Multi-Level Marketing as “Gig” Work: Worker Motivations, Characteristics, and Outcomes in the U.S.

Multi-level marketing (mlm) firms offer recruits the opportunity to earn compensation through starting their own direct selling business and often characterize mlm work as part of the “gig” economy. mlm promotes flexibility, autonomy, and income potential but data suggest that most participants fail to make money. Decisions are made under uncertainty as there is asymmetric information on potential outcomes and their respective likelihood. We use the first nationally representative survey (N = 1016) to understand the motivations for participating in mlm “gigs,” the social and financial outcomes of participation, and the correlates of those outcomes. While approximately three-fourths of mlm workers report that they joined for financial returns, a similar share reported that they did not earn any profit. Results identify a mismatch between expectations and outcomes and underscore decision biases in the context of uncertain financial rewards alongside broader gig economy regulatory concerns.

Humanized Recommender Systems: State-of-the-art and Research Issues

Psychological factors such as personality, emotions, social connections , and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Personality and emotions of users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends), group composition and social connections among group members can affect the outcome of a group decision. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.

A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli

Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory.

A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli

Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory.

Computational mechanisms for context-based behavioral interventions: A large-scale analysis

Choice context influences decision processes, and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and design behavioral interventions to influence and improve choices. In this paper we analyze the effects of context-based behavioral interventions on the computational mechanisms underlying decision making. We collect data from two very large laboratory studies involving nineteen prominent behavioral interventions, and model the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allows us to parametrize the biases induced by each intervention, interpret these biases in terms of underlying decision mechanisms and their properties, quantify similarities between interventions, and predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision making.

A Qualitative Study on the US Forest Service’s Risk Management Assistance Efforts to Improve Wildfire Decision-Making

To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools in complex organizational environments. We assessed the perceived value of RMA and factors that affected its use to inform the literature on decision support for fire management. We sought to answer two questions: (1) What was the perceived value of RMA for line officers who received it?; and (2) What factors affected how RMA was received and used during wildland fire events? We conducted a qualitative study involving semi-structured interviews with decision-makers to understand the contextualized and interrelated factors that affect wildfire decision-making and the uptake of a decision-support intervention such as RMA. We used a thematic coding process to analyze our data according to our questions. RMA increased line officers’ ability to communicate the rationale underlying their decisions more clearly and transparently to their colleagues and partners. Our interviewees generally said that RMA data analytics were valuable but did not lead to changes in their decisions. Line officer personality, pre-season exposure to RMA, local political dynamics and conditions, and decision biases affected the use of RMA. Our findings reveal the complexities of embracing risk management, not only in the context of US federal fire management, but also in other similar emergency management contexts. Attention will need to be paid to existing decision biases, integration of risk management approaches in the interagency context, and the importance of knowledge brokers to connect across internal organizational groups. Our findings contribute to the literature on managing change in public organizations, specifically in emergency decision-making contexts such as fire management.

The Unintended Consequences of Headquarters’ Involvement in Decentralized Transfer Price Negotiations: Experimental Evidence

This study investigates how headquarters’ involvement affects the efficiency of transfer price negotiations. Although prior research explores autonomous transfer price negotiations, evidence suggests that headquarters can become involved in these negotiations, particularly after they fail. Although the likely intention of headquarters’ involvement is to overcome inefficiencies arising from decentralized managers’ inability to agree on a transfer price, we suggest that such involvement can reduce agreement frequency and the efficiency of transfer pricing in coordinating transfers between divisions. Reduced agreement may occur because involvement can reduce managers’ perceived responsibility for the negotiation outcome and because they may expect headquarters’ decision to be more favorable for them than a negotiated price. Headquarters’ involvement can also reduce the coordination efficiency of transfer pricing because of information asymmetries and headquarters’ decision biases in interpreting negotiation failure and using available information. In an experiment, we manipulate whether headquarters’ involvement is absent or present. We also manipulate whether headquarters suggests a nonbinding price (weak involvement) or whether it imposes a price on divisions (strong involvement). Consistent with our predictions, we find that headquarters’ involvement reduces the frequency of negotiation agreement and the coordination efficiency of transfer pricing. Efficiency is reduced more when involvement is strong rather than weak. We contribute to research by studying managers’ negotiation behavior in the realistic setting of potential headquarters’ involvement and by providing evidence on headquarters’ biased perceptions of negotiation impasse and the unintended consequences of its involvement. This paper was accepted by Brian Bushee, accounting.

Farm business management: the decisive farmer

This book, 'The decisive farmer, human behaviour change for better farming' covers the story of the experimental groups of farmers learning to remove their decision biases and improve their management styles to better achieve their objectives. This book discusses an experiment that covers an essential theme, on removing farmers' decision biases, including improving their personal characteristics which lead to well-developed intuitive skills and optimal decision-making.