Suchergebnisse
Filter
8 Ergebnisse
Sortierung:
SSRN
Working paper
SSRN
Working paper
Ontology management and evolution for business intelligence
In: International journal of information management, Band 30, Heft 6, S. 559-566
ISSN: 0268-4012
The generic information business model
In: International journal of information management, Band 23, Heft 4, S. 323-336
ISSN: 0268-4012
Data Base Management Systems (DBMSs): Meeting the requirements of the EU data protection legislation
In: International journal of information management, Band 23, Heft 3, S. 185-199
ISSN: 0268-4012
Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach
In: Journal of service research, Band 17, Heft 3, S. 278-295
ISSN: 1552-7379
Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.
Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach
In: Ordenes , F V , Theodoulidis , B , Burton , J , Gruber , T & Zaki , M 2014 , ' Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach ' Journal of Service Research , vol 17 , no. 3 , pp. 278-295 . DOI:10.1177/1094670514524625
Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment. © The Author(s) 2014.
BASE
Customer Engagement in a Big Data World
In: Journal of Services Marketing (Forthcoming)
SSRN