The weakness of strong ties : sampling bias, social ties, and nepotism in family business succession

Decision-making is a complex cognitive activity filled with bias. Leader decision-making is unique because it occurs in a social context. We examine how biases resulting from social network dynamics complicate leaders' decision-making. In particular, we focus on a specific case of leader cognition: nepotism in the succession decisions in the context of family businesses. Succession often leads to a decline in performance because leaders frequently choose family members as their successor, a form of nepotism. We show that even when a leader can overcome individual decision biases, a bias in sampling resulting from families' strong ties can still allow a leader to wrongly conclude that family members are better qualified than external candidates when the opposite is true. We demonstrate this phenomenon using simulation modeling and explore solutions to family business succession planning

The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems

In this paper we conceptualize the impact of information technology on marketing decision-making. We argue that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision-making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes. Our analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management. Implications of our analysis are discussed.decision making;decision biases;information technology;marketing management support systems

An Empirical Study of Illusion of Control and Self-Serving Attribution Bias, Impact on Investor’s Decision Making: Moderating Role of Financial Literacy

According to traditional financial theory investors are supposed to be rational and make decisions that reflect all available information but prospect theory explained a number of biases which affect the investor’s behavior and investors lead to irrational decision making. This study aims to investigate the influence of behavioral biases (self-attribution, illusion of control) on investment decisions with the moderating role of financial literacy in context of Pakistan. The relationship was examined by administering a questionnaire and by collecting empirical data from investors about their own perception of these biases. Questionnaire was distributed among the sample of 220 investors and two statistical tools correlation analysis and regression analysis were used to analyze the collected data. The study was found that the Illusion of control bias has significant positive impact on individual investor investment decision and no support were found for the positive impact of self-serving attribution bias on investment decision. It is also found that financial literacy moderates the relationship between illusion of control bias and investment decision so that it weaken the relationship. The findings of this study will helpful for investors to identify these biases which interrupted his decision making level and then formulate different strategies to overcome these biases and reduce irrational behavior. Other implications and limitations of the study are also discussed. Keywords: Illusion of Control Bias, Self-Serving Attribution Bias, Financial Literacy, Investment Decision, Pakistan

Review of Scott Plous, \u3cem\u3eThe Psychology of Judgment and Decision Making\u3c/em\u3e

Where can one find basic marketing principles? Up to now, my favorite source has been Influence(Cialdini 1984, 1993). In The Psychology of Judgment and Decision Making, Plous (rhymes with house) adds to Cialdini by summarizing important principles in an effective manner. The book contains a wonderful selection of the classic studies on psychology. These are organized into six sections: (1) perception, memory, and context; (2) how questions affect answers; (3) models of decision making; (43 heuristics and biases; (5) the social side of judgment and decision making; and (6) common traps

The Mediating Role of Risk Perception among Cognitive Biases towards Decision to Start A New Venture

The Objective of the study is to investigate the relationship of overconfidence bias and illusion of control bias towards the start of new venture, with the mediating role of risk perception in context of Pakistan. To understand the relationship, this study developed and tested hypothesis by correlation and regression analysis. This study’s sample consisted of 170 students pursuing a Masters of Business Administration. The students’ responses to a survey based on a case study regarding a decision to start a venture were examined. This study found illusion of control and risk perceptions have significant effect on decision to start new venture. Conversely, overconfidence bias has insignificant relationship with decision to start new venture. The positive and negative impact of biases and perceiving low levels of risk suggest the importance of exploring the area of venture formation. As this study has incorporated two biases but many other biases should also be considered that effect human decision making process like self-efficacy, availability heuristics, law of small numbers and escalation of commitment. Keywords: Cognitive biases, Risk perception & Decision to start a new ventur

