Yuliya Kolomoyets

Kolomoyets, Yuliya
  • Assistant Professor
Tourism and Service Management

Short BIO

Yuliya Kolomoyets is an Assistant Professor at the School of Tourism and Service Management. Yuliya joined Modul University in 2015 as a Researcher and Lecturer and PhD candidate. In 2021 she was awarded PhD in Socio-Economic Science and Business from Modul University (with honors. Title: Comprehending, Evaluating, and Recovering the Value of Service Experiences). Yuliya also holds an Erasmus Mundus European Master in Tourism Management Program jointly offered by the University of Southern Denmark, University of Ljubljana, and University of Girona (with honors. Title: Effect of Gamification on Tourism Experiences). Before joining Modul University, Yuliya has gathered practical experience working in education, travel online media and hospitality in Ukraine, USA, and Hungary.

Research

Yuliya’s research evolves around value co-creation, in particular the role of digital technology in understanding (digital) consumer behavior, capturing service value and designing engaging experiences for tourism destination, hospitality, museums and service at large. She works with a variety of quantitative methodologies, such as text mining, surveys, experiments, but also qualitative methodologies. Yuliya is a certified Lego © Serious Play © facilitator.

Yuliya’s research has been published in the Journal of Business Research, presented at international scientific conferences, like the Interactive marketing research conference (IMRC), SERVSIG, EMAC and TTRA Europe, and recognized with the runner-up award at the IFITT 2019 PhD summer school and ENTER 2019 PhD Workshop. Yuliya also serves as an ad-hoc reviewer for academic journals and conferences.

Current/recent projects:

  • SmartCulTour: Smart Cultural Tourism as a Driver of Sustainable Development of European Regions. Lalicic, L., von Zumbusch, J., Dickinger, A. & Kolomoyets, Y. European Commission. (01/01/2020 → 30/06/2023)
  • DIGITAL-2021-PREPACT: Preparations for the Dataspace for Tourism. Stienmetz, J. L., Dan, D., Dickinger, A., Gunter, U., Kolomoyets, Y., Wöber, K. & Zekan, B. European Commission (01/11/2022 → 31/10/2023)

Recent publications:

  • Kolomoyets, Y., & Dickinger, A. (2023). Understanding value perceptions and propositions: A machine learning approach. Journal of Business Research, 154, 113355.

Courses

  • Tourism and Hospitality Business Analysis
  • Designing Experiences for Tourism and Events
  • Information Technology for Tourism
  • Digital Consumer Behavior
  • Social Media Marketing

Projects

Yuliya Kolomoyets

Doctoral Thesis

Comprehending, Evaluating, and Recovering the Value of Service Experiences

Organisations
MODUL University Vienna, School of Tourism and Service Management
Date
2021
Managed By
MODUL University Vienna

The preamble and the three studies of the dissertation are designed to answer two research questions. First, "What is the anatomy of service experience value from customer and provider perspectives?, followed by "How do contextual factors influence the service providers' capacity to recover value when services fail?" The preamble introduces the concept of value, outlining its evolution, emphasizing the critical disparities among the dominant research perspectives, and justifying the urgency of the present research. The dissertation adopts the Grönroos-Voima value model to tap into value co-creation processes across three spheres: customers, service providers, and joint sphere. This is done by uncovering the structure of the customer value-in-use perceptions (Study 1), evaluating service providers' value proposition against those perceptions (Study 2), and assessing the impact of service recovery actions on customers forgiveness and service expectations in the context of varying prior service experiences and harm direction (Study 3). The dissertation identified the fifteen key attributes describing the value of the hotel experiences and uncovered the critical discrepancies between the value perceptions and value proposition narratives. It also determined the varying effect of prior service recovery experience and service recovery actions on forgiveness on victims and observers of service transgressions. Each study outlined the limitations of the empirical research along with the theoretical and managerial implications of the findings.


