticipatory emotions didn’t respond to changes in the value of risky gains and responded to risky losses less strongly compared to anticipated emotions. Anticipated emotions, on the other hand, responded very consistently to the changes in the value of risky gains across the studies and models, and very strongly to changes in the value of risky losses in the incentivized study.

In Study 1, the findings were generally similar for intensity ratings. The only exception was that neither anticipatory emotions nor anticipated emotions for the sure option revealed a significant response to changes in value in the loss domain when they are measured as intensity. Due to resource constraints we did not look at intensity in the second study and future research could explore if similar findings hold for large, incentive compatible choices.

The findings indicated that both anticipatory and anticipated emotions explained the effects of the value on choice for the sure gain and sure losses. The proportion mediated was larger for anticipatory emotions compared to anticipated emotions. Results were consistent across studies (1 and 2). For the risky gain, neither anticipatory nor anticipated emotions had a consistent mediating role in the effects of value on choice. For risky losses, anticipatory emotions were a consistent mediator, and anticipated emotions towards the prospect became a significant mediator only in incentivized study, although the proportion mediated was much smaller than anticipatory emotions.

The findings from both studies (1 and 2) indicated that all emotions towards the sure and the gamble option mediated the effect of framing on choice while anticipatory emotions mediated a larger portion of the effect. Previous research by Young et al. ( 2019 ) has found that only anticipatory emotions for the sure option explained the effects of framing on choice. We did find that anticipatory emotions towards the sure are indeed the strongest explanatory factor although anticipated emotions also show some mediation.

Learning and implications

These findings have important implications for marketers and practitioners. Anticipated and anticipatory emotions may support marketers in their communication strategies, as they could increase the effectiveness of one-to-one communication, especially online, based on consumer personality variables, inferred from their online behaviour data. For example, marketers can use social networks likes or language to infer personality traits of agreeableness and conscientiousness, which are correlated to the self-control trait. In addition, in order to make better predictions, marketing researchers should consider a broad range of emotions likely to be salient at different points of the purchase and consumption process (Bee & Madrigal, 2013 ; Bettiga & Lamberti, 2020 ). It is important that marketers consider both how a consumer feels when they decide and how they expect to feel as a result of the decision, and how these interplay with each other and features of the decision such its importance and significance.

These findings can also be useful to other practitioners. For example, health interventions, especially those that target prevention behaviours, can include an emotional narrative that makes people believe that they will regret or worry eventually if they do not perform the suggested behaviours. People could be informed that how they feel when they decide affects their choice in addition to the expected outcomes of that choice and its importance and significance, which could alter the influence of emotions. When people are exposed to a risk, the psychological immune system will motivate people to cope with negative feelings and thoughts unconsciously. Since people are often unaware of their psychological defences systems, they tend to exaggerate their future emotional responses but not their current emotional and cognitive reactions (Xu & Guo, 2019 ).

To conclude, the study lays a critical foundation for understanding how emotions, especially the anticipation of losses, influence risky choices. By integrating these findings with real-world factors such as incentives and consequences, researchers and practitioners can develop targeted strategies to nudge behaviour and promote informed decision-making. For instance, health messages could be framed to emphasize potential benefits rather than risks, thereby encouraging positive behaviour change. Additionally, providing consumers with tools to identify and mitigate emotional biases in their decision-making processes can lead to more informed and rational choices.

Conclusions and implications

Overall, anticipatory and anticipated emotions responded very similarly to changes in value for the sure gains in both studies (1 and 2). The findings also indicated that both anticipatory and anticipated emotions explained the effects of the value on choice for the sure gain and sure losses, while both mediated the effect of framing on choice towards the sure and the gamble option. Although anticipatory emotions mediated a larger portion of the effect, which was expected according to the existing literature, anticipated emotions also show some mediation. Marketers and other practitioners can use these results to understand and influence people’s choices.

Data availability

The data presented in this study are available upon reasonable request from the corresponding author.

