Purpose This paper aims to advance the current understanding of social media (SM) brand engagement. Specifically, it validates the dimensionality of SM brand engagement, examines its drivers and explores the impact of SM brand engagement on brand equity. Design/methodology/approach A survey was conducted with 433 Generation Y (Gen Y) SM users. Findings The study results validate SM brand engagement as a multidimensional construct comprising utilitarian, hedonic and social dimensions. Three categories of SM engagement antecedents were identified: social factors (social identity and tie-strength), user-based factors (service, product and price information, hedonic motives and prior experience with SM) and firm-generated information (personalized advertising, mass advertising, promotional offers and price information). Finally, SM brand engagement was positively related to brand equity. Research limitations/implications This study focused on Gen Y SM users in India. This study should be replicated in other contexts to establish the generalizability of the findings. Practical implications A better understanding of the dimensionality and drivers of SM brand engagement can help managers to enhance their SM strategies to build brand equity. Originality/value This is the first study to provide a comprehensive examination of the dimensions, drivers and consequences of SM brand engagement.

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Wireless Sensor Networks are extremely densely populated and have to handle large bursts of data during high activity periods giving rise to congestion which may disrupt normal operation. It usually occurs when most of the data packets follow one route to reach from source to destination. Thus, there is a need of some new approach which could control congestion to meet increasing traffic demand and improved utilization of existing resources. Chance of congestion increases when both source and sink node are mobile. Due to mobility of source or sink, there is a need of determining optimal path every time when source or sink changes its position. So selection of optimal path is necessary in order to mitigate chance of congestion in the network. This paper employs new genetic algorithm based approach to determine an optimal path from source to destination for different scenarios of source or/and sink node mobility. Concept of connection value and localization region has been employed to determine an optimal path each time the data packet is being sent. An optimal path is the path that has minimum number of connections. In order to send the data packet from source to destination, there is requirement of genetic algorithm that automatically controls congestion. Simulations are performed for different scenarios of source or/and sink mobility. Significant improvements have been observed in terms of congestion value for genetic algorithm. Simulations results determine best route with minimum connection value by incorporating genetic algorithm.