Journal articles: 'Prioritization factors'

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Ul Hassan, Shams. "Factors affect on Requirement Prioritization." IOSR Journal of Engineering 02, no. 12 (December 2012): 28–33. http://dx.doi.org/10.9790/3021-021222833.

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Chong, Alan Kim Wing, Abdul Hakim Mohammed, Mat Naim Abdullah, and Mohd Shahril Abdul Rahman. "Maintenance prioritization – a review on factors and methods." Journal of Facilities Management 17, no. 1 (February 4, 2019): 18–39. http://dx.doi.org/10.1108/jfm-11-2017-0058.

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PurposeMaintenance is a compulsory cost in any asset's operation and requires sufficient budget to be supported. However, insufficient maintenance allocation would lead to maintenance prioritization being practiced as a solution to relief the maintenance demands. The purpose of this paper is to review and discuss the related literature on the factors considered and methods practiced in maintenance prioritization, especially in asset managements.Design/methodology/approachA thorough literature search related to factors and methods used in maintenance prioritization was conducted through several journal databases. The keywords maintenance priority, maintenance prioritization, priority management and maintenance planning were used. Publications related to maintenance priority from the period of 1990 to 2016 were reviewed and filtered out. Literature focusing on methods and factors related to maintenance prioritization were analyzed and discussed.FindingsThis study presented the trend and publications related to maintenance priority throughout 1990 to 2016. Publications related with methods and factors were analyzed. The most used priority-setting methods include analytical hierarchy process; priority criterion; priority matrix; and failure mode and effect analysis. Factors for maintenance priority were categorized into four categories which are technical, financial, social and political category. A discussion on the strategic direction of maintenance prioritization was conducted to highlight future research and possible improvements.Originality/valueThe paper contains a state of the art on publications and discussion related to the factors, methods and suggestions on the strategic aspect of maintenance prioritization. It offers insights and information on the current body of knowledge to academics, researchers, maintenance managers, practitioners and stakeholders concerned with asset management.

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Song, Bang-weon, Byeong-gyu Song, and Seok-joong Kang. "Influential Factors on Prioritization of Weapon Systems." Journal of the Korea Institute of Information and Communication Engineering 20, no. 8 (August 31, 2016): 1607–14. http://dx.doi.org/10.6109/jkiice.2016.20.8.1607.

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Chung, Bryan, and Steven F. Morris. "Factors influencing prioritization for carpal tunnel syndrome consultation." Canadian Journal of Plastic Surgery 21, no. 1 (March 2013): 33–36. http://dx.doi.org/10.1177/229255031302100111.

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Pinho, Micaela, and Ana Pinto Borges. "A Scoring Index of Prioritization Factors Between Patients." Health Care Manager 38, no. 3 (2019): 267–75. http://dx.doi.org/10.1097/hcm.0000000000000274.

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Dimzon, Mario S., Nicole You, Fernando Gonzalez, Ali Poosti, and Adel H. Hagekhalil. "Developing Spill Risk Factors for Sewer Renewal Prioritization." Proceedings of the Water Environment Federation 2012, no. 13 (January 1, 2012): 3014–21. http://dx.doi.org/10.2175/193864712811727102.

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Colvin, Kurt, Ken Funk, and Rolf Braune. "Task Prioritization Factors: Two Part-Task Simulator Studies." International Journal of Aviation Psychology 15, no. 4 (October 2005): 321–38. http://dx.doi.org/10.1207/s15327108ijap1504_2.

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Raju, S., and G. V. Uma. "An Efficient Method to Achieve Effective Test Case Prioritization in Regression Testing using Prioritization Factors." Asian Journal of Information Technology 11, no. 5 (May 1, 2012): 169–80. http://dx.doi.org/10.3923/ajit.2012.169.180.

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Roongruang, Siripong, and Jirapun Daengdej. "A Test Case Prioritization Method with Practical Weight Factors." Journal of Software Engineering 4, no. 3 (June 15, 2010): 193–214. http://dx.doi.org/10.3923/jse.2010.193.214.

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Singh, Dinesh, and Aman Jatain. "An Interactive Approach to Requirements Prioritization Using Quality Factors." International Journal in Foundations of Computer Science & Technology 3, no. 6 (November 30, 2013): 25–40. http://dx.doi.org/10.5121/ijfcst.2013.3603.

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Lai, John, Nicole J. Olynk Widmar, Michael A. Gunderson, David A. Widmar, and David L. Ortega. "Prioritization of farm success factors by commercial farm managers." International Food and Agribusiness Management Review 21, no. 6 (July 14, 2018): 817–32. http://dx.doi.org/10.22434/ifamr2017.0035.

