Search

Within the competitive foodservice industry, the ability to accurately measure the meal process known as turn-time is critical to the success of the firms in the industry. This is traditionally done through linear techniques such as multiple least squares (aka linear regression) or analysis of variance (ANOVA). However, linear techniques have theoretical properties that can potentially lead to bias in measurements of time duration variables, while survival models were designed for that purpose. This study utilized simulated data of a dine-in restaurant to test and compare the ability of linear regression to five survival models (proportional hazard models) for predicting the duration of turn-time. The results from the simulated trials show that while some of the survival models held incremental improvements, linear regression performed adequately for predicting the duration of turn-time even when taking the biased predictions into account. For operators who are in their infancy of developing resta...

According to the United Nations World Tourism Organization (UNWTO, 2017), over one billion tourists traveled the globe in 2016. In spite of this increase of travelers, airlines are faced with declining levels of customer service and quality of customer experience (American Customer Satisfaction Index [ACSI], 2012). Frontline service employees, like flight attendants, create a critical impression of the service which affects customer perceptions and satisfaction. Nevertheless, many unknowns exist about what creates such impression and how it can be improved. In particular, no study has investigated the effects of Competency-Based Training (CBT) on flight attendants' performance and consequently passengers' satisfaction. The goal of this research was to examine the effect of CBT on flight attendants' performance and consequently passengers' satisfaction. A group of 109 flight attendants was trained in four competences: managing stress, dealing with conflict situations, displaying human relations skills, and delivering quality customer service. Pre-and post-training measures of flight attendants' performance and customer satisfaction were taken. Random sampling was employed to administer questionnaires to passengers traveling between the Caribbean and North America. Bivariate analysis revealed that there was a positive association between flight attendant performance and customer satisfaction. However, flight attendant performance was not positively associated with CBT. Further analysis revealed that customer satisfaction is associated with CBT. Consequently, theoretical and practical implications were developed.

Many social studies analyze attitude responses using the linear regression model. This model typically treats questionnaire data as continuous scales, although the data is merely ordinal. One type of regression model that is more appropriate to analyze rank-order responses is the Ordinal Logistic Regression (OLR) model. In addition to the use of regression, the Artificial Neural Network (ANN) model has recently been applied in various studies. This paper delivered comparative descriptions of both the ANN and OLR models. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the disadvantages and advantages of both models were analytically reviewed. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3.]

The primary objective of this study was to examine how perceived risk could be managed in the destination choice context. Relationships among risk perceptions, uncertainty (information quality), and price premium were explored by using ordinal logistic regression. Eight types of perceived risk were investigated: health/life risk, financial risk, personal satisfaction risk, social risk, time risk, technical risk, political risk, and terrorism risk. Findings showed that perceived risks were decreased by information quality but various patterns were identified for different types of perceived risk. Results additionally suggested that travelers were willing to pay extra for products and services if more safety and security are provided. Managerial implications for how the findings can be incorporated into risk management strategies are also presented in this paper.

A student-operated restaurant has to balance the achievement of its objectives as a profit generator and as a learning centre. This unique characteristic distinguishes a student-operated restaurant from other types of restaurant. This study aims to build a model to predict overall customer satisfaction in a student-operated restaurant. The input variables consist of 32 dining service attributes, which are derived from DINESERV factors. Data was collected using a close-ended questionnaire and was distributed using a convenience random sampling approach. A neural network model and a logistic regression model were built to predict overall customer satisfaction. The result shows that the best neural network model built in this study was the MLP neural network model with two hidden layers. The correct classification rate of this model was 80.65% and 69.81% for the training and testing data set. The top three important attributes that influence overall customer satisfaction are customer satisfaction toward service, responsive service and excellent service. In addition, the best logistic regression built in this study was a stepwise approach. This model had a correct classification rate at 73.39% and 69.17% for training and testing data set. The result of logistic regression shows that two significant dining attributes that influence overall customer satisfaction are customer satisfaction with service quality and food quality. Based on the correct classification rate, this study concludes that a neural network model has a better performance to predict overall customer satisfaction than a logistic regression model. However, a neural network model may not be the best model to determine the most significant input variable toward an output variable since it cannot be proven using a statistic method.