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People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe.

In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe.

The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month.

The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies.

Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic.

The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.

The purpose of this research is to examine the effect of heuristic biases on investment decisions through multiple mediation mechanisms of risk tolerance and financial literacy in the Tanzanian stock market.

A sample of 316 individual investors in the Tanzanian stock market was obtained through questionnaires. The data were analyzed using structural equation modeling (SEM).

The findings show that financial literacy mediates insignificantly the effects of overconfidence, availability, anchoring and representativeness heuristics on investment decisions. Further, financial literacy does not influence the effect of risk tolerance and investment decisions. Risk tolerance is confirmed as a positive mediator of overconfidence, availability, anchoring and representativeness heuristics in investment decisions. Also, the study shows that overconfidence exerts a stronger influence on investment decisions, followed by availability, representativeness, risk tolerance, anchoring and financial literacy.

The study deals with real investors. Therefore, it uses fewer items to measure the constructs in order to avoid respondent bias. Further research could examine the effects of heuristic biases on investment decisions by adding or modifying the items of particular constructs and studying institutional investors.

The findings can help individual investors to analyze and evaluate their behavior toward stock selection. Securities institutions can use this research to understand investors' behavior, evaluate future market trends and provide advice to the investors.

Previous studies have examined the impact of heuristics on the investment decisions of individual investors. The unique empirical analysis developed in this paper is that it examines the multiple mediation mechanisms of risk tolerance and financial literacy with respect to heuristic biases and investment decisions in the Tanzanian stock market.

The paper addresses the following research question: how do decision-makers use heuristics in their international business (IB) environment? Whereas, the literature has focused on entrepreneurial companies, here contrasting approaches to learning and using heuristics in international marketing (IM) decisions are examined and discussed.

The paper aims to address a gap in the study of micro-foundations of internationalization, exploiting research from other disciplinary fields. It combines a multidisciplinary literature review and longitudinal case studies to illustrate different approaches in learning and using heuristics by international marketers.

International marketers can adopt “closed” heuristics that are consolidated and consistently followed, or “open” heuristics, which are constantly being adapted and learned. Established multinationals learn heuristics in international marketing decision-making, following both “closed” and “open” models.

This paper offers an original contribution by presenting different approaches not yet examined in the literature, focusing on how international marketers make decisions through learning and using heuristic rules. The focus is on established exporters, in contrast to the literature that has largely paid attention to the effectiveness of heuristics in new entrepreneurial firms.

Dynamic lot sizing is the problem of determining the quantity and timing of ordering items while satisfying the demand over a finite planning horizon. This paper considers the problem with two practical considerations: minimum order size and lost sales. The minimum order size is the minimum amount of items that should be purchased and lost sales involve situations in which sales are lost because items are not on hand or when it becomes more economical to lose the sale rather than making the sale. The objective is to minimize the costs of ordering, item , inventory holding and lost sale over the planning horizon. To solve the problem, we suggest a heuristic algorithm by considering trade-offs between cost factors. Computational experiments on randomly generated test instances show that the algorithm quickly obtains near-optimal solutions.

This paper proposes a theory-based process model for the generation, articulation, sharing and application of managerial heuristics, from their origin as unspoken insight, to proverbialization, to formal or informal sharing, and to their adoption as optional guidelines or policy.

A conceptual paper is built using systematic and non-systematic review of literature. This paper employs a three-step approach to propose a process model for the emergence of managerial heuristics. Step one uses a systematic review of empirical studies on heuristics in order to map extant research on four key criteria and to obtain, by flicking through this sample in a moving-pictures style, the static stages of the process; step two adapts a knowledge management framework to yield the dynamic aspect; step three assembles these findings into a graphical process model and uses insights from literature to enrich its description and to synthesize four propositions.

The paper provides insights into how heuristics originate from experienced managers confronted with negative situations and are firstly expressed as an inequality with a threshold. Further articulation is done by proverbialization, refining and adapting. Sharing is done either in an informal way, through socialization, or in a formal way, through regular meetings. Soft adoption as guidelines is based on expert authority, while hard adoption as policy is based on hierarchical authority or on collective authority.

The findings are theory-based, and the model must be empirically refined.

