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The purpose of this study is to investigate how electronic word of mouth (eWOM) affects purchase intention and brand equity, and to further examine the mediating role of brand equity between eWOM and purchase intention among Indian consumers of branded apparel.

The data was collected from 303 consumers of branded apparel using an online questionnaire, and data were analyzed through structural equation modeling with the help of SPSS v24 and AMOS v23.

The findings of this study demonstrated that eWOM has a positive and significant influence on brand equity and purchase intention. Simultaneously, brand equity partially mediates between the eWOM and purchase intention of consumers of apparel brands.

The study's data set is limited in its generalizability as it is based on specific responses from Indian consumers of branded apparel via an online survey. The results of this study would help marketing practitioners and apparel manufacturers to augment their sales and design their promotional strategy in accordance with consumers' traits.

To the best of the authors’ knowledge, this study is one of the first to propose an integrative model that studies relationships between eWOM, brand equity and purchase intention by incorporating the Elaboration Likelihood Model among Indian consumers of branded apparel. Furthermore, this novel piece of research explores the relationship between eWOM and purchase intention with brand equity as a mediator, particularly for branded apparel selected by Indian consumers.

Fused Filament Fabrication (FFF) is one of the growing technologies in additive manufacturing, that can be used in a number of applications. In this method, process parameters can be customized and their simultaneous variation has conflicting impacts on various properties of printed parts such as dimensional accuracy (DA) and surface finish. These properties could be improved by optimizing the values of these parameters.

In this paper, four process parameters, namely, print speed, build orientation, raster width, and layer height which are referred to as “input variables” were investigated. The conflicting influence of their simultaneous variations on the DA of printed parts was investigated and predicated. To achieve this goal, a hybrid Genetic Algorithm – Artificial Neural Network (GA-ANN) model, was developed in C#.net, and three geometries, namely, U-shape, cube and cylinder were selected. To investigate the DA of printed parts, samples were printed with a central through hole. Design of Experiments (DoE), specifically the Rotational Central Composite Design method was adopted to establish the number of parts to be printed (30 for each selected geometry) and also the value of each input process parameter. The dimensions of printed parts were accurately measured by a shadowgraph and were used as an input data set for the training phase of the developed ANN to predict the behavior of process parameters. Then the predicted values were used as input to the Desirability Function tool which resulted in a mathematical model that optimizes the input process variables for selected geometries. The mean square error of 0.0528 was achieved, which is indicative of the accuracy of the developed model.

The results showed that print speed is the most dominant input variable compared to others, and by increasing its value, considerable variations resulted in DA. The inaccuracy increased, especially with parts of circular cross section. In addition, if there is no need to print parts in vertical position, the build orientation should be set at 0° to achieve the highest DA. Finally, optimized values of raster width and layer height improved the DA especially when the print speed was set at a high value.

By using ANN, it is possible to investigate the impact of simultaneous variations of FFF machines’ input process parameters on the DA of printed parts. By their optimization, parts of highly accurate dimensions could be printed. These findings will be of significant value to those industries that need to produce parts of high DA on FFF machines.