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This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users).

A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results.

Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items.

In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

Oblique impacts which occur in many situations in mineral industries leads to material removal and fail of mechanical parts. Studies will be helpful in optimal design to have minimum machine malfunctions.

In the present work, the Hertz-Di Maio Di Renzo nonlinear model of contact is used to simulate the impact phenomenon as a micro-sliding process. The modified Archard equation is used to evaluate wear over the impact. The wear coefficient is evaluated by a pin-disk machine. An impact-wear tester is used to validate the model results.

The measurements indicate an increase in surface hardness because of the several impacts. It is considered in the wear predictive model.

The model predictions compared with the experimental data, obtained from the impact-wear tester, show that the model well predicts the impact wear and can be used as a predictive tool to study the practical design problems and to explain some phenomena associated with the percussive impact.

This study attempts to analyze the overall effectiveness of Lean System (LS)-based India post facility layout design (FLD) selection.

We have grouped all Lean Six Sigma elements into three different categories: Lean Technical Practices, Lean Workplace Environment Practices, and Lean Ergonomics Practices based on literature support and field surveys in the Indian postal service. Further, it employs a mathematical model known as the graph theory (GT) method. From the GT approach, interactions were identified through LS tools represented through the digraph, matrix model approach. This study was directed at the National Sorting Hub in Mysuru, Karnataka, in the southern part of India. A number of insights were given in the GT area where FLD is possible to evaluate.

The results showed that Layout five has the highest value, followed by Layout one from the GT approach. The relative relevance and effect values are given as inputs to the permanent function in the digraph technique, and an evaluation index is obtained as an output.

When solving any optimization issue with a high number of attributes, the digraph and matrix technique is successful. The layouts that have been identified have yielded valuable insights into how to enhance critical decision-making processes, which are necessary to accomplish company strategic objectives like growing postal business services.

The process operations carried out in India post service as well as its heterogeneity of articles and the intangible measures of production are the significant challenges to keenly analyze the layout design through lean service principles.

Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud.

This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency.

This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities.

Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case study.

The purpose of this study is to provide fresh insights into whether there is an expectation gap between external auditors' and other stakeholders' perceptions of external auditors' responsibilities in an emerging economy, in light of recent changes to the global audit landscape.

A quantitative approach in the positivistic paradigm was adopted, and a structured questionnaire was used to gather data.

The findings suggested that there was a statistically significant discrepancy between external auditors' and social groups' perceptions of the responsibilities of external auditors. More than half of the gap was due to deficiency in standards, 19% due to unreasonable expectations by society, while 25% of the gap was found to be due to deficient performance.

The study focused on the duties of external auditors and not on the duties of other types of auditors while examining the audit expectation-performance gap (AEG), and this was due to the drastic differences in the scope of their duties.

The findings of this study are likely to have direct policy implications for regulators, authorities, educators and auditing professionals, who should take immediate actions and measures to reduce the AEG in light of the current global audit landscape advancements and changes.

The present study used a substantially updated model to measure the AEG to suit the contemporary changes in the auditing landscape, and could be considered as a pioneering study that measures the AEG in an emerging economy amid recent changes.

Due to mitigate against natural disasters like earthquake and to distribute relief items, designing humanitarian relief chain networks is an attentional issue. Agile and efficient distribution of relief items after occurring a disaster is significant, especially when some of the relief items are perishable. Therefore, the purpose of this paper is to create a resilient and integrated decision-making structure to distribute relief items at demand points, considering two dimensions of sustainability, under disruption.

This study developed a mixed-integer nonlinear mathematical model to handle the pre- and post-disaster planning when a disaster occurs. The represented model has two objective functions: minimizing weighted unmet demand and total costs. Therefore, to convert this multi-objective problem into a single objective one, the e-constraint method was applied.

The main results showed that considering some resilience strategies has a significant effect in reducing the weighted amount of unmet demand and saves the total costs. More precisely, considering resilience strategies results in a 60% reduction in total unmet demand and 11% reduction in total pre-positioning costs. On the other hand, reducing the maximum response time with applying resilience strategies is another achievement of the present study. For these reasons, the use of these strategies can reduce people’s pain and suffer from natural disasters. In general, the application and effectiveness of sustainability dimensions and resilience strategies in the introduced humanitarian relief chain network were analyzed.

