The impact of religious salience on purchase intentions: evidence from the UAE

2019 ◽  
Vol 11 (6) ◽  
pp. 1339-1350
Author(s):  
Catherine Nickerson ◽  
Anup Menon Nandialath

Purpose The purpose of this paper is to explore the role of religious salience on consumer purchase intentions in the multicultural environment of the UAE, more specifically on the willingness of a Muslim consumer to purchase a product labelled or packaged to include an Islamic appeal, i.e. an appeal with a heightened religious salience. While some attempts have been made in the literature to examine the impact of religious salience on purchase intentions, research amongst Muslim consumers remains under-explored. Design/methodology/approach The authors used a randomized survey experiment administered to 148 Emirati educated female nationals. The survey consisted of pairs of advertisements, where each advertisement promoted the same product and the same brand, varying on whether they included an Islamic appeal or not in the labelling, packaging or slogan. The respondents were asked about their attitude to the different versions of the advertisements, as well as their willingness to purchase the product. The authors used causal mediation analysis to explore the mechanisms through which causal effects on purchase intentions are determined. Findings This study shows that including an Islamic appeal, and therefore increasing the religious salience in product promotion, leads to higher purchase intentions amongst Muslim consumers. The authors also identified a number of additional moderating factors that influenced the consumer’s purchase intentions, such as product and/or brand awareness and the type of product being promoted, as well as the nature of the artefact that was included in the ad as the Islamic appeal. Finally, the causal mediation analysis suggests that Islamic appeals increases product attractiveness, which in turn leads to higher purchase intentions. Originality/value This paper investigates the effect of religious salience on consumer behaviour and their purchase intentions. This paper makes an empirical contribution to understanding consumer behaviour with particular relevance to retail hubs with a majority Muslim population.

2017 ◽  
Author(s):  
Krisztián Pósch

Objectives: Review causal mediation analysis as a method for estimating and assessing direct and indirect effects in experimental criminology. Test procedural justice theory by examining the extent to which procedural justice mediates the impact of contact with the police on various outcomes. Apply causal mediation analysis to better interpret data from a field experiment that had suffered from a particular type of implementation failure.Methods: Data from a block-randomised controlled trial of procedural justice policing (the Scottish Community Engagement Trial) were analysed. All constructs were measured using surveys distributed during roadside police checks. The treatment implementation was assessed by analysing the treatment effect consistency and heterogeneity. Causal mediation analysis and sensitivity analysis were used to assess the mediating role of procedural justice.Results: First, the treatment effect was consistent and fairly homogeneous, indicating that the systematic variation in the study is attributable to the design. Second, procedural justice acts as a mediator channelling the treatment’s effect towards normative alignment (NIE=-0.207), duty to obey (NIE=-0.153), sense of power (NIE=-0.078), and social identity (NIE=-0.052), all of which are moderately robust to unmeasured confounding. The NIEs for risk of sanction and personal morality were highly sensitive, while for coerced obligation and sense of power they were non-significant. Conclusions: Causal mediation analysis is a versatile tool that can salvage experiments with systematic yet ambiguous treatment effects by allowing researchers to “pry open” the black box of causality. Most of the theoretical propositions of procedural justice policing were supported. Future studies are needed with more discernible causal mediation effects.


2017 ◽  
Vol 60 (2) ◽  
pp. S123-S124
Author(s):  
Joella W. Adams ◽  
Chanelle J. Howe ◽  
Andrew R. Zullo ◽  
Akilah Dulin Keita ◽  
Brandon D.L. Marshall

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Anne Kavanagh ◽  
Nicola Fortune ◽  
George Disney ◽  
Zoe Aitken ◽  
Samia Badji

