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When the probability of each model is known, a natural idea is to select the most probable model. However, in many practical situations, the exact values of these probabilities are not known; only the intervals that contain these values are known. In such situations, a natural idea is to select some probabilities from these intervals and to select a model with the largest selected probabilities. The purpose of this study is to decide how to most adequately select these probabilities.

It is desirable to have a probability-selection method that preserves independence. If, according to the probability intervals, the two events were independent, then the selection of probabilities within the intervals should preserve this independence.

The paper describes all techniques for decision making under interval uncertainty about probabilities that are consistent with independence. It is proved that these techniques form a 1-parametric family, a family that has already been successfully used in such decision problems.

This study provides a theoretical explanation of an empirically successful technique for decision-making under interval uncertainty about probabilities. This explanation is based on the natural idea that the method for selecting probabilities from the corresponding intervals should preserve independence.

Confidence intervals play a crucial role in economics and finance, providing a credible range of values for an unknown parameter along with a corresponding level of certainty. Their applications encompass economic forecasting, market research, financial forecasting, econometric analysis, policy analysis, financial reporting, investment decision-making, credit risk assessment and consumer confidence surveys. Signal-to-noise ratio (SNR) finds applications in economics and finance across various domains such as economic forecasting, financial modeling, market analysis and risk assessment. A high SNR indicates a robust and dependable signal, simplifying the process of making well-informed decisions. On the other hand, a low SNR indicates a weak signal that could be obscured by noise, so decision-making procedures need to take this into serious consideration. This research focuses on the development of confidence intervals for functions derived from the SNR and explores their application in the fields of economics and finance.

The construction of the confidence intervals involved the application of various methodologies. For the SNR, confidence intervals were formed using the generalized confidence interval (GCI), large sample and Bayesian approaches. The difference between SNRs was estimated through the GCI, large sample, method of variance estimates recovery (MOVER), parametric bootstrap and Bayesian approaches. Additionally, confidence intervals for the common SNR were constructed using the GCI, adjusted MOVER, computational and Bayesian approaches. The performance of these confidence intervals was assessed using coverage probability and average length, evaluated through Monte Carlo simulation.

The GCI approach demonstrated superior performance over other approaches in terms of both coverage probability and average length for the SNR and the difference between SNRs. Hence, employing the GCI approach is advised for constructing confidence intervals for these parameters. As for the common SNR, the Bayesian approach exhibited the shortest average length. Consequently, the Bayesian approach is recommended for constructing confidence intervals for the common SNR.

This research presents confidence intervals for functions of the SNR to assess SNR estimation in the fields of economics and finance.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

The results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE.

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The complex network structure causes several disruptions in the supply chain that make risk management essential for supply chain management including halal supply chain (HSM). During risk management, several challenges are associated with the risk assessment phase, such as incomplete and uncertain information about the system. To cater this, the authors propose a risk assessment framework that addresses the issues of uncertainty using neutrosophic theory and demonstrated the applicability of the proposed framework through the case of halal supply chain management (HSCM).

The proposed framework is using the capabilities of the neutrosophic number which can handle uncertain, vague and incomplete information. Initially, the risk related to the HSC is identified through a literature review and expert’s input. Further, the probability and impact of each HSM-related risk are assessed using experts’ input through linguistic terms. These linguistic values are transformed into single-value trapezoidal neutrosophic numbers (SVTNNs). Finally, the severity of each HSM-related risk is determined through the multiplication of the probability and impact of each risk and prioritised the risks based on their severity.

A comprehensive risk assessment framework is developed that could be used under uncertainty. Initially, 16 risks are identified related to the HSM. Further, the identified risks are prioritised using the severity of the risks. The high-priority risk is “raw material status”, “raw material wholesomeness” and “origin of raw material” while “information integrity” and “people integrity” are low-priority risks.

HSM risk can be effectively assessed through the proposed framework. The proposed framework applied neutrosophic numbers to represent real-life situations, and it could be used for other supply chains as well.

The proposed method is effectively addressing the issue of linguistic subjectivity, inconsistent information and uncertainty in the expert’s opinion. A case study of the HSC is adopted to illustrate the efficiency and applicability of the proposed risk framework.

The study investigates the effect of behavioural uncertainty on the environmental sustainability of restaurant businesses in Tanzania. Also, the study examines the moderating role of purchasing technical knowledge on the main relationship between the study variables.

The quantitative approach was used and cross-sectional data were collected at a specific time from restaurant businesses in Dodoma, Tanzania. The PROCESS macro was used to analyse the relationships between behavioural uncertainty, purchasing technical knowledge and environmental sustainability.

