TaxoPublish: Towards a solution to automatically personalize taxonomies in e-catalogs

Angermann, Heiko; Ramzan, Naeem

doi:10.1016/j.eswa.2016.08.058

Cited by 4 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the expert system proposed by Lee and Kwon (2008) can provide intelligent support in transactions negotiation using advanced reasoning logic. Further, Angermann and Ramzan (2016) proposed an expert system that serves the customer side – using the knowledge provided by the CRM system to predict customer preferences. This system predicts customer needs in a B2B environment, generates personalized electronic catalogs for customers and increases the probability of sales occurring.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

Chen

Jiang

Jia

et al. 2021

JBIM

View full text Add to dashboard Cite

Purpose The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing. Design/methodology/approach A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed. Findings This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing. Originality/value This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

Chen

Jiang

Jia

et al. 2021

JBIM

View full text Add to dashboard Cite

show abstract

“…The dependence between sibling concepts can vary from being more likely an antonym (e.g., "Coffee" and "Beer") or synonym (e.g., "Coffee" and "Tea"), and being a hypernym ("Hot Drink") or hyponym ("Decaffeinated") to other sibling concepts (see Fig. 2) [6].…”

Section: Taxonomic Dependenciesmentioning

confidence: 99%

“…This results in the reduction of sub ( sub ) and super ( super ) concepts, and finally the reduction of the complete taxonomy ( ), respectively. The reduction efficiency is in addition compared with the latest work on personalized directories, presented by [6], in the following named Personalized Directories. We do this by comparing their proposed operations to reduce the taxonomy, with the operations proposed in the work at hand.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Personalized directories are using different taxonomy modification rules, i. a. operations to represent the taxonomy according to a user-specific requirement, e.g. in [6,21,22,28,34,47,62]. Hereby, the main focus is to effectively reduce the distance of preferred concepts to the root distance.…”

Section: Analytical Techniques Paradigmsmentioning

confidence: 99%

“…Techniques of personalized directories are overcoming most of the before-mentioned problems, as those are aiming to modify the taxonomy according to users' personal requirements, e.g. in [6,21,22,28,34,47,62]. The most recent technique is providing various modification rules on subsets of concepts, which allows to not semantically reduce the taxonomy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TaxoMulti: Rule-Based Expert System to Customize Product Taxonomies for Multi-Channel E-commerce

Angermann

2022

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

Taxonomies are used as product categories to facilitate users navigating through an e-commerce portal with the help of hierarchically structured concepts. However, the identical taxonomy is shown to each customer regardless of the channel used. This is challenging for the customers in terms of user experience, as the screen size is rigid, and has not a flexible format like a printed catalog. Simply reducing the taxonomy as suggested in existing works is not sufficient, as it leads to semantic misrepresentation of the product domain. To overcome the inflexibility of product taxonomies, the rule-based expert system TaxoMulti is presented in this paper. The main objectives of our descriptive research are the formulation of the taxonomy over- and undersize problem in multi-channel context, before different types of flexible mediator concepts are discussed that allow overcoming these challenges. Using our novel method, marketing experts can now provide different taxonomies including the same semantics to be shown on different channels. The method is implemented using logic programming, allowing the integration of an inference engine utilizing background knowledge without changing the underlying logic of the used information (management) system. The comprehensive experiments on three public and private databases highlight the improvement when adding different types of mediator concepts for the adaption process. Compared to existing best performing works in related fields, TaxoMulti has achieved an improvement of + 26.31 % for the reduction of the taxonomy, + 60 % for the enlargement of the taxonomy, and + 21.21 % in terms of flexibility.

show abstract

Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning

2023

View full text Add to dashboard Cite

Digital technologies have the potential to transform all aspects of firms’ operations. The emergence of advanced digital technologies such as Artificial Intelligence and Machine Learning raises questions about whether and when micro-businesses should adopt these technologies. In this paper we focus on how firms’ adoption decisions on Artificial Intelligence and Machine Learning influence their innovation capabilities. Using survey data for over 6,000 micro-businesses in the UK, we identify two groups of adopters based on the timing of their adoption of Artificial Intelligence and Machine Learning. ‘first movers’ – early adopters of the new technologies - and ‘second movers’- later adopters of the new technology. Probit models are used to investigate the innovation benefits of first and second mover adoption strategies. Our results suggest strong and positive impacts of adopting Artificial Intelligence and Machine Learning on micro-businesses’ innovation outcomes and innovation processes. We highlight the differential benefits of first mover and second mover strategies and highlight the role of technology characteristics as the differentiating factor. Our results emphasize both the innovation enabling role of digital technologies and the importance of an appropriate strategic approach to adopting advanced digital technologies.

show abstract

TaxoPublish: Towards a solution to automatically personalize taxonomies in e-catalogs

Cited by 4 publications

References 32 publications

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

TaxoMulti: Rule-Based Expert System to Customize Product Taxonomies for Multi-Channel E-commerce

Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning

Contact Info

Product

Resources

About