In general, effect sizes were small to moderate. For example, in this case, F(1,310) = 240.9, p  < 0.001, r 2 within = 0.18, r 2 between = 0.007, overall = 0.10. Further details are available upon request.

Bagozzi, R. P., Dholakia, U. M., & Basuroy, S. (2003). How effortful decisions get enacted: The motivating role of decision processes, desires, and anticipated emotions. Journal of Behavioral Decision Making , 16 (4), 273–295.

Article   Google Scholar  

Baumgartner, H., Pieters, R., & Bagozzi, R. P. (2008). Future-oriented emotions: Conceptualization and behavioral effects. European Journal of Social Psychology , 38 (4), 685–696.

Bee, C. C., & Madrigal, R. (2013). Consumer uncertainty: The influence of anticipatory emotions on ambivalence, attitudes, and intentions. Journal of Consumer Behaviour , 12 (5), 370–381.

Bell, D. E. (1982). Regret in decision making under uncertainty. Operations Research , 30 (5), 961–981.

Bell, D. E. (1985). Disappointment in decision making under uncertainty. Operations Research , 33 (1), 1–27.

Bettiga, D., & Lamberti, L. (2020). Future-oriented happiness: Its nature and role in consumer decision-making for new products. Frontiers in Psychology , 11 .

Buckert, M., Schwieren, C., Kudielka, B. M., & Fiebach, C. J. (2014). Acute stress affects risk taking but not ambiguity aversion. Frontiers in Neuroscience , 8 , 82.

Article   PubMed   PubMed Central   Google Scholar  

Caplin, A., & Leahy, J. (2001). Psychological expected utility theory and anticipatory feelings. The Quarterly Journal of Economics, 116 (1), 55–79.

Charness, G., Gneezy, U., & Imas, A. (2013). Experimental methods: Eliciting risk preferences. Journal of Economic Behavior Organization , 87 , 43–51.

Charpentier, C. J. (2016). The interaction between emotion and economic decision-making . UCL (University College London).

Charpentier, C. J., De Neve, J. E., Li, X., Roiser, J. P., & Sharot, T. (2016). Models of affective decision making: How do feelings predict choice? Psychological Science , 27 (6), 763–775. https://doi.org/10.1177/0956797616634654 .

Article   PubMed   Google Scholar  

Cheng, H., Burns, C., & Revie, M. (2022). Risky-choice framing and its null effect on integral emotions. Journal of Risk Research , 25 (4), 453–467.

Cheung, E., & Mikels, J. A. (2011). I’m feeling lucky: The relationship between affect and risk-seeking in the framing effect. Emotion , 11 (4), 852.

Damasio, A. R., & Sutherland, S. (1994). Descartes’ error: Emotion, reason and the human brain. Nature , 372 (6503), 287–287.

Google Scholar  

Davis, T., Love, B. C., & Maddox, W. T. (2009). Anticipatory emotions in decision tasks: Covert markers of value or attentional processes? Cognition , 112 (1), 195–200.

De Groot, K., & Thurik, R. (2018). Disentangling risk and uncertainty: When risk-taking measures are not about risk. Frontiers in Psychology , 9 , 2194.

Dolan, P., Hallsworth, M., Halpern, D., King, D., Metcalfe, R., & Vlaev, I. (2012). Influencing behaviour: The mindspace way. Journal of Economic Psychology, 33 (1), 264–277.

Dorison, C. A., Klusowski, J., Han, S., & Lerner, J. S. (2020). Emotion in organizational judgment and decision making. Organizational Dynamics , 49 .

Duxbury, D., Gärling, T., Gamble, A., & Klass, V. (2020). How emotions influence behavior in financial markets: A conceptual analysis and emotion-based account of buy-sell preferences. The European Journal of Finance , 1–22.

Ellsberg, D. (1961). Risk, ambiguity, and the savage axioms. The Quarterly Journal of Economics , 75 (4), 643–669.

George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior Human Decision Processes , 136 , 47–55.

