Big Data Analytics and Now-casting: A Comprehensive Model for Eventuality of Forecasting and Predictive Policies of Policy-making Institutions
Maryam
Hajipour Sarduie
Ph.D. Candidate in Information Technology Management/ Business Intelligence, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
author
Mohammadali
Afshar Kazemi
Associate Professor, Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
author
Mahmood
Alborzi
Associate Professor, Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
author
Adel
Azar
Professor, Department of Management, Tarbiat Modares University, Tehran, Iran.
author
Ali
Kermanshah
Associate Professor, Department of Management, Sharif University of Technology, Tehran, Iran.
author
text
article
2019
eng
The ability of now-casting and eventuality is the most crucial and vital achievement of big data analytics in the area of policy-making. To recognize the trends and to render a real image of the current condition and alarming immediate indicators, the significance and the specific positions of big data in policy-making are undeniable. Moreover, the requirement for policy-making institutions to produce a structured model based on big data analytics for now-casting and eventuality of predictive policies is growing rapidly. The literature review demonstrates that a comprehensive model to assist policy-making institutions by providing all components and indicators in now-casting of predictive policies based on big data analytics is not devised yet. The presentation of the model is the main finding of this research. This research aims to provide a comprehensive model of now-casting and eventuality of predictive policies based on big data analytics for policy-making institutions. The research findings indicate that the dimensions of the comprehensive model include: the alignment of now-casting strategies and the big data analytics’ architecture, now-casting ecosystem, now-casting data resources, now-casting analytics, now-casting model and now-casting skill. The results of using the model were analyzed and the recommendations were presented.
Journal of Information Technology Management
Faculty of Management, University of Tehran
2980-7972
11
v.
2
no.
2019
1
42
https://jitm.ut.ac.ir/article_73946_b036da2933836ca2a6cd8405f675f40c.pdf
dx.doi.org/10.22059/jitm.2019.284645.2376
Text Analytics of Customers on Twitter: Brand Sentiments in Customer Support
Iman
Raeesi Vanani
Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.
author
text
article
2019
eng
Brand community interactions and online customer support have become major platforms of brand sentiment strengthening and loyalty creation. Rapid brand responses to each customer request though inbound tweets in twitter and taking proper actions to cover the needs of customers are the key elements of positive brand sentiment creation and product or service initiative management in the realm of intense competition. In this research, there has been an attempt to collect near three million tweets of inbound customer requests and outbound brand responses of international enterprises for the purpose of brand sentiment analysis. The steps of CRISP-DM have been chosen as the reference guide for business and data understanding, data preparation, text mining, validation of results as well as the final discussion and contribution. A rich phase of text pre-processing has been conducted and various algorithms of sentiment analysis were applied for the purpose of achieving the most significant analytical conclusions over the sentiment trends. The findings have shown that the sentiment of customers toward a brand is significantly correlated with the proper response of brands to the brand community over social media as well as providing the customers with a deep feeling of reciprocal understanding of their needs in a mid-to-long range planning.
Journal of Information Technology Management
Faculty of Management, University of Tehran
2980-7972
11
v.
2
no.
2019
43
58
https://jitm.ut.ac.ir/article_73947_161dfbbd02dc246360bf20660ae7c959.pdf
dx.doi.org/10.22059/jitm.2019.291087.2410
A Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis
Fatemeh
Abbasi
Postdoctoral researcher, Department of Social and Economic, Alzahra University, Tehran, Iran.
author
Ameneh
Khadivar
Associate Professor, Department of Social and Economic, Alzahra University, Tehran, Iran.
author
Mohsen
Yazdinejad
Ph.D. Student, Faculty of Computing Engineering, University of Isfahan , Iran.
author
text
article
2019
eng
Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.
Journal of Information Technology Management
Faculty of Management, University of Tehran
2980-7972
11
v.
2
no.
2019
59
78
https://jitm.ut.ac.ir/article_73948_eac728ff5cdfbea6a1a002dcfcd48575.pdf
dx.doi.org/10.22059/jitm.2019.289271.2402
Identifying the Determinant Factors of E-Service Innovations: A Qualitative Meta-Synthesis
Seyed Mohammadbagher
Jafari
Assistant Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran.
author
Mona
Jami Pour
Assistant Professor, Department of Management, Hazrat-e Masoumeh University (HMU), Qom, Iran.
author
Reyhanezahra
Esfandiyarpour
PhD Candidate, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran.
author
text
article
2019
eng
Recently, significant technological changes, greater customer demand and the rise of new business models have triggered a rapid increase in electronic service (e-service) innovations. Now, innovation in the provision of e-services has become one of the priorities of managers in order to gain a competitive advantage. However, few studies so far have explored the determinant factors needed in the organization in order to innovate and implement e-services. The purpose of this study is to provide a comprehensive framework that integrates the multiple factors of e-service innovation. Using the qualitative meta-synthesis research method and after a systematic review of the literature and examination of 61 articles, all factors needed for innovation in e-services have been identified and classified in 4 capabilities, 9 concepts, and 30 codes. The results show that e-service innovation depends on networking, informational, operational and supporting, and strategic capabilities. These capabilities create the required platform for innovation in e-services in the organization. This study contributes to current e-service researches by offering theoretical advances related to innovation in e-services. Furthermore, the capabilities, concepts, and codes identified in this study would be useful as a comprehensive conceptual framework for developers of e-service innovation to plan and evaluate their related initiatives.
Journal of Information Technology Management
Faculty of Management, University of Tehran
2980-7972
11
v.
2
no.
2019
79
110
https://jitm.ut.ac.ir/article_73949_5668d66072b5cdca0891c27703276c19.pdf
dx.doi.org/10.22059/jitm.2019.288828.2401
Employing the Technology Acceptance Model to Explore the Trends of Social Media Adoption and its Effect on Perceived Usefulness and Perceived Ease of Use
Manal
Alduaij
Assistant Professor, Management Department, College of Business Studies, The Public Authority for Applied Education and Training, Kuwait.
author
text
article
2019
eng
The purpose of this research is to explore the social media trend in communication in Kuwait by utilizing the technology acceptance model. Social media has been gaining extraordinary adoption in past years that requires further investigation into user’s adoption habits, the various kinds of social media, and its effect on their perceived usefulness and ease of use of social media. The study consists of a total of 250 participants that were asked to complete a questionnaire in a random sample. Important findings indicate that the highest number of participants uses Facebook, and the second highest number of participants use Twitter. In terms of usage habits, the highest number of participants uses social media for chatting and connecting with family and friends. The second highest number of participants uses social media for reading posts. In terms of perceived usefulness, the highest numbers of participants perceive social media as ‘usefulness’, and the second highest numbers of participants feel that ‘social media is faster’. In terms of perceived ease of use the highest numbers of participants feel that social media is an easy way to communicate, and the second highest numbers of participants feel that social media does not require a lot of effort. In terms of gender it has been evident that females feel higher perceived usefulness and perceived ease of use of social media than males. The study bears theoretical and practical implications that show TAM can be successfully applied to examine social media in the context of Kuwait population. Furthermore, results of this study can be further generalized to neighboring GCC countries as they share similar geographic, economic, cultural, and financial factors.
Journal of Information Technology Management
Faculty of Management, University of Tehran
2980-7972
11
v.
2
no.
2019
129
143
https://jitm.ut.ac.ir/article_73951_1ee39d95b1fdf5f162cb6893d9d0de3c.pdf
dx.doi.org/10.22059/jitm.2019.290075.2405