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This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

A significant body of literature suggests that shipping companies operate in an extremely volatile and risky environment, relying on the effective use of information to remain competitive. However, decision-making in this market is demanding because of the high uncertainty, market competition and significant capital investments. Moreover, the rapid spread of COVID-19 renders information uncertainty a daunting challenge for companies engaged in global trade. Hence, this study aims to explore the information behavior of managers in a time of crisis seems compelling.

This study provides novel insights into the information behavior of senior managers by adopting a qualitative approach. Forty-nine semi-structured face-to-face interviews with individuals from Hellenic shipping companies were conducted. Moreover, this study explores the extant theory qualitatively, using the grounded theory methodology and shows that an unprecedented event (pandemic crisis) can redefine the information behavior of managers.

This study highlights the importance of information in decision-making. Moreover, the results show that, during a pandemic, managers resort to alternative information sources, adopt collaborative information behaviors and take advantage of digital technology.

There is limited research in exploring the information behavior of managers in times of pandemics. This research underscores the fact that during a crisis, managers seek information from digital information resources and decision-making assumes a more decentralized form. This study concludes with a discussion of the theoretical and practical implications of these findings.

This book on uncertainty comprises the initial volume in a series titled “New Horizons in Managerial and Organizational Cognition”. We asked Frances Milliken and Gerard P. Hodgkinson, two well-known scholars who have made important contributions to our understanding of uncertainty to join us in this opening chapter to introduce this project. The brief bios found at the end of this volume cannot do justice to the broad range of their contributions, but our conversation gives a flavor of the kind of insights they have brought to managerial and organizational cognition (MOC). The editors thank them for helping launch the series with a decisive exploration of what defining uncertainty involves, how that might be done, why it is important, and how the task is changing. We were interested to discover that all five of us are currently involved in research that considers the nature and impact of uncertainty, and we hope that readers similarly find that paying attention to uncertainty contributes to their current projects. Working together, we can advance understanding of organizational settings and effective action, both for researchers and practitioners.

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.

In real life, we only know the consequences of each possible action with some uncertainty. A typical example is interval uncertainty, when we only know the lower and upper bounds on the expected gain. A usual way to compare such interval-valued alternatives is to use the optimism–pessimism criterion developed by Nobelist Leo Hurwicz. In this approach, a weighted combination of the worst-case and the best-case gains is maximized. There exist several justifications for this criterion; however, some of the assumptions behind these justifications are not 100% convincing. The purpose of this paper is to find a more convincing explanation.

The authors used utility approach to decision-making.

The authors proposed new, hopefully more convincing, justifications for Hurwicz’s approach.

This is a new, more intuitive explanation of Hurwicz’s approach to decision-making under interval uncertainty.

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.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

In many real-life situations, we do not know the exact values of the expected gain corresponding to different possible actions, we only have lower and upper bounds on these gains – i.e., in effect, intervals of possible gain values. The purpose of this study is to describe all possible ways to make decisions under such interval uncertainty.

The authors used both natural invariance and additivity requirements.

The authors demonstrated that natural requirements – invariance or additivity – led to a two-parametric family of possible decision-making strategies.

This is a first description of all reasonable strategies for decision-making under interval uncertainty – strategies that satisfy natural requirements of invariance or additivity.

The purpose of this guest editorial is to present an overview of the contributions in this special issue and proposes a positive approach to heuristics deriving from the growing interest in the decision-making topic with respect to the new challenges emerging in uncertain environments in management and marketing research.

The authors explore the reasons for a positive view of business actors' judgments and choices based on heuristics, not only in terms of effectiveness in practice, but their fit with human cognition and behavior, and the potential distinctiveness in contexts where technological devices and algorithms are more widespread, but not necessarily more appropriate.

The authors present and discuss the emergence and evolution of heuristics as a topic in the management literature, and the themes and insights proposed in the papers published in this special issue contributing to research aimed at systemizing a managerial perspective of the concepts and tools that may be useful for practitioners and researchers in this field.

The paper discusses the positive role that heuristics can play, offering some propositions for future research by framing heuristics as a set of tools (toolbox) for business actors in uncertain contexts, without constituting a cognitive limitation for effective solutions.

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.