Creating Memories: The Interactive Process of Service Delivery

A model is presented that captures the complex nature of the service experience in an attempt to aid in the design, development and evaluation of service delivery personnel and systems.

Model of Service Quality: Customer Loyalty for Hotels

The authors investigate the relationship between loyalty and perceived service quality of hotel customers and discus managerial implications to develop strategies to enhance loyalty of hotel customers. A survey was conducted among customers in the San Francisco Bay Area. Results indicate that customer loyalty is dependent on perceived service quality which is observed in terms of timelines, facilities, and ambience.

Examining the Impact of Service Times on Overall Guest Satisfaction Perception in the Casual Dining Environment

A mystery shopper study was used to examine the influence of service times on customer satisfaction. The impact of management emphasis on service quality was also examined. In the restaurants studied, service time influenced customer satisfaction. Management attention to service time improved performance in direct relationship to the level of emphasis.

The Influence of Selected Antecedents of Frontline Employee's Perceptions of Service Recovery Performance

The purpose of this study is to determine the potential impact of selected organizational factors on boundary-spanning-role employees’ perceptions of service recovery performance. This study also aims to assess the impact of service recovery performance on the intention to leave the job and extrinsic job satisfaction. This study uses a sample of frontline employees in Belek, Antalya, Turkey. The empirical findings revealed that education, team work and role ambiguity as frontline job perceptions were found to exert positive influences on the service recovery performance, but, empowerment, reward, and organizational commitment were found to have negative effects on the service recovery performance.

Implementation of service -learning in higher education courses: Perceptions of faculty

This study examined the motivation of college and university faculty to implement service-learning into their traditional courses. The benefits derived by faculty, as well as those issues of maintenance, including supports and/or obstacles, were also investigated in relation to their impact on motivation. The focus was on generating theory from the emerging data. ^ Data were collected from interviews with 17 faculty teaching courses that included a component of service-learning. A maximum variation sampling of participants from six South Florida colleges and universities was utilized. Faculty participants represented a wide range of academic disciplines, faculty ranks, years of experience in teaching and using service-learning as well as gender and ethnic diversity. For data triangulation, a focus group with eight additional college faculty was conducted and documents, including course syllabi and institutional service-learning handbooks, collected during the interviews were examined. The interviews were transcribed and coded using traditional methods as well as with the assistance of the computerized assisted qualitative data analysis software, Atlas.ti. The data were organized into five major categories with themes and sub-themes emerging for each. ^ While intrinsic or personal factors along with extrinsic factors all serve to influence faculty motivation, the study's findings revealed that the primary factors influencing faculty motivation to adopt service-learning were those that were intrinsic or personal in nature. These factors included: (a) past experiences, (b) personal characteristics including the value of serving, (c) involvement with community service, (d) interactions and relationships with peers, (e) benefits to students, (f) benefits to teaching, and (g) perceived career benefits. Implications and recommendations from the study encompass suggestions for administrators in higher education institutions for supporting and encouraging faculty adoption of service-learning including a well developed infrastructure as well as incentives, particularly during the initial implementation period, rewards providing recognition for the academic nature of service-learning and support for the development of peer relationships among service-learning faculty. ^

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

Palliative Medicine:Scoping the level and extent of service provision to people with intellectual disability at end of life Poster Presentation

Background: Concerns exist about the end of life care
that people with intellectual disabilities receive. This population
are seldom referred to palliative care services and
inadequate data sets exist about their place of death.
Aim: To scope the extent of service provision to people
with intellectual disabilities at the end of life by specialist
palliative care and intellectual disability services in one
region of the United Kingdom.
Methods: As part of a larger doctoral study a regional survey
took place of a total sample (n=66) of specialist palliative
care and intellectual disability services using a postal
questionnaire containing forty items. The questionnaire
was informed by the literature and consultation with an
expert reference group. Data were analysed using SPSS to
obtain descriptive statistics.
Results: A total response rate from services of 71.2%
(n=47) was generated. Findings showed a range of experience
among services in providing end of life care to people
with intellectual disabilities in the previous five years, but
general hospitals were reported the most common place of
death. A lack of accessible information on end of life care
for people with learning disabilities was apparent. A few
services (n=14) had a policy to support this population to
make decisions about their care or had used adapted Breaking
Bad News guidelines (n=5) to meet their additional
needs. Both services recognised the value of partnership
working in assessing and meeting the holistic needs of
people with intellectual disabilities at end of life.
Conclusions: A range of experience in caring for people
with intellectual disabilities was present across services,
but more emphasis is required on adapting communication
for this population to facilitate them to participate in their
care. These findings could have international significance
given that studies in other countries have highlighted a
need to widen access to palliative care for this group of
people.

