Scale of religiosity for Muslims: an exploratory study
Bibliographic record
Abstract
Purpose Islam plays a powerful symbolic and cultural role in the constitution of consumer preferences, especially in Muslim countries. To quantitatively study this role in the consumption patterns of Muslim consumers we need a suitable scale for religiosity. However, the existing scales of religiosity have been developed primarily for Christian/Jewish respondents and cannot provide valid results for Muslim consumers. This study aims to address these challenges by re-conceptualizing the religiosity construct for Muslims and conducting an exploratory study to generate an initial scale. Design/methodology/approach This paper initialized the scale development exercise with a systematic review of the existing Islamic literature to ensure that we use Islamic categories to build the scale. Once the authors had a large pool of items, they consulted experts on Shariah (Islamic law) to evaluate these items for clarity, face and content validity. Next, they conducted five focus groups to (a) determine if they had covered the full terrain of Muslim religiosity; (b) identify if the items correspond with the actual experiences of the target respondents; and (c) ensure linguistic compatibility. This was followed by administering an exploratory survey designed to test psychometric properties of the new scale and to analyze the underlying dimensionality of the inventory of items. Findings To extract a manageable number of latent dimensions in the survey data, an exploratory factor analysis (EFA) procedure was conducted. This resulted in the extraction of five different factors which were named as Mu’amalat_societal ethics, Roshan Khayali (enlightened moderation), Ibadaat (prayers), Mu’amalat_societal laws, Azeemat (a state exhibiting scrupulous faithfulness) and Mu’amalat_business dealings. There is a divide between Ibadaat (individual and collective worship) and Muamlaat (social relations) that emerged in the data from the cluster analysis procedure. Originality/value Religion can be an important part of decision-making of a typical consumer. This paper proposes a new scale for Muslims to tap into their religiosity, as existing scales are not embedded in the Islamic literature. This study also distinguishes Muslim religiosity from its Western counterpart and thus helps in clarifying the Muslim religiosity construct.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".