Gigerenzer, G. (2004). Dread risk, September 11, and fatal traffic accidents. Psychological Science , 15 (4), 286–287. https://doi.org/10.1111/j.0956-7976.2004.00668.x .

Harinck, F., Van Dijk, E., Van Beest, I., & Mersmann, P. (2007). When gains loom larger than losses: Reversed loss aversion for small amounts of money. Psychological Science, 18 (12), 1099–1105.

Hellevik, O. (2009). Linear versus logistic regression when the dependent variable is a dichotomy. Quality & Quantity , 43 (1), 59–74.

Hillebrandt, A., & Barclay, L. J. (2017). Comparing integral and incidental emotions: Testing insights from emotions as social information theory and attribution theory. Journal of Applied Psychology , 102 (5), 732–752. https://doi.org/10.1037/apl0000174 .

Kahneman, D., & Tversky, A. (1979). Prospect Theory: An analysis of decision under risk. Econometrica , 47 (2), 263–291. https://doi.org/10.2307/1914185 .

Knight, F. H. (1921). Risk, uncertainty and profit (Vol. 31). Houghton Mifflin.

Kocher, M. G., Krawczyk, M., & van Winden, F. (2014). Let me dream on!’Anticipatory emotions and preference for timing in lotteries. Journal of Economic Behavior Organization , 98 , 29–40.

Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annu Rev Psychol , 66 , 799–823. https://doi.org/10.1146/annurev-psych-010213-115043 .

Loewenstein, G. F., & Lerner, J. S. (2003). The role of affect in decision making. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Series in affective science. Handbook of affective sciences (pp. 619–642). Oxford University Press.

Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin , 127 (2), 267.

Loomes, G., & Sugden, R. (1982). Regret theory: An alternative theory of rational choice under uncertainty. The Economic Journal, 92 (368), 805–824.

McGraw, A. P., Larsen, J. T., Kahneman, D., & Schkade, D. (2010). Comparing gains and losses. Psychological Science, 21 (10), 1438–1445.

Mellers, B., Schwartz, A., Ho, K., & Ritov, I. (1997). Decision affect theory: Emotional reactions to the outcomes of risky options. Psychological Science , 8 (6), 423–429.

Mellers, B., Schwartz, A., & Ritov, I. (1999). Emotion-based choice. Journal of Experimental Psychology: General , 128 (3), 332.

Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science, 6 (42).

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods , 40 (3), 879–891.

Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences , 111 (33), 12252–12257.

Schlösser, T., Dunning, D., & Fetchenhauer, D. (2013). What a feeling: The role of immediate and anticipated emotions in risky decisions. Journal of Behavioral Decision Making , 26 (1), 13–30.

Simon, H. A. (1983). Reason in human affairs . Basil Blackwell.

Stark, E., Baldwin, A. S., Hertel, A. W., & Rothman, A. J. (2017). Understanding the framing effect: Do affective responses to decision options mediate the influence of frame on choice? Journal of Risk Research , 20 (12), 1585–1597.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science , 211 (4481), 453–458.

Vlaev, I., Kusev, P., Stewart, N., Aldrovandi, S., & Chater, N. (2010). Domain effects and financial risk attitudes. Risk Analysis: An International Journal , 30 (9), 1374–1386.

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior . Princeton University Press.

Xu, Z., & Guo, H. (2019). Advantages of anticipated emotions over Anticipatory Emotions and cognitions in Health decisions: A Meta-analysis. Health Communication , 34 (7), 774–781. https://doi.org/10.1080/10410236.2018.1434738 .

Yechiam, E., Telpaz, A., & Hochman, G. (2014). The complaint bias in subjective evaluations of incentives. Decision, 1 (2), 147–160.

Young, N. A., Shuster, M. M., & Mikels, J. A. (2019). The sure thing: The role of integral affect in risky choice framing. Emotion , 19 (6), 1035–1043. https://doi.org/10.1037/emo0000505 .

Zaleskiewicz, T., & Traczyk, J. (2020). Emotions and financial decision making. Psychological perspectives on financial decision making (pp. 107–133). Springer.