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This study elicits U.S. agricultural producer preferences for five key management success factors: managing output prices; managing production; controlling costs; managing land/equipment/facilities; and managing people. The objective of this analysis was to determine the relative importance of each of the five profit-centric functional areas of management among U.S. farm managers. Significant heterogeneity in preferences was observed over the management areas. Farm managers, on average, placed the highest importance in controlling costs (29% preference share). Differences emerged among groups of farmers in a latent class model where managing people became relatively important to the viability of the agribusiness.

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Mohammadkarim, Bahadori, Salesi Mahmood, Ravangard Ramin, Hosseini Seyed Mojtaba, Raadabadi Mehdi, Hojati Dana Amin, and Ameryoun Ahmad. "Prioritization of Factors Affecting Time Management among Health Managers." International Journal of Travel Medicine and Global Health 3, no. 4 (October 15, 2015): 169–74. http://dx.doi.org/10.20286/ijtmgh-0304142.

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Srivastva, Praveen Ranjan, Krishan Kumar, and G. Raghurama. "Test case prioritization based on requirements and risk factors." ACM SIGSOFT Software Engineering Notes 33, no. 4 (July 2008): 1–5. http://dx.doi.org/10.1145/1384139.1384146.

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Muir, Andrew J., Linda L. Sanders, Michael A. Heneghan, Paul C. Kuo, William E. Wilkinson, and Dawn Provenzale. "An examination of factors predicting prioritization for liver transplantation." Liver Transplantation 8, no. 10 (October 2002): 957–61. http://dx.doi.org/10.1053/jlts.2002.35545.

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Laliberté, M., B. Mazer, T. Orozco, G. Chilingaryan, M. Hunt, B. Williams-Jones, and D. E. Feldman. "Factors influencing prioritization, frequency and duration of physiotherapy services." Physiotherapy 101 (May 2015): e811. http://dx.doi.org/10.1016/j.physio.2015.03.3697.

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Minouei, Amin, and Mohd Zaidi abd Rozan. "University entrepreneurship center identity factors prioritization using TOPSIS method." International Journal of Engineering & Technology 7, no. 3 (July 8, 2018): 1325. http://dx.doi.org/10.14419/ijet.v7i3.12252.

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University Entrepreneurship Center Identity (UECI) is a set of meanings by which a UECI allows itself to be known, and through which it allows people to describe, remember and relate to it. There are many factors stated in many studies related to UECI, but their importance level is not mentioned. In this study, the UECI factors are ranked by using TOPSIS method. A quantitative study was conducted to obtain experts’ views on UECI factors. The UECI factors were prioritized and ranked by using the “Technique for order of Preference by Similarity to Ideal Solution” (TOPSIS) from Multi-Criteria Decision Making (MCDM) method. The result of performing TOPSIS is as a novelty which assists UECI presentation. The high priority factors were introduced as “Innovativeness”, “Industry Interactions”, “Producing Highly Qualiﬁed Graduates”, “Proactiveness”, “Attract Entrepreneurial Faculty”, “Teamwork”, “Consulting”, and “Risk Taking”. This study proposes the importance level of UECI factors. At the end, it is recommended to evaluate the effect of UECI perception in different studies which contain university entrepreneurship center identity perception.

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Etumnu, Chinonso, and Allan W. Gray. "A Clustering Approach to Understanding Farmers’ Success Strategies." Journal of Agricultural and Applied Economics 52, no. 3 (March 18, 2020): 335–51. http://dx.doi.org/10.1017/aae.2020.4.

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AbstractThis paper examines the prioritization of management factors by farmers that lead to the success of their farm business. The research also examines the heterogeneity of producer prioritization of different success strategies. Using a hierarchical clustering approach, we found that each farmer group prioritizes either managing costs, managing output prices, managing people, managing production, or managing assets. The results indicate that the farmer groups are heterogeneous in their prioritization of five strategic management factors. These results provide useful insights for farmers, farm input providers, and farm service providers.

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Anthony Jnr., Bokolo. "Validating the usability attributes of AHP-software risk prioritization model using partial least square-structural equation modeling." Journal of Science and Technology Policy Management 10, no. 2 (June 3, 2019): 404–30. http://dx.doi.org/10.1108/jstpm-06-2018-0060.

