List of Articles Mobile application

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  1 - Fostering Academic Vocabulary Learning: Opportunities for Explicit Learning through a Mobile-Assisted App in the Field of Applied Linguistics
  زینب عبداله پور ننفیسه اسدراده ملکی
  10.30495/jfl.2023.700437
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• Open Access Article
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  2 - The Strategic Marketing Model Based on the Suggested Values of Mobile Religious Applications
  Zohreh dehdashti shahrokh vahid khashei Soroush Ghazinoori amir aslani afrashteh
  The aim of the current research is to provide a strategic model for marketing based on proposed values ​​in religious mobile phone applications. Based on Grounded theory research method, the data is collected through in-depth semi-structured interviews and the target co More

  The aim of the current research is to provide a strategic model for marketing based on proposed values ​​in religious mobile phone applications. Based on Grounded theory research method, the data is collected through in-depth semi-structured interviews and the target community is among founders and executives of religious mobile applications and marketing researchers in the field of religious business marketing. The sampling method is snowball, which reached theoretical saturation after 15 interviews. Data analysis was done in three stages of open, central and selective coding. The results showed that the core category of proposed values ​​(economic, social, functional, religious, informational, cognitive, emotional, communication) is influenced by causal factors (attitudinal factors in religious audiences, competitive factors in the market of religious applications, behavioral factors in users of religious applications and technical factors of religious applications). Also, the marketing model by strategies (content marketing (idea, creation and distribution of religious content), design and production of religious applications, pricing and revenue of religious applications, organizational sales in religious applications and communication strategies with users of religious applications , optimization of performance in the digital space, market research and identifying the behavior of religious consumers), contextual factors (factors related to the product, factors related to user behavior, factors related to institutions, factors related to the market and organizational factors), intervening factors (legal and audit factors, technical, social and cultural, political), and the consequences are realized at four levels of consequences (individual, organizational, national and transnational). Manuscript profile
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  3 - Designing a mobile tourism program and achieving sustainable development
  Yaser Ebazadeh Reza Alayi Maedeh Kiani Sakaleh
  10.22034/ap.2021.1929091.1103
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• Open Access Article
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  4 - Comparison of Pre-Trained models in Extractive Text Summarization of Mobile User Reviews
  Mehrdad Razavi Dehkordi hamid rastegari Akbar Nabiollahi Najafabadi Taghi Javdani Gandomani
  Since the inception of mobile apps, user feedback has been extremely valuable to app developers as it contains users' feelings, bugs, and new requirements. Due to the large volume of reviews, summarizing them is very difficult and error-prone. So far, many works have be More

  Since the inception of mobile apps, user feedback has been extremely valuable to app developers as it contains users' feelings, bugs, and new requirements. Due to the large volume of reviews, summarizing them is very difficult and error-prone. So far, many works have been done in the field of extractive summarization of users' reviews; However, in most researches, old methods of machine learning or natural language processing have been used, or if a model has been trained for summarizing using transformers, it has not been determined whether this model is useful for summarizing the reviews of mobile users. No? In other words, the model for summarizing texts has been presented in a general purpose form, and no investigation has been carried out for its use in special purpose summarization. In this article, first, 1000 reviews were randomly selected from the Kaggle database of user reviews, and then given to 4 pre-trained models bart_large_cnn, bart_large_xsum, mT5_multilingual_XLSum, and Falcon'sai Text_Summrization for summarization, and the criteria Rouge1, Rouge2 and RoungL were calculated separately for each of the models and finally it was found that the pre-trained Falcon's AI model with a score of 0.6464 in the rouge1 criterion, a score of 0.6140 in the rouge2 criterion and a score of 0.6346 in rougeL The best model for summarizing users' reviews is the Play Store. Manuscript profile