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Conceptualization: AY, IV, QW; Methodology: AG, AY; Writing - original draft preparation: AG, AY; Writing - review and editing: AG, LK; GK; Supervision: IV, QW.

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The Risks of Using AI to Interpret Human Emotions

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People are complicated.

Companies have historically used focus groups and surveys to understand how people felt. Now, emotional AI technology can help businesses capture the emotional reactions of both employees and consumers in real time — by decoding facial expressions, analyzing voice patterns, monitoring eye movements, and measuring neurological immersion levels, for example. The ultimate outcome is a much better understanding both of workers and customers. But, because of the subjective nature of emotions, emotional AI is especially prone to bias. AI is often also not sophisticated enough to understand cultural differences in expressing and reading emotions, making it harder to draw accurate conclusions. For instance, a smile might mean one thing in Germany and another in Japan. Confusing these meanings can lead businesses to make wrong decisions. Imagine a Japanese tourist needing assistance while visiting a shop in Berlin. Using emotion recognition to prioritize which customers to support, the shop assistant might mistake their smile — a sign of politeness back home — as an indication that they don’t require help. If left unaddressed, conscious or unconscious emotional biases like this can perpetuate stereotypes and assumptions at an unprecedented scale.

What do people really feel?

• MP Mark Purdy is a thought leader and advisor on issues at the intersection of economics, technology and business. He is Managing Director of Purdy & Associates, an independent consultancy focusing on economic and technology policy issues.
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How do you move from an emotionally charged moment in a negotiation to a mutually beneficial agreement?

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In Beyond Reason , authors Roger Fisher and Daniel Shapiro illustrate the five “core concerns” that motivate people: appreciation, affiliation, autonomy, status, and role. Through recognizing and dealing with these core concerns, negotiators can move emotions in a constructive direction. Identifying and managing these core concerns does take practice. The Teaching Negotiation Resource Center (TNRC) has a variety of role simulations that can help students practice how to deal with emotions in negotiation.

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To learn more about this simulation, download a free preview copy of the Hospital Committee Teacher’s Package .

Rose Lane – Featured Simulation

This two-party, email-based, multi-issue issue negotiation deals with a dispute between neighbors over one sharing their home on a home-sharing website, and having difficult conversations in relationships with low trust. Schmidt, a branch manager of a local bank, reaches out via email to their next-door neighbor, Harberer, about the use of their home on the popular home-sharing site, HomeBNB. Schmidt has grown increasingly frustrated by the prevalence of frequent large groups of rock climbers, who party and play music late into the night. Schmidt has attempted to reach out to Haberer through various channels but has not been able to make contact. Frustrated with their inability to contact Haberer, Schmidt mobilized the neighborhood’s social media group where they initially received significant support. After two years of increasing frustration, Schmidt has reached out to the local government and was surprised with their response: come to a resolution within 30 days or we’ll bring your case to the larger governing body and make a region-wide ruling. Major lessons of this simulation include:

• Identifying challenges related to negotiating via email and generating strategies to overcome these challenges.
• Negotiating with very weak alternatives (BATNA).
• Having difficult conversations in relationships with low trust.

To learn more about this simulation, download a free preview copy of the Rose Lane Teacher’s Package .

Casino Two – Featured Simulation

This two-party, two hour intra-organizational discussion, an updated version of the original, is between a newly promoted manager and her division vice-president over work performance, responsibility for a new computer game project, and office environment issues. Jamie and Allison are both employees at Digital Development, a male-dominated Silicon Valley start-up that makes profitable phone apps. Jamie is the vice president for Programming and recently promoted Allison, moving her from the kids and family app team to the gaming team. Jamie feels that Allison has not been performing well in her new position. The two are meeting to discuss her performance and then negotiate next steps. Major lessons of this simulation include:

• Those parties willing to consider the perceptions and interests of the other party as relevant can usually engage effectively in mutually beneficial joint problem solving.
• The skills involved in separating the people from the problem are especially important in this negotiation as emotions between formerly friendly people may run high.
• If the participants choose to try to resolve workplace environment difficulties, they must face the fact that whatever they decide will have an impact on those around them.

