Identifying and exploring the relationship among the critical success factors of sustainability toward consumer behavior

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-08-22 DOI:10.1108/jm2-06-2022-0153
S. Zaman, Simonov Kusi‐Sarpong
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引用次数: 2

Abstract

Purpose The purpose of this study is to find out what is the relationship between sustainability toward consumer behavior. Consumer behavior is the method of choosing, buying and using goods and services with an attachment to needs and wants. Now consumers are aware about sustainability, they make purchase decisions according to environmental safety, benefit to the society and increase economic growth. Design/methodology/approach This study validates the result through experts in textile industry by using the Decision-Making Trial and Evaluation Laboratory approach. This method has many benefits which provide decision makers and experts to understand the interdependence and influential relation between the criteria by hierarchical approach. Findings According to the results, green culture (F8) and green brand (F3) are the most influential (causal) factors and exert a substantial amount of influence over other factors for achieving organizational performance and sustainability. On the other hand, past experience (F14) and time pressure (F12) are the most influenced (effect) factors that are highly influenced by other factors. Practical implications The study conducted in Pakistan underscores the significance of maintaining a healthy and pristine environment for future generations. Both consumers and organizations play a vital role in this endeavor. It is imperative that they actively promote and support goods and services that advocate for sustainability. Social implications Mangers should use long-term strategies that meet the high product value to enhance the organization’s reputation, so it will have positive consumer perception. If managers make policies to implement natural resources in their raw material, so this policy avoids conflicts and maintains a balance in our society. Originality/value This research delves into the complexities and subtleties associated with the identification and examination of the interconnections between the success factors of sustainability and consumer behavior.
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识别和探索可持续发展对消费者行为的关键成功因素之间的关系
目的本研究的目的是找出可持续性与消费者行为之间的关系。消费者行为是一种选择、购买和使用商品和服务的方法,对需求和愿望有依恋。现在消费者意识到了可持续性,他们根据环境安全做出购买决定,造福社会,促进经济增长。设计/方法/方法本研究通过纺织行业的专家使用决策试验和评估实验室的方法来验证结果。该方法具有许多优点,使决策者和专家能够通过分层方法了解标准之间的相互依赖性和影响关系。结果表明,绿色文化(F8)和绿色品牌(F3)是最具影响力的(因果)因素,对实现组织绩效和可持续性的其他因素有很大影响。另一方面,过去的经验(F14)和时间压力(F12)是受其他因素影响最大的(影响)因素。实际意义在巴基斯坦进行的这项研究强调了为子孙后代保持健康和原始环境的重要性。消费者和组织都在这方面发挥着至关重要的作用。它们必须积极促进和支持倡导可持续性的商品和服务。社会影响管理者应该使用符合高产品价值的长期策略来提高组织的声誉,这样它才会有积极的消费者感知。如果管理者制定政策,在他们的原材料中使用自然资源,那么这项政策可以避免冲突,并在我们的社会中保持平衡。独创性/价值这项研究深入探讨了与识别和检查可持续性的成功因素与消费者行为之间的相互联系相关的复杂性和微妙性。
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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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