To verify the applicability of this study, this model is applied on a probable real-life case study in Tehran. Finally, some managerial insights are discussed to help humanitarian organizations, managers and stakeholders to make better decisions to reduce negative effects of natural disasters.

This paper introduced a two-stage stochastic mathematical model for designing a resilient humanitarian relief chain network under disruption, at pre- and post-disaster stages. Also, economic and social dimensions of sustainability are considered in this study. Moreover, assembling perishable and im-perishable relief items as relief kits, dynamically is a main contribution of this research.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Organizations experience various issues with the optimum use of data. This study is qualitative research to identify and provide a helpful pattern for increasing the performance of sustainable supply chain management (SSCM).

The statistical population in the qualitative section includes managers and experts in the supply chain (SC) and food production. The data were collected via semi-structured interviews, and data saturation happens after the tenth interview. Then, the data were coded using grounded theory and qualitative research analysis. 384 questionnaires were distributed among employees via random sampling. SmartPLS software is used to investigate and analyze the relationships in the mentioned model through 13 core categories.

The findings indicate that organizational productivity and SC deficiencies are among the effective factors in the SSCM primarily identified by this study. Moreover, the findings propose that industry SC, macro policies, organizational performance, social factors, economic factors, organizational factors, political factors, technological factors, production and customer are likely to positively impact the SSCM, which have previously been documented by studies.

The model and concepts extracted from the responses of research participants show well that there are reasons and motivations for increasing the performance of SSCM. Also, the designed model shows well that the motives and reasons for turning to this system are satisfied due to its implementation.

This study aims to identify the determinants of the expectation gap between financial statement users (investors and bankers) and auditors from a developing country perspective with Cameroon as the case study.

This study makes use of the survey instrument to identify the determinants of the expectation gap in Cameroon. The research method and research design used for this study are similar to that adopted in Schelluch, Best et al., Fadzly and Ahmed, Desira and Baldacchino and Dixon et al.

The results indicate that audits and audited financial statements and auditors’ skills are good predictors of the audit expectation gap (AEG), whereas gender, years of experience and occupation (investors and accountants) do not have any significant influence on the AEG. It follows that the expectation gap is further widened by an increase in the regulation and duties of auditors concerning the reliability and usefulness of audits and audited financial statements and auditors’ skills.

A limitation of this study is the sample size, which is limited in scope, with only 400 potential respondents. In addition, this study adopted a survey method used in countries with different economic views and cultural values from Cameroon.

This study contributes to current knowledge by identifying the determinants of the expectation gap in Cameroon, thus facilitating the adoption of measures aimed at mitigating this gap such as educating the Cameroonian public on the auditors’ duties, especially each time a new audit regulation is adopted. The paper is a critical reference point for future research on the subject in Cameroon.

This study contributes to the expectation gap discourse by uncovering the determinants of the expectation gap from a developing country perspective of Cameroon with a different economic and cultural outlook.

This research aims to design a comprehensive resilience model in the banking industry for identifying the dimensions and components that can enhance organizational resilience in the industry, which can contribute to the existing literate as a promising comprehensive model.

After reviewing the literature and studying the models of organizational resilience, semistructured interviews were conducted with managers and prominent experts in the banking industry. To analyze the interviews, the thematic analysis technique was used with three coding stages. After designing the research model in two main dimensions of micro and macro management in the banking industry, the relation between the main components and subcomponents was identified by using Interpretive Structural Modeling (ISM) and DEMATEL techniques.

The study findings indicating that proper observation and predicting the bank's problems and making suitable connections with the government are two major indicators of the resilience of the banking network, which can realize through influencing the components of risk management, financial resource management and system corruption. The results of this research can lead to the expansion of theoretical foundations of the past research and the concept of organizational resilience in the field of financial services and especially the banking industry.

This paper provides the components with a more significant impact, which bank managers should consider the relationship among them to enhance organizational resilience for more effectiveness of their decisions.