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In practice, the concept of incentives is extensively applied in the allocation process for guiding the behaviours of an organization's units to satisfy the goals of the organization. However, this concept is rarely considered in data envelopment analysis (DEA)-based allocation research. This paper proposes a two-step incentive approach for allocating common revenues or fixed costs. The first step is performance evaluation. Considering the noncooperative game relationship of decision-making units (DMUs), a DEA game cross-efficiency method is selected to measure the efficiency scores of DMUs in this paper. The second step is incentive allocation. Based on the performance evaluation, we propose our incentive method for allocating revenues or fixed costs. We further provide simple equations to calculate the global optimal solution for our nonlinear programme allocation models. Several properties are explored, and we i) obtain the allocation interval rule of DMUs with the incentives, ii) investigate the quantitative relationship between the allocation gap and the optimal allocation plan, and iii) prove that the optimal allocation plan obtained by our allocation model is unique. The results of an empirical application highlight the applicability of our allocation method and solution approach. In this study, we obtain several important practical insights, including that (i) our method has positive effects on performance improvement and (ii) our method can work well even in an information asymmetric decision-making environment.

In the new situation of Chinese economic development, how to achieve sustainable development of enterprises has become an important issue concerned by managers and investors. This paper selects the listed companies of agriculture, tourism and renewable energy industry as research samples, employs the MVAIC (Modified value-added intellectual coefficient) model and runs the panel data to explore the role of capital employed efficiency, intellectual capital efficiency and its components on corporate sustainable growth. The results show that in the full sample, the capital employed efficiency, intellectual capital efficiency and its components have a significant positive effect on corporate sustainable growth. In the industries samples, the impact of the relationship capital efficiency on corporate sustainable growth of agricultural enterprises is not significant, and the impact of structural capital efficiency on corporate sustainable growth of tourism enterprises is not significant. While there is a significant positive impact of intellectual capital efficiency and its components on corporate sustainable growth of renewable energy enterprises. Based on the conclusions, this paper proposes policy suggestions for promoting the sustainable development of enterprises, aiming to provide theoretical guidance and empirical evidence for managers to invest and develop intellectual capital.

In academic communication, editors exert a significant influence on a journal’s mission and content. We examined how the composition of editorial board members, in particular diversity in terms of institution, is related to journal quality. Our sample comprised 6916 editors who were affiliated with 246 economics journals. Using Stirling Index of Diversity, we provided a single numeric index (DI) to measure the diversity of institutions which is composed of variety, balance, and disparity. Then we related it to journal quality, as reflected in three widely used indices in economics: the five-year impact factor, the association of business schools’ (ABS) journal quality guide, and the eigenfactors. The results show that academic journals in the field of economics are heavily dominated by US institutions, but in terms of geographic distribution, there are more institutions in Europe than in North America. Surprisingly, we found that the diversity of editorial board members in terms of institution is negatively related to ABS ranking, but unrelated to the five-year impact factor and the eigenfactors. While when we removed the US journals from the sample, there was a significant positive impact between institutional diversity and the five-year impact factor. Our study extends the scarce knowledge on the composition of editorial teams and their relevance to journal quality by study the correlation between the institutional diversity index and three different journal quality indices. The implication of this study is that more effort is needed to increase the diversity in the composition of editorial teams in order to ensure transparency and promote equity.

From the initial idea, writing, submitting, and reviewing to the online presentation of a research paper takes a long time. The identified intellectual structure of a research paper may have a certain time lag. In view of this problem, scholars have suggested that research grants may be an alternative way to identify intellectual structure as early as possible. However, these comments are mentioned qualitatively. Few researchers have verified the research grant by early identification of the intellectual structure of a field with a quantitative method. Therefore, this paper proposes a new method framework to confirm the lead-lag relationship quantitatively between intellectual structures identified by the research grant and the research paper. In empirical analysis, Operations Research and Management Science in China was selected as a specific research area. The results show that the intellectual structure identified by the research grant leads the intellectual structure of a research paper by approximately 1–2 years. These discoveries, to some extent, confirm the early identification of intellectual structure based on the research grant. In addition, the results also indicate that there is high similarity between intellectual structure identified by the research grant and that in the research paper in the previous year.

