Modelling Consumer Knowledge: the Role of Ontology

Kudryavtsev, Dmitry; Gavrilova, Tatiana; Smirnova, Maria; Golovacheva, Ksenia S.

doi:10.1016/j.procs.2020.08.052

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…In modelling consumer knowledge, many steps are involved in ontology development. Those are ontology requirement specification, analysis of reusable knowledge resources, conceptualisation and formalisation, programme implementation and testing and validation (Kudryavtsev et al, 2020). In our study, the requirement is to understand the factors influencing housing type choices and a thorough analysis is conducted to understand the existing ontologies in high involvement product behaviours in the next stage.…”

Section: Implemented Methodologymentioning

confidence: 99%

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Arachchige

Quach

Roca

et al. 2022

J of Consumer Behaviour

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Adopting the conservation of resources theory, this research explores the joint influence of multiple factors affecting the choice of housing, an instance of a high involvement product. We develop an innovative Machine Learning approach to identify the most significant set of factors affecting consumers' housing choices. It was found that housing choices were primarily bound by energy resources and secondarily by other personal, conditional, and object resources. The study identified 25 key factors, many of which previous studies have not explored. The new factors include government payments, vehicle possession, time living with children, marriage duration, current location duration, and health conditions. The results suggest that joint effects of factors are more prominent than individual effects in influencing the choice of high‐involvement products. Further, the study suggests that ML methods are more robust than traditional methods and can be applied to analyse other types of high involvement products. The findings can assist real estate investors, policymakers and other stakeholders to understand sophisticated market behaviours to develop better and tailored housing strategies.

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Section: Implemented Methodologymentioning

confidence: 99%

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Arachchige

Quach

Roca

et al. 2022

J of Consumer Behaviour

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“…Ontology for opinion mining (Alfrjani et al, 2016) It is used to perform various information extraction tasks in order to improve products and services. RDF/RDFS, OWL, SPARQL, Linked data ONTARIS ontology (Ontology of Innovative Products and Services' Consumer Knowledge) (Kudryavtsev et al, 2020) ONTARIS ontology supports the design of innovative products and services; the design of a system of interaction between a company and a user.…”

Section: Owlmentioning

confidence: 99%

“…XML 7 (Osterwalder and Pigneur, 2002), (Gottgtroy and Gottgtroy, 2003), (Jarrar et al, 2003), (Osterwalder and Pigneur, 2003), (Caliusco et al, 2004), (Smirnov et al, 2005), (Cao et al, 2011) RDF/RDFS 22 (Jarrar et al, 2003), (Caliusco et al, 2004), (Baida et al, 2005), (Damme et al, 2007), (Lee et al, 2007), (Hepp, 2008), (Cao et al, 2011), (Ardito et al, 2011), (Liang et al, 2011), (Razmerita, 2011), (Wu, 2012), (Yılmaz and Alptekin, 2013), (Hladky and Maltseva, 2013), (Hladky et al, 2014), (Thakor and Sasi, 2015), (Alfrjani et al, 2016), (Bassiliades et al, 2018), (Haav and Maigre, 2018), (Kudryavtsev et al, 2020), (Ali et al, 2020), (Fensel et al, 2020), (Blankenberg et al, 2022) OWL 31 (Caliusco et al, 2004), (Lee et al, 2007), (Goy et al, 2008), (Hepp, 2008), (Frasincar et al, 2009), , (Yan and Zha, 2010), (Picot-Clémente et al, 2010), (Cao et al, 2011),…”

Section: Semantic Web Technologies Count Of Publications Publicationsmentioning

confidence: 99%

“…Improved pricing 1 (Wang and Zhou, 2014) Improved sales force efficiency and effectiveness 1 (Hladky et al, 2014) Improved products and services 2 (Alfrjani et al, 2016), (Ali et al, 2020) Innovative products and services 1 (Kudryavtsev et al, 2020) Customer complaint management 2 (Jarrar et al, 2003), (Thakor and Sasi, 2015) Customer segmentation 1 (Yılmaz and Alptekin, 2013) Supporting users of CRM systems 11 (Osterwalder and Pigneur, 2002), (Gottgtroy and Gottgtroy, 2003), (Osterwalder and Pigneur, 2003), (Caliusco et al, 2004), (Damme et al, 2007), (Yan and Zha, 2010), , (Wu, 2012), (Tiryaki and Atalay, 2020), (Blankenberg et al, 2022), (Fernández-Cejas et al, 2022) Supporting CRM projects 1 (Lee et al, 2007) Integration of information in a CRM system 2 (Hladky and Maltseva, 2013), (Tiryaki and Atalay, 2020) Description of business processes 2 (Goy et al, 2008), (Magro and Goy, 2012) Personalization of business processes 1 (Liang et al, 2011) Social branding 1 (Zailskaitė-Jakštė and Damaševičius, 2014) From the data represented in Table 4, it can be concluded that each of the listed CRM benefits B1-B8 is addressed in at least one study selected for the systematic literature review.…”

Section: Benefits For Crm Count Of Publications Publicationsmentioning

confidence: 99%

“…In (Kudryavtsev et al, 2020), the creation of an ontology of user knowledge for innovative products and services is proposed. Its main aim is to support the design of innovative products and services; the facilitation of research activities and the integration of research data.…”

Section: Benefits For Crm Count Of Publications Publicationsmentioning

confidence: 99%

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The purpose of the present paper is to summarize the current state of the existing research on the application of Semantic web technologies in supporting customer relationship management. Its achievement includes providing answers to a set of research questions as a result of conducting a systematic literature review. A total of 44 scientific publications are identified as relevant to the topic and included in the review. Information is extracted from the selected literature sources, which is then summarized, systemized and analyzed according to the predefined research questions and finally reported. The conducted systematic literature review determines that the development of Semantic web technologies is provoked interest among researchers, as a result of which the advantages of using them for descriptions useful for various CRM purposes are investigated and practically confirmed. In addition to defining semantic models for descriptions supporting a variety of CRM activities and processes (such as customized products and services; supporting users of CRM systems; integrated offerings across channels; improved and innovative products and services; customer complaint management, etc.), various research works identify new approaches to support CRM, that can be achieved through the application of appropriate Semantic web technologies. The detailed study represented in this paper contributes to familiarization with the existing experience in the application of Semantic web technologies in supporting customer relationship management, as well as facilitates the discovery of trends and directions for future research. This is the reason for the expected interest from scientists whose research area cover the considered and similar fields; software engineers implementing CRM systems; data analysts exploring CRM domain.

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Application of Classification to Determine the Level of Awareness of the Foresight Process

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2022

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Modelling Consumer Knowledge: the Role of Ontology

Cited by 9 publications

References 14 publications

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Application of Semantic Web Technologies for Supporting Customer Relationship Management: a Systematic Literature Review

Application of Classification to Determine the Level of Awareness of the Foresight Process

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