ORIGINAL_ARTICLE
Explaining the Competitive Advantage of Enterprise Resource Planning Adoption: Insights Egyptian Higher Education Institutions
Organizations nowadays focus on, not implementing ERP systems, but also leveraging ERP systems as part of their digital strategy. They holistically address people, processes, and technology for a digital transformation. Meanwhile, higher education institutions (HEIs) are also facing an imperative need for the implementation of modern technologies to stay competitive and differentiate them as an innovation leader. Higher education management is challenged with maintaining high-level information systems. These systems can generate complex real-time reports for effective resource allocation and better decision making. Enterprise Resource Planning (ERP) systems can help HEIs manage their resources and operations effectively. A study of ERP among 112 HEIs in Egypt was conducted. This study originally investigates the Egyptian HEIs’ perception of the ERP system as a new integrating tool for its value. The results showed that Egyptian HEIs are still at the embryonic level as the majority have not adopted these systems yet. However, ERP value has been undoubtedly perceived by HEIs’ managers. Therefore, the present study fruitfully reflected HEIs’ understanding of the imperative need of ERP systems as strategic systems for their competitiveness. Consequently, the study suggests that Egyptian HEIs and ERP vendors take steps to remove any barriers and accelerate the ERP adoption process. Also, this research contributes to the advancement of ERP concepts and characteristics from HEIs' standpoint and a grants practical verification to the higher education context. Overall, the study advances existing knowledge and research on ERP, strategic management systems, and HEIs.
https://jitm.ut.ac.ir/article_78398_d198143f727b69b57514b4d55ded7d0c.pdf
2020-12-01
1
21
10.22059/jitm.2020.292788.2424
Higher Education
HEIs
ERP
Competitive Advantage
Perceived value
RBT
Mohamed Soliman
Soliman
soliman2002s@yahoo.com
1
Ph.D. Candidate, Department of Operations Management, Universiti Sains Malaysia (USM), Penang, Malaysia.
LEAD_AUTHOR
Karia
Noorliza
noorliza@usm.my
2
School of Management, Universiti Sains Malaysia, Penang, Malaysia
AUTHOR
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ORIGINAL_ARTICLE
P-V-L Deep: A Big Data Analytics Solution for Now-casting in Monetary Policy
The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated where it considers eventuality. So, it is necessary to consider the highly data-driven technologies and to use new methods of analysis, like machine learning and visualization tools, with the ability of interaction and connection to different data resources with varieties of data regarding the type of big data aimed at reducing the risks of policy-making institution’s investment in the field of IT. The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. Represented and reconstructed data in the generative network of variational auto-encoder to determine the performance of long-short-term memory in the forecasting of the economic indicators were compared to principal data of the net. The findings of the research argue that reconstructed data which are derived from variational auto-encoder embody shorter training time and outperform of prediction in long short-term memory compared to principal data.
https://jitm.ut.ac.ir/article_78399_f7ac08337fe2f08967e323952eb1abe6.pdf
2020-12-01
22
62
10.22059/jitm.2020.293071.2429
Big data analytics
Deep learning
Now-casting
monetary policy
Maryam
Hajipour Sarduie
maryam.hajipour@srbiau.ac.ir
1
Ph.D. Candidate, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Mohammadali
Afshar Kazemi
m_afsharkazemi@iauec.ac.ir
2
, Associate Prof., Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
Mahmood
Alborzi
m.alborzi@srbiau.ac.ir
3
Associate Prof., Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Adel
Azar
azara@modares.ac.ir
4
Prof., Department of Management, Tarbiat Modares University, Tehran, Iran.
AUTHOR
Ali
Kermanshah
akermanshah@sharif.edu
5
Associate Prof., Department of Management, Sharif University of Technology, Tehran, Iran.
AUTHOR
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ORIGINAL_ARTICLE
Identification and Prioritization of Factors Contributing in Cloud Service Selection Using Fuzzy Best-worst Method (FBWM)
The introduction of cloud computing techniques revolutionized the current of information processing and storing. Cloud computing as a competitive edge provides easy and automated access to the vast ocean of resources through standard network mechanisms to businesses and organizations. Due to the vast diversity of service providers and their respective variety of available services with different qualities, top managements often face difficulty for choosing the best available option. So, considering the growing significance of the mentioned issue, this study aims to identify and rank contributing factors in selection of cloud service providers. In that attempt, this research approaches its goal by going through three major phases. Firstly, in phase one, prior studies are reviewed for extracting related elements of selection. Secondly, by employing Fuzzy Delphi method and obtaining results by interviewing experts in this field such as IT managers and technicians, this study tries to finalize the list of contributing factors. Lastly, by utilizing Fuzzy best-worst multi-criteria decision-making method, which is one of the most recent techniques employed to statistically rank variables, this research introduces a list of vital factors for cloud service selection. Based on the findings of this study, there are five major categories involved in the selection process which are: performance, security, data management, personal data protection and environmental-organizational. The finalized result of ranking shows that, performance related factors such as accessibility, response time and capacity are the first priority. The runner-up is security with reliability and governance. Environmental-organizational variables lands in the third place by considering rental and network costs.
https://jitm.ut.ac.ir/article_78400_a45682458228a73035a8238abdf96a0c.pdf
2020-12-01
63
89
10.22059/jitm.2020.294526.2439
Cloud service selection
Cloud service providers
Fuzzy best-worst method
Multi-criteria decision-making method
Fuzzy Delphi
Ali Asghar
Salarnezhad
a.salarnejad@iau-tnb.ac.ir
1
PhD Candidate, Department of Information Technology Management, Tehran North Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Maryam
Shoar
m_shoar@iau-tnb.ac.ir
2
Associate Prof., Department of Information Technology Management, Tehran North Branch, Islamic Azad University, Tehran, Iran.