Risk measures play significant roles in determining the magnitude of risks. The traditional risk measures consider only the consequence $(C)$ and the probability $(P)$ and ignore the support of the knowledge behind to estimate $C$ and $P$ . Several researchers have suggested adding knowledge as a third dimension in the risk measures. However, the issues of how to embed the dimension of knowledge in the risk measures to output an explicit expression of the risk measure and how to measure the strength of knowledge remain unresolved. This paper proposes a new risk measure incorporating the dimension of knowledge, apart from $C$ and $P$ . It is shown that the proposed risk measure has the form of traditional risk measures when the risk assessor has full knowledge. In addition, a fuzzy multicriteria decision-making (MCDM) method is employed to assess the strength of knowledge. In the fuzzy MCDM method, an entropy optimization problem is solved to obtain fuzzy measures, which are critical for determining the score of the strength of knowledge. Finally, the proposed method is applied to a project risk assessment, showing the feasibility of the method.

Risk matrices have been proven as useful risk management tools, especially in the cases where data are not sufficient. Current usage of risk matrices in both literature and practice is related to s...

Meta-syntheses from experts’ judgements and quantitative metrics are two main forms of evaluation. But they both have limitations. This paper constructs a framework for mapping the evaluation results between quantitative metrics and experts’ judgements such that they may be solved. In this way, the weights of metrics in quantitative evaluation are objectively obtained, and the validity of the results can be testified. Weighted average percentile (WAP) is employed to aggregate different experts’ judgements into standard WAP scores. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to map quantitative results into experts’ judgements, while WAP scores are equal to the final closeness coefficients generated by the TOPSIS method. However, the closeness coefficients of TOPSIS rely on the weights of quantitative metrics. In this way, the mapping procedure is transformed into an optimization problem, and a genetic algorithm is introduced to search for the best weights. An academic journal ranking in the field of Supply Chain Management and Logistics (SCML) is used to test the validity obtained by mapping results. Four prominent ranking lists from Association of Business Schools, Australian Business Deans Council, German Academic Association for Business Research, and Comite National de la Recherche Scientifique were selected to represent different experts’ judgements. Twelve indices including IF, Eigenfactor Score (ES), H-index, Scimago Journal Ranking, and Source Normalized Impact per Paper (SNIP) were chosen for quantitative evaluation. The results reveal that the mapping results possess high validity for the relative error of experts’ judgements, the quantitative metrics are 43.4%, and the corresponding best weights are determined in the meantime. Thus, some interesting findings are concluded. First, H-index, Impact Per Publication (IPP), and SNIP play dominant roles in the SCML journal’s quality evaluation. Second, all the metrics are positively correlated, although the correlation varies among metrics. For example, ES and NE are perfectly, positively correlated with each other, yet they have the lowest correlation with the other metrics. Metrics such as IF, IFWJ, 5-year IF, and IPP are highly correlated. Third, some highly correlated metrics may perform differently in quality evaluation, such as IPP and 5-year IF. Therefore, when mapping the quantitative metrics and experts’ judgements, academic fields should be treated distinctively.

Extracting log template is a very effective method to deal with massive system logs. In this paper, based on Web system logs, the template extraction method based on label recognition tree is used to extract log template, and on this basis, the log preprocessing and template expression generation method are studied and improved. Aiming at the complex structure problem of system logs, the preprocessing method based on text similarity is adopted, and the classification of a log message is realized. The use of the maximum template matching method solves the problem of inconsistent log format and insufficient template matching caused by word segmentation. In the end, the log template extraction method is evaluated. The results show that the accuracy of the method is up to 96.4%, and the template matching degree is greatly increased.

Studies on software risk response theories and methods play an important role in improving the success rate of software project. With the requirement of software risk management, the existing single-objective risk response strategies section model is difficult to manage software risks effectively. This paper regards software risk response cost and software risk exposure as optimization objectives and proposes a multiobjective risk response strategies optimization model for software project. Furthermore, it analyzes the risk correlation from the perspective of risk probability dependence and risk loss interaction and puts forward a multiobjective risk response strategies optimization model for software project from the perspective of risk correlation. Empirical analysis results show that there is a trade-off relationship between the software risk exposure and software risk response cost. The software manager can identify the corresponding optimal risk response strategies according to the actual risk response budget. The results also indicate that the consequence of the multiobjective risk response strategies optimization model for software project considering risk correlation can better describe the actual situation of risk management.

