This study aims to examine the effects of electronic word of mouth communication (eWOM) among consumers on purchase intention and brand image, specifically, Generation Y and Z groups in relation to smartphone brands. The study utilizes an empirical research model using data collected from 402 valid respondents among consumers who use smartphone brands in North Cyprus. The study uses structural equation modeling (SEM) to explore and conduct the analysis. The results confirm the significant effects of eWOM on purchase intention through brand image and the moderating role of product type among eWOM, purchase intentions and brand image. The study also recommends that firms and marketers must concentrate on online communication channels to affect consumers’ intention toward purchasing brands and brand image. Moreover, the current study model suggests that future study can be extended in different context, countries (i.e. developed, emerging, developing), industries (i.e. banks, e-commerce, tourism) and different social me-dia platforms sites (i.e. Facebook, Twitter).

Reverse logistics plays a very critical role in the overall strategy of a business and hence need to be very effective in meeting its objectives. Studies have come up with various insights to optimize reverse logistics arrangements within a specific industry or a sector, but presently there is no study which provides an approach to share knowledge drawn out of reverse logistics arrangements, across dissimilar industries and sectors. Such a study is significant because the response to a reverse logistics arrangement is not uniform in an industry or sector in all the countries, due to different market maturity levels, dissimilar consumer behaviour, and the state of the economy itself. Therefore, the purpose of this paper is to provide a guide for logistics planners through which they can utilize the learning outcomes that emerge from dissimilar industries or sectors within the same economy also. The research findings show that the reverse logistics arrangements can be categorised into various types on the basis of origin and reason for return. It is shown that the products with dissimilar characteristics can be grouped together into six types depending on the common supply chain member interests. Further, the reverse logistics arrangements change from one type to another as a product moves across its life stages. It highlights an approach using which the knowledge drawn from a reverse logistics type in one sector/industry can be applied to the same type in another sector/industry, by focusing on the product types, whose return share similar supply chain member interests. Logistics network planners can apply the insights that have emerged from this analysis to effectively design reverse logistics channels.