LEAD_AUTHOR
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41
Senarathna I., Wilkin C., Warren M., Yeoh W., Salzman S. (2018). Factors That Influence Adoption of Cloud Computing: An Empirical Study of Australian SMEs.
42
Tang M., Dai X., Liu J., Chen J. (2016). Towards a trust evaluation middleware for cloud service selection. Future Generation Computer Systems. Doi: http://dx.doi.org/10.1016/j.future.2016.01.009
43
Weins, K. (2018). RightScale 2018 State of the Cloud Report. (RightScale) Retrieved 6 9, 2018, from RightScale: https://www.rightscale.com/lp/state-of-the-cloud?campaign=7010g0000016JiA.
44
Whaiduzzaman M., Gani A., Anuar N.B., Shiraz M., Haque M.N., Haque I.T. (2014). Cloud Service Selection Using Multicriteria Decision Analysis. The Scientific World Journal. Doi:
45
Wu X., (2018). Context-Aware Cloud Service Selection Model for Mobile Cloud Computing Environments. Wireless Communications and Mobile Computing. Doi: https://doi.org/10.1155/2018/3105278.
46
Yarlikas S., (2014) Cloud computing Effectiveness Assessment. PhD. Dissertation.
47
Yoo S. K., Kim B. K. (2018). A Decision-Making Model for Adopting a Cloud Computing System. Sustainability. Doi: 10.3390/su10082952.
48
ORIGINAL_ARTICLE
A Novel Scheme for Improving Accuracy of KNN Classification Algorithm Based on the New Weighting Technique and Stepwise Feature Selection
K nearest neighbor algorithm is one of the most frequently used techniques in data mining for its integrity and performance. Though the KNN algorithm is highly effective in many cases, it has some essential deficiencies, which affects the classification accuracy of the algorithm. First, the effectiveness of the algorithm is affected by redundant and irrelevant features. Furthermore, this algorithm does not consider the differences between samples, which led the algorithm to have inaccurate predictions. In this paper, we proposed a novel scheme for improving the accuracy of the KNN classification algorithm based on the new weighting technique and stepwise feature selection. First, we used a stepwise feature selection method to eliminate irrelevant features and select highly correlated features with the class category. Then a new weighting method was proposed to give authority value to each sample in train dataset based on neighbor categories and Euclidean distances. This weighting approach gives a higher preference to samples that have neighbors with close Euclidean distance while they are in the same category, which can effectively increase the classification accuracy of the algorithm. We evaluated the accuracy rate of the proposed method and analyzed it with the traditional KNN algorithm and some similar works with the use of five real-world UCI datasets. The experiment results determined that the proposed scheme (denoted by WAD-KNN) performed better than the traditional KNN algorithm and considered approaches with the improvement of approximately 10% accuracy.
https://jitm.ut.ac.ir/article_78401_aa4ee9377b284f7eaf141c5a92adbe96.pdf
2020-12-01
90
104
10.22059/jitm.2020.296305.2455
Data Mining
KNN algorithm
Classification algorithm
Weighted KNN
Saeid
Sheikhi
s.sheikhi@outlook.com
1
MSc, Department of Computer, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
AUTHOR
Mohammad Taghi
Kheirabadi
mtkheirabadi383@gmail.com
2
Assistant Prof., Department of Computer, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
LEAD_AUTHOR
Amin
Bazzazi
bazzazi@gorganiau.ac.ir
3
Assistant Prof., Department of Computer, Gorgan Branch, Islamic Azad University, Gorgan, Iran.
AUTHOR
Abbasi, F., Khadivar, A., &Yazdinejad, M. (2019). A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis. Journal of Information Technology Management, 11(2), 59-78.
1
Alpaydin, E. (1997). Voting over multiple condensed nearest neighbors. In Lazy learning (pp. 115-132). Springer, Dordrecht.
2
Angiulli, F. (2005, August). Fast condensed nearest neighbor rule. In Proceedings of the 22nd international conference on Machine learning (pp. 25-32). ACM.
3
Bagui, S. C., Bagui, S., Pal, K., & Pal, N. R. (2003). Breast cancer detection using rank nearest neighbor classification rules. Pattern recognition, 36(1), 25-34.
4
Bailey, T. (1978). A note on distance-weighted k-nearest neighbor rules. Trans. on Systems, Man, Cybernetics, 8, 311-313.
5
Biswas, N., Chakraborty, S., Mullick, S. S., & Das, S. (2018). A parameter independent fuzzy weighted k-nearest neighbor classifier. Pattern Recognition Letters, 101, 80-87.
6
Cheng, Y., Chen, K., Sun, H., Zhang, Y., & Tao, F. (2018). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration, 9, 1-13.