A perfect combination of fact-based and value-based judgments is what multiple criteria decision-making (MCDM) issues pursue. Tolerability constraints, an important part of value-based judgments, are easily observable and have drawn attention in theory. However, their practical applications in the MCDM process are still in their infancy. This paper aims to fill the gap between theory and application. A framework for tolerability constraints in the context of non-independent criteria is proposed and operated for the first time. In this framework, global tolerability constraints on the overall decision-making process and local constraints on a single criterion are considered at the same time. Furthermore, mandatory/ sufficient requirements are regarded as different aspects of tolerability constraints. Two cases are given to illustrate the application of the proposed framework. In short, the proposed framework deepens the understanding of MCDM issues and extends its application, especially with respect to tolerability requirements.

In order to better cope with the problem of material procurement, this paper establishes a multi-objective optimization model in a systematic analysis framework for material procurement considering supply risk. This paper firstly combs and identifies the influencing factors of supply risk, and constructs a supply risk evaluation system from the dimensions of quality, price, delivery, service and technology. Secondly, based on the linguistic scale and fuzzy theory, this paper measures the supply risk of the candidate suppliers, and estimates the relevant parameters of the multi-objective optimization model by using the triangular fuzzy numbers. In addition, traditional intelligent algorithms are easily falling into a local optimal solution when solving programming problems. Through numerical simulation experiments, it is verified that the optimization model established in this paper can effectively simulate the operation of the enterprise in actual business. At the same time, the proposed model is feasible and useful for the selection of candidate suppliers and the portfolio optimization of material procurement.

The inequality in research funding is an important issue, in which the measurement of inequality is the basis. The literature has mostly investigated the inequality in research funding by providing overall values of inequality but has rarely explored this topic through the internal structure of the overall inequality. In this paper, a three-stage nested Theil index is employed to decompose the overall inequality in research funding into the between and within components. Moreover, a decomposition framework of university-institute sub-group is constructed to investigate the inherent structure of overall inequality in research funding. The data from the National Natural Science Foundation of China between 2013 and 2017 at the individual researcher level are collected as empirical data. The empirical results indicate that the overall Theil index of research funding in China is 1.97 (equal to 0.87 in the Gini index), a value higher than in the United States but lower than in Japan. The decomposition results regarding the inequality in research funding indicate that the inequality within institutions dominates and contributes 80.0% of the overall inequality. Moreover, ‘Universities’ contributes more than ‘Institutes’ to the inequality, and the ‘World-class universities’ contributes the most among the six groups of the Within-B component.

Project risk response planning is an important component of project risk management to help ensure project success. However, thus far, the relevant research has ignored the correlations between risk factors during different project development subprocesses. This study proposes a novel risk response method by opening the black box of the project process and considering the risk correlations among different subprocesses to help practitioners create a practical risk response plan. In our method, we first identify process risk factors from two-dimensional criteria called risk categories and project development subprocesses. Then, we consider the risk correlations between different subprocesses to assess the true effect of each process risk factor. Finally, we build a multiobjective risk response model under resource constraints to minimize the total expected losses, the total expected schedule delay, and the total expected quality reduction. Based on the model, we obtain a set of Pareto-optimal risk response plans. Project managers can select a satisfactory risk response plan based on their experience and objective project requirements. Finally, we apply the proposed method to a real software development project. We find that the risk response effects are better if managers control the process risks earlier. Moreover, the number of initial risk factors in each subprocess is an important reference in risk response resource allocation.

Assessing the scholarly impact of academic institutions has become increasingly important. The achievements of editorial board members can create benchmarks for research excellence and can be used to evaluate both individual and institutional performance. This paper proposes a new method based on journal editor data for assessing an institution’s scholarly impact. In this paper, a journal editorship index (JEI) that simultaneously accounts for the journal rating (JR), editor title (ET), and board size (BS) is constructed. We assess the scholarly impact of economics institutions based on the editorial boards of 211 economics journals (which include 8640 editorial board members) in the ABS Academic Journal Guide. Three indices (JEI/ET, JEI/JR, and JEI/BS) are also used to rank the institutions. It was found that there was only a slight change in the relative institutional rankings using the JEI/ET and JEI/BS compared to the JEI. The BS and ET weight factors did not have a substantial influence on the ranking of institutions. It was also found that the journal rating weight factor had a large effect on the ranking of institutions. This paper presents an alternative approach to using editorial board memberships as the basis for assessing the scholarly impact of economics institutions.

