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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.

The purpose of this paper is to explore two financial inclusion measures adopted within the local welfare context of the city of Milan, Italy, examining their functioning and underpinning representations. The aim is also to understand how such representations take concrete shape in the practices of local actors, and their implications for the opportunities and constraints regarding individuals' effective inclusion. To this end, this paper takes a wide-ranging look at the interplay between the rise of financial inclusion and the individualisation and responsibilisation models informing welfare policies, within the broader context of financialisation processes overall.

This paper draws on the sociology of public action approach and provides a qualitative analysis of two case studies, a social microcredit service and a financial education programme, based on direct observation and semi-structured interviews conducted with key policy actors.

This paper sheds light on the rationale behind two financial inclusion services and illustrates how the instruments involved incorporate and tend to reproduce, individualising logics that reduce the problem of financial exclusion, and the social and economic vulnerability which underlies it, to a matter of personal responsibility, thus fuelling depoliticising tendencies in public action. It also discusses the contradictions underlying financial inclusion instruments, showing how local actors negotiate views and strategies on the problems to be addressed.

The paper makes an original contribution to the field of sociology and social policy by focusing on two under-researched instruments of financial inclusion and improving understanding of the finance-welfare state nexus and of the contradictions underpinning attempts at financial inclusion of the most vulnerable.

The purpose of this study is to explore and understand, how strong financial literacy influences the cognitive biases of students in Germany while investing. Second, it also evaluates the most influential cognitive biases that students encounter when undertaking their investment decisions within this environment.

A quantitative approach is used to assess the relationship between financial literacy and students’ investment-related cognitive biases by using the frameworks proposed by Clercq (2019) and Pompian (2012).

The results advocate that the students’ financial literacy positively impacts their cognitive biases within the investment process. It additionally revealed the most significant biases regarding students’ investment decision-making and proposed the possible reasons behind their behavioral distortions.

The study provides a detailed review of the behavioral tendencies of the younger generation while investing and creates recommendations for prospective researchers.

This research lies at the junction of the behavioral finance field, suggesting that it assists in developing a theoretical framework of cognitive biases within students’ financial decisions. Furthermore, it serves as an addition to the financial management subject course that would provide valuable insights about, first and foremost, financial literacy and subsequently, the theory behind the investment process.

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.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.

In this article, the aim is to document the divergences/convergences between the market perceptions of quality and the financial estimations for office buildings relative to the notion of centrality and the distance to the central business district (CBD).

Based on a hierarchical approach that decomposes and estimates the perceived quality of buildings from the stakeholders’ perspectives, we study the geographies of perceived quality measures in the Greater Paris Metropolis and compare them to the financial geography.

The perceived location quality decreases with distance from the CBD whereas judgments on the built structure and the workplace do not, exhibiting a ring-shaped pattern. The gradient of the components of the perceived quality are heterogeneous, having positive, negative or null values. Appraisers tend only to consider the quality of location in their estimations.

This article raises the issue of fair spatial judgments by appraisers and the financial market. Monocentricity is not the rule in the market perceptions of quality. It suggests that financial estimates are strongly biased, with mental representation of centrality as a judgmental heuristic.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

Many in the private and public sectors see headhunting as a “necessary evil.” A tour of investors, business schools, executive and supervisory board directors throws up unflattering descriptions. Accusations of “opacity” are probably linked to the industry’s duty of discretion. Other observers signal a need for improvement: “pumping the same names around,” “emphasis on reputation, not merit,” “lack of quality” and “low innovation.” The battle to professionalize is underway. We look at the avenues ahead for executive hiring, the demise of the “old boys’ network” and the need for diversity and sustainability in modern organizations. We present the difficulties of running a headhunting firm, and distinguishing factors of the best players. We argue that selecting a headhunter demands the same rigor as choosing a lawyer, and six areas of improvement for executive search firms. Unpacking the Spartan marathon to the boardroom, we reveal the surprising estimates of the number of new high potentials entering the market annually. We conclude by looking at possible blind spots on both sides of the hiring equation that can undermine company interests, and continue to examine the march to professionalize the executive search industry.

An earlier form of this chapter by the author was published in Dutch in “Bestemming Boardroom: over zoeken en gevonden worden” (Boom, Amsterdam, 2018) and in English (online) by the Amrop Partnership (2021) as “Destination Boardroom 3: Mapping the Executive Search World.”