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PurposeThis study aims to develop a software risk prioritization model using analytic hierarchy process (AHP) and further validate the usability attributes of the model in prioritizing operational, technical, technological, strategic and environmental software risks.Design/methodology/approachQuestionnaire was used to collect data from software practitioners to evaluate the usability attributes of the AHP-software risk prioritization model. Accordingly, partial least square-structural equation modeling was used to analyze the collected data.FindingsResults reveal that the developed AHP-software risk prioritization model is efficient and effective in facilitating software risk factor prioritization. In addition, results suggest that the experts are satisfied with the learnability, accessibility and navigation capability of the model. Besides, results indicate that the model provides a useable interface and system design for content availability of information needed by software practitioners in evaluating and prioritizing operational, technical, technological, strategic and environmental risk. Furthermore, results show that the experts intend to adopt the model to prioritize identified software risk in their firm.Research limitations/implicationsMethodologically, the developed AHP-software risk prioritization model is faced with issues such as inconsistency in judgments, weakness of confronting ambiguities and uncertainties of high complexity. Empirically, data were collected from software practitioners in Malaysia to validate the AHP-software risk prioritization model. Hence, results from this study cannot be generalized to other software practitioners in different countries.Practical implicationsThis study developed a software risk prioritization model to evaluate and prioritize software risks that occur in software organizations by deploying AHP to carryout risk factor priority selection. Moreover, the model provides risk knowledge as guidelines for evaluating software risks in software organizations.Social implicationsThe developed AHP-software risk prioritization model computes risk prioritization factor priority selection and further supports software practitioners and evaluates risks and associated risk factors. Besides, this study develops an instrument that can be used in project risk management to validate the usability attributes of software risk approaches.Originality/valueThis research designs use case and class diagram to show how the AHP-software risk prioritization model evaluates and prioritizes software risks factors by using risk evaluation questions. Additionally, the AHP-software risk prioritization model computes, evaluates and prioritizes software risk factors using risk factor priority selection for software project management.

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V.S.V.V.R.K, Prasad Chowdary. "Prioritization of Maintenance Metric Factors and Improve the Supply Chain Performance through Cluster Analysis and ANOVA." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2490–97. http://dx.doi.org/10.5373/jardcs/v12sp7/20202381.

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Kabango Christian, Mbayo. "Characteristics and Critical Success Factors Prioritization of MSMEs in African Agribusiness: A Case of DR. Congo." International Journal of Management Science and Business Administration 1, no. 6 (2015): 31–43. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.16.1003.

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Due to the pertinent question of development of micro small and medium enterprises (MSMEs) and self food supply over the world and especially in Africa, this paper intends to analyze the key characteristics of the Congolese MSMEs, the identification of the perceived critical success factors and their prioritization for accurate and well-oriented performance action. To respond to the focus issues of this paper, an investigation on 259 enterprises was made in Kinshasa and the critical success factor methodology was used to cease and categorize all perceived factors from entrepreneurs. From this, 45 identified CSFs emerged throughout the operational channel of MSMEs and where categorized into four groups which are externalities, strategy, finance and organization. The Analytical Hierarchy Process is used to prioritize these CSFs per operational channel sections which are supply chain, production and distribution. And findings show that the most critical path is the supply chain while the highest critical axis is the finance area followed respectively by strategy, externalities and organization axes.

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Zhang, Guoxi, and Robert G. Feyen. "A Conceptual Framework for Dynamic Prioritization in Multiple-Task Scenarios." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 12 (September 2005): 1172–75. http://dx.doi.org/10.1177/154193120504901216.

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Prioritizing tasks appropriately is particularly critical when performing multiple tasks concurrently. Although necessary to achieve one's goals or avoid serious consequences, prioritization has not received much attention in the research literature, especially with respect to modeling human performance computationally. A conceptual framework that integrates several motivational theories, empirical studies, and neuroscience research is proposed to guide future studies of dynamic prioritization in multiple-goal contexts. Rooted in control theory, the proposed framework illustrates self-regulation processes in prioritizing tasks and explicitly shows important factors affecting the prioritization process so that empirical results can be integrated into the framework and future studies can be inferred. By illustrating information flow in the self-regulation processes and the brain structures associated with prioritization, the framework should help facilitate development of robust computational models of task prioritization.

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BROOKES, V. J., V. J. DEL RIO VILAS, and M. P. WARD. "Disease prioritization: what is the state of the art?" Epidemiology and Infection 143, no. 14 (April 16, 2015): 2911–22. http://dx.doi.org/10.1017/s0950268815000801.

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SUMMARYDisease prioritization is motivated by the need to ensure that limited resources are targeted at the most important problems to achieve the greatest benefit in improving and maintaining human and animal health. Studies have prioritized a range of disease types, for example, zoonotic and foodborne diseases, using a range of criteria that describe potential disease impacts. This review describes the progression of disease prioritization methodology from ad hoc techniques to decision science methods (including multi-criteria decision analysis, conjoint analysis and probabilistic inversion), and describes how these methods aid defensible resource allocation. We discuss decision science in the context of disease prioritization to then review the development of disease prioritization studies. Structuring the prioritization and assessing decision-makers' preferences through value trade-offs between criteria within the decision context are identified as key factors that ensure transparency and reproducibility. Future directions for disease prioritization include the development of validation techniques, guidelines for model selection and neuroeconomics to gain a deeper understanding of decision-making.