Justified assessments of service provider reputation

Abstract Reputation, influenced by ratings from past clients, is crucial for providers competing for custom. For new providers with less track record, a few negative ratings can harm their chances of growing. In the JASPR project, we aim to look at how to ensure automated reputation assessments are justified and informative. Even an honest balanced review of a service provision may still be an unreliable predictor of future performance if the circumstances differ. For example, a service may have previously relied on different sub-providers to now, or been affected by season-specific weather events. A common way to ameliorate the ratings that may not reflect future performance is by weighting by recency. We argue that better results are obtained by querying provenance records on how services are provided for the circumstances of provision, to determine the significance of past interactions. Informed by case studies in global logistics, taxi hire, and courtesy car leasing, we are going on to explore the generation of explanations for reputation assessments, which can be valuable both for clients and for providers wishing to improve their match to the market, and applying machine learning to predict aspects of service provision which may influence decisions on the appropriateness of a provider. In this talk, I will give an overview of the research conducted and planned on JASPR. Speaker Biography Dr Simon Miles Simon Miles is a Reader in Computer Science at King's College London, UK, and head of the Agents and Intelligent Systems group. He conducts research in the areas of normative systems, data provenance, and medical informatics at King's, and has published widely and manages a number of research projects in these areas. He was previously a researcher at the University of Southampton after graduating from his PhD at Warwick. He has twice been an organising committee member for the Autonomous Agents and Multi-Agent Systems conference series, and was a member of the W3C working group which published standards on interoperable provenance data in 2013.

Accounting for the Multi-Period Impact of Service When Determining Employee Requirements for Labor Scheduling

Providing good customer service, inexpensively, is a problem commonly faced by managers of service operations. To tackle this problem, managers must do four tasks: forecast customer demand for the service; translate these forecasts into employee requirements; develop a labor schedule that provides appropriate numbers of employees at appropriate times; and control the delivery of the service in real-time. This paper focuses upon the translation of forecasts of customer demand into employee requirements. Specifically, it presents and evaluates two methods for determining desired staffing levels. One of these methods is a traditional approach to the task, while the other, by using modified customer arrival rates, offers a better means of accounting for the multi-period impact of customer service. To calculate the modified arrival rates, the latter method reduces (increases) the actual customer arrival rate for a period to account for customers who arrived in the period (in earlier periods) but have some of their service performed in subsequent periods (in the period). In an experiment simulating 13824 service delivery environments, the new method demonstrated its superiority by serving 2.74% more customers within the specified waiting time limit while using 7.57% fewer labor hours.

Applying Career Concepts to Strengthen the Work-Attitudes of Service Professionals

Given the nature of employment relationships today, service organizations can strengthen the organization commitment levels and reduce the turnover intentions of its professionals through providing job features important to their careers. These features include opportunities to perform challenging work, experience trusting relationships with customers/clients, and obtain extrinsic rewards. Using a sample of alumni from a hospitality business program, hypotheses that these features impact organizational commitment and turnover intentions, partially through strengthening professionals' career commitment, are developed and tested. Findings suggest that challenging work opportunities impact these attitudes both directly and indirectly. So too trusting relationships with customers and clients indirectly impact organization commitment and intent to turnover (ITO). Results also suggest that, as a whole, satisfaction with extrinsic rewards has no effect. However, an analysis of multigroup mediation results revealed that for professionals working in professional service firms, satisfaction with pay reduces both attitudes. Implications for research in organization commitment and ITO, specifically the role and impact of career-based antecedents, are discussed.

Shining Examples of Service When the Lights Went Out: Hotel Employees and Service Recovery during the Blackout of 2003

The electrical outage in the summer of 2003 that interrupted power to thousands of hotels wrought a variety of facilities failures and service-process problems. Fortunately, strong service-recovery efforts from hotel employees mitigated the worst of the blackout’s effects. Using survey data from hotel managers who experienced the blackout, this study highlights those employee actions that most contributed to immediate service recovery; however, the study also reveals limited organizational learning or efforts to failsafe hospitality service from the eventuality of future power failures.