Decision Support Systems XIV. Human-Centric Group Decision, Negotiation and Decision Support Systems for Societal Transitions

Supply chain resilience (SCRes) has received considerable attention from scholars and practitioners because organizations and supply chains are facing increasing disasters, uncertainties, and risks. They seek to survive disruptions and return to their original or a better state, and thereby achieve competitive advantage. However, existing studies investigate SCRes mainly from organizational and supply chain perspectives, which limits scholars’ and practitioners’ understanding and presents an incomplete picture of SCRes. Therefore, we conducted a systematic literature review (SLR) to advance SCRes knowledge through the theoretical lens of grand theory (GT). A total of 102 SCRes relevant, high-quality journal papers published between 2004 and 2023 were selected to synthesize existing knowledge and identify future research directions. Our study makes several novel contributions to existing SCRes knowledge. First, we believe that SCRes is determined by interactions between micro-level individuals, meso-level organizations, and macro-level environments. Thus, this study differs from existing SCRes studies by understanding it from the individual, organizational, and supply chain perspectives. Second, from the macro-level perspective, we conclude that SCRes is influenced by social, economic, technological, policy, and cultural environments. Third, from the micro-level perspective, employees’ learning orientation, risk perceptions, self-leadership, and trust may impact on organizational resilience and SCRes. Finally, this study is one of first to apply GT to extend existing SCRes knowledge. We also suggest future research directions advancing SCRes knowledge.

Effectively evaluating the impact of process interventions on business outcomes is crucial for assessing the effectiveness and return on investment of process improvement initiatives. However, this task is challenging due to the complex interplay of factors influencing process execution and performance. This paper presents a comprehensive and versatile tool that combines propensity score weighting and event logs to enhance causal inference in business processes. Propensity score weighting balances the treatment and control groups based on their observed characteristics, mitigating bias and improving the precision of causal estimates. Event logs are the input source of process mining methods, which enable the analysis and understanding of how a process works. Our tool assists practitioners in selecting the most suitable weighting method, assessing treatment-control group balance, and evaluating covariate balance before and after adjustments. We apply the approach and tool to a synthetic dataset, demonstrating their effectiveness and illustrating key insights gleaned from the analysis. We discuss the implications and benefits of this approach for advancing causal inference in business processes, alongside limitations and potential future developments for the tool.

The multi-criteria decision analysis (MCDA) landscape is fraught with complexity and challenges, particularly in diverse decision-making environments. Practitioners often face the challenging tasks of selecting appropriate MCDA methodologies, navigating complicated computational processes, and effectively synthesizing inputs from a variety of stakeholders. The existing landscape of MCDA tools, which are typically limited to specific methodologies, exacerbates these challenges, often resulting in fragmented workflows and steep learning curves. To overcome these hurdles, the MCDA Calculator (https://​mcda-calculator.​psi.​ch) emerges as a novel decision support system (DSS), providing a unified and streamlined platform tailored to increase the efficiency and effectiveness of computational process for experienced practitioners in applying MCDA. The MCDA Calculator features a streamlined computational workflow that blends different MCDA methodologies into a cohesive unit. This approach ensures a consistent and intuitive user experience, effectively eliminating the need for complex, time-consuming configurations. The tool’s design philosophy focuses on simplifying the MCDA calculation process. In this paper, we introduce our DSS and detail the workflow of the developed web-based tool. To illustrate the practical benefits and real-world applicability of the MCDA Calculator, the paper presents a numerical example which illustrates the tool’s ability to streamline calculation processes, and produce insightful, actionable results.

A cost estimation method for launch vehicles is proposed combining the concepts of machine learning, aims to provide assistance in strategic decision-making processes pertaining to satellite launch activities. First, the characteristics of existing methods for estimating the cost of launch vehicles are analyzed, and draws out the machine learning methods based on the characteristics of the current development of launch vehicles in China. Next, a model algorithm based on a dynamic neural network and grey relational analysis is introduced. This algorithm simplifies the network structure by iteratively eliminating low correlation coefficient nodes, effectively addressing the issue of overfitting in small sample data. Finally, the proposed method is validated through a case study about prediction of the Long March series launch vehicle, demonstrating its feasibility and effectiveness.

Paratransit systems are designed to cater to individuals with reduced mobility, and have played a pivotal role in providing essential mobility and accessibility for those who often encounter challenges with conventional public transport. These systems rely on flexible transport, and are characterised by their high configurability and uncertainty stemming from factors such as low demand, complexity, and the specific mobility needs of the targeted group. Unfortunately, some of these systems have encountered failures due to structural and parameterisation errors.

This paper proposes a Decision Support System (DSS) to support the strategic and tactical decisions of conception and design of paratransit systems, as well as to support operational management. This system aims to integrate the interests of different stakeholders during the decision-making process in order to obtain more robust solutions.

Particular focus is given to a segment of the DSS that interfaces with operational management and simulation components. This integration facilitates the assessment and comparison of alternative scenarios, utilizing diverse criteria to achieve the most suitable parameterisation and comprehend the associated risks tied to specific decisions. The tool provides assistance across various decision-making scenarios, and within this context, we will scrutinize its efficacy in identifying and comprehending the impacts of varying time window sizes on the systems’ performance.