Abstract Focus and outcomes for participants The symposium will focus on the role of epidemiologists in building an evidence base to improve the health of the 15% of the world’s population with disability who currently experience vast health inequalities. Participants will be introduced to new ways of conceptualising disability in epidemiology; state of the art approaches to monitoring disability-related socio-economic and health inequalities; methodological challenges and solutions to address the biases due to misclassification, confounding and reverse causation; and the application of causal mediation analysis and natural experiments in identifying potential policy solutions. Participants will gain a greater understanding of how epidemiological methods can be applied to improve the health of people with disability, as well as insights and ideas for their research. A network of epidemiologists interested in this topic will be generated to foster ongoing communication and collaborative opportunities. Rationale for the symposium, including for its inclusion in the Congress The health of disabled people has largely been ignored by epidemiologists. This is despite emerging evidence that people with disability experience poorer health because of factors unrelated to their impairment, including socio-economic disadvantage, discrimination, and violence. However, turning epidemiologists’ efforts to the health of people with disability presents conceptual and methodological challenges, some of which are unique to the content area. Participants will be shown a suite of approaches that can be deployed to address these problems. Participatory methods and innovative graphical and statistical methods for analysing disability-related health inequalities, approaches rarely used in epidemiology, will be covered. The symposium will also concentrate on the application of methods to optimise causal inference in the presence of multiple potential biases, and methods that simulate randomised controlled trial conditions to model policy interventions. Presentation program The presentations are from researchers from the CRE-DH, funded through Australia’s National Health and Medical Research Council organised four themes. Theme 1: Conceptualisation of disability We will present findings from a scoping review of original articles in epidemiology journals and will argue that, while, disability is usually conceptualised in epidemiology as an outcome, reconceiving of disability as an exposure, mediator and/or effect modifier can provide important insights on the determinants of health of people with disability. Theme 2: Monitoring disability-related inequalities We will demonstrate how the CRE-DH has used participatory methods, where people with disability are ‘experts through lived experience’, to develop indicators to monitor disability-related inequalities and design a National Community Attitudes survey. We will demonstrate innovative ways to graphically illustrate prevalence, absolute and relative inequalities simultaneously, and discuss how hierarchical Bayesian methods can be used to overcome inadequate power due to disaggregation and assess inequalities under uncertainty. Theme 3: Approaches to minimising bias We will talk about how biases can affect estimates of disability prevalence and disability-outcome associations, including reverse causation, confounding and misclassification. We will discuss a range of approaches we have used to address these challenges including modelling incident (rather than prevalent) disability, using fixed effects models and propensity score approaches, and approaches to addressing misclassification bias drawing on examples from our program of research. Theme 4: Identification of policy interventions We will discuss methods that can be used to model the impact of policies on the health of people with disability using examples from our research. We will present the results of a causal mediation analysis modelling the impact of different employment policy interventions on mental health outcomes. We will illustrate the value of natural policy experiments for estimating effects of policy changes on employment and health of people with disability using two examples – the 2014 reassessment of Disability Support Pensioners under stricter impairment tables and the introduction of Australia’s National Disability Insurance Scheme. The symposium will conclude with a facilitated discussion focussed on how epidemiologists can come together internationally to grasp the opportunities and address the challenges in research focussed on the health of people with disabilities. Names of presenters Professor Anne Kavanagh, PhD Dr Nicola Fortune, PhD Dr George Disney, PhD Dr Zoe Aitken Dr Samia Badji, PhD


2020 ◽  
Vol 8 (1) ◽  
pp. 131-149
Author(s):  
Soojin Park ◽  
Esra Kürüm

AbstractEstimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of treatment noncompliance. Existing studies based on the IV-based method focus on identifying the mediated portion of the intention-to-treat effect, which relies on several identification assumptions. However, little attention has been given to assessing the sensitivity of the identification assumptions or mitigating the impact of violating these assumptions. This study proposes a two-stage joint modeling method for conducting causal mediation analysis in the presence of treatment noncompliance, in which modeling assumptions can be employed to decrease the sensitivity to violation of some identification assumptions. The use of a joint modeling method is also conducive to conducting sensitivity analyses to the violation of identification assumptions. We demonstrate our approach using the Jobs II data, in which the effect of job training on job seekers’ mental health is examined.


