Intention to Purchase Counterfeit Products: The Impact of Unethical Beliefs, Social Status and Perceived Risk

Counterfeiting has become an economic problem as an increasing number of goods are being illegally produced. However, there is a dearth of studies investigating counterfeiting in the Arab World. This study uses a five-point Likert scale to examine the impact of moral beliefs, social status and perceived risk on the intention to purchase counterfeits of luxury brands in the Arab Gulf (n = 448). Structural equation modelling was employed for data analysis. Results show that consumers’ unethical beliefs and perceived risk have a negative, while status consumption has a positive, impact on the intention to purchase counterfeit products. Additionally, users’ demographic measures, such as religiosity, cultural background and socioeconomic status, do not have a significant impact on the intent to purchase counterfeit products. This study provides a new theoretical foundation for studying the purchase of counterfeit products in a non-Western culture, and also provides companies with a number of factors that might help in discouraging counterfeit product consumption

Intention to Purchase Counterfeit Products: The Impact of Unethical Beliefs, Social Status and Perceived Risk

Counterfeiting has become an economic problem as an increasing number of goods are being illegally produced. However, there is a dearth of studies investigating counterfeiting in the Arab World. This study uses a five-point Likert scale to examine the impact of moral beliefs, social status and perceived risk on the intention to purchase counterfeits of luxury brands in the Arab Gulf (n = 448). Structural equation modelling was employed for data analysis. Results show that consumers’ unethical beliefs and perceived risk have a negative, while status consumption has a positive, impact on the intention to purchase counterfeit products. Additionally, users’ demographic measures, such as religiosity, cultural background and socioeconomic status, do not have a significant impact on the intent to purchase counterfeit products. This study provides a new theoretical foundation for studying the purchase of counterfeit products in a non-Western culture, and also provides companies with a number of factors that might help in discouraging counterfeit product consumption

An integrated approach to unravel the structural controls on groundwater potentialities in hyper-arid regions using satellite and land-based geophysics: a case study in southwestern desert of Egypt

Published versio

Modeling bivariate geyser eruption system with covariate-adjusted recurrent event process

Geyser eruption is one of the most popular signature attractions at the Yellowstone National Park. The interdependence of geyser eruptions and impacts of covariates are of interest to researchers in geyser studies. In this paper, we propose a parametric covariate-adjusted recurrent event model for estimating the eruption gap time. We describe a general bivariate recurrent event process, where a bivariate lognormal distribution and a Gumbel copula with different marginal distributions are used to model an interdependent dual-type event system. The maximum likelihood approach is used to estimate model parameters. The proposed method is applied to analyzing the Yellowstone geyser eruption data for a bivariate geyser system and offers a deeper understanding of the event occurrence mechanism of individual events as well as the system as a whole. A comprehensive simulation study is conducted to evaluate the performance of the proposed method

Bayesian Estimation for Parameters of Truncated Data Based on a Two-Phase Sampling Plan

This paper provides insights to researchers when conducting a survey and the response rate is very low, and it is of interest to increase response rate and to estimate the optimal population mean. The paper considers a population that consists of two strata, respondents, and non-respondents. The sampling design is to select a random sample of size n, then when only r observations respond to the survey, select an optimal random sample of size m that minimizes the expected loss, from the remaining (n-r) observations. A truncated Bayesian approach sampling plan is considered [1], such that the posterior distribution of the first stage is treated as a prior to the second stage, and an over-all mean is estimated. The paper illustrates Ericson approach to two random data sets with two sets of priors where the estimated overall mean is obtained for each stage and the expected loss is computed for the two prior sets. It is concluded that priors on means affect the optimal estimate for the mean; under the selected two priors, the final covariance matrix is approximately the same, and the losses are approximately equal when the r responses are more than 20%

Copula-frailty models for recurrent event data based on Monte Carlo EM algorithm

Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers in a clinical trial for skin cancer prevention. The aims in those applications are to characterize features of the marginal processes, evaluate covariate effects, and quantify both the within-subject recurrence dependence and the dependence among different event types. We use copula-frailty models to analyze correlated recurrent events of different types. Parameter estimation and inference are carried out by using a Monte Carlo expectation-maximization (MCEM) algorithm, which can handle a relatively large (i.e., three or more) number of event types. Performances of the proposed methods are evaluated via extensive simulation studies. The developed methods are used to model the recurrences of skin cancer with different types

Low dose sulphonylurea plus DPP4 inhibitor lower blood glucose and enhance beta cell function without hypoglycaemia

Context: Low dose sulphonylureas have been found to augment the classical incretin effect, increase glucose sensitivity and late phase incretin potentiation.Objective: To evaluate potential synergy between low dose sulphonylurea plus DPP4 inhibitor.Design: Unblinded randomised crossover studySetting: Clinical Research Centre, University of DundeeParticipants: 30 participants with T2DM (HbA1c < 64 mmol/mol) treated with diet or metformin.Intervention: Participants completed four, 14-day blocks in a random order: control, gliclazide 20 mg (SU), sitagliptin 100 mg (DPP4i), or combination (SUDPP4i). A mixed meal test was conducted after each intervention.Main Outcome Measure: The primary outcome was the effect of treatment on beta-cell glucose sensitivity. Secondary outcomes included frequency of glucose <3 mmol/l on continuous glucose monitoring, sub-analyses by genotype (KNCJ11 E23 K), gender and body mass index.Results: SU combination with DPP4i showed additive effect on glucose lowering: Mean glucose AUC (mean 95% CI) (mmol/l) was: Control 11.5 (10.7–12.3), DPP4i 10.2 (9.4–11.1), SU 9.7 (8.9–10.5), SUDPP4i 8.7 (7.9–9.5) (p < 0.001). Glucose sensitivity mirrored the additive effect (pmol min-1 m-2mM-1): Control 71.5 (51.1–91.9), DPP4i 75.9 (55.7–96.0), SU 86.3 (66.1–106.4), SUDPP4i 94.1 (73.9–114.3) (p = 0.04). The additive effect was seen in men but not women. Glucose time in range <3 mmol/l on CGM (%) was unaffected: Control 1 (2-4), DPP4i 2 (3-6), SU 1 (0-4), SUDPP4i 3 (2–7) (p = 0.65)Conclusions: Low dose sulphonylurea plus DPP4i has potent glucose lowering effect through augmentation of beta cell function. A double-blind randomised controlled trial would formalise efficacy and safety of this combination, which may avoid negative aspects of SU