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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 study aims to investigate the effects of organizational culture factors on the selection of software process development models and develops a conceptual model for selecting and adopting process development models with an organizational culture approach, using 12 criteria and their sub-criteria defined in Fey and Denison’s model (12 criteria).

The research hypotheses were investigated using statistical analysis, and then the criteria and sub-criteria were selected based on Fey and Denison’s model and the experts’ viewpoints. Afterward, the organizational culture of the selected company was measured using the data from 2016 and 2017, based on Fey and Denison’s questionnaire. Due to the correlation between the criteria, using the decision-making trial and evaluation technique, the correlation between sub-criteria were determined, and by analytical network process method and using Super-Decision software, the process development model was preferred to the 12 common models in information systems development.

Results indicated a significant and positive effect of organizational culture factors (except the core values factor) on the selection of development models. Also, by changing the value of organizational culture, the selected process development model changed either. Sensitivity analysis performed on the sub-criteria implied that by changing and improving some sub-criteria, the organization will be ready and willing to use the agile or risk-based models such as spiral and win-win models. Concerning units where the mentioned indicators were at moderate and low limits, models such as waterfall, V-shaped and incremental worked more appropriately.

While many studies were performed in comparing development models and investigating their strengths and weaknesses, and the impact of organizational culture on the success of information technology projects, literature indicated that the impact of organizational sub-culture prevailing in the selection of development process models has not been investigated. In this study, new factors and indicators were addressed affecting the selection of development models with a focus on organizational culture. Correlation among the factors and indicators was also investigated and, finally, a conceptual model was proposed for proper adoption of the models and methodologies of system development.

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.

In demand-driven markets, customer value, sometimes called perceived use value or consumer surplus, is defined by the customer rather than the firm. The value a firm can appropriate, its profits, is driven by the customer’s willingness to pay for the value they receive, adjusted by costs. This paper introduces a conceptual framework that helps understand value creation and appropriation in demand-driven markets and shows how to influence them through strategic decision-making.

This paper uses an axiomatic approach combined with an extended analytical formulation of the jobs-to-be-done framework to contextualise demand-driven markets. It mathematically derives implications for managerial decision-making concerning selecting customer segments, optimising customer value creation and maximising firm value appropriation in a competitive environment.

Rooting strategic decision-making in the jobs-to-be-done framework allows distinguishing between what customers want to achieve (goal), what product attributes need to be satisfied (opportunity space/constraints) and what value creation criteria related to features are important (utility function). This paper shows that starting from a job-to-be-done, the problem of identifying which customer segments to serve, what product to offer and what price to charge, can be formulated as an optimisation problem that simultaneously (rather than sequentially) solves for the three decision variables, customer segments, product features and price, by maximising the value that a firm can appropriate, subject to maximising customer value creation and constrained by the competitive environment.

Applying the derived results to simultaneously deciding which customer segments to target, what product features to offer and what price to charge, given a set of competing products, allows managers to increase their chances of winning the competitive game.

This paper shows that starting from a job-to-be-done and simultaneously focusing on customers, product features, price and competitors enhances firm profitability. Strategic decision-making is formulated as an optimisation problem based on an axiomatic approach contextualising demand-driven markets.

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions.

The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research.

To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring.

This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation.

This research may contribute to more safe operations and functions in the aviation industry.

Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

The purpose of the article is to identify relevant criteria for decision support in the implementation of waste-to-energy (WtE)-based systems.

The methodology is a simple case study with a qualitative approach. Five experts involved in the project of a thermoelectric power plant qualitatively evaluated, on a Likert scale, a decision model with 15 indicators derived from recent studies. The research object was the first stage of a project to implement a thermoelectric plant employing municipal solid waste (MSW) in southern Brazil.

The study identified 15 criteria supporting the decision-making process regarding WtE implementation for MSW in a mid-sized city in southern Brazil. The study identified that compliance with MSW legislation, compliance with energy legislation, initial investment and public health impact are the most influential criteria. The study offered two models for decision processes: a simplified one and a complete one, with ten and fifteen indicators, respectively.

The study concerns mid-sized municipalities in southern Brazil.

Municipal public managers have now a methodology based on qualitative evaluation that admits multiple perspectives, such as technical, economic, environmental and social, to support decision-making processes on WtE technologies for MSW.

MSW management initiatives can yield jobs and revenues for vulnerable populations and provide a correct destination for MSW, mainly in developing countries.

The main originality is that now municipal public decision-makers have a structured model based on four constructs (technical, economic, environmental and social) deployed in 15 indicators to support decision-making processes involving WtE and MSW management.

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

This paper covers the development of a novel defect model for concrete highway bridges. The proposed defect model is intended to facilitate the identification of bridge’s condition information (i.e. defects), improve the efficiency and accuracy of bridge inspections by supporting practitioners and even machines with digitalised expert knowledge, and ultimately automate the process.

The research design consists of three major phases so as to (1) categorise common defect with regard to physical entities (i.e. bridge element), (2) establish internal relationships among those defects and (3) relate defects to their properties and potential causes. A mixed-method research approach, which includes a comprehensive literature review, focus groups and case studies, was employed to develop and validate the proposed defect model.

The data collected through the literature and focus groups were analysed and knowledge were extracted to form the novel defect model. The defect model was then validated and further calibrated through case study. Inspection reports of nearly 300 bridges in China were collected and analysed. The study uncovered the relationships between defects and a variety of inspection-related elements and represented in the form of an accessible, digitalised and user-friendly knowledge model.

The contribution of this paper is the development of a defect model that can assist inexperienced practitioners and even machines in the near future to conduct inspection tasks. For one, the proposed defect model can standardise the data collection process of bridge inspection, including the identification of defects and documentation of their vital properties, paving the path for the automation in subsequent stages (e.g. condition evaluation). For another, by retrieving rich experience and expert knowledge which have long been reserved and inherited in the industrial sector, the inspection efficiency and accuracy can be considerably improved.