Practical advice for managers on how to develop and share their portfolio of heuristics makes this paper valuable for practitioners.

This study addresses the less-researched aspect of heuristics creation, transforms static insights from literature into a dynamic process model, and, in a blended-theory approach, considers insights from a distant, but relevant literature – paremiology (the science of proverbs).

This paper aims to develop a conceptual framework on how the representativeness heuristic operates in the decision-making process. Specifically, the authors unbundle representativeness into its building blocks: search rule, stopping rule and decision rule. Furthermore, the focus is placed on how individual-level cognitive and behavioral factors, namely experience, intuition and overconfidence, affect the functioning of this heuristic.

From a theoretical standpoint, the authors build on dual-process theories and on the adaptive toolbox view from the “fast and frugal heuristics” perspective to develop an integrative conceptual framework that uncovers the mechanisms underlying the representativeness heuristic.

The authors’ conceptualization suggests that the search rule used in representativeness is based on analogical mapping from previous experience, the stopping rule is the representational stability of the analogs and the decision rule is the choice of the alternative upon which there is a convergence of representations and that exceeds the decision maker's aspiration level. In this framework, intuition may help the decision maker to cross-map potentially competing analogies, while overconfidence affects the search time and costs and alters both the stopping and the decision rule.

The authors develop a conceptual framework on representativeness, as one of the most common, though still poorly investigated, heuristics. The model offers a nuanced perspective that explores the cognitive and behavioral mechanisms that shape the use of representativeness in decision-making. The authors also discuss the theoretical implications of their model and outline future research avenues that may further contribute to enriching their understanding of decision-making processes.

We consider the problem of assigning ship assembly blocks to storage locations at a ship yard while allowing one-way movement of the blocks. The objective is to minimize the number of blocks obstructing other blocks’ movements. We show that the integer program in a previous study contains errors in identifying obstructive blocks and therefore, suggest another integer program without the error. Also, we suggest construction heuristics and a tabu search algorithm using properties characterizing optimal solutions of subproblems. Computational experiment results indicate that our heuristics outperform the existing algorithm in terms of solution quality.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

This paper proposes an approach to solve the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) in the context of the Physical Internet (PI) supply chain. The main objective is to minimize the total distribution costs (transportation cost and holding cost) to supply retailers from PI hubs.

Mixed integer programming (MIP) is proposed to solve the problem in smaller instances. A random local search (RLS) algorithm and a simulated annealing (SA) metaheuristic are proposed to solve larger instances of the problem.

The results show that SA provides the best solution in terms of total distribution cost and provides a good result regarding holding cost and transportation cost compared to other heuristic methods. Moreover, in terms of total carbon emissions, the PI concept proposed a better solution than the classical supply chain.

The sustainability of the route construction applied to the PI is validated through carbon emissions.

This approach also relates to the main objectives of transportation in the PI context: reduce empty trips and share transportation resources between PI-hubs and retailers. The proposed approaches are then validated through a case study of agricultural products in Thailand.

This approach is also relevant with the reduction of driving hours on the road because of share transportation results and shorter distance than the classical route planning.

This paper addresses the VRPSPD problem in the PI context, which is based on sharing transportation and storage resources while considering sustainability.

This paper aims to provide a wide picture of studies on heuristics for international decision-making with a focus on foreign market entry. This paper systematically reviews studies published in the international business and international marketing domain to examine heuristically based decisions for foreign market entry.

This paper proposes a systematic literature review and an in-depth analysis of 32 papers published between 1997 and 2021 dealing with foreign market entry and the use of heuristics for international decision-making.

Even if the marketing and management literature is in many ways permeable to the debate around heuristics developed in experimental psychology and cognitive science, international business and international marketing studies on the one hand recognize that international decision-making, especially when dealing with foreign market entry, is strongly characterized by uncertainty, on the other hand, there isn’t a developed and systematized literature about it. This paper shows key topics and areas fundamental to foreign market entry in which heuristics are applied by decision makers and their effectiveness.

A systematic review of the use of heuristics for foreign market entry decision-making can represent a useful step for a more organic development of knowledge about the more general use of heuristics for international decision-making. Understanding the decision-making process on the modes of entry in foreign markets is a key topic for international marketing and international business scholars and practitioners.