To learn more about this simulation, download a free preview copy of the Casino Two Teacher’s Package .

Neighborhood Care – Featured Simulation

This two-party, three-hour negotiation or mediation is between church and neighborhood representatives over the possible use of church facilities to provide services for the mentally challenged. Neighborhood Care, Inc. is a non-profit mental health organization that provides counseling and recreational health services to mentally challenged adults and teenagers. Neighborhood Care would like to rent space in a local church, and the church is interested. Local residents oppose the idea and plan on staging a protest at the next zoning hearing, when the church will seek a permit to operate a Neighborhood Care facility. The situation is also complicated by the fact that the church is located in a neighborhood in which most residents are of a different religious faith. Major lessons of this simulation include:

• Partisan perceptions: This exercise illustrates how and why groups with competing interests or concerns can view the same situation in different ways.
• Mediator issues: The difficulties facing mediators trying to gain entry into community disputes are illustrated, especially the problem of maintaining neutrality.
• Identifying success: The prospects for developing written agreements in community conflicts are presented. The difficulties of defining a “good” outcome in a community dispute are also highlighted.
• Implementation: Review of the agreement reached in the real-life case highlights the problem of implementing informed negotiated agreements.

To learn more about this simulation, download a free preview copy of the Neighborhood Care Teacher’s Package .

In addition to these simulations, also check out Beyond Reason: Using Emotions as You Negotiate by Roger Fisher and psychologist Daniel Shapiro, winner of the 2005 CPR Award for Excellence in ADR (Outstanding Book Category).

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TNRC  negotiation exercises and teaching materials are designed for educational purposes. They are used in college classroom settings or corporate training settings; used by mediators and facilitators seeking to introduce their clients to a process or issue; and used by individuals who want to enhance their negotiation skills and knowledge.

Negotiation exercises and role-play simulations introduce participants to new negotiation and dispute resolution tools, techniques and strategies.  Our videos, books, case studies, and periodicals are also a helpful way of introducing students to key concepts while addressing the theory and practice of negotiation.

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Preparing for negotiation.

Understanding how to arrange the meeting space is a key aspect of preparing for negotiation. In this video, Professor Guhan Subramanian discusses a real world example of how seating arrangements can influence a negotiator’s success. This discussion was held at the 3 day executive education workshop for senior executives at the Program on Negotiation at Harvard Law School.

Guhan Subramanian is the Professor of Law and Business at the Harvard Law School and Professor of Business Law at the Harvard Business School.

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Harvard researchers help explain link between emotion and addictive substance use

In four integrated studies, researchers find sadness, more than other negative emotions, heightens craving to smoke and likelihood of relapse

What drives a person to smoke cigarettes—and keeps one out of six U.S. adults addicted to tobacco use, at a cost of 480,000 premature deaths each year despite decades of anti-smoking campaigns? What role do emotions play in this addictive behavior? Why do some smokers puff more often and more deeply or even relapse many years after they've quit? If policymakers had those answers, how could they strengthen the fight against the global smoking epidemic?

A team of researchers based at Harvard University now has fresh insights into these questions, thanks to a set of four interwoven studies described in a new report published in the Proceedings of the National Academy of Sciences: The studies show that sadness plays an especially strong role in triggering addictive behavior relative to other negative emotions like disgust.

The studies range from analysis of data from a national survey of more than 10,000 people over 20 years to laboratory tests examining the responses of current smokers to negative emotions. One study tested the volume and frequency of actual puffs on cigarettes by smokers who volunteered to be monitored as they smoked. While drawing from methodologies from different fields, the four studies all reinforce the central finding that sadness, more than other negative emotions, increases people's craving to smoke.