7
Dua, D., & Graff, C. (2017). UCI machine learning repository (2017). URL http://archive. ics. uci. edu/ml.
8
Gates, G. (1972). The reduced nearest neighbor rule (Corresp.). IEEE transactions on information theory, 18(3), 431-433.
9
Gowda, K., & Krishna, G. (1979). The condensed nearest neighbor rule using the concept of mutual nearest neighborhood (Corresp.). IEEE Transactions on Information Theory, 25(4), 488-490.
10
Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003, November). KNN model-based approach in classification. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 986-996). Springer, Berlin, Heidelberg.
11
Kafaf, D. A., Kim, D. K., & Lu, L. (2017). B-knn to improve the efficiency of kNN. In Proceedings of the 6th international conference on data science, technology and applications. Science and Technology Publications (pp. 126-132).
12
Kotenko, I., Saenko, I., &Branitskiy, A. (2018). Framework for Mobile Internet of Things Security Monitoring Based on Big Data Processing and Machine Learning. IEEE Access, 6, 72714-72723.
13
Kumar, M., Rath, N. K., &Rath, S. K. (2016). Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier. Journal of biomedical informatics, 60, 395-409.
14
Lin, W. C., Ke, S. W., & Tsai, C. F. (2015). CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowledge-based systems, 78, 13-21.
15
Pan, Z., Wang, Y., & Ku, W. (2017). A new general nearest neighbor classification based on the mutual neighborhood information. Knowledge-Based Systems, 121, 142-152.
16
Parvin, H., Alizadeh, H., &Minati, B. (2010). A modification on k-nearest neighbor classifier. Global Journal of Computer Science and Technology.
17
Reshi, J. A., & Singh, S. (2019). Investigating the Role of Code Smells in Preventive Maintenance. Journal of Information Technology Management, 10(4), 41-63.
18
Serpen, G., &Aghaei, E. (2018). Host-based misuse intrusion detection using PCA feature extraction and kNN classification algorithms. Intelligent Data Analysis, 22(5), 1101-1114.
19
Sun, C., Yao, C., Shen, L., & Yu, X. (2016). Improving classification accuracy using missing data filling algorithms for the criminal dataset. International Journal of Hybrid Information Technology, 9(4), 367-374.
20
Wu, X., Yang, J., & Wang, S. (2018). Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm. Multimedia Tools and Applications, 77(3), 3745-3759
21
Xueli, W., Zhiyong, J., &Dahai, Y. (2015, September). An Improved KNN Algorithm Based on Kernel Methods and Attribute Reduction. In 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) (pp. 567-570). IEEE.
22
Zeng, Y., Yang, Y., & Zhao, L. (2009). Pseudo nearest neighbor rule for pattern classification. Expert Systems with Applications, 36(2), 3587-3595.
23
Zhao, M., & Chen, J. (2016). Improvement and comparison of weighted k nearest neighbors classifiers for model selection. Journal of Software Engineering, 10(1), 109-118.
24
ORIGINAL_ARTICLE
Sentiment Analysis of Social Networking Data Using Categorized Dictionary
Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed. A categorized dictionary is developed for the sentiment classification and further calculation of sentiment accuracy. The concept of categorized dictionary involves the creation of dictionaries for different categories making the comparisons specific. The categorized dictionary includes words defining the positive and negative sentiments related to the particular category. It is used by the mapper reducer algorithm for the classification of sentiments. The data is collected from social networking site and is pre-processed. Since the amount of data is enormous therefore a reliable open-source framework Hadoop is used for the implementation. Hadoop hosts various software utilities to inspect and process any type of big data. The comparative analysis presented in this paper proves the worthiness of the proposed method.
https://jitm.ut.ac.ir/article_78402_d5d943afd9753b7ce691d4d0be85e122.pdf
2020-12-01
105
120
10.22059/jitm.2020.297562.2465
Hadoop
Big data
HDFS
Map-Reduce
Facepager
Sentiment analysis
Akansha
Singh
akanshasing@gmail.com
1
Associate Prof., Department of CSE, ASET, Amity University Uttar Pradesh, Noida.
LEAD_AUTHOR
Aastha
Sharma
aastha.sharma97@gmail.com
2
The North Cap University, Gurgaon, India.
AUTHOR
Krishna
Singh
krishnaiitr2011@gmail.com
3
Associate Prof., Department of ECE, KIET Group of Institutions, Ghaziabad, India.
AUTHOR
Anuradha
Dhull
anuradha@ncuindia.edu
4
Assistant Prof., The North Cap University, Gurgaon, India.
AUTHOR
Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
1
Chang, V. (2018). A proposed social network analysis platform for big data analytics. Technological Forecasting and Social Change, 130, 57-68.
2
Chawda, R. K., & Thakur, G. (2016, March). Big data and advanced analytics tools. In 2016 symposium on colossal data analysis and networking (CDAN) (pp. 1-8). IEEE.
3
Dasgupta, S. S., Natarajan, S., Kaipa, K. K., Bhattacherjee, S. K., & Viswanathan, A. (2015, October). Sentiment analysis of Facebook data using Hadoop based open source technologies. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-3). IEEE.
4
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
5
Goswami, S., Nandi, S., & Chatterjee, S. (2019). Sentiment analysis based potential customer base identification in social media. In Contemporary Advances in Innovative and Applicable Information Technology (pp. 237-243). Springer, Singapore.
