Multichannel services Research Papers - Academia.edu (original) (raw)

Purpose-The purpose of this research is to analyse the influence of mobile word of mouth (m-WOM), received at the physical store, which "challenges" the consumer's preferences in a webrooming experience. The impacts of the social... more

Purpose-The purpose of this research is to analyse the influence of mobile word of mouth (m-WOM), received at the physical store, which "challenges" the consumer's preferences in a webrooming experience. The impacts of the social relationship between the sender and the receiver of the m-WOM and product category (electronics versus fashion accessories) are examined. Design/methodology/approach-An online experiment was carried out which manipulated the presence and type of challenging m-WOM, and product category, in a 3 3 2 between-subjects factorial design. The participants were 204 consumers recruited through a market research agency. Their perceptions about the helpfulness of the m-WOM, and their product preferences and choices, were analysed. Findings-Receiving in-store m-WOM was perceived as helpful by webroomers and affected their preferences and choices. For electronics online reviews posted by anonymous customers were more influential than friends' opinions, whereas the opposite was the case with fashion accessories. The trustworthiness and expertise of the m-WOM source may explain the effects of m-WOM. Practical implications-m-WOM entails challenges and opportunities for retailers in the omnichannel era. The findings suggest that allowing customers to access m-WOM may be beneficial; however, retailers must consider the type of m-WOM that may be most suitable for their businesses. Recommendations for referral and review sites are also offered. Originality/value-This study examines the impact of challenging m-WOM on shopping experiences, combining online, mobile and physical channels. The results revealed the importance of the information source and product category in the determination of consumers' perceptions of helpfulness, preferences and choice.

Wireless mesh network (WMN) has become an important leading technology which provides several types of useful applications such as community network, broadband home network and internet access, etc. The rise in the size of users in WMN... more

Wireless mesh network (WMN) has become an important leading technology which provides several types of useful applications such as community network, broadband home network and internet access, etc. The rise in the size of users in WMN has created a degradation of efficiency in a network especially in dense areas due to the clumsy channel allocation and hence creating many challenges for enhancing the users experience, network quality and throughput. Therefore in this paper, we proposed OCA based AIF model that can access the channel information and then it process to improve the RF channel association. The proposed OCA-AIF will function for each period when some interference is detected via AIF and we further extend this analysis by taking in to consideration the influence of interference to provide a high quality indicator in network. The analysis of result shows the optimization by our proposed approach which increases as per the increment of relay nodes (RNs).

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty context-capture. However, bursty context... more

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty context-capture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in practice, many applications need to process multi-valued data, such as temperature and reflection level used for lightening prediction. To address these challenges, the Markov modulated Poisson process (MMPP) and multi-valued logic are introduced into WSNs to perform context-capture multi-valued decision fusion. The overall decision fusion is decomposed into two parts. The first part is the context-capture model for WSNs using superposition MMPP. Through this procedure, the fusion center has a higher probability to get useful local decisions from sensors. The second one is focused on multi-valued decision fusion. Fault detection can also be performed based on MVL. Once the fusion center detects the faulty nodes, all their local decisions are removed from the computation of the likelihood ratios. Finally, we evaluate the capability of context-capture and fault tolerant. The result supports the usefulness of our scheme.