"The conventional wisdom in the field was that any type of negative feeling, whether it's anger, disgust, stress, sadness, fear, or shame, would make individuals more likely to use an addictive drug," said lead researcher Charles A. Dorison , a Harvard Kennedy School doctoral candidate. "Our work suggests that the reality is much more nuanced than the idea of 'feel bad, smoke more.' Specifically, we find that sadness appears to be an especially potent trigger of addictive substance use."

Senior co-author Dr. Jennifer Lerner , the co-founder of the Harvard Decision Science Laboratory and Thornton F. Bradshaw Professor of Public Policy, Decision Science, and Management at Harvard Kennedy School, said the research could have useful public policy implications. For example, current anti-smoking ad campaigns could be redesigned to avoid images that trigger sadness and thus unintentionally increase cigarette cravings among smokers.

Lerner is the first tenured psychologist on the faculty of the Kennedy School. She was the chief decision scientist for the U.S. Navy in 2018–19. Lerner has studied the impact of emotions on decision making since the 1990s, examining issues including whether generalized negative emotions trigger substance abuse or whether a subset of specific emotions such as sadness are more important factors in addiction.

The other co-authors include Ke Wang, a doctoral student at the Kennedy School; Vaughan W. Rees, director of the Center for Global Tobacco Control at Harvard T.H. Chan School of Public Health; Ichiro Kawachi, the John L. Loeb and Frances Lehman Loeb Professor of Social Epidemiology at the Chan School; and Associate Professor Keith M.M. Ericson at the Questrom School of Business at Boston University. The work was funded by grants from the National Science Foundation and the National Institutes of Health.

Here are further details on the techniques and key findings of the four studies:

• Examining data from a national survey that tracked 10,685 people over 20 years, the researchers found that self-reported sadness among participants was associated with being a smoker and with quitters relapsing into smoking one and two decades later. The sadder individuals were, the more likely they were to be smokers. Notably, other negative emotions did not show the same relationship with smoking.
• Then the team designed an experiment to test causality: Did sadness cause people to smoke, or were negative life events causing both sadness and smoking? To test this, 425 smokers were recruited for an online study. One-third were shown a sad video clip about the loss of a life partner. Another third of the smokers were shown a neutral video clip, about woodworking; the final third were shown a disgusting video involving an unsanitary toilet. All participants were asked to write about a related personal experience. The study found that individuals in the sadness condition—who watched the sad video and wrote about a personal loss—had higher cravings to smoke than both the neutral group and the disgust group.
• A similar approach in the third study measured actual impatience for cigarette puffs rather than mere self-reported craving. Similar to the second study, nearly 700 participants watched videos and wrote about life experiences that were either sad or neutral, and then were given hypothetical choices between having fewer puffs sooner or more puffs after a delay. Those in the sadness group proved to be more impatient to smoke sooner than those in the neutral group. That result built upon previous research findings that sadness increases financial impatience, measured with behavioral economics techniques.
• The fourth study recruited 158 smokers from the Boston area to test how sadness influenced actual smoking behavior. Participants had to abstain from smoking for at least eight hours (verified by carbon monoxide breath test). They were randomly assigned to sadness or neutral control groups; smokers sat in a private room at the Harvard Tobacco Research Laboratory, watched a sad video and wrote about great loss, or watched a neutral video and wrote about their work environment. Then they smoked their own brand of cigarette through a device that tested the total volume of puffs and their speed and duration. The results: Smokers in the sadness condition made more impatient choices and smoked greater volumes per puff.

Lerner said the research team was motivated in part by the deadly realities of smoking: Tobacco use remains the leading cause of preventable death in the United States despite five decades of anti-smoking campaigns. The global consequences are also dire, with 1 billion premature deaths predicted across the world by the end of this century.

"We believe that theory-driven research could help shed light on how to address this epidemic," Dorison said. "We need insights across disciplines, including psychology, behavioral economics and public health, to confront this threat effectively." 

Photo by Zhang Rong

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Does AI help humans make better decisions?

Psychopaths are not incapable of feeling emotions, like regret and disappointment, but what they cannot do is make accurate predictions about the outcomes of their choices, according to a study co-authored by Joshua Buckholtz, associate professor of psychology at Harvard.

