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The purpose of this research is to explore the objectivity and reliability of Entrepreneur Magazine's Franchise 500^® ranking system.

Using data from 1997 to 2004 rankings, regression analysis was used to determine the extent to which key variables explained the rank of franchise firms.

Several quantifiable measures, categorized by the publishers as “most important” or “important” to a firm's rankings, were found to have little or no explanatory power in the regression model. Longitudinal analysis revealed inconsistencies in the ranking among the top 100 ranked franchises, which question the ranking system's reliability.

Only a subset of the variables used to calculate the rankings are disclosed by the publisher, yet these variables explain a substantial portion of any given franchise's rank. Only the top 100 ranked firms were included in the study.

While considered to be important to a firm's rank, the amount of pending litigation and the percentage of terminations within the system, found to be indicators of conflict between franchisor and franchisees, appear to have little effect on a franchise's rank. Also, size of the franchise system appears to have a strong but inconsistent relationship with rank, both within any given year and over the time period covered by this study. Lastly, the relationship between growth rate and rank, another factor considered by the publisher to be most important, also appears inconsistent, both in terms of number of outlets added and percentage of growth attained over the previous year.

Due to the wide popularity of the ranking system by practitioners and researchers a more systematic examination of the ranking appears justified to understand the underlying research implications of franchising research as it relates to the Franchise 500.

The purposes of this study were to measure the relevance status of Index Islamicus, evaluate the semantic correlation between a query and documents and inquire the basis of its rank. Sorting the retrieved results from the most relevant to the least relevant is the common option of an information retrieval system. This sorting mechanism or relevance judgment is computed by measuring closeness of query with its documents.

Forming up 100 queries on Islamic History and Civilizations, with two indexing elements (keyword and concept), a laboratory experiment was generated on its first ten items of the rank. Throughout an experimental research design, the relevance status value formula was used to measure system-computed rank and compare it with mean average precision.

The results showed that the average status value of Index Islamicus’s ranking on relevance criterion was 18 per cent effective in terms of retrieving precise documents. Despite the main focus of this study being only on one subject domain and the items calculated were only 1,000, this small percentage of its ranking mechanism proved that semantic correlations between queries with subject domain did not achieve the satisfactory level.

Implication of this study could be a guideline for further research on ranking mechanism of other search engines because the limitation of this study was Index Islamicus being the only database, which was the focus of this study.

Throughout this study, Index Islamicus would be benefited knowing the status of its ranking mechanism as well as other databases can make further research on their own ranking method following this study.

Researchers and vendors of online databases can ensure their users a true platform of search engine with a proper ranking list.

Relevance status value model for Index Islamicus on Islamic History and Civilization that allows the system to rank documents according to the match between document and query and gives the idea of a better index. The model improves the system’s ranking mechanism, and promotes the use of semantic relationships. This research promotes the computation of relevance status value by domain for capturing subject-specific relevance criteria and semantic relationships.

Ranking of retrieved documents in the descending order of their association with the inquiry has been known in theory for a very long time, but so far it has found only limited use in operational online systems, Because of the ranking, the documents that are probably relevant are concentrated at the top of the set of documents retrieved and submitted for display. This affects the practical strategy of online retrieval that differs essentially from the search strategy in the traditional Boolean‐oriented systems: the inquirer does not need to reformulate his inquiry if it yields too many documents nor to be too anxious at the selection of inquiry terms. Consequently, types of inquiry can be used that are inadmissible in Boolean‐oriented systems or in systems that rank only a limited number of documents.

In a system-based approach, replicating the web would require large test collections, and judging the relevancy of all documents per topic in creating relevance judgment through human assessors is infeasible. Due to the large amount of documents that requires judgment, there are possible errors introduced by human assessors because of disagreements. The paper aims to discuss these issues.

This study explores exponential variation and document ranking methods that generate a reliable set of relevance judgments (pseudo relevance judgments) to reduce human efforts. These methods overcome problems with large amounts of documents for judgment while avoiding human disagreement errors during the judgment process. This study utilizes two key factors: number of occurrences of each document per topic from all the system runs; and document rankings to generate the alternate methods.

The effectiveness of the proposed method is evaluated using the correlation coefficient of ranked systems using mean average precision scores between the original Text REtrieval Conference (TREC) relevance judgments and pseudo relevance judgments. The results suggest that the proposed document ranking method with a pool depth of 100 could be a reliable alternative to reduce human effort and disagreement errors involved in generating TREC-like relevance judgments.

Simple methods proposed in this study show improvement in the correlation coefficient in generating alternate relevance judgment without human assessors while contributing to information retrieval evaluation.