6
Gupta, P., Kumar, P., & Gopal, G. (2015). Sentiment analysis on Hadoop with Hadoop streaming. International Journal of Computer Applications, 121(11).
7
Gupta, P., Sharma, A., & Grover, J. (2016, September). Rating based mechanism to contrast abnormal posts on movies reviews using MapReduce paradigm. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 262-266). IEEE.
8
Kang, G. J., Ewing-Nelson, S. R., Mackey, L., Schlitt, J. T., Marathe, A., Abbas, K. M., & Swarup, S. (2017). Semantic network analysis of vaccine sentiment in online social media. Vaccine, 35(29), 3621-3638.
9
Kumar, B. (2015). An encyclopedic overview of ‘big data’analytics. International Journal of Applied Engineering Research, 10(3), 5681-5705.
10
Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in human behavior, 31, 527-541.
11
Patidar, K., & Sharma, I. (2015). Study of Big Data Analysis Tools and Techniques.
12
Selvan, L. G. S., & Moh, T. S. (2015, June). A framework for fast-feedback opinion mining on Twitter data streams. In 2015 International Conference on Collaboration Technologies and Systems (CTS) (pp. 314-318). IEEE.
13
Tayal, D. K., & Yadav, S. K. (2016, March). Fast retrieval approach of sentimental analysis with implementation of bloom filter on Hadoop. In 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) (pp. 14-18). IEEE.
14
ORIGINAL_ARTICLE
A Model for Effectiveness of E-learning at University
In the digital age, e-learning systems have been employed as new equipment in the higher education system in different universities. Consideringthe importance of optimization of this system, this research is aimed at providing a modelfor the effectiveness of e-learning at in higher education systems. The study is a descriptive survey study in terms of its data collection method.The population includes all the students of electronic courses at the University of Tehran. This population includes 1481 students of the University of Tehran in the academic year 2019-2020. Regarding the population size, 300 students were selectedbased onstratified sampling, using Cochran’s formula.Lisrel and Amos softwarewere used for data analysis. In the first step, byliterature review, and based on the collected information, 87 components were identified to be related toe-learning effectiveness. Then, based on the highest frequency of the identified components in one hand, and their significance from the experts’ viewpoints, on the other hand, 14 components were finally selected and classified in three major classes including;pedagogical, individual and technicalrelated factors.
https://jitm.ut.ac.ir/article_78403_ffec3179d9136b9ef52489a8820c8f1d.pdf
2020-12-01
121
140
10.22059/jitm.2020.298696.2479
E-learning
Effectiveness
students
Higher Education
University of Tehran
Marzieh
Aali
m.aali@ut.ac.ir
1
Assistant Prof., Department of Philosophy of Education, University of Tehran, Tehran, Iran.
AUTHOR
Fatemeh
Narenji Thani
fnarenji@ut.ac.ir
2
Assistant Prof., Department of Educational Planning and Administration, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
Mohammad Reza
Keramati
mkeramaty@ut.ac.ir
3
Associate Prof., Department of Educational Planning and Administration, University of Tehran, Tehran, Iran.
AUTHOR
Armin
Garavand
armingaravand70@gmail.com
4
MA., Department of Educational Planning and Administration, University of Tehran, Tehran, Iran.
AUTHOR
Abdellatief, M., Sultan, A.B.M., Jabar, M. A., & Abdullah, R. (2011). A technique for quality evaluation of E-learning from developers’ perspective. American Journal of Economics and Business Administration, 3 (1), 157-164.
1
Ahmed, H. M. S. (2010). Hybrid E-Learning acceptance model: Learner perceptions. Decision Sciences Journal of Innovative Education, 8 (2), 313-346.
2
Alhabeeb, A., & Rowley, J. (2018). E-learning critical success factors: Comparing perspectives from academic staff and students. Computers & Education, 127, 1–12.
3
Ali, M., Hossain, S. K., & Ahmed, T. (2018). Effectiveness of E-learning for university students: Evidence from Bangladesh. Asian Journal of Empirical Research, 8 (10), 352-360.
4
Almarzooqi, J. M. H. Y. (2020). An evaluation of the effectiveness of face-to-face versus e-learning in the UAE Civil Defence sector (Doctoral dissertation).
5
Al-rahmi, W. M., Othman, M. S., & Yusuf, L. M. (2015). The effectiveness of using E-learning in Malaysian higher education: A case study Universiti Teknologi Malaysia. Mediterranean Journal of Social Sciences, 6 (5), 625-625.
6
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2011). Measuring E-learning system success (Research in progress). In Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS 2011) (pp. 1-15). Queensland University of Technology.
7
Al-Samarraie, H., Teng, B.K., Alzahrani, A. I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: a unified perspective from instructors and students. Studies in higher education, 43 (11), 2003-2019.
8
Altarawneh, M. (2013). Factors Affecting the Adoption of E-learning: Jordanian Universities Case Study. Computer Engineering and Intelligent Systems, 4 (3). 32-40.
9
Anderson, T. (2004). Towards a theory of online learning. Theory and practice of online learning, 2, 109-119.
10
Arkorful, V., & Abaidoo, N. (2015). The role of E-learning, advantages, and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12 (1), 29-42.
