Faramarz Kazemainy - Department of English language and Literature, Ayatollah Amoli branch, Islamic Azad University, Amol, Iran
Hamed Barjesteh - Department of English language and Literature, Ayatollah Amoli branch, Islamic Azad University, Amol, Iran
Nassim Golaghaei - ELT Department, Roudehen Branch, Islamic Azad University, Roudehen, Iran
Atefeh Nasrollahi Mouzirji - Department of English language and Literature, Ayatollah Amoli branch, Islamic Azad University, Amol, Iran

Juxtaposing the ubiquitous facets of mobile technology as everyware (Greenfield, ۲۰۱۰) and vocabulary as one of the most axial aspects of language learning (Bowles & Cogo, ۲۰۱۶; Schmitt & Schmitt, ۲۰۲۰), besides the importance of considering learners’ attributes and needs (Taghizadeh, ۲۰۱۹) while developing a mobile vocabulary application (app), necessitates analyzing the relationship and impact of all these elements in a single structural model. To tackle the issue, first, via a mixed-methods design, the researcher developed a bespoke mobile application using the task model and the principles of agile methodology. and then investigated the impact of using the app on a sample of ۶۲ Iranian EFL university students’ vocabulary recognition and recall, the results of which were published in two other articles. In this study, the researcher integrated the obtained data within a proposed structural model and assessed the model's fitness to investigate the interaction and interrelationship among the latent variables mentioned above. The results obtained from SEM-PLS analyses revealed that within the unified structural model, the learners’ preferences and needs were favorably influenced by their learning style orientation and technology savviness. Similarly, the findings verified the positive impact of considering learners’ preferences and needs during the agile app development lifecycle on the target participants’ vocabulary knowledge, encompassing vocabulary recognition and recall. Finally, the fitness of the proposed structural model was verified based on the criteria for model assessment mentioned by Sparks and Alamer (۲۰۲۲). The SEM-PLS data analyses and the implications of the study are presented and discussed.

learning styles, technology savviness, Needs analysis, custom-made mobile applications, vocabulary knowledge