This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.

The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.

The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.

This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

The purpose of this paper is to examine the role of university ranking systems as instruments of university quality assessment. Some controversy surrounds the methodology used to compile such instruments. Accordingly, different compilers have adopted different methods to produce these rankings. This study examines to what extent this diversity in methodology is now converging in the context of Spanish university rankings.

To conduct this research, a two-step approach was adopted. First, the indicators used in four Spanish rankings were examined. Second, empirical analysis was used to identify differences between university rankings.

Results reveal that, despite the vast number and variety of indicators, there is a positive, significant relationship between rankings. Spanish university rankings thus show some degree of convergence.

Because rankings influence behavior and shape institutional decision making, a better understanding of how these assessment tools are devised is essential. Research on these ranking systems therefore offers an important contribution to improving the quality of higher education institutions.

This paper presents the results of a comprehensive survey of Spanish university rankings. It offers a new perspective of the state of the art of the Spanish university ranking system. The paper also presents a set of managerial implications for improving these benchmarking tools.

It is often suggested that information retrieval systems should rank documents rather than simply retrieving a set. Two separate reasons are adduced for this: that relevance itself is a multi‐valued or continuous variable; and that retrieval is an essentially approximate process. These two reasons lead to different ranking principles, one according to degree of relevance, the other according to probability of relevance. This paper explores the possibility of combining the two principles, but concludes that while neither is adequate alone, nor can any single all‐embracing ranking principle be constructed to replace the two. The only general solution to the problem would be to find an optimal ranking by exploring the effect on the user of every possible ranking. However, some more practical approximate solutions appear possible.

Research on green certificate rankings in the MENA region primarily focuses on building scale, relying on the certified project count. This assessment approach overlooks the spatial factor, failing to capture their influence on the urban built environment, thus potentially undermining other efforts not reflected by the project count. This research aims to rank countries in the Middle East and Northern Africa (MENA) region based on their ongoing efforts regarding green neighbourhood certification.

This study employs a three-phase methodology to rank MENA countries' adoption of green neighbourhood certification systems: content analysis, multicriteria analysis (MCA) using the analytical hierarchy process (AHP) and spatial analysis.

Based on the content analysis, four major performance indicators were identified and the conventional ranking using projects count was presented. Using AHP, the MCA could rank the countries in the region according to their unique performance indicators score, clarifying the differences between conventional and AHP-based rankings. Finally, the spatial analysis phase uncovers shortcomings in the traditional ranking method, revealing inaccuracies and misrepresentations for several countries.

The study presents an innovative ranking methodology to monitor the green neighbourhood actions of countries in future development and establish a pioneering framework to evaluate the impact of green certifications within the region.

The purpose of this paper is to present a system that analyzes trustworthiness and ranks applications to improve the search experience.

The system adopts pointwise mutual information to calculate comment semantics. It examines subjective (signed opinions, anonymous opinions and star ratings) and objective factors (download numbers, reputation ratings) before filtering, ranking and displaying). The authors invited three experts to check three categories and compared the results using Spearman and two statistics.

A high correlation between the proposed system and the expert ranking system suggests that the system can act as decision support.

First, the authors have only tested the correlation between the proposed system and an expert ranking system; user satisfaction was not evaluated. The authors plan to conduct a later survey to gather user feedback. Second, the ranking system evaluates applications using fixed weights and disregards time. Therefore, in the future, the authors plan to enable their system to weight recent records over older ones.

User discussion forums, although helpful, have drawbacks. Not all reviews are trustworthy, and forums provide no filtering mechanisms to combat information overload. The solution to this is the authors’ system that crawls a forum, filters information, analyzes the trustworthiness of each comment and ranks the application for the user.

This paper develops a formula to analyze the trustworthiness of opinions, enabling the system to act as decision support when no professional advice is available.

The purpose of this paper is to introduce a novel framework for visual-aided ontology-based multidimensional ranking and to demonstrate a case study in the academic domain.

The paper presents a method for adapting semantic web technologies on multiple criteria decision-making algorithms to endow to them dynamic characteristics. It also showcases the enhancement of the decision-making process by visual analytics.

The semantic enhanced ranking method enables the reproducibility and transparency of ranking results, while the visual representation of this information further benefits decision makers into making well-informed and insightful deductions about the problem.

This approach is suitable for application domains that are ranked on the basis of multiple criteria.

The discussed approach provides a dynamic ranking methodology, instead of focusing only on one application field, or one multiple criteria decision-making method. It proposes a framework that allows integration of multidimensional, domain-specific information and produces complex ranking results in both textual and visual form.