File photo by Stephanie Mitchell/Harvard Staff Photographer

A revised portrait of psychopaths

Peter Reuell

Harvard Staff Writer

Study finds that they do feel regret, but it doesn’t affect their choices

When most people hear the word psychopath, they immediately think of a Hannibal Lecter–style serial killer who is cold, calculating, emotionless, willing to do or say anything to get their desire.

More like this

Pinpointing punishment

And they’re not alone. For decades, researchers studying psychopathy have characterized the disorder as a profound inability to process emotions such as empathy, remorse, or regret.

A recent study , though, suggests that psychopaths are not incapable of feeling emotions like regret and disappointment. What they cannot do, it seems, is make accurate predictions about the outcomes of their choices.

The study, co-authored by Joshua Buckholtz, associate professor of psychology at Harvard, and Arielle Baskin-Sommers, assistant professor of psychology and of psychiatry at Yale University, offers a new model of the disorder that could shed important light on the decision-making process of psychopaths. The study was published in the Proceedings of the National Academy of Sciences.

“The assumption has always been that they make these bad choices because they can’t generate negative emotions like fear, or appropriately respond to emotional signals generated by other people … but we turned that idea on its head.”

Using an economic game, Buckholtz and Baskin-Sommers were able to show that while psychopaths have normal, or even enhanced, emotional responses in situations that typically elicit regret, they have trouble extracting information from the environment that would indicate that an action they’re about to take will result in the experience of regret.

“There are two components to regret,” Buckholtz explained. “There is retrospective regret, which is how we usually think about regret — the emotional experience after you learn you could have received a better outcome if you had made a different choice. But we also use signals from our environment to make predictions about which actions will or won’t result in regret. What differentiated psychopaths from other people was their inability to use those prospective regret signals, to use information about the choices they were given to anticipate how much regret they were going to experience, and adjust their decision-making accordingly.

“It’s almost like a blindness to future regret,” he added. “When something happens, they feel regret, but what they can’t do is look forward and use information that would tell them they’re going to feel regret to guide their decision-making.”

“These findings highlight that psychopathic individuals are not simply incapable of regret [or other emotions], but that there is a more nuanced dysfunction that gets in the way of their adaptive functioning,” Baskin-Sommers said. “By appreciating this complexity, we are poised to develop more accurate methods for predicting the costly behavior of psychopathic individuals.”

Using a measure of prospective regret sensitivity, Buckholtz and Baskin-Sommers were also able to predict whether and even how many times study participants had been incarcerated.

“Contrary to what you would expect based on these basic emotional-deficit models, their emotional responses to regret didn’t predict incarceration,” Buckholtz said. “We know psychopathy is one of the biggest predictors of criminal behavior, but what we found was that behavioral regret sensitivity moderated that, raising the suggestion that intact behavioral regret sensitivity could be a protective factor against incarceration in psychopathic individuals.”

While the study upends the pop-culture image of psychopaths, Buckholtz is hopeful that it will also provide a new direction for scientists who hope to understand how psychopaths make decisions.

“We actually know very little about how psychopaths make choices,” he said. “There have been all sorts of research into their emotions and emotional experience, but we know next to nothing about how they integrate information that we extract from the world as a matter of course and use it to make decisions in daily lives. Getting better insight into why psychopaths make such terrible choices, I think, is going to be very important for the next generation of psychopathy research.”