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A.YMuhsen, Rami. "Human-Perception of Web Content Trust Factors Identification and Prioritization." International Journal of Computer Applications 75, no. 17 (August 23, 2013): 16–21. http://dx.doi.org/10.5120/13202-0424.

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Tyagi, Manika, and Sona Malhotra. "An Approach for Test Case Prioritization Based on Three Factors." International Journal of Information Technology and Computer Science 7, no. 4 (March 8, 2015): 79–86. http://dx.doi.org/10.5815/ijitcs.2015.04.09.

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Topaz, Maxim, Mary D. Naylor, John H. Holmes, and Kathryn H. Bowles. "Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare." CIN: Computers, Informatics, Nursing 38, no. 2 (February 2020): 88–98. http://dx.doi.org/10.1097/cin.0000000000000576.

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Khalilzadeh, Mohammad, Shiba Masoumi, and Isa Masoumi. "Identification and prioritization of factors influencing organization risk tolerance level." Journal of Advances in Management Research 16, no. 4 (October 23, 2019): 417–35. http://dx.doi.org/10.1108/jamr-07-2018-0061.

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Purpose Identifying and prioritizing the risks are considered as critical issues in risk management; otherwise, non-considering the risks will lead to the problems such as delays in project implementation, increased costs, loss of reputation, loss of clients, reduced revenue and liquidity and even bankruptcy. The paper aims to discuss these issues. Design/methodology/approach In this paper, the factors influencing the organization risk tolerance level were identified. Then, the factors increasing and decreasing the risk tolerance level were determined by a decision-making model. Finally, a comprehensive model was considered for risk measuring and preparing a risk failure structure chart, in order to determine the factors influencing it as well as the measurement criteria and then they were ranked using the taxonomy method. In this study, the size of the statistical population was 130 (six small and medium manufacturer and service provider companies). Based on Cochran’s sample size formula, 97 questionnaires containing 30 questions were randomly distributed among the population. Validity and reliability of the questionnaire were confirmed. The data were analyzed by SPSS 22. Findings Given the hypotheses of this study, the first hypothesis was rejected and the other hypotheses were accepted. The final ranking was done using the taxonomy method; the personality of the project manager was ranked at first; income, credit and capital were ranked second and the number of personnel was ranked third. Moreover, the TOPSIS method was used for ranking to compare the results. Originality/value In this research, the identification and ranking of these factors have taken place in several small- and medium-sized organizations; in addition, the rankings are conducted using the taxonomy decision-making method.

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GÜLER, Mehmet Emre. "Prioritization of Revenue Management Factors: A Synthetic Extent Analysis Approach." Ege Akademik Bakis (Ege Academic Review) 12, no. 2 (April 1, 2012): 161. http://dx.doi.org/10.21121/eab.2012219531.

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Srikanth, Hema, Charitha Hettiarachchi, and Hyunsook Do. "Requirements based test prioritization using risk factors: An industrial study." Information and Software Technology 69 (January 2016): 71–83. http://dx.doi.org/10.1016/j.infsof.2015.09.002.

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Hittle, Beverly M., Gordon L. Gillespie, Holly J. Jones, and Amit Bhattacharya. "Time lost: Factors influencing advanced practice provider’s prioritization of sleep." Work 68, no. 3 (March 26, 2021): 653–65. http://dx.doi.org/10.3233/wor-203400.

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BACKGROUND: Prescribing patient care providers regularly experience insufficient sleep, putting them at increased risk of committing occupational injuries, accidents, and errors and developing chronic health conditions. OBJECTIVE: Identify antecedents to short sleep (≤6-hours sleep in 24-hour period) in the understudied population of hospital-based Advanced Practice Providers (APPs). METHODS: Using an ethnographic research design, data included APP and key stakeholder interviews, hospital observations, and relevant documents. Interview data were analyzed using modified constant comparative method. RESULTS: Nine APPs were interviewed, revealing four themes: Social/Family Obligations and Value of Connectivity, Community Value of Sleep, Organizational Value of Sleep, and Individual Biology and How the Body Values Sleep. APP decisions to prioritize sleep are based on an interplay of societal, professional, organizational, and personal values. Triangulated data verified results, except regarding how APP sleep deficit can lead to mood disturbances and the lack sleep consideration in patient care error reporting. CONCLUSIONS: Findings demonstrate the importance of consistency in messaging, action, and policy when promoting occupational sleep health among healthcare workers. Implications include instituting worker sleep education, leadership modeling healthy sleep habits, and inclusion of sleep in root cause analyses. Additional consideration includes evaluating the influence of nursing culture on nurse practitioners’ sleep habits.

