The Influence of Servicescape and Local Food Attributes on Pleasure and Revisit Intention in an Upscale-Casual Dining Restaurant

The current study looks at the relationship between servicescape, emotional product involvement, perceived quality of local foods, the positive emotion of pleasure, and revisit intention in an upscale buffet style restaurant on a university campus in the Southeastern U.S. Test results show positive relationships between all of the constructs in the proposed conceptual model. The study also gives practitioners and academics insights into practices that can help to market the use of local foods through the restaurant environment in order to engage emotionally involved customers. This marketing can illicit pleasurable feelings and increase perceived product quality of local foods with the purpose of getting customers to revisit the restaurant. Suggestions for further research on the subject are proposed.

Seasonal Fish Dispersal in Ephemeral Wetlands of the Florida Everglades

We hypothesized that fishes in short-hydroperiod wetlands display pulses in activity tied to seasonal flooding and drying, with relatively low activity during intervening periods. To evaluate this hypothesis, sampling devices that funnel fish into traps (drift fences) were used to investigate fish movement across the Everglades, U.S.A. Samples were collected at six sites in the Rocky Glades, a seasonally flooded karstic habitat located on the southeastern edge of the Everglades. Four species that display distinct recovery patterns following drought in long-hydroperiod wetlands were studied: eastern mosquitofish (Gambusia holbrooki) and flagfish (Jordanella floridae) (rapid recovery); and bluefin killifish (Lucania goodei) and least killifish (Heterandria formosa) (slow recovery). Consistent with our hypothesized conceptual model, fishes increased movement soon after flooding (immigration period) and just before drying (emigration period), but decreased activity in the intervening foraging period. We also found that eastern mosquitofish and flagfish arrived earlier and showed stronger responses to hydrological variation than either least killifish or bluefin killifish. We concluded that these fishes actively colonize and escape ephemeral wetlands in response to flooding and drying, and display species-specific differences related to flooding and drying that reflect differences in dispersal ability. These results have important implications for Everglades fish metacommunity dynamics.

Stereotype Threat and the Standardized Testing Experiences of African American Children at an Urban Elementary School

Stereotype threat (Steele & Aronson, 1995) refers to the risk of confirming a negative stereotype about one’s group in a particular performance domain. The theory assumes that performance in the stereotyped domain is most negatively affected when individuals are more highly identified with the domain in question. As federal law has increased the importance of standardized testing at the elementary level, it can be reasonably hypothesized that the standardized test performance of African American children will be depressed when they are aware of negative societal stereotypes about the academic competence of African Americans. This sequential mixed-methods study investigated whether the standardized testing experiences of African American children in an urban elementary school are related to their level of stereotype awareness. The quantitative phase utilized data from 198 African American children at an urban elementary school. Both ex-post facto and experimental designs were employed. Experimental conditions were diagnostic and non-diagnostic testing experiences. The qualitative phase utilized data from a series of six focus group interviews conducted with a purposefully selected group of 4 African American children. The interview data were supplemented with data from 30 hours of classroom observations. Quantitative findings indicated that the stereotype threat condition evoked by diagnostic testing depresses the reading test performance of stereotype-aware African American children (F[1, 194] = 2.21, p < .01). This was particularly true of students who are most highly domain-identified with reading (F[1, 91] = 19.18, p < .01). Moreover, findings indicated that only stereotype-aware African American children who were highly domain-identified were more likely to experience anxiety in the diagnostic condition (F[1, 91] = 5.97, p < .025). Qualitative findings revealed 4 themes regarding how African American children perceive and experience the factors related to stereotype threat: (1) a narrow perception of education as strictly test preparation, (2) feelings of stress and anxiety related to the state test, (3) concern with what “others” think (racial salience), and (4) stereotypes. A new conceptual model for stereotype threat is presented, and future directions including implications for practice and policy are discussed.

An Examination of Psychological Meaningfulness, Safety, and Availability as the Underlying Mechanisms linking Job Features and Personal Characteristics to Work Engagement

The present study tested a nomological net of work engagement that was derived from its extant research. Two of the main work engagement models that have been presented and empirically tested in the literature, the JD-R model and Kahn’s model, were integrated to test the effects that job features and personal characteristics can have on work engagement through the psychological conditions of meaningfulness, safety, and availability. In this study, safety refers to psychological perceptions of safety and not workplace safety behaviors. The job features that were tested in this model included person-job fit, autonomy, co-worker relations, supervisor support, procedural justice, and interactional justice, while the personal characteristics consisted of self-consciousness, self-efficacy, extraversion, and neuroticism. Thirty-four hypotheses and a conceptual model were tested in order to establish the viability of this nomological net of work engagement in which it was expected that meaningfulness would mediate the relationships between job features and work engagement, safety would mediate the relationships that job features and personal characteristics have with work engagement, and availability (physical, emotional, and cognitive resources) would mediate the relationships that personal characteristics have with work engagement. Furthermore, analyses were run in order to determine the factor structure of work engagement, assess whether or not it exhibits differential validity from organizational commitment and job satisfaction, and confirm that it is positively related to the outcome variable of organizational citizenship behavior (OCB). The final sample consisted of 500 workers from an online labor market who responded to a questionnaire composed of measures of all constructs included in this study. Findings show that work engagement is best represented as a three-factor construct, composed of vigor, dedication and absorption. Furthermore, support was found for the distinction of work engagement from the related constructs of organizational commitment and job satisfaction. With regard to the proposed model, meaningfulness proved to be the strongest predictor of work engagement. Results show that it partially mediates the relationships that all job features have with work engagement. Safety proved to be a partial mediator of the relationships that autonomy, co-worker relations, supervisor support, procedural justice, interactional justice, and self-efficacy have with work engagement, and fully mediate the relationship between neuroticism and work engagement. Findings also show that availability partially mediates the positive relationships that extraversion and self-efficacy have with work engagement, and fully mediates the negative relationship that neuroticism has with work engagement. Finally, a positive relationship was found between work engagement and OCB. Research and organizational implications are discussed.

The effects of attribute quality information and sources of information on consumers' perceptions and behavior: A model of Pre-purchase Information Utilization in Service Physical Environments

The search-experience-credence framework from economics of information, the human-environment relations models from environmental psychology, and the consumer evaluation process from services marketing provide a conceptual basis for testing the model of "Pre-purchase Information Utilization in Service Physical Environments." The model addresses the effects of informational signs, as a dimension of the service physical environment, on consumers' perceptions (perceived veracity and perceived performance risk), emotions (pleasure) and behavior (willingness to buy). The informational signs provide attribute quality information (search and experience) through non-personal sources of information (simulated word-of-mouth and non-personal advocate sources).^ This dissertation examines: (1) the hypothesized relationships addressed in the model of "Pre-purchase Information Utilization in Service Physical Environments" among informational signs, perceived veracity, perceived performance risk, pleasure, and willingness to buy, and (2) the effects of attribute quality information and sources of information on consumers' perceived veracity and perceived performance risk.^ This research is the first in-depth study about the role and effects of information in service physical environments. Using a 2 x 2 between subjects experimental research procedure, undergraduate students were exposed to the informational signs in a simulated service physical environment. The service physical environments were simulated through color photographic slides.^ The results of the study suggest that: (1) the relationship between informational signs and willingness to buy is mediated by perceived veracity, perceived performance risk and pleasure, (2) experience attribute information shows higher perceived veracity and lower perceived performance risk when compared to search attribute information, and (3) information provided through simulated word-of-mouth shows higher perceived veracity and lower perceived performance risk when compared to information provided through non-personal advocate sources. ^

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.