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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.