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Patel, Anand S., and Kaushik M. Patel. "Prioritization of Lean Six Sigma Success Factors using Pareto Analysis." IOP Conference Series: Materials Science and Engineering 1070, no. 1 (February 1, 2021): 012133. http://dx.doi.org/10.1088/1757-899x/1070/1/012133.

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Adebanjo, Dotun, Premaratne Samaranayake, Fereshteh Mafakheri, and Tritos Laosirihongthong. "Prioritization of Six-Sigma project selection." Benchmarking: An International Journal 23, no. 7 (October 3, 2016): 1983–2003. http://dx.doi.org/10.1108/bij-09-2015-0086.

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Purpose With increasing choice from a range of programs, improvement project selection within broader supply chain context and resource constraints has become a major research challenge. The purpose of this paper is to investigate the different criteria for selecting Six-Sigma (SS) projects based on previous studies. The study is supported by two grounded theories: resource-based view and institutional norms. The criteria include: first, business drivers for improvement and the common performance metrics deployed; second, the organization’s stakeholders needs; and third, process owner’s needs. Design/methodology/approach To determine the relative importance of influencing factors, opinions were collected from 30 experienced practitioners including SS champions/master black-belts, company directors, consultants, and process owners through a series of interviews in small, medium, and large organizations including multi-national organizations. The evaluation of criteria is based on analytical hierarchy process. Findings The results show that impact on customer, financial impacts, and impact on operational goals are the most significant factors in selecting SS improvement project. Originality/value This study is a first attempt to determine the relative weight among SS project selection criteria, which help the practitioner to allocate their limited resources in implementing SS project.

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Beltman, Suzanne, Suzanne M. Vosslamber, Albert Molderink, and Matthijs L. Noordzij. "Toward the Design of an Energy Consumption Feedback System." Ergonomics in Design: The Quarterly of Human Factors Applications 24, no. 3 (July 2016): 9–16. http://dx.doi.org/10.1177/1064804615611283.

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In this article, we examine two novel issues in user requirements analysis for a feedback system on household energy consumption: (a) microgeneration and (b) the “Must, Should, Could, Won’t have” (MoSCoW) method. We report on a qualitative user requirements analysis using the MoSCoW method for the prioritization of user requirements. Fifteen interviews resulted in three user groups that were abstracted into personas with different user requirements. Finally, we show that changes in cutoff values resulted in MoSCoW prioritization changes for a maximum of only 5.9%. These are promising results for the robustness of the MoSCoW method.

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Pasandideh, Reza, and Kobra Hoseini. "Evaluation and prioritization the key factors of Human Resources Information System in Bu Ali hospital in Tehran." International Academic Journal of Science and Engineering 05, no. 02 (December 19, 2018): 182–91. http://dx.doi.org/10.9756/iajse/v5i1/1810035.

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KRISHNAMOORTHI, R., and S. A. SAHAAYA ARUL MARY. "REQUIREMENT BASED SYSTEM TEST CASE PRIORITIZATION OF NEW AND REGRESSION TEST CASES." International Journal of Software Engineering and Knowledge Engineering 19, no. 03 (May 2009): 453–75. http://dx.doi.org/10.1142/s0218194009004222.

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Test case prioritization schedules the test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also detects the most severe faults at the earliest in its testing life cycle. Test case prioritization techniques have proved to be beneficial for improving regression testing activities. While code coverage based prioritization techniques are found to be studied by most scholars, test case prioritization based on requirements in a cost effective manner has not been used for studies so far. Hence, in this paper, we propose to put forth a model for system level Test Case Prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system test cases based on six factors: customer priority, changes in requirement, implementation complexity, usability, application flow and fault impact. The proposed prioritization technique is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.

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Yadav, H. C., Rajeev Jain, Sandarbh Shukla, Shwetank Avikal, and P. K. Mishra. "Prioritization of aesthetic attributes of car profile." International Journal of Industrial Ergonomics 43, no. 4 (July 2013): 296–303. http://dx.doi.org/10.1016/j.ergon.2013.04.008.

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Jermsittiparsert, Kittisak, and Waurasit Poothong. "Political Ideology and Prioritization of Qualities for Boyfriends-to-be Among Thai Female University Students." Global Journal of Health Science 9, no. 10 (September 6, 2017): 127. http://dx.doi.org/10.5539/gjhs.v9n10p127.

