Restaurant Industry Perspectives on Pro-social Rule Breaking: Intent versus Action

The resounding message extracted from the service literature is that employees serve pivotal functions in the overall guest experience. This is of course due to the simultaneous delivery of personalized service provision with resultant consumption of those services. This simultaneous delivery and consumption cycle is at times challenged by a perceived desire to accommodate guest request that may violate, to a greater or lesser degree, an organizational rule. This is important to note because increased interactions with customers enable frontline employees to have a better sense of what customers want from the company as well as from the company itself (Bitner, et al, 1994). With that platform established, then why are some employees willing to break organizational rules and risk disciplinary action to better service a customer? This study examines the employee personality, degree of autonomy, job meaning, and co-worker influence on an employee's decision to break organizational rules. The results of this study indicate that co-worker influence exerted a minimal influence on employee decision to break rules while the presence of societal consciousness exerted a much stronger influence. Women reported that they were less likely to engage in rule divergence, and significant correlations were present when filtered by years in current position, and years in the industry.

A Review of Merger and Acquisition Wave Literature: Proposing Future Research in the Restaurant Industry

The purpose of this study is to identify research trends in Merger and Acquisition waves in the restaurant industry and propose future research directions by thoroughly reviewing existing Merger and Acquisition related literature. Merger and Acquisition has been extensively used as a strategic management tool for fast growth in the restaurant industry. However, there has been a very limited amount of literature that focuses on Merger & Acquisition in the restaurant industry. Particular, no known study has been identified that examined M&A wave and its determinants. A good understanding of determinants of M&A wave will help practitioners identify important factors that should be considered before making M&A decisions and predict the optimal timing for successful M&A transactions. This study examined literature on six U.S M&A waves and their determinants and summarized main explanatory factors examined, statistical methods, and theoretical frameworks. Inclusion of unique macroeconomic factors of the restaurant industry and the use of factor analysis are suggested for future research.

An Evaluation of Three Nutrition Labeling Formats for Restaurant Menus

This study evaluated three menu nutrition labeling formats: calorie only information, a healthy symbol, and a nutrient list. Daily sales data for a table-service restaurant located on a university campus were recorded during a four-week period from January to February 2013 to examine changes in average nutritional content of the entrees purchased by customers when different nutrition labels were provided. A survey was conducted to assess the customers’ use of nutrition labels, their preferences among the three labeling formats, their entree selections, their cognitive beliefs with regard to healthy eating, and their demographic characteristics. A total of 173 questionnaires were returned and included in data analysis. Analysis of Variance (ANOVA) and regression analyses were performed using SAS. The results showed that favorable attitudes toward healthy eating and the use of nutrition labels were both significantly associated with healthier entrée selections. Age and diet status had some effects on the respondent’s use of nutrition labels. The calorie only information format was the most effective in reducing calories contained in the entrees sold, and the nutrient list was most effective in reducing fat and saturated fat content of the entrees sold. The healthy symbol was the least effective format, but interestingly enough, was most preferred by respondents. The findings provide support for future research and offer implications for policy makers, public health professionals, and foodservice operations.

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.

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Are Consumers Ready for Mobile Payment? An Examination of Consumer Acceptance of Mobile Payment Technology in Restaurant Industry

As an emerging payment method, mobile payment technology is perceived to be a secure and effective substitute of traditional debit/credit card payment. Although several reports and scholars claimed that mobile payment technology would become a major future payment method, consumers rather caught on this trend slowly, and little is known about key determinants of consumers’ acceptance of mobile payment. To close that gap, the current study extended the classic Technology Acceptance Model by adding four additional predictors that are relevant to hospitality industry. The study results suggested that compatibility with lifestyle was the strongest predictor of consumers’ intention to adopt mobile payment technology in restaurants, followed by perceived usefulness, subjective norm, security, and previous experience with mobile payment. Important theoretical and practical implications were provided based on our findings.

Perceived Value of Fast-food Restaurant Franchises in the USA

The main research objective of this study was to find out whether perceived value significantly affects consumers’ purchase intention. Additionally, this study examined if there are any significant differences in perceived value for different fast-food restaurant brands and attempted to identify which fast-food restaurant is perceived to be the industry leader. A total number of six fast-food restaurants (McDonalds, Subway, Starbucks, Wendy’s, Burger King, and Taco Bell) were selected. Findings showed that among the five perceived service value dimensions, Starbucks is the leader in terms of quality, emotional response, and reputation. Multivariate analysis of variance (MANOVA) and multiple regression analysis were performed to test the study hypotheses. Results indicated that there were significant differences in perceived value for different fast-food restaurant brands. Besides, monetary and behavioral price significantly affects consumers’ purchase intention. Findings are expected to help hospitality marketers to strategically manage their brands.

Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

The Effects of Teppanyaki Restaurant Stimuli on Diners’ Emotions and Loyalty

This research examines the influence of restaurant stimuli (i.e., chefs, service staff, other customers, food quality, and atmospherics) on diners’ emotions and loyalty to teppanyaki restaurants. In teppanyaki restaurants, chefs take orders from diners, prepare food in front of diners, and serve dishes to diners. Although the importance of chefs has been acknowledged by scholars, empirical research on the influence of chefs on diners has been scarce. To augment the literature on how chefs influence diners, this research incorporates “chef’s image” into an extended Mehrabian-Russell model (M-R model) to conceptualize diner loyalty to teppanyaki restaurants. A total of 308 diners from Taiwan were recruited. After examining their completed questionnaires, this study found that chef’s image, service quality, and food quality can affect the positive and negative emotions of diners. Moreover, other diners and restaurant atmospherics affect only the negative emotions of diners. Both positive and negative emotions can affect diner loyalty to teppanyaki restaurants. In addition, the managerial implications of this study are discussed.

The Business Case for (and Against) Restaurant Tipping

Recent interest in replacing tipping with service charges or higher service-inclusive menu pricing prompted this review of empirical evidence on the advantages and disadvantages to restaurants of these different compensation systems. The evidence indicates that these different pricing systems affect the attraction and retention of service workers, the satisfaction of customers with service, the actual and perceived costs of eating out, and the costs of hiring employees and doing business. However, the author comes away from the data believing that the biggest reason for restaurateurs to replace tipping is that the practice takes revenue away from them in the form of lower prices and gives it to servers in the form of excessively high tip income. The biggest reason for restaurateurs to keep tipping is that it allows them to reduce menu prices, which increases demand. Thus, restaurateurs’ decisions to keep voluntary tipping or not should ultimately depend on the relative strengths of these benefits. The more that a restaurant’s servers are overpaid relative to the back of house and the wealthier and less price-sensitive a restaurant’s customers are, the more the owner of that restaurant should consider abandoning tipping. By this reasoning, many upscale, expensive restaurants (especially those in states with no or small tip credits) probably should replace tipping with one of its alternatives.

Black-White Wage Gap Among Restaurant Servers: A Replication, Extension, and Exploration of Consumer Racial Discrimination in Tipping

There is a rich history of social science research centering on racial inequalities that continue to be observed across various markets (e.g., labor, housing, and credit markets) and social milieus. Existing research on racial discrimination in consumer markets, however, is relatively scarce and that which has been done has disproportionately focused on consumers as the victims of race-based mistreatment. As such, we know relatively little about how consumers contribute to inequalities in their roles as perpetrators of racial discrimination. In response, in this paper we elaborate on a line of research that is only in its’ infancy stages of development and yet is ripe with opportunities to advance the literature on consumer racial discrimination and racial earnings inequities among tip dependent employees in the United States. Specifically, we analyze data derived from a large exit survey of restaurant consumers (n=378) in an attempt to replicate, extend, and further explore the recently documented effect of service providers’ race on restaurant consumers’ tipping decisions. Our results indicate that both White and Black restaurant customers discriminate against Black servers by tipping them less than their White coworkers. Importantly, we find no evidence that this Black tip penalty is the result of interracial differences in service skills possessed by Black and White servers. We conclude by delineating directions for future research in this neglected but salient area study.

Pooling Restaurant Reservations to Increase Service Efficiency

This paper examines whether restaurant reservations should be locked to specific tables at the time the reservation is made, or whether the reservations should be pooled and assigned to tables in real-time. In two motivating studies, we find that there is a lack of consensus in the restaurant industry on handling reservations. Contrary to what might be expected based on research that shows the benefits of resource pooling in other contexts, a survey of 425 restaurants indicated that over 80% lock reservations to tables. In two simulation studies, we determine that pooling reservations enables a 15-minute reduction in table turn times more than 15% of the time, which consequently increases service efficiency and enables a restaurant to serve more customers during peak periods. Pooling had the most consistent advantage with higher customer service levels, with larger restaurants, with customers who arrive late, and with larger variation in customer arrival time.

Front end development for building automation systems using JavaScript Frameworks

Automation technologies are widely acclaimed to have the potential to significantly reduce energy consumption and energy-related costs in buildings. However, despite the abundance of commercially available technologies, automation in domestic environments keep on meeting commercial failures. The main reason for this is the development process that is used to build the automation applications, which tend to focus more on technical aspects rather than on the needs and limitations of the users. An instance of this problem is the complex and poorly designed home automation front-ends that deter customers from investing in a home automation product. On the other hand, developing a usable and interactive interface is a complicated task for developers due to the multidisciplinary challenges that need to be identified and solved. In this context, the current research work investigates the different design problems associated with developing a home automation interface as well as the existing design solutions that are applied to these problems. The Qualitative Data Analysis approach was used for collecting data from research papers and the open coding process was used to cluster the findings. From the analysis of the data collected, requirements for designing the interface were derived. A home energy management functionality for a Web-based home automation front-end was developed as a proof-of-concept and a user evaluation was used to assess the usability of the interface. The results of the evaluation showed that this holistic approach to designing interfaces improved its usability which increases the chances of its commercial success.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.