Search results

As for academic papers, the customary methods for assessing the impact of books are based on citations, which is straightforward but limited to the coverage of databases. Alternative metrics can be used to avoid such limitations, such as blog citations and library holdings. However, content-level information is generally ignored, thus overlooking users’ intentions. Meanwhile, abundant academic reviews express scholars’ opinions on books, which can be used to assess books’ impact via fine-grained review mining. Hence, this study aims to assess books’ use impacts by conducting content mining of academic reviews automatically and thereby confirmed the usefulness of academic reviews to libraries and readers.

Firstly, 61,933 academic reviews in Choice: Current Reviews for Academic Libraries were collected with three metadata metrics. Then, review contents were mined to obtain content metrics. Finally, to identify the reliability of academic reviews, Choice review metrics and other assessment metrics for use impact were compared and analysed.

The analysis results reveal that fine-grained mining of academic reviews can help users quickly understand multi-dimensional features of books, judge or predict the impacts of mass books, so as to provide references for different types of users (e.g. libraries and public readers) in book selection.

Book impact assessment via content mining can provide more detail information for massive users and cover shortcomings of traditional methods. It provides a new perspective and method for researches on use impact assessment. Moreover, this study’s proposed method might also be a means by which to measure other publications besides books.

The purpose of this paper is to explore a host of issues related to the use of marketing metrics and firm performance in the context of the Middle East. Specifically, it seeks to explore which marketing metrics relate to perceived performance, to understand how frequency of metric reporting impacts perceived performance, to identify the impact that marketing dashboards have on perceived firm performance and to analyze how measurement ability relates to perceived performance.

This paper used an online survey administered to marketing managers at firms located in the Middle East. A total of 55 participants provided usable data. Participants provided the frequency at which 71 different marketing metrics are reported by their firms and their assessments of the firm’s performance with respect to sales growth, market share growth, and profitability. In addition, they indicated whether or not a marketing dashboard was used to report these metrics, and if so, how long ago the dashboard had been implemented. They also assessed their firm’s holistic ability to measure and use metrics compared to their competition.

As expected, marketers in the Middle East found the marketing metrics examined to vary in their usefulness as judged by their relationship to perceived performance. For those metrics that were perceived to be useful, their utility tended to peak at a moderate level of reporting frequency. These findings also varied by the type of performance considered. The use a marketing metric dashboard did not relate to perceived performance, but the frequency with which the dashboards were reported was found to have a negative linear relationship to perceived performance. Overall, the more capable respondents judged their firms to be with respect to measuring and reporting metrics, the higher their perceived performance.

This paper offers new insights into the usefulness of a wide variety of marketing metrics to marketers in the Middle East. It also provides guidance on the ideal reporting frequency for those metrics. The findings suggest that marketers in the Middle East should focus on reporting key metrics at an appropriate frequency, regardless of whether or not a dashboard format is used. If a dashboard is used, the results of this paper suggest that care should be taken that it not be reported too frequently.

This paper contributes to our understanding of how marketing metrics relate to performance. As the first such study undertaken in the context of Middle Eastern marketers, it represents an important replication and extension of previous findings in other contexts.

Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.

In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.

The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.

One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.

In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

The roles recommendation systems play in health care have become crucial in achieving effective care and in meeting the needs of modern care giving. As a result, efforts have been geared toward using recommendation systems in the management of chronic diseases. Effectiveness of these systems is determined by evaluation following implementation and before deployment, using certain metrics and criteria. The purpose of this study is to ascertain whether consideration of criteria during the design of a recommendation system can increase acceptance and usefulness of the recommendation system.

Using survey-style requirements gathering method, the specific health and technology needs of people living with chronic diseases were gathered. The result was analyzed using quantitative method. Sets of harmonized criteria and metrics were used along with requirements gathered from stakeholders to establish relationship among the criteria and the requirements. A matching matrix was used to isolate requirements for prioritization. These requirements were used in the design of a mobile app.

