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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.

This study aims to explore and clarify the mechanism by which recognition-based heuristic biases influence the investment decision-making and performance of individual investors, with the mediating role of fundamental and technical anomalies.

The deductive approach was used, as the research is based on behavioral finance's theoretical framework. A questionnaire and cross-sectional design were employed for data collection from the sample of 323 individual investors trading on the Pakistan Stock Exchange (PSX). Hypotheses were tested through the structural equation modeling (SEM) technique.

The article provides further insights into the relationship between recognition-based heuristic-driven biases and investment management activities. The results suggest that recognition-based heuristic-driven biases have a markedly positive influence on investment decision-making and negatively influence the investment performance of individual investors. The results also suggest that fundamental and technical anomalies mediate the relationships between the recognition-based heuristic-driven biases on the one hand and investment management activities on the other.

The results of the study suggested that investment management activities that rely on recognition-based heuristics would not result in better returns to investors. The article encourages investors to base decisions on investors' financial capability and experience levels and to avoid relying on recognition-based heuristics when making decisions related to investment management activities. The results provides awareness and understanding of recognition-based heuristic-driven biases in investment management activities, which could be very useful for decision-makers and professionals in financial institutions, such as portfolio managers and traders in commercial banks, investment banks and mutual funds. This paper helps investors to select better investment tools and avoid repeating the expensive errors that occur due to recognition-based heuristic-driven biases.

The current study is the first to focus on links recognition-based heuristic-driven biases, fundamental and technical anomalies, investment decision-making and performance of individual investors. This article enhanced the understanding of the role that recognition-based heuristic-driven biases plays in investment management. More importantly, the study went some way toward enhancing understanding of behavioral aspects and the aspects' influence on investment decision-making and performance in an emerging market.

This study aims to use a qualitative approach to explore and clarify the mechanism by which heuristic-driven biases influence the decisions and performance of individual investors actively trading on the Pakistan Stock Exchange (PSX). It also aims to identify how to overcome the negative effect of heuristic-driven biases, so that finance practitioners can avoid the expensive errors which they cause.

This study adopts an interpretative approach. Qualitative data was collected in semistructured interviews, in which the target population was asked open-ended questions. The sample consists of five brokers and/or investment strategists/advisors who maintain investors’ accounts or provide investment advice to investors on the PSX, who were selected on a convenient basis. The researchers analyzed the interview data thematically.

The results confirm that investors often use heuristics, causing several heuristic-driven biases when trading on the stock market, specifically, reliance on recognition-based heuristics, namely, alphabetical ordering of firm names, name memorability and name fluency, as well as cognitive heuristics, such as herding behavior, disposition effect, anchoring and adjustment, repetitiveness, overconfidence and availability biases. These lead investors to make suboptimal decisions relating to their investment management activities. Due to these heuristic-driven biases, investors trade excessively in the stock market, and their investment performance is adversely affected.

This study provides a practical framework to explore and clarify the mechanism by which heuristic-driven biases influence investment management activities. To the best of authors’ knowledge, the current study is the first to focus on links between heuristic-driven biases, investment decisions and performance using a qualitative approach. Furthermore, with the help of a qualitative approach, the investigators also highlight some factors causing an increased use of heuristic variables by investors and discuss practical approaches to overcoming the negative effects of heuristics factors, so that finance practitioners can avoid repeating the expensive errors which they cause, which also differentiates this study from others.

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an 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 their financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

The purpose of this article is to clarify the mechanism by which underconfidence heuristic-driven bias influences the short-term and long-term investment decisions of individual investors, actively trading on the Pakistan Stock Exchange.

Investors' underconfidence has been measured using a questionnaire, comprising numerous items, including indicators of short-term and long-term investment decision. In order to establish the influence of underconfidence on the investment decisions in both the short and long run, a 5-point Likert scale questionnaire has been used to collect data from the sample of 203 investors. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling technique.

This article provides further empirical insights into the relationship between heuristic-driven biases and investment decision-making in the short and long run. The results suggest that underconfidence bias has a markedly negative influence on the short-term and long-term decisions made by investors in developing markets. It means that heuristic-driven biases can impair the quality of both short-term and long-term investment decisions.

This article encourages investors to avoid relying on cognitive heuristics, namely, underconfidence or their feelings when making short-term and long-term investment strategies. It provides awareness and understanding of heuristic-driven biases in investment management, 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. They can improve the quality of their decision-making by recognizing their behavioral biases and errors of judgment, to which we are all prone, resulting in more appropriate investment strategies.

The current study is the first to focus on links between underconfidence bias and short-term and long-term investment decision-making. This article enhanced the understanding of the role that heuristic-driven bias plays in the investment management and more importantly, it went some way toward enhancing understanding of behavioral aspects and their influence on the investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance specifically the role of heuristics in investment strategies; this field is in its initial stage, even in developed countries, while, in developing countries, little work has been done.

