{"title":"Modeling the key attributes for improving customer repurchase rates through mobile advertisements using a Fuzzy mixed approach","authors":"","doi":"10.1108/jm2-02-2023-0022","DOIUrl":null,"url":null,"abstract":"\nPurpose\nDue to the high use of mobile devices, the market share of mobile advertisements (Ads) is significantly growing. Although mobile Ads can contact potential customers at any time and in any location depending on their unique demands, one of the biggest problems for advertisers is how to improve customer repurchases with their Ads. The development and empirical support of customer repurchase through mobile Ads context have not been addressed. Therefore, the purpose of this paper is to define and identify the key attributes of customer repurchase in a mobile Ads context.\n\n\nDesign/methodology/approach\nIn this research, the set of attributes was derived from a systematic literature review and finalized by applying the Fuzzy Delphi method. To develop a hierarchical model and classify the cause/effect groups among identified key attributes, the Fuzzy mixed approach uses a combination of Fuzzy interpretive structural modeling-decision-making trial and evaluation laboratory.\n\n\nFindings\nThe findings suggest that language, type of website and social media are classified to as essential attributes for improving customer repurchase through mobile Ads.\n\n\nResearch limitations/implications\nThe focus of the current research is limited to identify and develop the hierarchical interrelationships between customer repurchase attributes that are unique to the mobile Ads business context. Additional research may be conducted for various media contexts and other products/services categories.\n\n\nOriginality/value\nThis study illustrated how multicriteria decision-making techniques could be used effectively using Fuzzy theory to explore the research area of customer repurchase in mobile Ads concept.\n","PeriodicalId":16349,"journal":{"name":"Journal of Modelling in Management","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modelling in Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jm2-02-2023-0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
Due to the high use of mobile devices, the market share of mobile advertisements (Ads) is significantly growing. Although mobile Ads can contact potential customers at any time and in any location depending on their unique demands, one of the biggest problems for advertisers is how to improve customer repurchases with their Ads. The development and empirical support of customer repurchase through mobile Ads context have not been addressed. Therefore, the purpose of this paper is to define and identify the key attributes of customer repurchase in a mobile Ads context.
Design/methodology/approach
In this research, the set of attributes was derived from a systematic literature review and finalized by applying the Fuzzy Delphi method. To develop a hierarchical model and classify the cause/effect groups among identified key attributes, the Fuzzy mixed approach uses a combination of Fuzzy interpretive structural modeling-decision-making trial and evaluation laboratory.
Findings
The findings suggest that language, type of website and social media are classified to as essential attributes for improving customer repurchase through mobile Ads.
Research limitations/implications
The focus of the current research is limited to identify and develop the hierarchical interrelationships between customer repurchase attributes that are unique to the mobile Ads business context. Additional research may be conducted for various media contexts and other products/services categories.
Originality/value
This study illustrated how multicriteria decision-making techniques could be used effectively using Fuzzy theory to explore the research area of customer repurchase in mobile Ads concept.
期刊介绍:
Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.