Frontline Employees’ Informal Learning and Customer Relationship Skills in Macao Casinos: An Empirical Study

This study uses qualitative methods to better understand how the informal learning of frontline employees influenced their customer relationship skills in dealing with patrons at gaming tables, in the hope of achieving positive customer experiences in a competitive environment in Macao. As casino operators need to get their employees to work after limited formal training, they might find that their emphasis on formal training might be insufficient to provide patrons with customized service in Macao. In this context, the concept of informal learning, which is determined and directed by learners themselves to further improve what they have learned from their formal training, is likely to be of special significance in Macao. Based upon a constructivistic framework, this study used semi-structured interviews to gather data from 49 frontline employees. The study relied upon the Miles and Huberman (1994) framework to analyze qualitative data. Data analysis suggested that informal learning among frontline employees would lead to four strategies: (i) to be polite and respect patrons; (ii) to uncover patrons’ emotional status from their body language; (iii) to manage patrons’ emotions in their gaming pursuit; and (iv) to self-regulate emotions to the demands of a service encounter

Facial-Expression Affective Attributes and their Configural Correlates: Components and Categories

The present study investigates the perception of facial expressions of emotion, and explores the relation between the configural properties of expressions and their subjective attribution. Stimuli were a male and a female series of morphed facial expressions, interpolated between prototypes of seven emotions (happiness, sadness, fear, anger, surprise and disgust, and neutral) from Ekman and Friesen (1976). Topographical properties of the stimuli were quantified using the Facial Expression Measurement (FACEM) scheme. Perceived dissimilarities between the emotional expressions were elicited using a sorting procedure and processed with multidimensional scaling. Four dimensions were retained in the reconstructed facial-expression space, with positive and negative expressions opposed along D1, while the other three dimensions were interpreted as affective attributes distinguishing clusters of expressions categorized as “Surprise-Fear,” “Anger,” and “Disgust.” Significant relationships were found between these affective attributes and objective facial measures of the stimuli. The findings support a componential explanatory scheme for expression processing, wherein each component of a facial stimulus conveys an affective value separable from its context, rather than a categorical-gestalt scheme. The findings further suggest that configural information is closely involved in the decoding of affective attributes of facial expressions. Configural measures are also suggested as a common ground for dimensional as well as categorical perception of emotional faces.Este estudio investiga la percepción de las expresiones faciales de la emoción y explora la relación entre las propiedades configurales de las expresiones y su atribución subjetiva. Los estímulos eran una serie de expresiones faciales transformadas por ordenador, interpuestas entre los prototipos de siete emociones (felicidad, tristeza, miedo, ira, sorpresa, asco y neutral) tomados de Ekman y Friesen (1976). Las propiedades topográficas de los estímulos se cuantificaron mediante el esquema Facial Expression Measurement (FACEM). Las disimilaridades percibidas entre las expresiones emocionales se elicitaron mediante un procedimiento de clasificación y se procesaron con escalonamiento multidimensional. Se retuvieron cuatro dimensiones en el espacio facial-expresión reconstruido, con expresiones positivas y negativas contrapuestas a lo largo de D1, y las restantes tres dimensiones se interpretaron como atributos afectivos, distinguiendo clusters de expresiones clasificadas como “Sorpresa/Miedo”, “Ira”, y “Asco”. Se hallaron relaciones significativas entre estos atributos afectivos y las medidas faciales objetivas de los estímulos. Los resultados apoyan un esquema explicativo componencial para el procesamiento de las expresiones, en el que cada componente de un estímulo facial conlleva un valor afectivo separable de su contexto, más que un esquema categórico de tipo Gestalt. Además sugieren que la información configural juega un papel importante en la decodificación de los atributos afectivos de las expresiones faciales Además, sugieren que las medidas configurales constituyen en terreno común de la percepción dimensional y categórica de las caras emocionales

A Review on Facial Expression Recognition Techniques

Facial expression is in the topic of active research over the past few decades. Recognition and extracting various emotions and validating those emotions from the facial expression become very important in human computer interaction. Interpreting such human expression remains and much of the research is required about the way they relate to human affect. Apart from H-I interfaces other applications include awareness system, medical diagnosis, surveillance, law enforcement, automated tutoring system and many more. In the recent year different technique have been put forward for developing automated facial expression recognition system. This paper present quick survey on some of the facial expression recognition techniques. A comparative study is carried out using various feature extraction techniques. We define taxonomy of the field and cover all the steps from face detection to facial expression classification

Exposure to models’ negative facial expressions whilst eating a vegetable decreases women’s liking of the modelled vegetable, but not their desire to eat

Introduction: Food enjoyment can be conveyed through facial expressions. Observing others’ enjoyment of food has been found to influence adults’ desirability of liked and disliked food. Exposing adults to other eaters enjoying nutritious foods that are typically disliked (e.g., vegetables) could enhance the consumption of vegetables by young adults. However, this remains to be examined in young adult populations. This study examined the effect of models’ facial expressions towards raw broccoli on young adult women’s change in liking and change in desire to eat a modelled vegetable (raw broccoli) and a non-modelled vegetable (cucumber).Methods: Young adult women (N = 205) were randomised to watch a video of unfamiliar adult models eating raw broccoli with a positive, negative, or neutral facial expression. Participants’ change in liking and change in desire to eat the modelled and non-modelled vegetable was examined.Results: Observing models conveying negative facial expressions whilst eating raw broccoli resulted in a statistically significant reduction in liking ratings of broccoli, but not cucumber. There was no effect of models’ facial expressions on the change in desire to eat foods.Discussion: These findings suggest that watching others express a negative facial expression whilst eating a raw vegetable reduces women’s liking of the modelled vegetable, in the absence of a significant change to their desire to consume these foods. This highlights the power of others’ negative facial expressions on food liking. Further work is needed to establish the effect of others’ facial expressions on vegetable intake

Classification of EEG signals for facial expression and motor execution with deep learning

Recently, algorithms of machine learning are widely used with the field of electroencephalography (EEG) brain-computer interfaces (BCI). The preprocessing stage for the EEG signals is performed by applying the principle component analysis (PCA) algorithm to extract the important features and reducing the data redundancy. A model for classifying EEG, time series, signals for facial expression and some motor execution processes had been designed. A neural network of three hidden layers with deep learning classifier had been used in this work. Data of four different subjects were collected by using a 14 channels Emotiv EPOC+ device. EEG dataset samples including ten action classes for the facial expression and some motor execution movements are recorded. A classification results with accuracy range (91.25-95.75%) for the collected samples were obtained with respect to: number of samples for each class, total number of EEG dataset samples and type of activation function within the hidden and the output layer neurons. A time series EEG signal was taken as signal values not as image or histogram, analysed and classified with deep learning to obtain the satisfied results of accuracy