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This research aims to (1) examine the political ideology and prioritization of qualities for men to be chosen as a boyfriend, (2) compare such prioritization among individuals by considering their personal factors, including class years, majors, hometowns, parents’ occupations, and household incomes, and (3) test the relationship between the political ideology and such prioritization. The research is conducted by collecting data from 400 female students of a private university in Pathumthani, Thailand who registered in the final semester of the 2016 academic year. The data are collected via questionnaires, and statistically analyzed by finding the frequencies, percentages, means, and standard deviations as well as by adopting the methods of one-way analysis of variance (ANOVA), Tukey’s Pairwise Comparison Test, and Pearson’s correlation coefficient analysis, with the statistical significance set at the 5-percent level. The results show that overall the sample’s political ideology leans slightly towards liberalism, and the sample gives a moderate priority to the qualities of men to be chosen as a boyfriend. The quality to which the sample gives the top priority is the personal characters of the men. It is also found that the five personal factors also affect the prioritization of qualities for men to be chosen as a boyfriend, and that the political ideology and the prioritization of qualities for the boyfriend-to-be are only weakly related.

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Raazm ara, Mohammad reza, MohammadReza Jani, Asiyeg Moudi, MohammadHadi Sarvari, Zhara Drogar, HamidReza Zolfaghri, and Ebrahim Rahmani moghadam. "Prioritization of contributing factors on patient safety by analytical hierarchy process." Quarterly Journal of Nersing Management 5, no. 1 (June 1, 2016): 78–70. http://dx.doi.org/10.29252/ijnv.5.1.78.

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Blankenburg, H., S. Diehl, F. Ramírez, I. Wörz, M. Poenisch, and R. Bartenschlager. "Discovery and prioritization of human cellular factors required for HCV infection." New Biotechnology 27 (April 2010): S24—S25. http://dx.doi.org/10.1016/j.nbt.2010.01.020.

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Rafiee, Marzieh, and Salman Abbasian-Naghneh. "Prioritization of critical individual factors influencing willingness to communicate: AHP method." Journal of Multilingual and Multicultural Development 40, no. 6 (October 16, 2018): 461–74. http://dx.doi.org/10.1080/01434632.2018.1521814.

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Pournaghi, Roya, and Fahimeh Babalhavaeji. "The factors and criteria for prioritization of GIS utilization by libraries." Electronic Library 33, no. 2 (April 7, 2015): 181–95. http://dx.doi.org/10.1108/el-05-2013-0092.

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Purpose – This paper aims to identify the impressive factors on choosing the type of the libraries and information centers and determining their significance and prioritizing them and finally determining the priority of each type according to the identified factors to implement geographic information system (GIS) in the library researches. Design/methodology/approach – A combination of documentary, Delphi and analytic methods was used in the current study. Due to the interdisciplinary nature of the topic in mind, different dimensions of the issue were firstly identified through reviewing the research literature. Then, the Delphi panel was formed and the factors and indices for choosing and prioritizing the libraries were extracted and finally the analytic-hierarchy and Topsis method was used for analyzing and ranking the data which were collected through a questionnaire. In this way, the instrument was devised by the researcher and the population under the study consisted of the professors in two fields of LIS and the GIS. The criteria include the “access to the library”, “being active”, “the number of sources”, “the size and space of the place”, “the library equipment”, “compatibility” each one include eight, seven, five, five, two and five factors, respectively. Findings – In the Delphi study, six factors including 32 indices were determined for prioritizing the libraries to implement the GIS in the library researches. The analytic-hierarchy method was applied to compare the factors correspondingly in terms of the degree of their significance. In this way, “access to the library sources”, “being active”, “the number of sources”, “the size and space of the place”, “the library equipment” and “compatibility” were identified as the factors on choosing the library type with the values of 0.39, 0.24, 0.16, 0.08, 0.07 and 0.06, respectively. Then, several types of the libraries were ranked and the findings show that academic libraries are prioritized as the first one for conducting the GIS projects. Originality/value – Employing the factors resulted from the conducted researches is recommended for choosing the libraries to implement the GIS, so that the researches in this interdisciplinary field become more organized. To extend the current study, the extracted factors and indices can be sent to the professors of the LIS from the other countries to elicit their insights and thereby, designing a standard international checklist for choosing the libraries and information centers in the GIS projects.

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Ribeiro, M. B., V. D. Duarte, E. G. Salgado, and C. V. Castro. "Prioritization of Critical Success Factors In The Process of Software Development." IEEE Latin America Transactions 15, no. 1 (January 2017): 137–44. http://dx.doi.org/10.1109/tla.2017.7827917.

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Dulange, S. R., A. K. Pundir, and L. Ganapathy. "Prioritization of factors impacting on performance of power looms using AHP." Journal of Industrial Engineering International 10, no. 4 (August 6, 2014): 217–27. http://dx.doi.org/10.1007/s40092-014-0080-8.

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Gulácsi, László, and Ottó Hajdú. "Prioritization Preferences among General Practitioners in Hungary." East European Politics and Societies: and Cultures 26, no. 1 (June 13, 2011): 20–32. http://dx.doi.org/10.1177/0888325410390207.

