Artificial neural networks: a tool for understanding green consumer behavior
| dc.centre | Faculty of Management | |
| dc.contributor.author | Mercy Samuel | |
| dc.date.accessioned | 2025-02-20T11:52:14Z | |
| dc.date.available | 2025-02-20T11:52:14Z | |
| dc.date.issued | Jul.2014 | |
| dc.description.abstract | Purpose - The purpose of this paper is to study the usefulness of neural network to explain the gap between behavior intention and actual behavior in the consumption of green products. The paper draws the base from theory of planned behavior (TPB) and social dilemma theory. Design/methodology/approach - Artificial neural networks were used to analyze the data. A survey instrument was developed to understand the behavior pattern of customers while purchasing energy-efficient products. The outputs and input variables were identified and the input variables were divided into binary and discreet inputs. Findings - The research attempts to identify the factors that drive as well as avoid green consumerism. It also details the measures that can be adapted to address the social dilemma of green consumerism. In general the paper identifies with the literature in eliciting that environmental consciousness does not drive green consumerism. Research limitations/implications - The results of the study have important implications for practitioners as well as researchers. It is observed that neural network also provides inconclusive evidence for the intention behavior gap. This can be further explored by identifying different elements of environment consciousness and further testing. Practical implications - Marketers need to have strategies interwoven with traditional influencers to promote their green offerings. The consumers expect a clear and measurable benefit to the green offerings that the marketers are marketing. Originality/value - The research has its conceptual base in the TPB and social dilemma theory to understand the drivers of purchase behavior while evaluating an electronic product available in both energy efficient non-energy efficient rating scenario. | |
| dc.identifier.doi | 10.1108/MIP-06-2013-0099 | |
| dc.identifier.issn | 0263-4503 | |
| dc.identifier.other | FP-087-JP | |
| dc.identifier.sourcelink | https://www.emerald.com/insight/content/doi/10.1108/MIP-06-2013-0099/full/html | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12725/27195 | |
| dc.issue.no | No.5 | |
| dc.journal.name | Marketing Intelligence & Planning | |
| dc.keywords | consumer behavior,artificial neural networks,green marketing strategies | |
| dc.pages | 552-566p. | |
| dc.publisher | Marketing Intelligence and Planning, Emerald | |
| dc.title | Artificial neural networks: a tool for understanding green consumer behavior | |
| dc.type | Article | |
| dc.vol.no | Vol. 32 |