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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.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

Business analytics (BA) is a new frontier of technology development and has enormous potential for value creation. Information systems research shows ample evidence of its positive business impacts and organizational performance. However, there is limited understanding of how decision-makers or users of BA outcomes actually engage with data analysts in the process of data-driven insight generation and how they improve their understanding of business environments using BA outcomes. To aid this engagement and understanding, this study investigates the interaction between decision-makers and data analysts when they attempt to uncover data capacities and business needs and acquire business insights from BA tools.

This study employs an interpretive field study with thematic analysis. The authors conducted interviews with 31 participants who all relied on BA in their daily decisions. The study participants were engaged in different BA roles, including data analysts and decision-makers. They validated the applicability and usefulness of our findings through a focus group with eight practitioners, including decision-makers and data analysts from the same companies.

This study proposes a process model of data-driven sensemaking and sensegiving based on Weick’s sensemaking framework. The findings exhibit that decision-makers are engaged in sensemaking by identifying areas of focus, determining BA scope, evaluating generated insights and turning BA into action. The findings also show that data analysts engage in sensemaking by consolidating data, data understanding, preparing preliminary outcomes and generating actionable reports. This study shows how sensemaking processes and sensegiving activities work together over time through immediate enactment, selection and decision cycles.

This study is a first attempt to understand interactions in the context of BA using the perspective of sensemaking and sensegiving.

This study aims to examine the nonlinear threshold effect of female board gender diversity (FBGD) on debt financing (DF) and equity financing (EF) decisions arguing that the effect of FBGD varies/changes depending on the numerical strength of the women on the board.

This study uses seemingly unrelated simultaneous panel equation modeling of 19 listed firms on the Ghana Stock Exchange (GSE) between 2010 and 2021. Although natural logs of equity and debts are used to proxy financing decisions, FBGD is measured as a percentage of total female board members to total board members.

This study reveals a nonlinear inverted U-shape effect of FBGD on EF and DF options. Although this result implies that the positive effects transit to negative effects when FBGD reaches numerical thresholds 34.20% and 35.11%, respectively, it also suggests that the risk averse nature of women on EF and DF usage becomes more visible and intense when the percentage of women on board increases above the mentioned thresholds, respectively. Clearly, the effect gender diversity on DF and EF depends on the numerical strength of the women on a board.

These results suggest that corporate entities and managers must be careful in the formation and implementation of gender diversity policies as gender diversity policies can influence/change debt and EF decisions. In addition, the thresholds show that a smaller number of women on board is required to lower EF compared to debt and this highlights risk-aversion nature women toward riskier financing decision. Also, the nonlinear inverted U-shape nexus from FBGD to EF and DF confirms the inverted U curve theory implying that the numerical strength of females on boards is critical for financing decisions.

This study contributes to the “gender diversity-financing decision” literature by simultaneously conceptualizing and modellng debt and EF structures and providing an emerging economy perspective on how gender diversity nonlinearly affects financing decisions.

The sustainable development of contractor organizations depends highly on bidding decision-making of projects. This current study, leveraging the risk decision-making theory, attempts to elucidate the process of contractors’ bid/no-bid decision-making and reveal how the process is influenced by their perception of risk. In particular, this study aims to explore the multiple mediating effects of contractors’ trust in owners and risk perception in explaining the relationship between contractual governance outlined in owners’ bidding documents and the bid/no-bid decisions.

A questionnaire survey was used to obtain data from the Chinese construction industry. The PLS-SEM technique was employed to analyze a dataset of 557 available questionnaires.

The findings indicate that (1) the contractual governance provided by owners’ bidding documents positively impacts contractors’ bid/no-bid decisions; (2) both risk perception and trust serve as multiple mediators in this relationship and (3) trust mediates the relationship between contractual governance and contractors’ risk perception.

Drawing upon the risk decision-making theory, this study proposes a multiple mediation model for understanding contractors’ bid/no-bid decision-making processes. It contributes to a better understanding of contractors’ bidding decision-making mechanisms, thereby offering theoretical guidance for contractors to make reasonable and informed risk decisions.

This study aims to explore the relationship between Generation Z (or Gen Z) consumers’ decision-making styles and electronic word-of-mouth (eWOM) use in the tourism sector. Drawing on the consumer style inventory (CSI) model and the theory of reasoned action (TRA), the research examines how specific decision-making styles influence Gen Z’s propensity to use eWOM recommendations for accommodation choices.

The study uses structural equation modelling to analyse data collected from 296 Gen Z users of Booking.com. The CSI model is adapted to the analysed context and attributes – impulsive, recreational, sustainable, fashion-conscious and perfectionist attitudes – are examined to determine their impact on eWOM use intention and actual eWOM use.

Three of the hypothesised relationships in the model were validated. Specifically, the results suggest that the attitudes of sustainable and perfectionist consumers influence the intention to use eWOM. Furthermore, use intention is positively associated with the actual use of eWOM.

For marketers and tourism businesses, understanding the decision-making styles of Gen Z can inform the development of targeted marketing strategies that emphasise quality and sustainability. Highlighting these aspects in online reviews and eWOM platforms can enhance engagement with Gen Z consumers.

This research advances the understanding of eWOM behaviour by integrating CSI and TRA theories in the context of Gen Z’s tourism decision-making. It provides empirical evidence on the significant role of perfectionist and sustainable attitudes in shaping eWOM intentions, contributing to the literature on consumer behaviour and digital marketing in tourism.

This chapter discusses how the real options theory can be useful for understanding the adoption of human resources management (HRM) practices. The authors review how the real options theory has provided insights into the processes through which firms manage uncertainties involved in the adoption of HRM practices. The authors offer propositions for future HRM research from the real options perspective. The authors contend that analyzing HRM practice adoptions through the lens of real options theory can enhance our understanding of the mechanisms through which firms choose which HRM practices to adopt and how they adjust the timing, scale, and methods of investment in these practices. Specifically, the authors suggest that differences in information relevant to valuation of HRM options are the source of distinct choices of HRM options across firms. Finally, the authors propose advancing knowledge on HRM practice adoptions by using a portfolio of options approach, as well as considering factors like competitors, path dependence, and switching options.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

This paper aims to contribute to the literature on corporate and leadership responsibility by proposing a relational business model for shared responsibility.

First, a literature review on corporate and leadership responsibility is presented and discussed. This is followed by an overview of existing public and private regulations and future perspectives that enforce and/or foster corporate and leadership responsibility. Based on the concepts of relational economics, relational leadership and proactive regulation, the theoretical foundations of a relational business model are derived. In addition, a decision model for the empirical application of the relational business model in ethical dilemma situations is developed and presented.

Theoretical elaboration of a relational business model and an associated relational decision-making approach.

This study contributes to a new way of doing business in terms of shared responsibility. Furthermore, corporate responsibility and leadership responsibility are usually researched as two distinct fields, with the former referring to the meso level and the latter to the micro level. A relational approach, which views leadership as a relational phenomenon, contributes to bridging both concepts.