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Cost-utility analyses use the quality-adjusted life-year (QALY) as a measure of health benefit. Normally, they treat every QALY gain equally, that is, attach the same weight (or value) to each QALY gained. However, it appears that this practice does not reflect the distributional preferences of the general public nor of health care professionals. Maximizing the QALY gain from a given budget is not the only aim in priority setting. This article presents a study into such distributional preferences of general practitioners (GPs) for prioritization at the patient level in Hungary. Given the special position GPs have in many health care systems, including the Hungarian, more knowledge of their preferences is important. The authors used a discrete choice experiment to study these preferences, focusing on factors related to the characteristics of the patients, the disease, and treatment effects. Results show that the most important factors influencing the GPs’ decision were the age of the patient, the mortality of the disease, the impact of the disease on patients’ quality of life, and the potential for the full restoration of the previous health status. The treatment of patients without comorbidities was preferred to that of patients with comorbidities. Importantly, these preferences in GPs may steer the actual distribution of health care.

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Mir, Umar Bashir, Swapnil Sharma, Arpan Kumar Kar, and Manmohan Prasad Gupta. "Critical success factors for integrating artificial intelligence and robotics." Digital Policy, Regulation and Governance 22, no. 4 (August 12, 2020): 307–31. http://dx.doi.org/10.1108/dprg-03-2020-0032.

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Purpose This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs. Design/methodology/approach This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs. Findings Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population. Research limitations/implications This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries. Practical implications Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF. Originality/value The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.

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Mulyono, B. "PENENTUAN STRATEGIK PRIORITAS PELAYANAN LABORATORIUM KLINIK MENGGUNAKAN TEKNIK SFAS (strAtEGIc FActOrs ANALYsIs sUMMArY) BERSARANA ACUAN SWOT." INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY 13, no. 2 (February 22, 2017): 82. http://dx.doi.org/10.24293/ijcpml.v13i2.749.

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To face the global competition of clinical laboratory services, some techniques are needed for strategic formulation. SWOT, external, and internal analysis have been known as techniques for those purposes. SFAS technique was introduced recently as a simple procedure to be used as complementary tool. To evaluate an program prioritization using SFAS technique. Qualitative descriptive case analysis. Observation was done into Clinical Pathology Department of Sardjito Hospital, Yogyakarta during its activities in the year of 2006. IFA and EFA matrices showed the total scores > 2.5 and SWOT analysis revealed that the position was in 1st quadrant of the matrix. SFAS technique showed the total score > 3.0 that mean the prioritization was appropiate. SFAS technique is valuable in supporting the formulation of strategy

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Renet, Sophie, Cynthia Tanguay, Kevin Hall, and Jean-François Bussières. "Prioritizing Pharmaceutical Activities." Journal of Pharmacy Practice 26, no. 4 (November 26, 2012): 367–75. http://dx.doi.org/10.1177/0897190012465952.

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Objectives: The primary objective was to examine the consistency of prioritization decisions made by pharmacy residents in a simulated environment where the available resources are constrained. Secondary objectives were to rank the factors that influenced their prioritization and to compare the residents’ results with those of Canadian pharmacy leaders. Methods: We have developed a prioritization exercise that aims at evaluating how pharmaceutical activities are prioritized. The simulation was conducted with hospital pharmacy residents in 2 Quebec universities in 2011. Results: Residents covered a similar number of activities in the prioritization simulation (mean 27 of 32). Teams tended to favor a broad range of services delivered less comprehensively. Participants ranked “perception of the favorable impact of the activity on health outcomes” higher than “conclusive evidence available to support the decisions.” The relative weight attributed per domain was similar between pharmacy residents and pharmacy leaders, but their ranking of factors that influenced their decisions was different. Conclusions: Pharmacy residents opted to provide a wide range of services, but at a low level of comprehensiveness. The high variation between each team’s coverage per activity in this simulation supports the observation that pharmacy residents do not agree on a core set of pharmaceutical activities that should be prioritized.

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Kahle, Erin M., Akshay Sharma, Stephen P. Sullivan, and Rob Stephenson. "HIV Prioritization and Risk Perception Among an Online Sample of Men Who Have Sex With Men in the United States." American Journal of Men's Health 12, no. 4 (May 21, 2018): 676–87. http://dx.doi.org/10.1177/1557988318776581.