International research collaboration is vital to the success of scientific development of China, and the identification of collaboration partners is the basic unit of collaboration. While many researchers have investigated international research collaboration using publication data, grant data have rarely been used. This paper explores the international research collaboration partners of China from a new data perspective, based on grant data. Using data from the National Natural Science Foundation of China (NSFC) joint grants between China and 75 countries from 2006 to 2016, this study examines the collaboration partners in three aspects: overall collaboration activity, relative research effort, and collaboration groups of countries. Some interesting results are obtained as follows. Among 75 partners, the top 15 account for 95.42% of the total grants, but they are quite uneven in collaborating grant numbers and shares. The USA ranks far ahead of the others with a share of 53.27%, but China maintained a steady, approximately average collaborative effort with it from 2006 to 2016. China shows a growing preference for collaborating with Australia, the Netherlands, and Spain, while the preferences for Japan, Germany, and Sweden are the opposite. The collaborative number of grants among five collaboration groups varies greatly because of different features of constituents’ countries. The G7 and Asia–Pacific both own more than 75% of all the grants, while the BRICS and “The Belt and Road” are relatively weak in research collaboration, but China has maintained a rising trend of collaboration with them over the past 11 years.

We consider multiple criteria decision aiding (MCDA) in the case of interactions between criteria. In dealing with interactions between criteria, fuzzy measures and integrals have demonstrated great advantages. Nevertheless, the determination of fuzzy measures has proven difficult because the capacities of not only single criterion but also all subsets of criteria need to be identified. Due to the value judgment essence of MCDA, the attitudes of the decision maker (DM) are typically modeled to identify fuzzy measures. In this paper, the tolerance attitudes of the DM, which implies a direct requirement instead of partial preference, are modeled with regard to the determination of fuzzy measures for the first time. With two scales developed in this paper, the DM can directly express the tolerance attitudes to certain criteria other than providing partial preference through pairwise comparison. As a result, it requires less prior knowledge and is more efficient to some extent. Further, the inherent interacting mechanism of criteria under different tolerance attitudes is explored. At last, the tolerance attitudes are applied to the process of multiple criteria analysis using a Choquet integral. A classic student evaluation problem is given as an example. The evaluation results are compared with additive models. This paper not only provides a new inspiration to the determination of fuzzy measures but also improves the descriptive capacity of fuzzy measures to the real world.

Disciplinary layout is of great significance for research institutes, which can show the tendency and emphasis of the research institutes and facilitate mutual learning and promote potential collaboration. In this study, we illustrate how to use hierarchical clustering method to cluster the research institutes of Chinese Academy of Sciences based on the paper data which embody the disciplinary layouts. Based upon the clustering methods, potential collaborators can be identified and unified administrative plans can be developed which are of great significance in the development of scientific institutes.

Software effort estimation (SEE) is the process of forecasting the effort required to develop a new software system, which is critical to the success of software project management and plays a significant role in software management activities. This study examines the potentials of the SEE method by integrating particle swarm optimization (PSO) with the case-based reasoning (CBR) method, where the PSO method is adopted to optimize the weights in weighted CBR. The experiments are implemented based on two datasets of software projects from the Maxwell and Desharnais datasets. The effectiveness of the proposed model is compared with other published results in terms of the performance measures, which are MMRE, Pred(0.25), and MdMRE. Experimental results show that the weighed CBR generates better software effort estimates than the unweighted CBR methods, and PSO-based weighted grey relational grade CBR achieves better performance and robustness in both datasets than other popular methods.

Periodically ranking institutional research productivity is necessary not only to understand the status of the development of related fields but also to identify gaps and take appropriate corrective steps. Many bibliometric indicators contribute to the assessment of institutional research productivity, but the appropriateness of the indicator and the relationships between different indicators are topics that have not been addressed. For this reason, an indicator framework for the ranking of institutional research performance, which consists of a count of published articles and quality weighted dimensions, is developed. Based on the literature review, 17 indicators in the framework are chosen for study. Based on these indicators, experiments are conducted to rank Chinese institutions in the field of Information Management. Kendall's tau rank correlation coefficient (τ) is calculated, and all pairwise correlations are between 0.34 and 0.98. There are three primary findings: (1) among the article count indicators, the Straight count indicator is significantly different than others; (2) the rankings based on the indicators which are weighted by quality are consistent with those based on the indicators using article count; and (3) the Paper citation weighted indicator is sensitive to the procedure used for institutional ranking.