This study aims to explore and clarify the mechanism by which cognitive heuristics influence strategic decision-making during the coronavirus disease 2019 (COVID-19) pandemic in an emerging economy.

Data collection was conducted through a survey completed by 213 top-level managers from firms located in the twin cities of Pakistan. A convenient, purposively sampling technique and snowball method were used for data collection. To examine the relationship between cognitive heuristics and strategic decision-making, hypotheses were tested by using correlation and regression analysis.

The article provides further insights into the relationship between cognitive heuristics and strategic decision-making during the COVID-19 pandemic. The results suggest that cognitive heuristics (under-confidence, self-attribution and disposition effect) have a markedly negative influence on the strategic decision-making during the COVID-19 pandemic in an emerging economy.

The article encourages strategic decision-makers to avoid relying on cognitive heuristics or their feelings when making strategic decisions. It provides awareness and understanding of cognitive heuristics in strategic decision-making, which could be very useful for business actors such as managers and entire organizations. The findings of this study will help academicians, researchers and policymakers of emerging countries. Academicians can formulate new behavioural models that can depict the solutions to dealing with an uncertain situation like COVID-19. Policymakers and strategic decision-making teams can develop crisis management strategies based on concepts from behavioral strategy to better deal with similar circumstances in the future, such as COVID-19.

The paper’s novelty is that the authors have explored the mechanism by which cognitive heuristics influence strategic decision-making during the COVID-19 pandemic in an emerging economy. It adds to the literature in strategic management, explicitly probing the impact of cognitive heuristics on strategic decision-making; this field is in its initial stage, even in developed countries, while little work has been done in emerging countries.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-10-2021-0636.

This article aims to clarify the mechanism by which herding behavior influences perceived market efficiency, investment decisions and the performance of individual investors actively trading on the Pakistan Stock Exchange (PSX).

The deductive approach was used in this study, as the research is based on the theoretical framework of behavioral finance. A questionnaire and cross-sectional design were employed to collect data from the sample of 309 investors trading on the PSX. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling (SEM).

The article provides further empirical insights into the relationship between herding behavior and investment management and perceived market efficiency. The results suggest that herding behavior has a markedly negative influence on perceived market efficiency and investment performance, while positively influencing the decision-making of individual investors.

The current study is the first to focus on links between herding behavior and investment management activities and perceived market efficiency. This article enhances the understanding of the role that herding behavior plays in investment management and, more importantly, it improves understanding of behavioral aspects and their influence on investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance, specifically the role of herding behavior in investment management; this field is in its initial stage, even in developed countries, while little work has been done in developing countries.

Research on choice overload with adult participants has shown that the presence of a brand significantly mitigates the phenomenon. The purpose of this study is to investigate whether these findings can be expanded to a population of adolescents, where it has already been shown that choice overload occurs in a similar way as adults.

Studies 1 and 2 aim to test whether the presence of a brand name mitigates the adverse consequences of choice overload in adolescents. In line with prior research on choice overload, in both studies, the authors compared between-subjects differences in the levels of reported dissatisfaction, difficulty and regret in a choice condition where adolescents chose among either 6 or 24 options associated with brand names and in another choice condition where adolescents chose among the same 6 or 24 options but not associated with brand names.

This paper presents evidence from two studies that when facing either a large or a small amount of choice options that are associated with brand names, choice overload disappears among adolescents. Conversely, when no brands are associated to the choice options, adolescents report choice overload, that is a greater dissatisfaction, difficulties and regret with larger (versus smaller) assortments.

Prior research on choice overload has led to recommendations that marketers and other choice architects should simply reduce choice options or assortments to improve consumers’ satisfaction. However, our finding suggests that this recommendation may be invalidated when brands are present, at least for certain age groups. Adolescents cope indeed very well with large assortments of branded products.

The research adds to the existing understanding of choice overload, demonstrating that the brand is a moderator of the phenomenon for adolescents, who currently represent a large portion of the market. A second important contribution of this work is that it extends prior research on choice overload to real-world consumer scenarios, where consumers choose among products with a brand, rather than among products described only by technical characteristics or nutritional values, as in classical studies on choice overload.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

This paper builds on a long-lasting research program on the micro-foundations of innovative decision making, founded on a development of a neglected epistemic aspect of Simon's work, and on contributions in epistemology, in which heuristics are not procedures that are uncertaintyavoiding, economizing on cognitive and search effort, and problem-space reducing, but procedures that are uncertainty-modeling, investing in research effort, and problem-expanding. The paper offers a summary of the main effective heuristics of that kind so far identified, as applied to real processes of innovative decision making under epistemic uncertainty, such as judging and investing in novel entrepreneurial projects. It argues and shows that, in contrast to the common view, a wide range of those procedures, usually thought to belong to different and rival models, can be fruitfully combined.