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Men who have sex with men (MSM) may prioritize interpersonal and structural factors, such as LGBTQ-related inequalities, housing instability, financial insecurity, and relationship seeking, over HIV prevention. The aim of this study was to assess how MSM prioritize HIV relative to other factors and the association between HIV prioritization, HIV testing and sexual risk behavior, and perceived risk. Data were collected from a national online survey of MSM in the United States assessing HIV knowledge and prioritization. Participants ranked HIV prevention relative to LGBTQ-related concerns and life issues (e.g., housing, job, relationship). Adjusted regression models were conducted to assess the association of HIV prioritization with HIV testing and condomless anal sex (CAS) and to evaluate associations between perceived risk and HIV risk behavior. Among 516 participants, HIV prevention was ranked significantly higher among MSM reporting recent CAS ( p = .04) and HIV testing within the past ( p = .02); HIV prevention was prioritized higher relative to life issues among MSM reporting last HIV test more than 1 year ago ( p = .02). Perceived HIV risk was significantly associated with higher HIV prioritization relative to LGBTQ concerns ( p = .001) and life issues ( p = .001). Higher HIV perceived risk was associated with lower odds of any CAS ( p < .001) and higher odds of having an HIV test in the past year ( p < .001). In this online study, HIV prioritization was identified as differentially associated with HIV testing, sexual risk behavior, and perceived HIV risk. HIV prevention programs should integrate strategies to address social and structural inequalities based on priority needs of MSM.

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Gallardo, R. Karina, Diem Nguyen, Vicki McCracken, Chengyan Yue, James Luby, and James R. McFerson. "An Investigation of Trait Prioritization in Rosaceous Fruit Breeding Programs." HortScience 47, no. 6 (June 2012): 771–76. http://dx.doi.org/10.21273/hortsci.47.6.771.

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Over 60 rosaceous crop breeding programs exist in North America, but no information has been available on which traits are targeted for selection or how breeders make such decisions. We surveyed all active rosaceous fruit breeding programs in the United States and Canada to determine: 1) the relative importance of over 50 plant traits that breeders select for 2) the likelihood of selection for the most important traits; and 3) the factors influencing breeders’ decisions. A double-bounded Tobit model was used to investigate the effect of supply chain parties, technical and socioeconomic challenges, and crop characteristics on the likelihood of selection for trait clusters. We found that consumer-driven forces positively impact the likelihood of selection for traits more than producer forces and a breeder’s own experience. Technical factors are as important as socioeconomic factors but less important than market-related factors. Our findings provide the first ever evidence that a socioeconomic approach in specialty crop breeding programs can contribute to an improved understanding of the effects of different supply chain factors on breeding programs’ trait priority setting.

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Richardson, Lorilei M., Jennifer N. Hill, Bridget M. Smith, Erica Bauer, Frances M. Weaver, Howard S. Gordon, Kevin T. Stroupe, and Timothy P. Hogan. "Patient prioritization of comorbid chronic conditions in the Veteran population: Implications for patient-centered care." SAGE Open Medicine 4 (January 1, 2016): 205031211668094. http://dx.doi.org/10.1177/2050312116680945.

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Objective: Patients with comorbid chronic conditions may prioritize some conditions over others; however, our understanding of factors influencing those prioritizations is limited. In this study, we sought to identify and elaborate a range of factors that influence how and why patients with comorbid chronic conditions prioritize their conditions. Methods: We conducted semi-structured, one-on-one interviews with 33 patients with comorbidities recruited from a single Veterans Health Administration Medical Center. Findings: The diverse factors influencing condition prioritization reflected three overarching themes: (1) the perceived role of a condition in the body, (2) self-management tasks, and (3) pain. In addition to these themes, participants described the rankings that they believed their healthcare providers would assign to their conditions as an influencing factor, although few reported having shared their priorities or explicitly talking with providers about the importance of their conditions. Conclusion: Studies that advance understanding of how and why patients prioritize their various conditions are essential to providing care that is patient-centered, reflecting what matters most to the individual while improving their health. This analysis informs guideline development efforts for the care of patients with comorbid chronic conditions as well as the creation of tools to promote patient–provider communication regarding the importance placed on different conditions.

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Bruce. E, John, and T. Sasi Prabha. "A Survey on Techniques Adopted in the Prioritization of Test Cases for Regression Testing." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 242. http://dx.doi.org/10.14419/ijet.v7i4.5.20078.

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Regression testing is testing the software with the intention to confirm that changes made on part of a module do not necessitate other parts of the module. Test case prioritization helps to reduce regression testing cost by ordering the test cases in such a way that it produces optimized results. Code Coverage and Fault detection being the factors behind the prioritization is dealt with techniques like Heuristic method, Meta Heuristic methods and Data mining techniques. The effectiveness of the techniques applied can be evaluated with the metrics like Average Percentage of Fault Detection (APFD) , Average Percentage Block Coverage (APBC), Average Percentage Decision Coverage (APDC) etc . In this paper,, a detailed survey on the various techniques adopted for the prioritization of test cases are presented.

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Journal articles on the topic 'Prioritization factors'

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