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Technological alliances have become a popular strategy to cope with competitive pressures, short product life cycles, high research and development (R&D) costs and entry barriers. Firms gain access to complementary technologies, insulate from environmental uncertainty, increase knowledge base, access new markets and preserve leadership by using strategic alliances. In this regard, this study aims to establish a framework for R&D strategic alliance partner selection by utilizing a hybrid multi-criteria decision-making (MCDM) approach. In this way, at first, related R&D strategic alliance partner selection criteria are collected from existing literature, and then by using the best-worst method (BWM), which is a novel MCDM method, the weights of these criteria are calculated. After that by using the COPRAS method, available partners for R&D strategic alliance, ranked, and the best ones introduced. The calculation procedure for the weighting and evaluation processes are proposed and validated by using an illustrative example of Iran's aviation industry. The proposed approach also provides a relatively simple and well-suited decision-making tool for this type of strategic decision-making problem. [ABSTRACT FROM AUTHOR]

This study seeks to investigate and understand why and how Iranian higher education students choose to study overseas. After critically analyzing the international student mobility literature, the important factors are distilled. Then, by using Fuzzy-Delphi Method (FDM), 15 factors are selected from the 21 factors initially selected by reviewing the literature. This study utilizes a novel Multicriteria Decision Making (MCDM) approach, based on Best-Worst Method (BWM), to prioritize the factors influencing the choice of Iranian students regarding international higher education. Therefore, by using BWM, the most and the least important factors and subfactors were identified and prioritized based on the experts' opinions. The findings of this quantitative research reveal that Iranian students identify "Aids and Scholarships," "Cost Issues," and "Environment of the Home Country" as the most important factors, respectively. Finally, a discussion is generated regarding the results and their implications.

In this study, the existence of anchoring bias—people's tendency to rely on, evaluate, and decide based on the first piece of information they receive—is examined in two multi‐attribute decision‐making (MADM) methods, simple multi‐attribute rating technique (SMART), and Swing. Data were collected from university students for a transportation mode selection. Data analysis revealed that the two methods, which have different starting points, display different degrees of anchoring bias. Statistical analyses of the weights obtained from the two methods show that, compared to Swing (with a high anchor), SMART (with a low anchor) produces lower weights for the least important attributes, while for the most important attributes, the opposite is true. Despite their differences in anchoring bias, analytical approaches supported by empirical studies suggest that both methods (SMART and Swing) overweigh the less important attributes and underweigh the more important attributes. As such, we examined whether the best‐worst method (BWM), which has two opposite anchors in its procedure (a possible promising anchoring debiasing strategy), could produce results that are less prone to anchoring bias. Our findings show that the BWM is indeed able to produce lower weights (compared to SMART and Swing) for the less important attributes and higher weights for the more important attributes. This study shows the vulnerability of MADM methods with a single anchor and supports the idea that MADM methods with multiple (opposite) anchors, like BWM, are less prone to anchoring bias. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Remote Sensing / Türkiye Uzaktan Algılama Dergisi is the property of Turkish Journal of Remote Sensing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most important steps in formulating and solving a multiattribute decision‐making (MADM) problem is weighting the attributes. Most existing weighting methods are based on judgments by experts/decision‐makers, which are prone to several cognitive biases, making it necessary to examine these biases in MADM weighting methods and develop debiasing strategies. This study uses experimental analysis to look at equalizing bias—one of the main cognitive biases, where decision‐makers tend to assign the same weight to different attributes—in MADM methods. More specifically, we look at AHP (analytic hierarchy process), BWM (best‐worst method), PA (point allocation), SMART (simple multiattribute rating technique), and Swing methods under two structuring formats, hierarchical and non‐hierarchical. To empirically examine the existence of equalizing bias in these methods, we formulate several hypotheses, which are tested using a public transportation mode selection problem among 146 university students. The results indicate that AHP and BWM have less equalizing bias than SMART, Swing, and PA, and that the hierarchical problem structuring leads to a reduction in the equalizing bias in all five methods and that such a reduction significantly varies among the methods. Our findings prove some debiasing strategies suggested in existing literature, which could be used by real decision‐makers (when selecting a method) as well as researchers (when developing new methods). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Decisions & Operations Research is the property of Ayandegan Institute of Higher Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental management systems have been established and developed based on ISO 14001 in order to assess and improve the organization's environmental performance via providing a framework for evaluation and monitoring of the processes for all aspects related to the environment. This article aimed at ranking the key success factors of implementation of the environmental management systems in Mazandaran Steel industry. After reviewing the literature, the most important success factors were identified and classified into 9 categories. Then this factors analyzed by Grey DEMATEL technique. To complete the questionnaire, used the opinions of five experts. The results showed that the key factors respectively are: managerial commitment and support, coordination and integration, education, participation and effective management of human resources, financial and non-financial resources and cost-benefit analysis, environmental goals and policies and documentation control. The key success factors in the implementation of the environmental management systems should be seriously considered by this industry. [ABSTRACT FROM AUTHOR]

Generally, geothermal production and injection wells of a geothermal doublet have different performances. To maximize the energy production rate the well with higher performance should be used for production. One of the disadvantages of this option is the increase of costs for energy during production caused by the need to increase injection pressure over time. A potential method to enhance well performance by means of increasing the permeability of carbonate rocks is the injection of CO2- enriched water during heat and/or electrical power production. Geoenergie Bayern GmbH and the Department of Hydrogeology of the Technische Universität Bergakademie Freiberg in cooperation with Hydroisotop GmbH have started a research project "Long-term improvement and maintenance of reservoir-pathways in deep geothermal energy" [Langfristige Verbesserung und Erhaltung von Reservoir- Wegsamkeiten in der Tiefen Geothermie (LERWTG)] to investigate the potential and the opportunities for CO2 injection at the geothermal pilot project Kirchweidach. Reactive transport modeling was done using TOUGHREACT to simulate the use of CO2 for prevention of scaling and enhancement of permeability. Simulation results show that the injection of CO2 reduces the pH which in turn inhibits precipitation inside the well and also in the aquifer pathways. [ABSTRACT FROM AUTHOR]