Yuliya Kolomoyets

Abstract

Please Forgive Me: Victims’ versus Observers’ Perspective on the Service Recovery Process

Organisations
MODUL University Vienna, School of Tourism and Service Management
Date
2022
Managed By
MODUL University Vienna

This experimental study examined how service recovery strategies (apology, apology with compensation, apology with excuse) affect forgiveness among victims and observers of service failure and recovery experiences in the hotel context. The results of surveying 471 respondents revealed the disproportional effect of recovery strategies on emotional and decisional forgiveness among victims and observers. Apology and compensation were the most compelling strategies for both groups, with the effect stronger for observers. Only for the latter recovery, specifically, apology and excuse led to emotional forgiveness. The study confirms the spillover effect of both tangible and psychological recovery strategies on customers indirectly involved with service failure and recovery experiences. Moreover, given the capacity of the psychological recoveries, such as an apology and excuse to facilitate emotional forgiveness among observers, service providers should pay special attention to the training of the frontline staff and create a welcoming and friendly climate throughout the customer journey.


Yuliya Kolomoyets

Abstract

Exploring consumer's perception of service quality through online reviews: Text mining approach

Organisations
MODUL University Vienna
Date
2018
Managed By
MODUL University Vienna

Online reviews are an important source of consumer opinions about the service experiences across industries. Growing number of research investigate the potential of online reviews to explain consumer behaviour and to predict business performance, while only few engage into the examination of the textual content of the reviews. Large volume and unstructured format of the textual data make it unamenable to analysis with traditional methods like survey. To fill the emerging gap, this study will rely on automated text mining methodology to explore how consumers reflect on the perceived service quality in the online reviews. Specifically, sentiment analysis, text classification and regression modelling will be applied to investigate the dimensions of perceived service quality together with their contribution to the overall rating of service experience (e.g. star rating). The results are expected to extend the understanding of perceived service quality; to illustrate the value of the automated text mining methods for maximising the usefulness of online reviews and helping businesses reach their strategic goals.


Yuliya Kolomoyets, Astrid Dickinger

Article

Understanding value perceptions and propositions: A machine learning approach

Organisations
MODUL University Vienna, School of Tourism and Service Management
Date
1.2023
Managed By
MODUL University Vienna

It is well established in marketing literature that aligning value creation with customers' aspirations promotes satisfaction, repurchasing, and competitiveness. This study employs structural topic modeling and sentiment analyses on online documents to provide an empirical account of value alignment from customers' and service providers' perspectives. This generates insights into i) the attributes valued by customers and service providers, respectively, ii) the valence of those attributes, iii) the sources of value formation, iv) the value alignment between customers and service providers, and v) the relative importance of value attributes for budget and upscale hotels. The results indicate that guests focus on interaction, cleanliness, and comfort, while service providers most frequently discuss service-related aspects; however, the first two attributes also affect hotel ratings. Furthermore, the sources of value differ in terms of valence. These insights show that structural topic modeling is a scalable approach to understanding value from both perspectives.


Yuliya Kolomoyets, Astrid Dickinger

Conference contribution

A Text Mining Approach to Measuring and Predicting Perceived Service Quality from Online Chatter

Organisations
MODUL University Vienna, School of Tourism and Service Management
Date
2019
Managed By
MODUL University Vienna


Yuliya Kolomoyets

Abstract

A Text Mining Approach to Measuring & Predicting Perceived Service Quality from Online Chatter

Organisations
MODUL University Vienna
Date
2018
Managed By
MODUL University Vienna

Online reviews have been studied for their potential to explain consumer behaviour and to predict business performance. However, large volume and unstructured format of the textual part of the reviews make them unamenable to analysis with traditional methods like survey. By employing the automated text mining methodology this study explores how consumers reflect on the perceived service quality in the online reviews. Specifically, sentiment analysis, text classification and predictive modelling will be applied to investigate the dimensions of perceived service quality together with their contribution to the overall rating of service experience. The results are expected to extend the understanding of perceived service quality; to illustrate the value of the automated text mining methods for maximising the usefulness of online reviews and helping businesses reach their strategic goals.


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