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헬스케어 서비스 리뷰를 활용한 서비스 품질 차원 별 중요 단어 파악 방안

Keyword identifications on dimensions for service quality of Healthcare providers

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참고문헌

키워드

주제분류경영학

초록

Studies on online review have carried out analysis of the rating and topic as a whole. However, it is necessary to analyze opinions on various dimensions of service quality. This study classifies reviews of healthcare services into service quality dimensions, and proposes a method to identify words that are mainly referred to in each dimension. Service quality was based on the dimensions provided by SERVQUAL, and patient reviews have collected from NHSChoice. The 2,000 sentences sampled were classified into service quality dimension of SERVQUAL and a method of extracting important keywords from sentences by service quality dimension was suggested. The RAKE algorithm is used to extract key words from a single document and an index is considered to consider frequently used words in various documents. Since we need to identify key words in various reviews, we have considered frequency and discrimination (IDF) at the same time, rather than identifying key words based only on the RAKE score. In SERVQUAL dimension, we identified the words that patients mentioned mainly, and also identified the words that patients mainly refer to by review rating.

Abstract

Studies on online review have carried out analysis of the rating and topic as a whole. However, it is necessary to analyze opinions on various dimensions of service quality. This study classifies reviews of healthcare services into service quality dimensions, and proposes a method to identify words that are mainly referred to in each dimension. Service quality was based on the dimensions provided by SERVQUAL, and patient reviews have collected from NHSChoice. The 2,000 sentences sampled were classified into service quality dimension of SERVQUAL and a method of extracting important keywords from sentences by service quality dimension was suggested. The RAKE algorithm is used to extract key words from a single document and an index is considered to consider frequently used words in various documents. Since we need to identify key words in various reviews, we have considered frequency and discrimination (IDF) at the same time, rather than identifying key words based only on the RAKE score. In SERVQUAL dimension, we identified the words that patients mentioned mainly, and also identified the words that patients mainly refer to by review rating.

참고문헌 (36)

  1. 김상만, "의료서비스산업에서의 고객지식 획득과 활용방안 : 기대 불일치 이론을 중심으로", 지식경영연구, 11(3), pp.59~76 (2010), 한국지식경영학회[DOI]
  2. 김상만, "Online 의료웹서비스 품질과 지식제공성과의 관계 연구", 지식경영연구, 11(1), pp.1~17 (2010), 한국지식경영학회[DOI]
  3. 박명석, "댓글이 음원 판매량에 미치는 차별적 영향에 관한텍스트마이닝 분석", 지식경영연구, 19(2), pp.91~108 (2018), 한국지식경영학회[DOI]
  4. Ruth Angelie B. Cruz, "The Brand Personality Effect: Communicating Brand Personality on Twitter and its Influence on Online Community Engagement", 지능정보연구, 20(1), pp.67~101 (2014), 한국지능정보시스템학회[DOI]
  5. Akter, S., "Service quality of mHealth platforms: development and validation of a hierarchical model using PLS", Electronic Markets, 20(3-4), pp.209~227 (2010)
  6. Andrade M., "Automatic extraction of keywords from scientific text:application to the knowledge domain of protein families", Bioinformatics, 14(7), pp.600~607 (1998)
  7. Babakus, E., "Adapting the SERVQUAL scale to hospital services: an empirical investigation", Health Services Research, 26(6), pp.767~786 (1992)
  8. Baeza-Yates, R., "Modern Information Retrieval: The Concepts and Technology Behind Search" (2010), Addison Wesley
  9. Cao, Q., "Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach", Decision Support Systems, 50(2), pp.511~521 (2011)
  10. Chen, K., "Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches", Electronic Commerce Research and Applications, 14(1), pp.58~74 (2015)
  11. "slowraker: A Slow Version of the Rapid Automatic Keyword Extraction (R AKE) Algorith"[URL]
  12. Cronin, J. J. Jr., "Measuring service quality: a reexamination and extension", Journal of Marketing, 56(3), pp.55~68 (1992)
  13. Dagger, T. S., "A hierarchical model of health service quality: scale development and investigation of an integrated model", Journal of Service Research, 10(2), pp.123~142 (2007)
  14. Dean, A. M, "The applicability of SERVQUAL in different health care environments", Health Marketing Quarterly, 16(3), pp.1~21 (1999)
  15. Ghose, A., "Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics", IEEE Transactions on Knowledge and Data Engineering, 23(10), pp.1498~1512 (2011)
  16. Greaves, F., "Associations between Internet-based patient ratings and conventional surveys of patient experience in the English NHS: an observational study", BMJ Quality & Safety, 21(7), pp.600~605 (2012)
  17. Greaves, F., "Harnessing the cloud of patient experience: using social media to detect poor quality healthcare", BMJ Quality & Safety, 22(3), pp.251~255 (2013)
  18. Greaves, F., "Tweets about hospital quality: a mixed methods study", BMJ Quality & Safety, 23(10), pp.838~846 (2014)
  19. Grönroos, C, "A service quality model and its marketing implications", European Journal of Marketing, 18(4), pp.36~44 (1984)
  20. Hu, N., "Overcoming the J-shaped Distribution of Product Reviews", Communications of the ACM, 52(10), pp.144~147 (2009)
  21. Hulth, A., "Improved automatic keyword extraction given more linguistic knowledge", Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (2003)
  22. Humphreys, A., "Automated Text Analysis for Consumer Research", Journal of Consumer Research, 44(6), pp.1274~1306 (2018)
  23. Ladhari, R, "A review of twenty years of SERVQUAL research", International Journal of Quality and Service Sciences, 1(2), pp.172~198 (2009)
  24. Lam, S. S, "SERVQUAL: Atool for measuring patients’ opinions of hospital service quality in Hong Kong", Total Quality Management, 8(4), pp.145~152 (1997)
  25. Lee, H., "Methods of measuring health-care service quality", Journal of Business Research, 48(3), pp.233~246 (2000)
  26. Lehtinen, U., "Service quality: a study of quality dimensions" (1982), Service Management Institute
  27. Netzer, O., "Mine Your Own Business:Market-Structure Surveillance Through Text Mining", Marketing Science, 31(3), pp.521~543 (2012)
  28. Mihalcea, R., "Textrank:Bringing order into texts", Proceedings of EMNLP 2004 (2004)
  29. Ohsawa, Y., "Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor", Proceedings of ADL’98 (1998)
  30. Palese, B., "The relative importance of service quality dimensions in E-commerce experiences", International Journal of Information Management, 40, pp.132~140 (2018)
  31. Parasuraman, A., "A conceptual model of service quality and its implications for future research", Journal of Marketing, 49(4), pp.41~50 (1985)
  32. Parasuraman, A., "SERVQUAL: A Multiple-Item Scalefor Measuring Consumer Perceptions of Service Quality", Journal of Retailing, 64(1), pp.12~40 (1988)
  33. Park, G. -W., "Patient-centric quality assessment framework for healthcare services", Technological Forecasting and Social Change, 113, pp.468~474 (2016)
  34. Stuart R., "Text Mining:Applications and Theory" (2010), John Wiley & Sons
  35. Sofaer, S., "Patient perceptions of the quality of health services", Annual Review of Public Health, 26, pp.513~559 (2005)
  36. Zeithaml, V. A, "Service quality, profitability, and the economic worth of customers: what we know and what we need to learn", Journal of the academy of marketing science, 28(1), pp.67~85 (2000)

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