11
Asoodar, M., Vaezi, S., & Izanloo, B. (2016). A framework to improve e-learner satisfaction and further strengthen E-learning implementation. Computers in Human Behavior, 63, 704-716.
12
Attwell, G. (2006). Evaluating E-learning: A Guide to the Evaluation of E-learning. Evaluate Europe Handbook Series, 2, 1610-0875.
13
Balyk, N., Oleksiuk, V., & Shmyger, G. (2017). Development of the E-learning Quality Assessment Model in Pedagogical University. ICTERI 2017 ICT in education, research and industrial applications. Integration, harmonization and knowledge transfer-2017, 440-450.
14
Bauer, P. (2004). E-learning for a better understanding of power quality problems and compensators. In 11th International Power Electronics and Motion Control Conference, EPE-PEMC'2004, 2-4 September, Riga, Latvia.
15
Castle, S. R., & McGuire, C. J. (2010). An analysis of student self-assessment of online, blended, and face-to-face learning environments: Implications for sustainable education delivery. International Education Studies, 3 (3), 36-40.
16
Cheawjindakarn, B., Suwannatthachote, P., & Theeraroungchaisri, A. (2013). Critical success factors for online distance learning in higher education: A review of the literature. Creative Education, 3 (08), 61-66.
17
Chiu, H., Sheng, C. and Chen, A. (2008). Modeling agent-based performance evaluation for e-learning systems. The Electronic Library, 26 (3), 345-362.
18
Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of the e-learning portal from the students’ perspective. Interactive Technology and Smart Education. 16 (2), 94-116.
19
Divjak, B., & Begicevic, N. (2006). Imaginative acquisition of knowledge-strategic planning of E-learning. In 28th International Conference on Information Technology Interfaces, (pp. 47-52). IEEE.
20
Doherty, I. (2006). E-Places: Creating a Space for Exemplary Teaching and Learning. In E-Learn: World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education (pp. 1157-1164). Association for the Advancement of Computing in Education (AACE).
21
Dominici, G., & Palumbo, F. (2013). How to build an E-learning product: Factors for student/customer satisfaction. Business Horizons, 56 (1), 87-96.
22
Ejaffe, R., Moir, E., Swanson, E., & Wheeler, G. (2006). E-mentoring for Student Success: Online mentoring and professional development for new science teachers. Online professional development for teachers: Emerging models and methods, Cambridge, Mass. Harvard Education Press.
23
Farid, S., Qadir, M., Ahmed, M. U., & Khattak, M. D. (2018). Critical Success Factors of E-learning Systems: A Quality Perspective. Pakistan Journal of Distance & Online Learning, 1, 1-20.
24
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25
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26
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27
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28
Hand, A. (2012). Evaluating the suitability of current Authoring Tools for developing E-learning Resources. Dissertation submitted as part of the requirements for the award of the degree of MSc in Information Technology (Business).
29
Islam, M. A., Chittithaworn, C., Rozali, A. Z., & Liang, H. (2010). Factors Affecting E-learning Effectiveness in a Higher Learning Institution in Malaysia. Jurnal Pendidikan Malaysia (Malaysian Journal of Education), 35 (2), 51-60.
30
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31
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32
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33
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34
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: a case study of the blackboard system, Computers & Education, 51 (2), 864-873.
35
Liu, Y., & Wang, H. (2009). A comparative study on e- learning technologies and products: from the East to the West. Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 26 (2), 191-209.
36
Martinez-Argüelles, M., Castán, J., & Juan, A. (2009). How do students measure service quality in E-learning? A case study regarding an internet-based university. In Proceedings of the European Conference on E-learning (366-373).
37
Mbarek, R., & El Gharbi, J. E. (2013). A Meta-analysis of E-learning effectiveness antecedent. International Journal of Innovation and Applied Studies, 3 (1), 48-58.
38
Mothibi, G. (2015). A Meta-Analysis of the Relationship between E-learning and Students' Academic Achievement in Higher Education. Journal of Education and Practice, 6 (9), 6-9.
39
Naidu, S. (2013). Instructional design models for optimal learning. Handbook of distance education, 3, 268-281.
40
Nichols, M. (2008). Institutionnel perspectives: The challenges of e‐learning diffusion. British journal of educational technology, 39 (4), 598-609.
41
Noesgaard, S. S., & Ørngreen, R. (2015). The Effectiveness of E-learning: An Explorative and Integrative Review of the Definitions, Methodologies, and Factors That Promote E-learning Effectiveness. Electronic Journal of E-learning, 13 (4), 278-290.
42
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53 (4), 1285-1296.
43
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of E-learning systems. Computers in Human Behavior, 30, 614-622.
44
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45
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24 (1), 509-530.
46
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47
Shopova, T. (2014). Digital literacy of students and its improvement at the university. Journal on Efficiency and Responsibility in Education and Science, 7 (2), 26-32.
48
Sridharan, B., Deng, H., Kirk, J., & Corbitt, B. (2010). Structural Equation Modeling for evaluating the user perceptions of E-learning effectiveness in Higher Education. 18th European Conference on Information Systems.
49
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50
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51
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52
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53
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54
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55
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56
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57
Wang, M. Y. (2003). The strategic role of digital libraries: issues in E-learning environments. Library Review, 52 (3), 111-116.
