Electronic Promotion
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Recent papers in Electronic Promotion
This study examines the mediating role of electronic word of mouth (E-WOM) on the relationship between digital marketing activities and intention to buy among Shopee's customers in Thailand. Digital marketing activities comprise content... more
This study examines the mediating role of electronic word of mouth (E-WOM) on the relationship between digital marketing activities and intention to buy among Shopee's customers in Thailand. Digital marketing activities comprise content marketing and electronic promotion (E-Promotion). The online questionnaires of four hundred and twenty-five (425) Shopee's customers in Thailand were employed for the quantitative study through convenience sampling. The collected data were analysed using the SPSS Version 27 and PLS-SEM program. The results show that E-Promotion can explain E-WOM better than content marketing. E-WOM is significantly influencing customers' intention to buy. Moreover, E-WOM is a significant mediator between digital marketing activities (content marketing and E-Promotion) and customers' intention to buy via the Shopee platform in Thailand. Digital marketers should consider content marketing and E-Promotion because these factors significantly influence E-WOM and indirectly affect customers' intention to buy via Shopee. The recommendation is to expand more sampling to study other online marketplaces. Therefore, the customers' intention to buy is related to digital marketing and a growing trend in this sector.
- by Pongsakorn Limna and +4
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- Promotions, Intention, Marketing Management, Word of Mouth
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models... more
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers’ profits in promotion and marketing models. In addition to the sellers’ profits, three important factors – annoyance of customers, sellers’ reputations, and customers’ anonymity – are considered in this study. Without considering the customer’s profile, we minimize unrelated and disliked offers to reduce the customer’s annoyance and elevate the seller’s reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information.