One of the major challenges involved in risk aggregation is the lack of risk data. Recently, researchers have found that mapping financial statements into risk types is a satisfactory way to resolve the problem of data shortage and inconsistency. Nevertheless, ignoring off-balance sheet (OBS) items has so far been regarded as the usual practice in risk aggregation, which may lead to deviations in conclusions. Hence, we improve the financial statements based risk aggregation framework by mapping OBS items into risk types. Based on 487 quarterly financial statements from all 16 listed Chinese commercial banks over the period 2007–2014, we empirically study whether the overall impact of OBS activities and the individual impact of each of the OBS risk types on total risk depend on bank size. Moreover, this research divides the sample into two subsets, during and after the subprime crisis, to find out how the subprime crisis affects risks of Chinese banks. Our empirical results show that although OBS credit risk is positively linked to total risk while OBS operational risk is negatively linked to total risk for both large and small banks, the overall impact of OBS activities on total risk depends on bank size. The overall OBS activities are positively related to the large bank’s total risk while they are negatively related to the small bank’s total risk. Besides, we also found that it is the increase of liquidity risk and market risk that leads to the larger total risk of Chinese banks during the subprime crisis.

Research funding is a significant support for the development of scientific research. The inequality of research funding is an intrinsic feature of science, and policy makers have realized the over-concentration of funding allocation. Previous studies have tried to use the Gini coefficient to measure this inequality; however, the phenomena of multiple funding sources and funding subdivision have not been deeply discussed and empirically studied due to limitations on data availability. This paper provides a more accurate analysis of the distribution inequality of research funding, and it considers all of the funding sources in the funding system and the subdivision of funding to junior researchers within research teams. We aim to determine the influence of these two aspects of the Gini results at the individual level. A dataset with 68,697 project records and 80,380 subproject records from the Chinese Academy of Sciences during the period from 2011 to 2015 is collected to validate the problem. The empirical results show that (1) the Gini coefficient for a single funding source is biased and may be overestimated or underestimated, and the most common data source, which is the National Natural Science Foundation of China (NSFC), causes the Gini coefficient to be underestimated; and (2) considering the subdivision of research funding lowers the inequality of research funding, with a smaller Gini coefficient, although the decrease is moderate.

Risk matrices have been widely used as a risk evaluation tool in many fields due to their simplicity and intuitive nature. Designing a rating scheme, i.e., determining the number of ratings used in a risk matrix and assigning different ratings to different cells, is an essential part of risk matrix construction. However, most of the related literature has focused on applying a risk matrix to various fields, instead of researching how to design risk matrices. Based on the analysis of several current rules, we propose a new approach, namely, the sequential updating approach (SUA), to design the rating scheme of a risk matrix in a reliable way. In this article, we propose three principles and a rating algorithm based on these principles. The three principles, namely, adjusted weak consistency, consistent internality, and continuous screening, characterize a good rating scheme. The resulting rating scheme has been proven to be unique. A global rating algorithm is then proposed to create the design that satisfies the three principles. We then explore the performance of the SUA. An illustrative application is first given to explain the feasibility of our approach. The sensitivity analysis shows that our method captures a resolution-reliability tradeoff for decisionmakers in choosing an appropriate rating scheme for a risk matrix. Finally, we compare the designs based on the SUA and Cox's axioms, highlighting the advantages of the SUA.

The design of risk matrices is a topic that has not reached a consensus, although risk matrices are widely used in practice. Several methods have been proposed to help design risk matrices. However, all the methods seem to have their own advantages, and it is difficult for the decision makers to choose one. In this paper, we compare two different risk matrix design methods from the perspective of applicability. Specifically, we give three detailed scenarios where different settings of the risk matrices are given, and then compare the performance of the methods. Results show that both the two methods have their own advantages, but they will fail to give an effect design sometimes.

The financial system plays a crucial role in development of countries, which makes its stability be heated around the world. However, traditional indices which help measure financial stability such as quantile, leverage ratio and liquidity have instinctive shortcomings. For example, these digital indices are usually unavailable and one-sided. Therefore, finding a new approach to quantifying and visualizing financial stability is necessary and desirable. Different from digital data, textual data is more available and usually implies more abundant information and intuitional senses. Since textual data is not visualized, it is vital to find out what texts talk about. Latent Dirichlet Allocation (LDA) is one of the most effective approaches to achieve above goal. In order to apply LDA to measure financial stability, China Financial Stability Report is selected to make an empirical analysis. The results are as follows. Firstly, it is reasonable that LDA model can be applied to analyze China Financial Stability Report. Secondly, dividing core terms of every topic into basic terms and particular terms, we can draw pictures of every embranchment in finance. And we can analyze topics rank of 5 years or in every single year, so that a designing matrix comes into being and we can study financial stability tendencies. At last, the macro-environment in finance can be depicted easily using word cloud.