Behavioural uncertainty has a significant and negative effect on the environmental sustainability of restaurant businesses. Purchasing technical knowledge, on the other hand, has a positive and significant effect on the environmental sustainability of restaurant businesses. Finally, purchasing technical knowledge has a positive and significant moderating effect on the relationship between behavioural uncertainty and environmental sustainability such that the negative effect of behavioural uncertainty is reduced with increasing purchasing technical knowledge.

This study considers purchasing skills in terms of purchasing technical knowledge as a moderating variable; hence, other studies may take into account other moderating variables to extend this study. Also, the study considered only environmental sustainability and hence is limited in terms of other dimensions of sustainability and provide an avenue for further research in social and economic sustainability.

Since purchasing technical knowledge reduces the negative effect of behavioural uncertainty on the relationship with environmental sustainability, restaurant managers should be encouraged to improve their purchasing technical knowledge by attending short- and long-term training on purchasing functions in the restaurant industry.

The social implications of the investigated link between behavioural uncertainty, purchasing technical knowledge and environmental sustainability in the restaurant industry include raising awareness, promoting sustainable practises and fostering an environmentally responsible culture. By addressing behavioural uncertainty, leveraging purchasing technical knowledge and embracing sustainability the industry can contribute to a more environmentally conscious society.

By providing empirical evidence from Tanzania, the study extends literature on examining the environmental sustainability of restaurant businesses. The study also establishes the interaction effect of purchasing technical knowledge as an important skill in reducing the negative effect of behavioural uncertainty on enhancing environmental sustainability in restaurant businesses.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

Academic literature calls for research on the impact of psychological states derived from mental illness on detrimental consumer behaviour. The purpose of this study is to assess the impact of anxiety on the consumer’s buying processes (compulsive and impulsive) and emotional regulation.

To carry out the statistical analysis, the data were obtained through an online survey (n = 726) of supermarket consumers. The treatment of the data was using partial least squares structural equation modelling (PLS-SEM).

The results obtained show that anxiety influences the generation of harmful behaviour, as it has a positive impact on compulsive and impulsive buying. In addition, compulsive and impulsive buying generate higher levels of consumers’ emotional regulation.

This study contributes to the management of anxiety as a priority element to reduce harmful behaviour. Therefore, it provides useful information for marketing managers and professionals in psychological and healthy consumer processes.

學術文獻不斷呼籲研究人員和學者去探討來自精神病的心理狀態如何產生有害的消費者行為。本研究擬評定焦慮對消費者購買流程 (強迫性購物和衝動購物) 和情緒調節所產生的影響。

為能進行統計分析,研究人員透過超級市場消費者的在線調查 (n = 726) 取得數據,繼而以結構方程 (PLS-SEM) 處理數據。

研究結果顯示,焦慮會導致有害行為的產生,這是因為焦慮對強迫性購物和衝動購物均產生積極的影響; 而且,強迫性購物和衝動購物會產生較高水平的消費者情緒調節。

本研究的貢獻在於把焦慮視為減少有害行為的優先元素而予以管理; 因此,本研究為市場經理以及於心理上的和健康的消費者進程的專業人員提供了有用的資料。

This study aims to analyze the influence of farmers’ degree of participation in collective action on their adoption decisions and waiting time regarding soil and water conservation (SWC) measures.

The Probit model and Generalized Propensity Score Match method are used to assess the effect of the degree of participation in collective action on farmers’ adoption decisions and waiting time for implementing SWC measures.

The findings reveal that farmers’ engagement in collective action positively influences the decision-making process regarding terrace construction, water-saving irrigation and afforestation measures. However, it does not significantly impact the decision-making process for plastic film and ridge-furrow tillage practices. Notably, collective action has the strongest influence on farmers’ adoption decisions regarding water-saving irrigation technology, with a relatively smaller influence on the adoption of afforestation and terrace measures. Moreover, the results suggest that participating in collective action effectively reduces the waiting time for terrace construction and expedites the adoption of afforestation and water-saving irrigation technology. Specifically, collective action has a significantly negative effect on the waiting time for terrace construction, followed by water-saving irrigation technology and afforestation measures.

The results of this study underscore the significance of fostering mutual assistance and cooperation mechanisms among farmers, as they can pave the way for raising funds and labor, cultivating elite farmers, attracting skilled labor to rural areas, enhancing the adoption rate and expediting the implementation of terraces, water-saving irrigation technology and afforestation measures.

Drawing on an evaluation of farmers’ degree of participation in collective action, this paper investigates the effect of participation on their SWC adoption decisions and waiting times, thereby offering theoretical and practical insights into soil erosion control in the Loess Plateau.