Modeling barriers to social responsibility accounting (SRA) and ranking its implementation strategies to support sustainable performance – a study in an emerging market

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-11-20 DOI:10.1108/jm2-12-2022-0287
Ahmad Khodamipour, Hassan Yazdifar, Mahdi Askari Shahamabad, Parvin Khajavi
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Abstract

Purpose

Today, with the increasing involvement of the environment and human beings business units, paying attention to fulfilling social responsibility obligations while making a profit has become increasingly necessary for achieving sustainable development goals. Attention to profit by organizations should not be without regard to their social and environmental performance. Social responsibility accounting (SRA) is an approach that can pay more attention to the social and environmental performance of companies, but it has many barriers. Therefore, the purpose of this study is to identify barriers to SRA implementation and provide strategies to overcome these barriers.

Design/methodology/approach

In this study, the authors identify barriers to social responsibility accounting implementation and provide strategies to overcome these barriers. By literature review, 12 barriers and seven strategies were identified and approved using the opinions of six academic experts. Interpretive structural modeling (ISM) has been used to identify significant barriers and find textual relationships between them. The fuzzy technique for order performance by similarity to ideal solution (TOPSIS) method has been used to identify and rank strategies for overcoming these barriers. This study was undertaken in Iran (an emerging market). The data has been gathered from 18 experts selected using purposive sampling and included CEOs of the organization, senior accountants and active researchers well familiar with the field of social responsibility accounting.

Findings

Based on the results of this study, the cultural differences barrier was introduced as the primary and underlying barrier of the social responsibility accounting barriers model. At the next level, barriers such as “lack of public awareness of the importance of social responsibility accounting, lack of social responsibility accounting implementation regulations and organization size” are significant barriers to social responsibility accounting implementation. Removing these barriers will help remove other barriers in this direction. In addition, the results of the TOPSIS method showed that “mandatory regulations, the introduction of guidelines and social responsibility accounting standards,” “regulatory developments and government incentive schemes to implement social responsibility accounting,” as well as “increasing public awareness of the benefits of social responsibility accounting” are some of the essential social responsibility accounting implementation strategies.

Practical implications

The findings of the study have implications for both professional accounting bodies for developing the necessary standards and for policymakers for adopting policies that facilitate the implementation of social responsibility accounting to achieve sustainability.

Social implications

This paper creates a new perspective on the practical implementation of social responsibility accounting, closely related to improving environmental performance and increasing social welfare through improving sustainability.

Originality/value

Experts believe that the strategies mentioned above will be very effective and helpful in removing the barriers of the lower level of the model. To the best of the authors’ knowledge, for the first time, this study develops a model of social responsibility accounting barriers and ranks the most critical implementation strategies.

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对社会责任会计(SRA)的障碍进行建模,并对其实施策略进行排名,以支持可持续绩效——一项在新兴市场的研究
今天,随着环境和人类业务单位越来越多地参与进来,在盈利的同时注重履行社会责任义务对实现可持续发展目标越来越有必要。组织对利润的关注不应忽视其社会和环境绩效。社会责任会计(SRA)是一种更关注企业社会和环境绩效的方法,但它存在许多障碍。因此,本研究的目的是确定SRA实施的障碍,并提供克服这些障碍的策略。在本研究中,作者确定了社会责任会计实施的障碍,并提供了克服这些障碍的策略。通过文献综述,确定了12个障碍和7个策略,并采用了6位学术专家的意见。解释结构建模(ISM)已被用于识别重要的障碍并找到它们之间的文本关系。基于理想解相似性的排序性能模糊技术(TOPSIS)被用于识别和排序克服这些障碍的策略。这项研究是在伊朗(一个新兴市场)进行的。这些数据是通过有目的的抽样从18位专家中挑选出来的,其中包括该组织的首席执行官、资深会计师和熟悉社会责任会计领域的活跃研究人员。在研究结果的基础上,引入文化差异障碍作为社会责任会计障碍模型的主要和潜在障碍。在下一个层面上,“公众对社会责任会计的重要性认识不足、社会责任会计实施规范缺失、组织规模不足”等障碍是社会责任会计实施的重要障碍。消除这些障碍将有助于消除这个方向上的其他障碍。此外,TOPSIS方法的结果显示,“强制性法规、指南和社会责任会计准则的出台”、“实施社会责任会计的监管发展和政府激励计划”以及“提高公众对社会责任会计效益的认识”是一些必不可少的社会责任会计实施策略。本研究的结果对专业会计机构制定必要的标准和政策制定者采取促进社会责任会计实施以实现可持续发展的政策具有启示意义。社会意义本文为社会责任会计的实际实施创造了一个新的视角,它与通过提高可持续性来改善环境绩效和增加社会福利密切相关。独创性/价值专家认为,上述策略将非常有效,有助于消除模型较低层次的障碍。据作者所知,本研究首次建立了社会责任会计障碍模型,并对最关键的实施策略进行了排名。
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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