Survey data on the Factors of Business Sustainability in the North Indian Leather Processing Cluster of Uttar Pradesh , India

Leather processing is a highly polluting industry contaminating the surface and ground water and affecting the life of the workers and the inhabitants in the adjoining areas. The north Indian cluster of leather processing is important despite being the smallest of the three clusters of India affecting the lives of around 50,000 people directly and 250,000 indirectly. The leather processing centres of Kanpur and Unnao cities are known to pollute the Ganges River basin. India has a large population of livestock and produces huge quantities of hides annually and the importance of leather processing cannot be overemphasised. This research investigates the factors having a causal relationship with sustainable business practices in leather processing. The research is motivated by environmental consciousness and calls for ethical consumption of such products.
500 industry workers, manufacturers, raw hide suppliers, marketers and exporters were presented with a questionnaire comprising nine demographic and 42 items, six each the seven constructs used in the study. The data was collected in a face-to-face setting where the potential respondents were explained the purpose of the study and their consent was taken before collecting the data. No personally identifiable information was collected, and participation was voluntary. The final data of 397 comprised mostly of males (93.2%). 7-point Likert type questions were framed to elicit responses on the seven constructs namely, Government Policies & Regulations, Environmental Regulations, Economic Conditions, Technological Advancements, Socio-Cultural Factors, Global Market Trends and Business Sustainability.
The preliminary data was analysed using SPSS 23 and the causal relationships were analysed using path analysis by structural equation modelling (SmartPLS 4.0). Bootstrapped (10000 iterations) histograms of the path coefficients satisfactorily followed normal distribution. All the path coefficients were significant except Technological Advancement. The highest impacts were caused by Socio Cultural factors (β=0.320), Global Market Trends (β=0.310) and Economic Conditions (β=0.280) respectively.
Data in .xlsx and .sav formats is appended. Also included are the consent form with questionnaire and the output of PLS model. The model was tested using Cross Validated Predictive Ability Test and PLSPredict procedures.