Matching criteria against requirements highlights three possible matches, namely, exact, inferential and zero matches. In any of these matches, no requirement was discarded. This allows priority features of the system to be isolated and accorded high priority during the design. This study highlights the possibility of increasing the acceptance rate and usefulness of a recommendation system by using metrics and criteria as a guide during the design process of recommendation systems in health care. This approach was applied in the design of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People. The result has shown that with this method, it is possible to increase acceptance rate, robustness and usefulness of the product.

Inability to know the evaluation criteria beforehand, inability to do functional analysis of requirements, lack of well-defined requirements and often poor cooperation from people living with chronic diseases during requirements gathering for fear of stigmatization, confidentiality and privacy breaches are possible limitations to this study.

The result has shown that with this method, it is possible to isolate more important features of the system and use them during the design process, thereby speeding up the design and increasing acceptance rate, robustness and usefulness of the system. It also helps to see in advance the likely features of the system that will enhance its usefulness and acceptance, thereby increasing the confidence of the developers in their ability to deliver a system that will meet users’ needs. As a result, developers know beforehand where to concentrate their efforts during system development to ascertain the possibility of increasing usefulness and acceptance rate of a recommendation system. In addition, it will also save time and cost.

This paper demonstrates originality by highlighting and testing the possibility of using evaluation criteria and metrics during the design of a recommender system with a view to increasing acceptance and enhancing usefulness. It also shows the possibility of using the metrics and criteria in system’s development process for an exercise other than evaluation.

This study aims to identify and assess the use of mergers and acquisitions (M&A) performance metrics for small- and medium-sized German pharmaceutical and biotechnology companies.

In depth qualitative interviews with senior key informant executives involved with recent M&A were conducted. Unlike previous studies, the role of the interviews was to identify and uncover the use of a broad set of success metrics to add to a fuller understanding of post-M&A behaviour, leading to a more accurate evaluation of post-M&A performance.

Results from interviews suggest that important M&A success metrics include having a successful organisation, integrated cultures, a high employee retention rate, gaining new technology knowledge, overall economic enhancement, increases in patent-protected products, increases in clinical success rates (perished rate assessment) and increases in the share price. In general, expert interviewees stated that they would use a combination of the success metrics and monetary tools to assess post-M&A success.

This study identifies and uncovers a series of different M&A performance success metrics being in prominent use. The use of a combination of metrics is highlighted as being a relatively unique research finding.

The purpose of this paper is to classify the most important metrics proposed for web information systems, with the aim of offering the user a global vision of the state of the research within this area.

WQM distinguishes three dimensions related to web features, lifecycle processes and quality characteristics. A range of recently published (1992‐2004) works that include web metrics definitions have been studied and classified within this model.

In this work, a global vision of web metrics is provided. Concretely, it was found that about 44 percent of metrics are related to “presentation” and that most metrics (48 percent) are usability metrics. Regarding the life cycle, the majority of metrics are related to operation and maintenance processes. Nevertheless, focusing on metrics validation, it was found that there is not too much work done, with only 3 percent of metrics validated theoretically and 37 percent of metrics validated empirically.

The classification presented tries to facilitate the use and application of web metrics for different kinds of stakeholders (developers, maintainers, etc.) as well as to clarify where web metric definition efforts are centred, and thus where it is necessary to focus future works.

This work tries to cover a deficiency in the web metrics field, where many proposals have been stated but without any kind of rigour and order. Consequently, the application of the proposed metrics is difficult and risky, and it is dangerous to base decisions on their values.

This paper aims to focus on the use of qualitative research methods to gain a better understanding of the performance management system (PMS) of one of the largest retailers in North America. The motivation for the research was to assess whether the PMS at one of the world’s largest retail companies was congruent with the most recent thinking and research in the management accounting literature.

Using open-ended interviews, the paper seeks to develop relevant hypotheses emerging from the dimensions of the Strauss and Corbin’s qualitative research methodology (1998). A qualitative methodology was used because it provides a structured approach and analytical techniques that can build upon existing theory and literature.

