This study aimed to identify financial and cash flow risks associated with SMEs and investigated how managers perceived these risks using the analytical hierarchical process (AHP). Accordingly, a three-level decision model was structured using two criteria, probability and consequences, and a list of six different types of risks as decision alternatives. Data were collected by a survey questionnaire from SME managers/owners and analyzed in accordance with the AHP method. The results show that the priority weight for risk criteria was 52% for probability and 48% for consequences. Further, with an average weight of 18.8%, the risk of an increase in bank charges ranked as the highest type of risk faced by SMEs. However, the risk of low or no profits was ranked as the lowest with an average weight of 13.4%. This study is one of the few, if not the first, to investigate SME managers’ perceptions using an AHP method and to provide insightful information on how SME managers/owners perceived various financial and cash flow risks. The study results may support the use of the AHP method in understanding managers’ perceptions and attitudes toward various types of risks associated with SMEs.

The global spread of coronavirus (COVID-19) has had a devastating impact on thousands of small businesses. Many businesspeople, especially those who own and run micro-businesses, have been hampered by the unprecedented scale of the lockdown of social activities and the restrictions placed on their freedom of movement. The reciprocity process between small rural businesses and residents is ultimately in the interest of improving agrotourism performance. Integrating the non-zero-sum games theory and the social exchange theory, this study aims to achieve the following: (1) testing the relationship between family micro-businesses resources and agritourism performance; (2) examining the impact of two mediating variables (resident–micro-business interaction and support for agritourism development); and (3) testing the intervention of one moderating variable (personal resident benefit) on the tested relationships. Dyads data was collected from 293 residents/family small businesses operators. Partial least squares-based structural equation modelling (PLS-SEM) with the SmartPLS program was employed to analyze the collected data. Family micro-business resources were found to have a positive and significant impact on agritourism performance, moreover, resident–micro-business interaction and support for agritourism development were found to partially mediate the relationship between family micro-business resources and agritourism performance, and the personal resident benefit significantly moderated the relationship between family micro-business resources and resident–micro-business interaction. Several implications for academics and policymakers were elaborated. The limitations and further study opportunities were also discussed.

One of the challenges, when investigating sensitive attributes, or information that people tend not to disclose, through surveys is the ethical obligation to preserve the privacy of respondents. Although the randomized response method, originally suggested by Warner, allows estimating the proportion of such attributes within the population while maintaining confidentiality, the variance of the estimate consistently increases if the likelihood of selecting the question about sensitive attribute increases. The purpose of this research is to introduce a new three-stage RR model which provides an efficient alternative to Warner's model that allows more credibility from a practical perspective and apply the model to estimate noncompliance ratio in compulsory motor insurance. For the two models, a measure of privacy protection was calculated and a relation between this measure and the efficiency of both models was introduced. Efficiency comparisons indicate that the proposed model can always be made more efficient than both Warner's and Mangat & Singh's RR models. The proposed model, with specific parameter selection, was applied on a selected population and proved a practical reliability. The noncompliance ratio to obtain compulsory motor insurance was estimated by both a point and a confidence interval. This estimate provides a basis to predict third party motor insurance inclusion.

The existence of potential incomplete truthful reporting emerges when dealing with topics of great sensitivity. This paper introduces a novel, efficient randomized response model to address the challenges of untruthful reporting. It is meant to improve the accuracy of estimating highly sensitive attributes. The suggested model represents a modified version of Aboalkhair's model (2024) that was found to be an efficient substitute for the models developed by Warner and Mangat & Singh. This study examines the circumstances under which the suggested model outperforms alternative models in terms of efficiency. Theoretical and numerical comparisons of efficiency with alternative models, assuming incomplete truthful reporting, revealed the superior performance of the suggested model. Furthermore, a measure of privacy protection is computed.

An extended version of the reciprocal Weibull model is proposed and thoroughly analyzed. Key statistical properties of the model are derived, and various estimation techniques are employed to estimate the unknownparameters. A simulation study is conducted to evaluate the performance of these methods. Additionally, two real-world datasets are utilized to compare the effectiveness of the competing estimation methods. The significance of the proposed model is highlighted through these applications, demonstrating its superior performance over other competing models in fitting the datasets. A sequential sampling plan based on truncated life testing is introduced, leveraging a newly developed probabilistic model to enhance quality control decisions. The plan determines the acceptance or rejection of a lot based on life test outcomes within a specified truncation time, optimizing inspection efforts. A series of numerical experiments are conducted to validate the proposed approach, demonstrating its effectiveness in minimizing sample sizes while maintaining desired risk levels. The results highlight the impact of key parameters on the sampling process, ensuring a balance between producer and consumer risks.

This paper suggests an innovative randomized response model utilizing customizable random tool. The suggested model offers a general framework for some previously pioneering randomized response models and generates new efficient models. Comparison of the efficiency between one of these newly generated models and other groundbreaking models through theoretical and numerical ways, demonstrates higher efficiency for the new generated model. Additionally, ethical considerations and privacy protection of the suggested model are examined.