For the sake of better coping with the problem of material procurement, a multi-objective optimisation model was established in a systematic analysis framework for material procurement considering supply risk. First, this paper combs and identifies supply risk factors and constructs a supply risk evaluation system from the dimensions of quality, price, delivery, service and technology. Second, based on the linguistic scale and fuzzy theory, this paper measures the supply risk of candidate suppliers and estimates the relevant parameters of the multi-objective optimisation model by using the triangular fuzzy numbers. In addition, an improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized in this paper to solve the multi-objective model, since traditional intelligent algorithms have slow convergence speed and are easily trapping into local optimisation. Finally, this paper conducts simulation experiments by setting three types of decision-makers with different risk preferences and provides material procurement combination schemes in different scenarios. Through numerical simulation experiments, it was verified that the optimisation model established in this paper was feasible and useful for the selection of candidate suppliers and the portfolio optimisation of material procurement.

For all of the many advantages and enormous benefits that information technology has brought to us, it has subjected to increasing risks that need enough attention. Bayesian network (BN) is an important probabilistic inference approach to support reasoning under uncertainty. This paper describes how BN is applied to quantify the occurrence probability of software process risk factors and the influence strength among the process risk factors in the context of Chinese trustworthy software. The information of 52 factors was obtained through a questionnaire survey of 93 project managers in five high Capability Maturity Model Integration (CMMI) level software companies. The focus of this paper is to present the key risk checklist and good timing for process risk control to improve software process risk management. Special effort has been put on the description of the experimental study, which provides the top 20 key risk factors in software process and critical software sub-processes for process risk management. The findings can provide the key risk checklist to software risk manager for risk identification and decision-making in process risk management. This is a general approach and, as such, it can be applied to a certain software project or some software enterprises with updated data.

Extant research has focused upon assessing individual risks with the aid of risk matrices. Although risk aggregation is an important issue in risk management, aggregation of risks measured by risk matrices remains unresolved despite the wide usage of risk matrices. This paper proposes a framework to resolve the problem. We start from modifying the two notions of non-aggregatability of risk matrices, namely, qualitative description of inputs and non-comparability of different types of consequences. Then, we explicate the strong connection between risk matrices and fuzzy sets and propose that the transformation from risk matrices to fuzzy sets clear some confusions encountered in the aggregation process. A framework which covers membership analysis, composing different risk and defuzzification of the aggregated membership function, is proposed to aggregate different risks. In the framework, we pay maximum attention to accurate estimation of memberships of different risks. Besides, technical problems of comp...

In this paper, a co-word method based on keywords from funded project is proposed to map the research trends. Firstly, the keywords of funded project are used to describe the rsearch topic statistically. Then, co-word analysis, including cluster analysis, social network analysis, is adopted to study the relationship of each research topic. The projects of Management Science and Engineering in National Natural Science Foundation of China during 2011-2015 are collected as the empirical data. The data is composed of General Project, Youth Project, and Regional Project. The results show that the focus of researches are Game Theory, Supply Chain Management, Complex Network, Data Mining, Optimize, Risk Management, and Data Envelopment Analysis. Moreover, Game Theory, Supply Chain Management, and Data Mining are hot topics. The research fields in Management Science and Engineering in China are varied, and the well developed and core research fields are fewer.

Ranking research productivity of institutions periodically is of a great necessity nowadays, for that can not only help understand the latest development level of related fields but also contribute to finding gaps and quickly improve. In previous studies, the number of publications and further impact factors of journals is two widely used indexes to measure research productivity. However, impact factors do not always tally with a quality of the journal, which will lead to a bias of research productivity. Given this, a new journal rating adjusted publications (JRAP) index is constructed in this paper, which is based on the journal rating of the ABS journal guide. It takes the quantity of publications and the quality of the publications into account at the same time. Compared with impact factors, academic journal guide can provide more authoritative and accurate measurement to the quality of journals. Experiments are conducted to rank Asia---Pacific institutions in business and management area based on JRAP, and it is the first time to rank Asia---Pacific institutions systematically. Ranking of institutions measured by three methods is also given. Compared the results obtained by three different rank methods, although institutions ranked in the top places keep the same, the specific rank differs. The results indicate that JRAP do prefer the institutions perform well in paper quantity and quality of journals.