Rozhin Houshiarian, Asra Amidi, Ehsaneh Nejad Mohammad Nameghi
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引用次数: 0
Abstract
Purpose
This study aims to examine the role of various Instagram contents on customer behavior. The studied case is associated with herbal teas sold on active Instagram pages.
Design/methodology/approach
A structured questionnaire is exploited to collect data from customers. The integration of two entropy weight methods and weighted sum method are used to evaluate the priority of contents. In addition, model development is illustrated through which Instagram broadcasting tools are prioritized benefiting from fuzzy cognitive map method.
Findings
The results illustrate that customer behavior is moderated by content types, which empirically enhances the profitability of the business. The results of this study reveal that educational live, show other ones experience at live as well as motivational lives are the most effective contents. This study is a pioneering one to practically assess the construct of social media engagement through the effects of content types on the Instagram platform.
Originality/value
The effects of various contents, including motivational content, other customers’ experiences, products, educational content and purchase bill, on customer behavior are studied.
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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