58
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59
Wu, W., & Hwang, L. Y. (2010). The effectiveness of e-learning for blended courses in colleges: a multi-level empirical study. International Journal of Electronic Business Management, 8 (4), 322-312.
60
Yazdani, F., Ebrahimzadeh, E., Zandi, B., Alipour, A., Zare, H. (2012). Recognizing of Fundamental Factors in Effectiveness of E-learning Systems. Journal of Information Processing and Management, 27 (2): 385-411.
61
Yunus, Y., & Salim, J. (2013). E-learning evaluation in the Malaysian public sector from the pedagogical perspective: Towards E-learning effectiveness. Journal of Theoretical and Applied Information Technology, 51 (2), 201-210.
62
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63
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64
Zhang, W., & Cheng, Y. L. (2012). Quality assurance in E-learning: PDPP evaluation model and its application. The International Review of Research in Open and Distributed Learning, 13 (3), 66-82.
65
ORIGINAL_ARTICLE
The Impact of Hospital Information System on Nurses' Satisfaction in Iranian Public Hospitals: the Moderating Role of Computer Literacy
This study aimed to investigate the impact of the hospital information system (HIS) on nurses' satisfaction in Iranian public hospitals and to examine the moderating effect of computer literacy. The study population consisted of nurses working in public hospitals in Iran. A sample of 385 Iranian public hospitals was surveyed and a total of 1912 questionnaires were collected over 9 months. The analytical method used to empirically test the proposed hypotheses was the SEM technique using SmartPLS3. Results showed that all four variables of nurses' attitude, system quality, information quality, and service quality had significant positive effects on nurses' satisfaction with HIS. The findings also indicated that the effects of nurses' attitude, system quality, and particularly service quality increased on nurses' satisfaction with HIS by rising computer literacy, and only the effect of (computer literacy × information quality) was not significant on satisfaction. The use of FIMIX, CTA, and permutation test analyses is the innovation of the analysis.
https://jitm.ut.ac.ir/article_78404_9e49a891ddcf4b6b8dcbaa8c3e0d1928.pdf
2020-12-01
141
159
10.22059/jitm.2020.299802.2491
HIS
Nurses’ satisfaction
Computer literacy
Nurses’ attitude
Abbas
Gholampour
abbasgholampoor@yahoo.com
1
MSc., Department of Management, Rasht Branch, Islamic Azad University, Rasht, Iran,
AUTHOR
Mir Hadi Moazen
Jamshidi
jamshidi.hadi@gmail.com
2
, Assistant Prof., Department of Management, Payame Noor University, Tehran, Iran.
LEAD_AUTHOR
Alireza
Habibi
tajbluediamond@gmail.com
3
Assistant Prof., Ahlul-Bayt International University, Tehran, Iran,
AUTHOR
Narges
Motamedi Dehkordi
motamedi421@gmail.com
4
PhD Candidate, Department of Management and Economics, Faculty of Business Management, Marketing Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Pejman
Ebrahimi
pejman.ebrahimi77@gmail.com
5
PhD Candidate, Doctoral School of Economic and Regional Sciences, Szent Istvan University, Hungary.
AUTHOR
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16
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28
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57
ORIGINAL_ARTICLE
Towards Supporting Exploratory Search over the Arabic Web Content: The Case of ArabXplore
Due to the huge amount of data published on the Web, the Web search process has become more difficult, and it is sometimes hard to get the expected results, especially when the users are less certain about their information needs. Several efforts have been proposed to support exploratory search on the web by using query expansion, faceted search, or supplementary information extracted from external knowledge resources. However, these solutions are not well explored for the general web search in an open-domain setting. In addition, they mostly focus on supporting search in content expressed in English and Latin based languages. In this research, we propose a fully automated approach that aims to support exploratory search over the Arabic web content. It exploits the Arabic version of Wikipedia to extract complementary information that supports visual representation and deeper exploration of the search engine's results. Key Wikipedia entities are extracted from the text snippets produced by the search engine in response to the user's query. Entities are then filtered and ranked by using a novel ranking algorithm that extends the conventional PageRank algorithm. Finally, a graph is built and presented to the user to visually represent highly ranked topics and their relationships. The proposed approach was realized by developing ArabXplore, a system that integrates with the web browser to support the web search process by executing our approach in query time. It was assessed over a dataset of 100 Arabic search queries covering different domains, and results were assessed and rated by human subjects. The underlying ranking algorithm was also compared with the conventional PageRank.
https://jitm.ut.ac.ir/article_78405_ed67f141f050abd0a9623e48927b01a7.pdf
2020-12-01
160
179
10.22059/jitm.2020.303225.2535
Exploratory Search
Arabic
Wikipedia
PageRank
Entity Ranking
Al-Agha
Iyad
ialagha@iugaza.edu.ps
1
Associate Prof., Department of Computer Science, Faculty of Information Technology, The Islamic University of Gaza, Palestine.
LEAD_AUTHOR
Abed
Ahmed
aabed91@gmail.com
2
MSc, Department of Computer Science, Faculty of Information Technology, The Islamic University of Gaza, Gaza Strip, Palestine.
