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Social enterprises are organizations striving to address social issues. These enterprises sell products to be self-sustainable. Social entrepreneurial success depends on the intention of consumers to purchase social enterprise products. This study aims to assess and understand the consumer intention to buy social enterprise products.

This descriptive study integrates consumers’ emotional values with the theory of planned behaviour as a framework. The intention is to investigate the role of customer emotion in mediating the relationship between predictor and predicted variables. A mixed sampling technique is adopted to select sample units from the consumers with a consideration for regional balance. Data from 336 respondents were collected using a survey instrument administered online.

Structural equation modelling shows that among the three predictor variables, only attitude has a direct impact on consumer’s purchase intention, while subjective norms have an indirect impact through the mediating variable (emotional value). Behavioural control showed no effect on consumer’s purchase intention of products sold by social enterprises.

The results of this study have theoretical as well as practical implications. The success of social enterprises depends on the acceptance of their products by consumers. The social enterprise can strategize its marketing communications to strengthen the emotional values, attitudes and subjective norms of the consumers so that the market acceptance for these products can be increased.

The purpose of this paper is to identify barriers to implement green supply chain and to understand their mutual relationship. Green supply chain management (GSCM) barriers are identified using available GSCM literature and on consultations with experts from industry and academician. Interpretive structural model (ISM) was developed to identify the contextual relationship among these barriers.

A group of experts from industries and academics was consulted and ISM is used to develop the contextual relationship among various GSCMBs for each dimension of GSCM implementation. The results of ISM are used as an input to fuzzy matrix of cross-impact multiplications applied to classification (MICMAC) analysis, to identify the driving and dependence power of GSCMBs.

This paper has identified 14 key GSCMBs and developed an integrated model using ISM and the fuzzy MICMAC approach, which helps to identify and classify the important GSCMBs and reveal the direct and indirect effects of each GSCMB on the GSCM implementation. ISM model provides only binary relationship among GSCMBs, while fuzzy MICMAC analysis provides precise analysis related to driving and dependence power of GSCMB, to overcome this limitation, integrated approach is developed.

ISM model development and fuzzy MICMAC analysis were obtained through the judgment of academicians and industry experts. It is the only subjective judgment and any biasing by the person who is judging the GSCMBs might influence the final result.

This is first kind of study to identify GSCMBs and further, to deploy ISM and fuzzy MICMAC to identify and classify the key GSCMEs that influence GSCM implementation in the organization. The results will be useful for business managers to understand the GSCMBs and overcome these GSCMBs during GSCM implementation in an organization.