Harvest to basket: understanding drivers of organic food purchase behaviour through interpretive structural modelling and MICMAC approach

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-04-15 DOI:10.1108/jm2-12-2023-0299
Dewan Mehrab Ashrafi, Jannatul Maoua
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Abstract

Purpose

The purpose of this study is to examine the determinants impacting consumer behaviour in organic food consumption in Bangladesh. This study aims to identify the key factors facilitating organic food consumption and establish a framework by analysing their contextual relationships.

Design/methodology/approach

The study used interpretive structural modelling (ISM), relying on expert perspectives from experienced academicians and marketing professionals. A Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis was performed to assess the driving forces and interdependencies among these determinants.

Findings

The MICMAC analysis grouped determinants influencing organic food purchases into four categories. The dependent factors, like attitude and food safety, showed moderate driving forces and high dependence. Linkage determinants, such as environmental concern and price, exerted considerable influence with moderate dependence. Independent variables, especially knowledge about organic food, had a strong impact with relatively low dependence.

Practical implications

This study’s insights offer valuable guidance for managers in the organic food industry, providing strategies to address consumer behaviour. Prioritising education on environmental benefits, transparent pricing, collaborating on policies, ensuring food safety and understanding determinants impacting purchase intent can aid in designing effective marketing strategies and product offerings aligned with consumer needs, ultimately promoting sustainability.

Originality/value

To the best of the authors’ knowledge, this study is the first to investigate the interconnections and relative significance of determinants influencing organic food purchases, using the ISM approach and MICMAC analysis. It delves into the previously unexplored territory of understanding the relationships and hierarchical significance of these determinants in shaping consumer behaviour towards organic food purchases.

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从收获到购物篮:通过解释性结构建模和 MICMAC 方法了解有机食品购买行为的驱动因素
目的本研究旨在探讨影响孟加拉国消费者有机食品消费行为的决定因素。本研究旨在确定促进有机食品消费的关键因素,并通过分析这些因素的背景关系建立一个框架。设计/方法/途径本研究采用解释性结构建模(ISM),依靠经验丰富的学者和营销专业人士的专家观点。研究结果MICMAC分析将影响有机食品购买的决定因素分为四类。从属因素,如态度和食品安全,显示出中等驱动力和高度依赖性。关联决定因素,如环境关注和价格,具有相当大的影响力和中等程度的依赖性。本研究的见解为有机食品行业的管理者提供了宝贵的指导,为解决消费者行为问题提供了策略。优先考虑环境效益教育、透明定价、政策合作、确保食品安全以及了解影响购买意向的决定因素,有助于设计有效的营销策略和提供符合消费者需求的产品,最终促进可持续发展。 原创性/价值 据作者所知,本研究首次使用 ISM 方法和 MICMAC 分析法,调查了影响有机食品购买的决定因素之间的相互联系和相对重要性。该研究深入了解了这些决定因素在塑造消费者有机食品购买行为方面的关系和等级意义,这是以前从未探索过的领域。
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
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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