How Deep Work Drives Student Engagement Amid Smartphone Distraction and Attention Control: A Mediation-Moderation Analysis

In the modern, digitally driven world, deep work (DW) offers a valuable antidote to pervasive interruptions, yet its role in academic settings remains underexplored. This study aims to empirically investigate the influence of deep work on student engagement (SE) by proposing a mediation-moderation framework. The mediating variables include basic psychological needs (BPN), dispositional mindfulness (DM), and meaning in life, while smartphone distraction (SD) and attention control (AC) are examined as moderators. A cross-sectional study with 482 university students (mean age = 22.23 years) was conducted using validated scales for DW, SE, BPN, DM, SD, and AC. Regression analysis, mediation, and moderation analysis were computed, which showed that DW predicted SE with DM and meaning in life having significant mediating effects, with meaning in life being the strongest contributor. Further, SD and AC had significant moderating effects, with the strongest effect demonstrated in the presence of high levels of SD and low levels of AC. These findings underline deep work as a potential protective factor against digital distractions and low attention control, pointing toward possible deep work importance in enhancing academic engagement. We conclude by emphasizing the finding value of shaping policies on device usage and developing interventions to enhance mindfulness, meaning, and focus, with benefits extending beyond academia.

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Publication Details

Journal
Psychological Reports
Published
2025-09-08
DOI
https://doi.org/10.1177/00332941251377396
Primary Topic
Impact of Technology on Adolescents
Type
article
Field-Weighted Citation Impact
0.00
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article

How Deep Work Drives Student Engagement Amid Smartphone Distraction and Attention Control: A Mediation-Moderation Analysis

Narendra Nath Samantaray, Sandhyarani Moirangthem, Afrin Mazid
Psychological Reports
Impact of Technology on Adolescents
article

How Deep Work Drives Student Engagement Amid Smartphone Distraction and Attention Control: A Mediation-Moderation Analysis

Narendra Nath Samantaray, Sandhyarani Moirangthem, Afrin Mazid
article en

Abstract

In the modern, digitally driven world, deep work (DW) offers a valuable antidote to pervasive interruptions, yet its role in academic settings remains underexplored. This study aims to empirically investigate the influence of deep work on student engagement (SE) by proposing a mediation-moderation framework. The mediating variables include basic psychological needs (BPN), dispositional mindfulness (DM), and meaning in life, while smartphone distraction (SD) and attention control (AC) are examined as moderators. A cross-sectional study with 482 university students (mean age = 22.23 years) was conducted using validated scales for DW, SE, BPN, DM, SD, and AC. Regression analysis, mediation, and moderation analysis were computed, which showed that DW predicted SE with DM and meaning in life having significant mediating effects, with meaning in life being the strongest contributor. Further, SD and AC had significant moderating effects, with the strongest effect demonstrated in the presence of high levels of SD and low levels of AC. These findings underline deep work as a potential protective factor against digital distractions and low attention control, pointing toward possible deep work importance in enhancing academic engagement. We conclude by emphasizing the finding value of shaping policies on device usage and developing interventions to enhance mindfulness, meaning, and focus, with benefits extending beyond academia.

Psychological Reports
Mizoram University (IN), National Institute of Mental Health and Neurosciences (IN)
Openalex Percentile: Top 68%
Impact of Technology on Adolescents
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How Deep Work Drives Student Engagement Amid Smartphone Distraction and Attention Control: A Mediation-Moderation Analysis — Narendra Nath Samantaray, Sandhyarani Moirangthem, et al. · Psychological Reports (2025) | TGRS Research Map | TGRS