The qualitative evidence collected during the course of the research indicates that financial measures were predominantly used by the company in its PMS, and that this reliance on financial measures may be an artifact of the industry in which the company operates. The retail industry is highly competitive, and it is very sensitive to changes in customer tastes and behavior, as well as shareholder and financial market pressures. In addition to financial measures, it was found that operational management developed certain non-financial performance measures and that this development may have been a response by operational managers to wider stakeholder pressures and external influences. However, these performance measures appear to be not fully integrated in the PMS and are therefore de-coupled and relatively unimportant in, or entirely absent from, top-level decision-making.

The conclusions of the paper provide support for the concepts of isomorphism and de-coupling as found in the literature of new institutional theory.

The case study approach has enabled to explore and gain further understanding of management accounting practices, particularly performance measurement and management, in their natural setting. Strauss and Corbin’s (1998) grounded theory methodology was adopted because it provides a structured set of analytical steps and systematic analytical techniques for handling and interpreting data and theory building.

e‐Supply chains are fast becoming a reality. In order to manage such supply chains efficiently and effectively, traditional measures of supply chain performance are not adequate. The literature search revealed lack of measures and metrics for e‐supply chains. The purpose of this paper is to develop new measures and metrics for monitoring the performance of e‐supply chains.

A framework based on the benefits of e‐supply chains has been used to develop the metrics and measures. The study makes use of focus group discussion by assembling eight experts and practitioners in the field of e‐supply chain to come up with the measures and metrics. A questionnaire is designed with these measures and metrics and is sent to about 300 electronic component manufacturing companies in Malaysia to obtain feedback from the industry practitioners. Appropriate reliability and validity tests are conducted to measure the reliability of the instrument and validity of the constructs.

Through the focus group discussion, this study identifies six metrics and 21 measures. Further validation through the industry practitioners, reveals that these measures are important and some are in use by the industries. The six metrics are: web‐enabled service, data reliability, time and cost, e‐response, invoice presentation and payment and e‐document management metrics.

The study uses a simple framework and a sound methodology to develop new measures and metrics that are relevant for e‐supply chains.

The purpose of this paper is to develop and describe the implementation of a novel method for creating interval-level metrics for objectively assessing police officer behaviors during an encounter with the public. These behaviors constitute officer performance and affect the probability of desirable encounter outcomes. The metrics measure concrete, micro-level performance in the common types of complex, dynamic, and low-information police-public encounters that often require immediate action using “naturalistic” decision making. Difficulty metrics also were developed to control for situational variability. The utility of measuring what officers do vs probabilistic outcomes is explored with regard to informing policymaking, field practice, and training.

Metric sets were developed separately for three types of police-public encounters: deadly force judgment and decision making, cross-cultural tactical social interaction, and crisis intervention. In each, “reverse concept mapping” was used with a different diverse focus group of “true experts” to authoritatively deconstruct implicit concepts and derive important variables. Variables then were scaled with Thurstone’s method using 198 diverse expert trainers to create interval-level metrics for performance and situational difficulty. Metric utility was explored during two experimental laboratory studies and in response to a problematic police encounter.

Objective, interval-level metric sets were developed for measuring micro-level police performance and encounter difficulty. Validation and further refinement are required.

This novel method provides a practical way to rapidly develop metrics that measure micro-level performance during police-public encounters much more precisely than was previously possible.

The metrics developed provide a foundation for measuring officers’ performance as they exercise discretion, engage people, and affect perceptions of police legitimacy.

Despite extensive use and ever‐increasing enthusiasm in the West, benchmarking for facilities management (FM) in the Asia Pacific region is only fledgling. The austere economic situation and fierce business competition after the Asian economic crisis of 1997, has made more companies realize that it is not only profit, but also cost‐effectiveness and the leap‐frog pattern of improvement in performance which are crucial for survival. Benchmarking is reconsidered in this context. This 1998 research project investigates the perception and current practice of FM benchmarking metrics in the region. Through critical review of the current FM benchmarking, improvement in the process will be identified. The study on the preference and use of individual metrics can be used as a guideline for the development of standard benchmarking metrics list for companies in the region.