Investigating customer behavior on Instagram to enhance online sale: modeling the effect of content types and broadcasting tools

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-06-26 DOI:10.1108/jm2-02-2024-0059

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.

查看原文本刊更多论文

调查 Instagram 上的客户行为以促进在线销售：建立内容类型和广播工具的影响模型

目的本研究旨在探讨各种 Instagram 内容对顾客行为的影响。研究案例与 Instagram 活跃页面上销售的凉茶有关。通过整合两种熵权法和加权和法来评估内容的优先级。研究结果结果表明，客户行为受内容类型的影响，这从经验上提高了企业的盈利能力。研究结果表明，教育性直播、展示他人经验的直播以及激励性直播是最有效的内容。本研究开创性地通过 Instagram 平台上内容类型的影响来实际评估社交媒体参与度的构建。原创性/价值研究了激励性内容、其他客户的体验、产品、教育性内容和购买账单等各种内容对客户行为的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

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.