2021 ◽  
Author(s):  
Lei Hou ◽  
Yuanyuan Yu ◽  
Xiaoru Sun ◽  
Xinhui Liu ◽  
Yifan Yu ◽  
...  

Causal mediation analysis aims to investigate the mechanism linking an exposure and an outcome. Dealing with the impact of unobserved confounders among the exposure, mediator and outcome has always been an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, making it more difficult to estimate of path-specific effects (PSEs). In this article, we propose a method (PSE-MR) to identify and estimate PSEs of an exposure on an outcome through multiple causally ordered and non-ordered mediators using Mendelian Randomization, when there are unmeasured confounders among the exposure, mediators and outcome. Additionally, PSE-MR can be used when pleiotropy exists, and can be implemented using only summarized genetic data. We also conducted simulations to evaluate the finite sample performances of our proposed estimators in different scenarios. The results show that the causal estimates of PSEs are almost unbiased with good coverage and Type I error properties. We illustrate the utility of our method through a study of exploring the mediation effects of lipids in the causal pathways from body mass index to cardiovascular disease.


2019 ◽  
Vol 29 (4) ◽  
pp. 1129-1148 ◽  
Author(s):  
Cedric E Ginestet ◽  
Richard Emsley ◽  
Sabine Landau

Causal mediation analysis aims to estimate natural direct and natural indirect effects under clearly specified assumptions. Traditional mediation analysis based on Ordinary Least Squares assumes an absence of unmeasured causes to the putative mediator and outcome. When these assumptions cannot be justified, instrumental variable estimators can be used in order to produce an asymptotically unbiased estimator of the mediator-outcome link, commonly referred to as a Two-Stage Least Squares estimator. Such bias removal, however, comes at the cost of variance inflation. A Semi-Parametric Stein-Like estimator has been proposed in the literature that strikes a natural trade-off between the unbiasedness of the Two-Stage Least Squares procedure and the relatively small variance of the Ordinary Least Squares estimator. The Semi-Parametric Stein-Like estimator has the advantage of allowing for a direct estimation of its shrinkage parameter. In this paper, we demonstrate how this Stein-like estimator can be implemented in the context of the estimation of natural direct and natural indirect effects of treatments in randomized controlled trials. The performance of the competing methods is studied in a simulation study, in which both the strength of hidden confounding and the strength of the instruments are independently varied. These considerations are motivated by a trial in mental health, evaluating the impact of a primary care-based intervention to reduce depression in the elderly.


PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3053
Author(s):  
Aolin Wang ◽  
Onyebuchi A. Arah

BackgroundThe macro environment we live in projects what we can achieve and how we behave, and in turn, shapes our health in complex ways. Policymaking will benefit from insights into the mechanisms underlying how national socioeconomic context affects health. This study examined the impact of human development on individual health and the possible mediating roles of education and body mass index (BMI).MethodsWe analyzed World Health Survey data on 109,448 participants aged 25 or older from 42 low- and middle-income countries with augmented human development index (HDI) in 1990. We used principal components method to create a health score based on measures from eight health state domains, used years of schooling as education indicator and calculated BMI from self-reported height and weight. We used causal mediation analysis technique with random intercepts to account for the multilevel structure.ResultsBelow a reference HDI level of 0.48, HDI was negatively associated with good health (total effect at HDI of 0.23:b =  − 3.44, 95% CI [−6.39–−0.49] for males andb =  − 5.16, 95% CI [−9.24,–−1.08] for females) but was positively associated with good health above this reference level (total effect at HDI of 0.75:b = 4.16, 95% CI [−0.33–8.66] for males andb = 6.62, 95% CI [0.85–12.38] for females). We found a small positive effect of HDI on health via education across reference HDI levels (branging from 0.24 to 0.29 for males and 0.40 to 0.49 for females) but not via pathways involving BMI only.ConclusionHuman development has a non-linear effect on individual health, but the impact appears to be mainly through pathways other than education and BMI.


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