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Note on Human Behavior: Reason and Emotion

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Feasibility analysis of charging stations for electric vehicles. Case study route E35, Loja-Cuenca in Ecuador

• Méndez-Jaramillo, Edgar
• Coronel-Villavicencio, Iván
• Paccha-Herrera, Edwin
• Briceño-Martínez, Bryan
• Tapia-Viñan, Erik
• Camacho-Muñoz, Jefferson

This paper analyzes a methodology to identify suitable charging stations for electric vehicles (EV) and locate them on the E35 Pan-American Highway, between the cities of Loja and Cuenca, in Ecuador, to promote the use of EVs and reduce dependence on fossil fuels. The study employs several selection criteria, such as government and international regulations, to determine the best type of charging station for the route. The literature review reveals that international standards shape the infrastructure of charging stations in Ecuador, and a fast-charging station with an-output power of 50 kW in mode 4, this concept refers to the arrangement in which the transformer is stationary at the charging point, and the connection is made exclusively on the vehicle's charging side; furthermore, it involves the use of significantly high charging capacities. The station satisfies international standard connectors for charging most EVs in use in Ecuador. In addition, the methodology analyzes several factors related to the environment, economy, society, electricity, geography, and road quality to identify the most suitable locations for charging stations. The analysis concludes that only four of the fourteen locations analyzed meet the requirements for suitable areas. It also highlights that the three-phase electric grid shared by two electric companies (EERSSA and CENTROSUR) meets the quality standards. EERSSA only has a three-phase network in Carigán and Saraguro, which are the two cities with the highest population or energy use.

• charging stations;
• electric vehicles;
• infrastructure;
• Ecuador highway;
• fast-charging station

No Sources Found

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Harvard Professor Says 95% of Purchasing Decisions Are Subconscious

When marketing a product to a consumer, it's most effective to target the subconscious mind..

Why do consumers buy one product over another? How do you develop brand loyalty ? How do you maximize customer engagement ?  

According to Harvard professor Gerald Zaltman , the answer to all these questions is directly related to the subconscious mind. In Zaltman's book, "How Customers Think: Essential Insights into the Mind of the Market," the professor reveals many exciting ideas that can be helpful to marketers and brands.

Contrary to popular belief, consumers aren't as savvy as they might like to believe. For example, while many consumers report comparing multiple competing brands and price points when evaluating a purchasing decision, Zaltman's research indicates that this is not actually the case.

Also, by studying consumer's unconscious physical reactions, Zaltman found that what they really think or feel often contradicts what they say.

Why aren't consumers truthful about their purchasing thoughts and feelings? Well, a big reason is that they are driven by unconscious urges, the biggest of which is emotion.

Emotion is what really drives the purchasing behaviors, and also, decision making in general.

Studies completed by neuroscientists have found that people whose brains are damaged in the area that generates emotions are incapable of making decisions.

This idea is of great importance because it helps us realize that human beings are not as logical as we might imagine. And understanding this has significant implications for marketing, sales, and branding.

For example, by only marketing the attributes of your product, you will likely generate lackluster results. And the poor results you receive are due to the fact you are completely missing the subconscious, human element in the decision-making process.

Humans are driven by feelings. So if you want the consumer to remember your product or brand, they must be engaged and impassioned by the interaction with your company. 

Good marketers utilize this concept all the time, and examples of emotion-based campaigns are everywhere. Think for a moment, what is actually being sold in most marketing campaigns. 

Luxury goods target our feelings of self-worth, acceptance, and status in the world. Communication devices excite us by offering a connection to friends, family, and a broader network of people. Athletic brands inspire by offering adventure and glory through the act of competition. And many other products, such as perfume, cologne and lingerie, target emotions related to love, relationships, and sexual desires.   

As marketers, we should still focus on the features of the product. But we must also sell the lifestyle and the feeling. The key is to highlight the emotional response a consumer will achieve by using the product.

As the old saying goes - sell the sizzle, not the steak.

And to achieve the highest emotional response, you should target your consumer through many different senses. For example, think about the colors and shapes on your logo, homepage, or product packaging. How do they make consumers feel? Consider the words and messaging carefully. Are they emotive and engaging? What is the experience of your retail location? These face-to-face interactions should give customers a certain feeling about your brand.

A lot of what drives consumers is subconscious behavior. But this shouldn't be perceived as a bad thing. There's nothing manipulative about helping consumers achieve desired states of emotion. Product and services are designed to satisfy a customer desire or want, and like it or not, those desires always contain an emotional component.  

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