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Record W4417434236 · doi:10.1080/10447318.2025.2595545

Islamic Lifestyle Applications: Meeting the Spiritual Needs of Modern Muslims

2025· article· en· W4417434236 on OpenAlexaff
Mohsinul Kabir, Mohammad Ridwan Kabir, Riasat Islam

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsQueen's University
Fundersnot available
KeywordsIslamGovernment (linguistics)Field (mathematics)Work (physics)

Abstract

fetched live from OpenAlex

We evaluated contemporary Islamic lifestyle applications supporting religious practices and motivation among Muslims. We reviewed 11 popular applications using self-determination theory and the technology-as-experience framework to assess their support for motivation and affective needs. Most applications lack features that foster autonomy, competence, and relatedness. We also interviewed ten devoted Muslim application users to gain insights into their experiences and unmet needs. Our findings indicate that existing applications fall short in providing comprehensive learning, social connections, and scholar consultations. We propose design implications based on our results, including spiritual empowerment through technology, designing for intrinsic motivation, culturally grounded design and community engagement. We aim to inform the design of Islamic lifestyle applications that better facilitate ritual practices, benefitting application designers and Muslim communities. Our research provides valuable insights into the untapped potential for lifestyle applications to act as religious companions supporting Muslims’ spiritual journey.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.363
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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