TY - JOUR
T1 - Text analytics and new service development
T2 - a hybrid thematic analysis with systematic literature review approach
AU - Rouhani, Saeed
AU - Bozorgi, Saba Alsadat
AU - Amoozad Mahdiraji, Hannan
AU - Vrontis, Demetris
N1 - Publisher Copyright:
© 2024, Emerald Publishing Limited.
PY - 2024
Y1 - 2024
N2 - Purpose: This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones. Design/methodology/approach: This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings. Findings: The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total. Originality/value: This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.
AB - Purpose: This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones. Design/methodology/approach: This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings. Findings: The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total. Originality/value: This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.
KW - Sentiment analysis
KW - Service quality
KW - Systematic review
KW - Text analytics
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85204029562&partnerID=8YFLogxK
U2 - 10.1108/EMJB-01-2024-0017
DO - 10.1108/EMJB-01-2024-0017
M3 - Review article
AN - SCOPUS:85204029562
SN - 1450-2194
JO - EuroMed Journal of Business
JF - EuroMed Journal of Business
ER -