The outcomes of these analyses underscore the significance of contemplating diverse parameters in decisions related to mobility. The tool provides valuable insights, enabling better alignment with genuine needs, and ensuring that devised solutions align with user preferences.

Portugal aims to reduce road crashes and fatalities through the implementation of the European Union’s Vision Zero strategy. However, for municipalities, choosing effective interventions at a reduced cost is a challenging task. The choice of the proper countermeasures obeys to a series of constraints imposed by local budgets, municipal governments, urban planning strategies, existing infrastructure, and others. To aid decision-makers in designing Municipal Road Safety Plans that maximize safety at reduced costs, a planning approach was built. The proposed approach presents structured sets of countermeasures, linking crash types and site characteristics with potential interventions. The work used real road crash data from three Portuguese municipalities and comprised three stages. First, a cluster analysis to identify and characterize road crashes according to crash type (e.g., vehicle type, number of vehicles) and crash site (e.g., road alignment, cross section, intersection type, visibility). Then, a literature review and an empirical study supported the identification of possible groups of countermeasures for each crash type, and the specification of sets of interventions for each countermeasure group. The final proposal was confirmed by selecting crash hot spots from the original database evaluate if the measures had been correctly assigned to each crash type, providing both exemplification and validation. This work highlights the potential for a structured approach to identify efficient and cost-effective solutions when planning road safety interventions to be included in Municipal Road Safety Plans.

In some decision situations, the use of Multi-Criteria Decision-Making/Aiding methods cannot capture the real cognitive process performed by Decision-Makers. In this context, behavioral studies can be performed to investigate Decision-Makers’ behavior during the decision process. This study has been undertaken to compare previous studies in order to investigate how Decision-Makers performed holistic evaluations using graphical and tabular visualization presented in the FITradeoff Decision Support System. Results may be applied to other methods using additive aggregation in the MAVT (Multi-Attribute Value Theory) context. The study aims to modify or transform the method based on the inclusion of behavioral aspects observed during the decision process. Thus, in this study an experiment has been designed to conduct holistic evaluations. Eight visualizations are presented arranged in bar graphs and tables, and two decision processes have been investigated – the selection of the best alternative and the elimination of the worst alternative. The experiment has been applied to 134 participants, and the data were collected and analyzed in terms of their probabilities of success and preferences.

Recent advancements in digital technology have significantly impacted healthcare, with the rise of chatbots as a promising avenue for healthcare services. These chatbots aim to provide prevention, diagnosis, and treatment services, thereby reducing the workload on medical professionals. Despite this trend, limited research has explored the variables influencing user experiences in the design of healthcare chatbots. While the impact of visual representation within chatbot systems is recognized, existing studies have primarily focused on efficiency and accuracy, neglecting graphical interfaces and non-verbal visual communication tools. This research aims to delve into user experience aspects of symptom checker chatbots, including identity design, interface layout, and visual communication mechanisms. Data was collected through a comprehensive questionnaire involving three distinct chatbots (Healthily, Mediktor and Adele – a self-developed solution) and underwent meticulous analysis, yielding valuable insights to aid the decision process when designing effective chatbots for symptom checking.

This paper investigated consumer perceptions and attitudes for decision making in Cultured Meat (CM), driven by the growing interest in innovative food products. The motivation stemmed from the anticipated challenges in consumer acceptance of CM, a novel alternative to traditional meat production. The research objective included to identify key factors influencing consumer behaviour in the context of the novel food product. The Systematic Literature Review methodically explored and synthesised existing research, giving insights to the factors affecting consumer perceptions and attitudes towards decisions on CM. Then, a tailored conceptual framework, the Cultured Meat Attitude and Perception Assessment (CAPA), has been developed to address the identified gaps and limitations in understanding consumer perceptions and attitudes. The results highlighted the complex and multidimensional nature of consumer attitudes, emphasising the role of knowledge (awareness, comprehension, familiarity), perception (disgust, neophobia, curiosity, fear, trust), and external factors (ethical issues, social factors, product attributes, information influence, perceived exclusivity, regulatory considerations) that could be used by decision makers such as food innovators and marketers. The CAPA framework integrated these factors to offer a holistic perspective on consumer behaviour, overcoming the limitations of existing work and offering insights to the decision makers in the industry.

This paper investigates the adequacy of using digital tools as a solution for knowledge transfer and especially for tacit knowledge transfer. Based on individual interpretations (sense-reading), tacit knowledge is more difficult to verbalize and transfer with information systems and digital tools. The expansion of such tools leads to a great risk of neglecting this type of knowledge which is, nevertheless, essential to decision-making and action.

In this article, we argue that the transfer of tacit knowledge through digital tools can be improved by understanding their actual tacit knowledge transfer potential and raising awareness on their limits. The efforts to adapt and use digital tools must consider the degree of knowledge tacitness and the role of people as knowledge brokers who facilitate the socialization between stakeholders to ensure common interpretations.

Indeed, digital tools supporting a social dimension significantly and positively affect tacit knowledge transfer when they ensure trust, reciprocity, and shared goals. Knowledge brokers, recognized for their role as intermediaries between different networks, can facilitate exchanges in these new and not so new spaces.