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The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This study distinguishes the academic influence of different papers published in journals of the same subject or field based on the modification of the journal impact factor.

Taking SSCI journals in library and information science (LIS) as the research object, the authors first explore the skewness degree of the citation distribution of journal articles. Then, we define the paper citation ratio as the weight of impact factor to modify the journal impact factor for the evaluation of papers, namely the weighted impact factor. The authors further explore the feasibility of the weighted impact factor in evaluating papers.

The research results show that different types of skewness exist in the citation distribution of journal papers. Particularly, 94% of journal paper citations are highly skewed, while the rest are moderately skewed. The weighted impact factor has a closer correlation with the citation frequency of papers than the journal impact factor. It resolves the issue that the journal impact factor tends to exaggerate the influence of low-cited papers in journals with high impact factors or weaken the influence of high-cited papers in journals with low impact factors.

The weighted impact factor is constructed based on the skewness of the citation distribution of journal articles. It provides a new method to distinguish the academic influence of different papers published in journals of the same subject or field, then avoids the situation that papers published in the same journal having the same academic impact.

This paper describes the use of an analytical hierarchy process (AHP) in determining the rational weights of importance of pavement maintenance priority ranking factors. These weights were obtained by capturing the local people’s perception towards this vital part of the pavement management system (PMS). In this regard, different groups of individuals were asked to estimate the weight of importance in pavement maintenance of different factors for ranking pavement sections. These factors were road class, pavement condition, operating traffic, riding quality, safety condition, maintenance cost, and the overall importance of the road section to the community. The AHP method of pair‐wise comparison was employed to get the factor weights, which were compared with the weights obtained from the direct assignment method. It was concluded that the two methods were statistically similar which confirms that the results of the direct assignment method can be used safely with a sound reliability and consistency. This conclusion comes from the fact that the AHP method has a high reputation and applications, and it uses a high‐precision technique for obtaining the weights (priorities) of alternatives or items. Priority factor weights were used in developing a pavement maintenance priority ranking procedure for a road network. This procedure was validated by real case studies, and found to be logically and efficiently able to handle the ranking of a huge number of pavement sections for maintenance and repair.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The purpose of this research is to focus on the evaluation of heritage buildings' sustainability. BIM modeling was necessary for the design of the sustainability assessment model for Heritage Buildings (SAHB). Using ArchiCAD®, energy simulations were performed for two case studies (Murabba Palace, Saudi Arabia, and Grey Nuns Building, Canada), and the developed model was validated through sensitivity analysis.

Heritage buildings (HBs) are unique and must be preserved for future generations. This article focuses on a sustainability assessment model and rating scale for heritage buildings in light of the need for their conservation. Regional variations were considered in the model development to identify critical attributes whose corresponding weights were then determined by fuzzy logic. Data was collected via questionnaires completed by Saudi Arabian and Canadian experts, and Fuzzy TOPSIS was also applied to eliminate the uncertainties present when human opinions are involved.

Results showed that regional variations were sufficiently addressed through the multi-level weight consideration in the proposed model. Comparing the nine identified factors that affect the sustainability of HBs, energy and indoor environmental quality were of equal weight in both case studies.

This study will be helpful for the design of a globally applicable sustainability assessment model for HBs. It will also enable decision-makers to prepare maintenance plans for HBs.

We examine whether variations in the most fundamental aspects of state corporate income tax regimes affect state economic activity as measured by personal income, gross state product, and total non-farm employment. We focus on a variety of statutory components of state corporate income taxes that apply broadly in most U.S. states and for most multi-state corporate taxpayers. Our econometric strategy consists of a series of fixed effects panel regressions using state-level data from 1996 through 2010. Our results reveal important interaction effects of tax rates and policies, suggesting that policy makers should avoid making decisions about tax rates in isolation. The results demonstrate a relatively consistent negative economic response to the combination of high tax rates with throwback rules and heavy sales factor weights. Combined reporting has no discernible effect on personal income, GSP, or employment after controlling for tax rates, apportionment, and throwback rules. In an effort to gauge the relative impacts of tax policies on the location of economic activity, we also estimate alternative models in which each state’s economic activity is measured as a share of the national economic activity in each year. Statistically significant effects for tax rates, apportionment formulas, and throwback rules in the shares models suggest that at least some of their impact involves the movement of activity across state lines, thereby leaving open the possibility of a zero-sum game among the states.

The purposes of this study were to formulate a hierarchical table of factors that influence adoption of an inter-organization system (IOS) by enterprises and to apply multi-criteria decision-making (MCDM) tools to find the weights of these factors and to objectively identify the critical success factors (CSFs) for the adoption of IOSs by small- and medium-sized enterprises (SMEs).

This study first used a literature review to collect the factors that affect an enterprise’s adoption of an IOS and then constructed a three-level hierarchical table of these factors, based on a technology – organization – environment framework. Fuzzy analytic hierarchy processing was used, based on the returned questionnaires, to determine the weights of the factors. The concept of VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) acceptable advantage was used to objectively identify the CSFs of SMEs that have adopted an IOS.

This study identifies six CSFs of SMEs that have adopted an IOS: industry knowledge and experience, the degree of application of information technology within the industry, system safety, the organizational infrastructure, customer relationships and ease of use. In addition, four findings are proposed.

The work has studied, in depth, the factors that influence the adoption of an IOS by SMEs and identified four practice implications that provide a useful guideline for SMEs when they plan to adopt an IOS.

The identification of CSFs is also an MCDM problem. However, very few previous articles have used MCDM tools to identify the CSFs. This study adopted MCDM tools to objectively identify these CSFs and determine their appropriate weights. The results can help the managers of SMEs allocate their resources, according to the weighting of these CSFs, when they are making plans to adopt an IOS.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.

A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.

The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.

Over the past two years, fraudulent financial reporting has become a major concern of both the Securities and Exchange Commission and investors. These concerns have been spurred by evidence that several high-profile companies such as Enron, Tyco, WorldCom, and HealthSouth have published false and/or misleading financial reports. Statement on Auditing Standards (SAS) No. 82 specifies that auditors have a responsibility to assess the likelihood of management fraud and identifies specific risk factors that should be considered when making that assessment. Apostolou et al. (2001b) examined how internal and external auditors rate the relative importance of these factors. This study extends Apostolou et al. (2001b) by examining how forensic experts at four Big 5 professional service firms assess the factors specified in SAS No. 82. These assessments produced two different models of relative importance: (a) a statistical model (produced by the Analytic Hierarchy Process); and (b) a subjective model (based on subjects’ assessment of the relative weights). These models are then used to assess the self-insight of and the degree of agreement among the forensic experts. The results indicate that forensic experts have a moderately high degree of self-insight. A moderate to high degree of consensus among experts’ judgments about the relative importance of fraud risk factors was noted.