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Many decision rules are rational but opaque, and many others are irrational but transparent. This paper aims to propose a theoretical framework to operationalize opacity in decision-making – the degree to which a decision rule is intelligible to the decision maker.

The authors operationalize opacity and discuss the implication of opaque decision-making in organizational settings through a typology, where decision rules can be rational or irrational and opaque or transparent.

The authors show that opacity is asymmetric as different organizational actors possess different degrees of knowledge about how the decision rules work. Organizational actors often opacify the decision rules to increase their power (based on asymmetric knowledge). Opacity also presents a significant impact on organizational accountability, as transparent organizations are more reputable.

This contribution represents the first theoretical and methodological articulation of opacity in decision-making, within a bounded and ecological rationality framework; it also sheds new light on the role of cognitive biases in organizational settings.

This paper examines the relationship between marketing automation emergence and the marketers' use of heuristics in their decision-making processes. Heuristics play a role for the integration of human decision-making models and automation in augmentation processes, particularly in marketing where automation is widespread.

This study analyzes qualitative data about the impact of marketing automation on the scope of heuristics in decision-making models, and it is based on evidence collected from interviews with twenty-two experienced marketers.

Marketers make extensive use of heuristics to manage their tasks. While the adoption of new automatic marketing tools modify the task environment and field of use of traditional decision-making models, the adoption of heuristics rules with a different scope is essential to defining inputs, interpreting/evaluating outputs and control the marketing automation system.

The paper makes a contribution to research on the relationship between marketing automation and decision-making models. In particular, it proposes the results of in-depth interviews with senior decision makers to assess the impact of marketing automation on the scope of heuristics as decision-making models adopted by marketers.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

The aim of this paper is to systematically review the literature published in recognized journals focused on recognition-based heuristics and their effect on investment management activities and to ascertain some substantial gaps related to them.

For doing research synthesis, systematic literature review approach was applied considering research studies published within the time period, i.e. 1980–2020. This study attempted to accomplish a critical review of 59 studies out of 118 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioural finance domain-related explicitly to recognition-based heuristics and their effect on investment management activities.

The survey and analysis suggest investors consistently rely on the recognition-based heuristic-driven biases when trading stocks, resulting in irrational decisions, and an investment strategy constructed by implementing the recognition-based heuristics, would not result in better returns to investors on a consistent basis. Institutional investors are less likely to be affected by these name-based behavioural biases in comparison to individual investors. However, under the context of ecological rationality, recognition-based heuristics work better and sometimes dominate the classical methods. The research scholars from the behavioural finance community have highlighted that recognition-based heuristics and their impact on investment management activities are high profile areas, needed to be explored further in the field of behavioural finance. The study of recognition-based heuristic-driven biases has been found to be insufficient in the context of emerging economies like Pakistan.

The skilful understanding and knowledge of the recognition-based heuristic-driven biases will help the investors, financial institutions and policy-makers to overcome the adverse effect of these behavioural biases in the stock market. This article provides a detailed explanation of recognition-based heuristic-driven biases and their influence on investment management activities which could be very useful for finance practitioners’ such as investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/ broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making its financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on recognition-based heuristic-driven biases displayed by investors. The current study is original in discussing the role of recognition-based heuristic-driven biases in investment management activities by means of research synthesis. This paper is useful to researchers, academicians, and those working in the area of behavioural finance in understanding the role that recognition-based heuristics plays in investment management activities.

In recent times, high demand for cloud-based services has led to substantial focus in extant literature from technological and business perspectives. However, the prevailing market imperfections have not drawn much interest. This study aims to emphasize on potential sources of market imperfections from new institutional economics (NIE) perspective and attempts to bring forth the importance of public policy in cloud computing ecosystem.

This study takes a review-based deductive approach to present a set of propositions which highlight potential causes leading to suboptimal performance of cloud-based services.

Lack of clarity around ownership and property rights, high asset specificity, existence of information asymmetry and bounded rationality of the provider and consumer, lead to higher transaction cost for providers and consumers, discouraging participation. This would lead to moral hazard and adverse selection and create market imperfections. Appropriate contractual guidelines, standards, legal framework and policy measures will reduce the risk of such imperfections.