AUTHOR
Abbache, A., Meziane, F., Belalem, G., & Belkredim, F. Z. (2018). Arabic query expansion using wordnet and association rules Information Retrieval and Management: Concepts, Methodologies, Tools, and Applications (pp. 1239-1254): IGI Global.
1
Abdelali, A., Darwish, K., Durrani, N., & Mubarak, H. (2016). Farasa: A fast and furious segmenter for arabic. Paper presented at the Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Demonstrations.
2
Agarwalla, L., Parikh, A., & Sai, A. P. V. (2018). Terms for query expansion using unstructured data: Google Patents.
3
Agichtein, E., Brill, E., & Dumais, S. (2006, August 06 - 10, 2006). Improving web search ranking by incorporating user behavior information. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, WA, USA.
4
Aletras, N., Baldwin, T., Lau, J. H., & Stevenson, M. (2014). Representing topics labels for exploring digital libraries. Paper presented at the Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries.
5
Alromima, W., Moawad, I. F., Elgohary, R., & Aref, M. (2016). Ontology-based query expansion for Arabic text retrieval. Int. J. Adv. Comput. Sci. Appl, 7(8), 223-230.
6
Amer, E., Khalil, H. M., & El-Shistawy, T. (2017). Enhancing Semantic Arabic Information Retrieval via Arabic Wikipedia Assisted Search Expansion Layer. Paper presented at the International Conference on Advanced Intelligent Systems and Informatics.
7
Apache. Apache Lucene. Retrieved 20-1-2020, 2020, from https://lucene.apache.org/
8
Azad, H. K., & Deepak, A. (2019a). A new approach for query expansion using Wikipedia and WordNet. Information Sciences, 492, 147-163.
9
Azad, H. K., & Deepak, A. (2019b). Query expansion techniques for information retrieval: a survey. Information Processing & Management, 56(5), 1698-1735.
10
Bouchoucha, A., Liu, X., & Nie, J.-Y. (2014). Integrating multiple resources for diversified query expansion. Paper presented at the European Conference on Information Retrieval.
11
Callender, P. M. a. J. (2010). Search Pattern: O'Reilly Media.
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Carpineto, C., & Romano, G. (2012). A survey of automatic query expansion in information retrieval. Acm Computing Surveys (CSUR), 44(1), 1.
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14
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15
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16
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17
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18
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27
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28
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34
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35
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36
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37
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40
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41
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43
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44
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45
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46
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47
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48
ORIGINAL_ARTICLE
An Investigation on the User Behavior in Social Commerce Platforms: A Text Analytics Approach
Nowadays, the tourism industry accounts for approximately 10% of the global GDP, while it only contributes 3% of the economy in Iran. Since the pressure of US sanctions increases day after day on the Iranian economy, the necessity of paying attention to this industry as a source of foreign currency is felt more than ever. The purpose of this research is to analyze the reviews of users of social commerce websites by using a combination of text mining and data mining techniques. For this purpose, the database of TripAdvisor website (TripAdvisor.com) was evaluated, and all profile information of users who commented on hotels in Iran was collected. These comments on all the content of the website, such as hotels, restaurants, and attractions, were then extracted and analyzed. The optimal number of clusters was considered four clusters by calculating the Davies-Bouldin index, namingly water therapy tourists, boutique hotels style and Iran urban tourists, travelholics and food tourists, business and health tourists. Every single cluster possesses unique attributes and features. Afterward, the association rules were further identified for each cluster according to the characteristics of each cluster and the information in the users' profiles. Finally, a solution is proposed to increase the participation of the users on the website, and targeted promotional plans are expressed in accordance with the well-known features of each cluster.
https://jitm.ut.ac.ir/article_78406_f48abe49f561cb26998d4df72eb24426.pdf
2020-12-01
180
199
10.22059/jitm.2020.296648.2458
Social commerce
TripAdvisor
Social media
Text mining
Data Mining
Amir
Arzy
amir_arzy@atu.ac.ir
1
Ph.D. Candidate, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
AUTHOR
Mohammad Taghi
Taghavifard
dr.taghavifard@gmail.com
2
Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
LEAD_AUTHOR
Zohreh
Dehdashti Shahrokh
dehdashtishahrokh@atu.ac.ir
3
Associate Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
AUTHOR
Iman
Raeesi Vanani
imanraeesi@atu.ac.ir
4
Assistant Prof., Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.
AUTHOR
Afrizal, A. D., Rakhmawati, N. A., &Tjahyanto, A. (2019). New Filtering Scheme Based on Term Weighting to Improve Object Based Opinion Mining on Tourism Product Reviews. Procedia Computer Science, 161, 805-812.
1
Annisa, R., &Surjandari, I. (2019). Opinion Mining on Mandalika Hotel Reviews Using Latent Dirichlet Allocation. Procedia Computer Science, 161, 739-746.
2
Chang, T., Hsu, P. Y., Cheng, M. S., Chung, C. Y., & Chung, Y. L. (2015, June). Detecting fake review with rumor model—Case study in hotel review. In International Conference on Intelligent Science and Big Data Engineering (pp. 181-192). Springer, Cham.
3
Cho, E., & Son, J. (2019). The effect of social connectedness on consumer adoption of social commerce in apparel shopping. Fashion and Textiles, 6(1), 14.