VFR Into IMC Through the Lens of Behavioral Economics

Decision-making can be the difference between life and death in all types of aviation, but in general aviation (GA), where most of the flying is conducted as single-pilot operations, the decision-making of one individual becomes fundamentally important. It is critical to consider, first, why pilots make bad decisions that can ultimately lead to weather-related aviation accidents or incidents; and second, whether a better understanding of weather-related decision-making can inform regulations that will improve decision-making and consequently reduce the frequency of pilot-error accidents. Behavioral economics (BE) aims to better understand individual decision-making to model decision-making pathways. As individual decision-making is central to aviation safety, better modeling of decision-making pathways should be a central aim not just for pilots, but also for aviation regulators, such as the Civil Aviation Safety Authority (CASA) in Australia. While there has been little analysis of pilot decision-making using BE, we argue that BE, with its focus on predictive models of individual decision-making, provides a rich framework to understand pilot decision-making and inform more targeted regulation. This argument is in four parts. The first part identifies that there is an ongoing safety issue with visual flight rules (VFR) pilots flying into instrument meteorological conditions (IMC). The second part introduces some of the core concepts of BE, such as the rejection of perfect rationality and the reliance upon certain behavioral biases in decision-making. We argue that VFR into IMC is an appropriate context in which to apply BE as there is an identifiable measure of a pilot’s welfare and concerns around paternalism fall short when dealing with protecting the welfare of those likely to be impacted by a pilot’s decision-making, such as passengers and aircraft owners. The third part reviews the existing research applying behavioral models of decision-making in respect of VFR into IMC and identifies three behavioral biases that—among others—can lead to poor decision-making: (i) environmental literacy, (ii) overconfidence, and (iii) prospect theory. The final part briefly introduces some potential avenues for BE to inform regulatory reform, including better education of pilots and regulators in respect of the psychological factors to which pilots may fall victim, as well as more directed training for pilots to address the environmental literacy concerns identified in this part. We conclude that the regulatory environment should be reformulated to adequately account for predictable behavioral biases

An Organizational Learning Approach to Perceiving and Addressing Algorithmic Bias in Agricultural Settings

Organizations are deploying artificial intelligence (AI) to improve decision-making and performance. AI-enabled systems are used to automate the decision-making process or assist human choice by providing algorithmically generated information through predictive analytics and recommendations. However, the ability of these systems to improve organizational performance is constrained by biases within the algorithms. This study proposes to use organizational learning as a theoretical lens to understand how users perceive and respond to these biases using their experiential learning and cognitive search processes. The research is set within the agricultural context, as farm organizations are increasingly adopting AI-enabled systems to improve agricultural productivity and sustainability. However, because of complexities associated with the natural environment, algorithmic biases in the recommendation could threat these outcomes. The study proposes to conduct multiple case studies to explore how users of AI-enabled agricultural systems perceive algorithmic bias and develop coping mechanisms to improve agricultural performance. Keywords Algorithmic bias, agriculture, artificial intelligence, cognitive search, experiential learning

Learning From Mistakes: Decision-making Biases Within the Primate Lineage

Humans and animals alike make thousands of decisions each day, and good decision-making is crucial to survive and thrive in a competitive world. Much research has focused on how to make ‘rational’ decisions based on stable and absolute preferences. In reality, however, human and animal decisions are extremely context dependent. We show and act on relative rather than absolute preferences (e.g., relative to irrelevant options, previous choices, or what others receive), and these tendencies can lead to consistently ‘irrational’ behavior. Studying the flaws in our cognitive system can help us learn how it works. This dissertation explored the extent to which we share several such decision-making biases with other primates. In a series of manual and computerized tasks, capuchin monkeys’ and rhesus macaques’ choices shifted in response to theoretically irrelevant factors like the presence of unattainable options, inferior options, or social partners; how much work they had previously invested; and how frequently different stimuli were encountered. These findings suggest that evolutionary ancient mechanisms can underlie similar biases in humans, highlighting the need to evaluate the potential function of decision-making strategies in a species’ physical and social environment. However, seemingly minor aspects of the experimental paradigms, like monkeys’ baseline preferences or whether information about the reward contingencies was signaled, affected the magnitude of these biases. Such methodological details may contribute to mixed evidence for decision-making biases in animals and need to be assessed systematically for comparative research to make valid inferences. In doing so, studying whether species other than humans make similar mistakes allows us to better understand the underlying cognitive mechanisms and the evolutionary forces that shape them