Modeling the key attributes for improving customer repurchase rates through mobile advertisements using a Fuzzy mixed approach

IF 1.8 Q3 MANAGEMENT
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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.
利用模糊混合方法对移动广告提高客户回头率的关键属性进行建模
由于移动设备的高度使用,移动广告(Ads)的市场份额正在显著增长。虽然移动广告可以根据潜在客户的独特需求随时随地联系到潜在客户,但广告主面临的最大问题之一是如何通过广告提高客户的再购买行为。通过移动广告环境进行客户再购买的开发和实证支持尚未得到解决。因此,本文的目的是定义和识别移动广告环境下客户再购买的关键属性。在本研究中,属性集是通过系统的文献综述得出的,并通过模糊德尔菲法最终确定。模糊混合方法采用模糊解释结构建模-决策试验和评价实验室相结合的方法,建立层次模型,并在确定的关键属性之间对因果组进行分类。研究结果表明,语言、网站类型和社交媒体被归类为通过移动广告提高客户再购买的基本属性。研究局限性/启示当前研究的重点仅限于识别和发展客户再购买属性之间的层次相互关系,这些属性是移动广告业务环境所特有的。可能会针对不同的媒体环境和其他产品/服务类别进行额外的研究。原创性/价值本研究说明了如何利用模糊理论有效地运用多准则决策技术来探索移动广告概念中客户再购买的研究领域。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
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.
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