4
Curty, R. G., & Zhang, P. (2011). Social commerce: Looking back and forward. Proceedings
5
Dickinger, A., &Lalicic, L. (2016). An analysis of destination brand personality and emotions: A comparison study. Information Technology & Tourism, 15(4), 317-340.
6
Emarketer. (2019). Internet to Hit 3 Billion Users in 2015, http://www.emarketer.com/Article/Internet-Hit-3-Billion-Users-2015/1011602.
7
Hajli, M. N. (2012). An integrated model for e-commerce adoption at the customer level with the impact of social commerce. International Journal of Information Science and Management (IJISM), 77-97.
8
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9
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21
ORIGINAL_ARTICLE
Visual attention to the Logos of Popular and Unknown Brands: An Eye-tracking Study during Decision-making
A logo epitomizes a brand and depicts the picture of a product; consequently, attention, as an initial step of the AIDA model, to the logo is a good-looking index to survey the cognitive processing during consumers’ decision-making. Eye tracker extracts the visual attention data. For these reasons and appreciated from fixation duration, in the present study, the process of visual attention to the logo of a brand during decision-making was studied. To analyze the effects of the popularity of a brand on consumers’ decision-making, visual attention of 53 undergraduate students was studied. The brands were selected from two categories of beverages: soft drinks and non-alcoholic beers. Prior to the test, the participants had declared their favorite categories. The results showed that 75% of the participants selected a popular brand of their unfavorite categories. On the other hand, the difference of fixation duration between logos of popular and unknown brands did not directly relate to the choices of the participants. Therefore, these results did not correspond with the AIDA model. Additionally, cognitive processing toward the unknown brands’ logos was more than the popular brands’ logos. Moreover, the consumers who changed their decision were subjected to more cognitive processing compared to the consumers who insisted on their selection. In contrast to the AIDA model which implies that decision-making is triggered by attention, the present study indicated that decision making can also be influenced by the popularity of the brand. This can be due to optimization of the working memory usage by the brain. Finally, these results could be helpful in creating a new brand. According to the AIDA model, the more attractive logo and package of the brand, the more chance for being selected by the customers. However, our findings emphasize the importance of the popularity of the brand.
https://jitm.ut.ac.ir/article_78407_2be9ae5426943321f0f86c0f475a71f8.pdf
2020-12-01
200
214
10.22059/jitm.2020.301942.2516
Visual attention
cognitive process
fixation duration
eye tracking
Decision- making
Mona
Salarifar
salari_mona@yahoo.com
1
Ph.D. Candidate, Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.
AUTHOR
Younos
Vakil Alroaia
y.vakil@semnaniau.ac.ir
2
Assistant Prof., Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.
LEAD_AUTHOR
Abolfazl
Danaei
a.danaei@semnaniau.ac.ir
3
Assistant Prof., Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.
AUTHOR
Gholam Hossein
Riazi
ghriazi@ibb.ut.ac.ir
4
Prof., Institute of Biochemistry and Biophysics University of Tehran- Tehran- Iran.
AUTHOR
Janaina
de Moura Engracia Giraldi
jgiraldi@usp.br
5
Associate Prof., School of Economics, Business Administration and Accounting, Ribeirao Preto, University of Sao Paulo, Brazil.
AUTHOR
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37
ORIGINAL_ARTICLE
A Mathematical Model for Multi-Region, Multi-Source, Multi-Period Generation Expansion Planning in Renewable Energy for Country-Wide Generation-Transmission Planning
Environmental pollution and rapid depletion are among the chief concerns about fossil fuels such as oil, gas, and coal. Renewable energy sources do not suffer from such limitations and are considered the best choice to replace fossil fuels. The present study develops a mathematical model for optimal allocation of regional renewable energy to meet a country-wide demand and its other essential aspects. The ultimate purpose is to minimize the total cost by planning, including power plant construction and maintenance costs and transmission costs. Minimum-cost flow equations are embedded in the model to determine how regions can supply energy to other regions or rely on them to fulfill annual demand. In order to verify the applicability of the model, it is applied to a real-world case study of Iran to determine the optimal renewable energy generation-transmission decisions for the next decade. Results indicate that the hydroelectric and solar power plants should generate the majority of the generated renewable electricity within the country, according to the optimal solution. Moreover, regarding the significant population growth and waste generation in the country’s large cities, biomass power plants can have the opportunity to satisfy a remarkable portion of electricity demand.
https://jitm.ut.ac.ir/article_78408_c2d0ea2f77d27d284b994738d481826c.pdf
2020-12-01
215
231
10.22059/jitm.2020.298258.2476
renewable energy
Generation Expansion Planning
Transmission
Mathematical Programming
Iran
Mohammadreza
Taghizadeh-Yazdi
mrtaghizadeh@ut.ac.ir
1
Associate Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
AUTHOR
Abdolkarim
Mohammadi-Balani
a_mohammadi@modares.ac.ir
2
PhD Candidate, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
LEAD_AUTHOR
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2
Aghahosseini, A., Bogdanov, D., Ghorbani, N., & Breyer, C. (2018). Analysis of 100% renewable energy for Iran in 2030: Integrating solar PV, wind energy and storage. International Journal of Environmental Science and Technology, 15(1), 17–36. https://doi.org/10.1007/s13762-017-1373-4
3
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