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Sneed, Phyllis JeanWork experience as evidence of competency in credentialing dietary managers /
Doctor of Philosophy, The Ohio State University, 1985, Graduate School

Committee:

Not Provided (Other)

Subjects:

Home Economics

Keywords:

Diet;Food service management;Food service management

Albanese, RobertA case study of an executive development program /
Doctor of Philosophy, The Ohio State University, 1962, Graduate School

Committee:

Not Provided (Other)

Subjects:

Economics

Keywords:

Executives;Assessment centers ;Food service management

Moharreri, KayhanAugmenting Collective Expert Networks to Improve Service Level Compliance
Doctor of Philosophy, The Ohio State University, 2017, Computer Science and Engineering
This research introduces and develops the new subfield of large-scale collective expert networks (CEN) concerned with time-constrained triaging which has become critical to the delivery of increasingly complex enterprise services. The main research contribution augments existing human-intensive interactions in the CEN with models that use ticket content and transfer sequence histories to generate assistive recommendations. This is achieved with a recommendation framework that improves the performance of CEN by: (1) resolving incidents to meet customer time constraints and satisfaction, (2) conforming to previous transfer sequences that have already achieved their Service Levels; and additionally, (3) addressing trust to encourage adoption of recommendations. A novel basis of this research is the exploration and discovery of resolution process patterns, and leveraging them towards the construction of an assistive resolution recommendation framework. Additional interesting new discoveries regarding CENs include existence of resolution workflows and their frequent use to carry out service-level-effective resolution on regular content. In addition, the ticket-specific expertise of the problem solvers and their dynamic ticket load were found to be factors in the time taken to resolve an incoming ticket. Also, transfers were found to reflect the experts' local problem-solving intent with respect to the source and target nodes. The network performs well if certain transfer intents (such as resolution and collective) are exhibited more often than the others (such as mediation and exploratory). The assistive resolution recommendation framework incorporates appropriate strategies for addressing the entire spectrum of incidents. This framework consists of a two-level classifier with the following parts: (1) content tagger for routine/non-routine classification, (2) A sequence classifier for resolution workflow recommendation, (3) Response time estimation based on learned dynamics of the CEN (i.e. Expertise, and ticket load), and (4) transfer intent identification. Our solution makes reliable proactive recommendations only in the case of adequate historical evidence thus helping to maintain a high level of trust with the interacting users in the CEN. By separating well-established resolution workflows from incidents that depend on experts’ experiential and `tribal' knowledge for the resolution, this research shows a 34% performance improvement over existing content-aware greedy transfer model; it is also estimated that there will be a 10% reduction in the volume of service-level breached tickets. The contributions are shown to benefit the enterprise support and delivery services by providing (1) lower decision and resolution latency, (2) lower likelihood of service-level violations, and (3) higher workforce availability and effectiveness. More generally, the contributions of this research are applicable to a broad class of problems where time-constrained content-driven problem-solving by human experts is a necessity.

Committee:

Jayashree Ramanathan (Advisor); Rajiv Ramnath (Committee Member); Srinivasan Parthasarathy (Committee Member); Gagan Agrawal (Committee Member)

Subjects:

Artificial Intelligence; Computer Science; Information Science; Information Technology

Keywords:

IT Service Management, Collective Expert Networks, Process Discovery, Ticket Routing Recommendations, Resolution Time Estimation, Event Mining, IT Service Support, Service Level Compliance, Human-in-the-loop, Learning from Enterprise Event Data

Stodnick, Todd MichaelDriving retail store peformance: a service profit chain perspective
Doctor of Philosophy, The Ohio State University, 2005, Business Administration
One service management model that has been gaining momentum in academic and practitioner circles alike is the service profit chain. First introduced in the early 1990’s, the service profit chain offers a structural framework to service management (Heskett et al, 1994). The theory basically asserts that providing employees with a superior internal working environment will lead to satisfied employees who are both loyal to the organization and able to provide the customer with an excellent service experience. Customers will recognize and value the outstanding service afforded them. Over time they will exhibit loyalty behaviors such as continued purchasing and increased referrals. These loyalty behaviors will generate both market share and profitability increases for the service firm. Despite its widespread adoption by many service industry leaders (e.g. Southwest Airlines, Progressive Insurance, etc) and a growing amount of academic literary attention to the topic, very little empirical research has attempted to validate the basic tenets within the service profit chain. As such, the primary objective of this research is to test the structural framework presented in the service profit chain. Two structural models, incorporating nine distinct hypotheses, are the means by which this objective is carried out. To support this primary objective, several secondary objectives must be met. Because this research will use several constructs that have yet to be rigorously validated, much time and attention must be devoted to scale development. The population frame used in this study will be one large retail chain within the women’s specialty apparel industry. Seven of the nine hypotheses are supported, two are not. The overall fit statistics of the two models employed suggest that the models do fit the data well, indicating support for the underlying theory behind the service profit chain. A summary of the hypotheses includes: 1.) internal service quality drives both employee satisfaction and loyalty, 2.) employee satisfaction drives employee loyalty 3.) total retail experience drives a customer’s perception of retail value and their satisfaction, 4.) customer satisfaction drives customer loyalty.

Committee:

David Collier (Advisor)

Subjects:

Business Administration, Management

Keywords:

Service Profit Chain; Service Management; Service Quality; Total Retail Experience; Structural Equation Modeling