As the focus of the study is to forward the propositions and not to empirically test them, future researchers can adopt data-driven studies to validate those propositions.

To ensure equity in the cloud-market, government and industry bodies should work towards enabling both the small and large players to use cloud-based services efficiently and effectively. Appropriate public policy measures can help remove potential market imperfections, encourage better participation and adoption of cloud-based services.

This study identifies potential market imperfections in cloud computing ecosystem through the lens of the theoretical frameworks of NIE.

Unicorn companies, such as Facebook, Uber, and Airbnb, significantly impact our economies. This happens although they had a dramatic initial start – at least in terms of financial performance – that would have let any other “conventional” business close. In other words, Unicorns challenge the start-ups’ problems traditionally associated with early failure (liability of newness). This paper aims to understand what helps Unicorn firms initially survive despite huge losses.

By adopting a behavioral lens, this historical case study article focuses on key strategic decisions regarding the famous social media Unicorn Snapchat from 2011 to 2022. The case combines secondary data and a thematic analysis of Snapchat founders’ and investors’ interviews/comments to identify the behavioral antecedents leading to Snapchat’s honeymoon.

Snapchat network effect triggered cognitive biases of Snapchat founders’ and investors’ decisions, leading them to provide initial assets (i.e. beliefs/goodwill, trust, financial resources and psychological commitment) to the nascent Unicorn. Therefore, the network effect and biases resulted in significant antecedents for Snapchat’s honeymoon.

The authors propose a general, theoretical framework advancing the possible impact of biases on Unicorns’ initial survival. The authors argue that some biases of the Unicorns’ founders and investors can positively support a honeymoon period for these new ventures. This is one of the first case studies drawing on a behavioral approach in general and on biases in particular to investigate the liability of newness in the Unicorns’ context.

This paper aims to survey literature on behavioral economics and finance, with particular emphasis on a selection of models, methods and tools that this strand of thought uses to approach and explain observable phenomena.

After a brief discussion on the meaning and context of behavioral economics, the manuscript identifies five topics of special interest: time preference, heuristics, emotions, finance and macro behavior. For each of these topics, relevant models, methods and tools are identified and scrutinized.

Behavioral economics and finance establish an effective bridge between orthodox economic thinking and new and revolutionary methods of analysis. Exploring the intricacies of human behavior can frequently be done by adapting the trivial and conventional intertemporal utility maximization models that economists insistently resort to, but to fully grasp such intricacies, a step forward is required. Agent-based models and other tools from complexity sciences constitute the analytical arsenal that is needed to improve our understanding of how behavioral issues attach to heterogeneity, local interaction, path-dependence, out-of-equilibrium dynamics and emergence.

Although surveys on behavioral economics and finance abound in the specialized literature, this study has the peculiarity of emphasizing five relevant topics that are particularly illustrative of the pivotal role of behavioral science in promoting the transition from the strict neoclassical perspective to a less mechanic and more organic view of economics and finance.

This study aims to explore the heuristics applied by tech entrepreneurs in the Middle East during the opportunity evaluation process.

A multiple case-based methodology was applied, which consisted of semi-structured interviews with entrepreneurial experts from different cities in the Middle East. Qualitative data analysis was then performed with inductive thematic coding using the Eisenhardt method.

The results suggest that entrepreneurs mostly use six heuristics to evaluate opportunities quickly. Three of them are related to the opportunity as an abstract idea, and three are connected with the person (s) involved in the opportunity. In addition, entrepreneurs in the Middle East were more interested in the personal characteristics of the opportunity presenter than in the opportunity itself.

Identifying the heuristics applied by experts may neglect the perspective of the community of entrepreneurs as a whole. Hence, future research should target a wider segment of entrepreneurs. Furthermore, the effect of applying such heuristics on the strategic growth of startups remains an open question.

The identified heuristics are aligned with the hands-on approach of entrepreneurship and can be applied as a decision-making technique for aspiring entrepreneurs who seek to succeed in this region.

This study explores the under-examined topic of heuristics in opportunity evaluation within the regional context of the Middle East, which has also been scarcely investigated. It sheds light on the importance of cultural factors in identifying the cognitive shortcuts used in a business context.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.