Medium Minimization Effect of Medium-type Reward in the Online Referral Reward Programs: A General Evaluability Perspective

Medium-type reward is a token that people receive as an immediate reward for their effort and can be traded for a desired outcome, and has been widely used in various promoting campaigns. However, our understanding of its impact remains limited. This research focuses on the effect of medium-type reward on individuals’ referral intention in online reward referral programs. Based on general evaluability theory, we propose the medium minimization effect, i.e., individuals have higher referral intention when the numerical value of medium-type reward is small (vs. large) and that the effect will be attenuated when the reward strategy does not care whether referral is successful or the actual reward is uncertain. Results of three experimental studies support our hypotheses. Findings put forward new insights into the medium effect, as well as its potential mechanism, and individuals’ referral behavior, and can help firms optimize the design of online reward referral program systems

The rize of electronic social networks and implications for advertisers

The rise of modern digital communication technologies, most notably electronic social networks, transforms structures through which consumers interact with one another. In this paper we distinguish between two channels through which product promotion affects sales. The direct channel always positively affects consumers' pre-purchase valuation. The indirect channel goes through word-of-mouth (WoM) and can be either positive or negative. The sentiment contained in WoM is generated by the complex interaction process and depends on the aggressiveness of the advertising campaign. We investigate the implications of the current changes in social network architectures for the effectiveness of the indirect channel. We show that changes in social structures have increased the efficiency of WoM across a host of industries. Our results call for “smart” advertising policie

Competition for attention in online social networks: Implications for seeding strategies

Many firms try to leverage consumers’ interactions on social platforms as part of their communication strategies. However, information on online social networks only propagates if it receives consumers’ attention. This paper proposes a seeding strategy to maximize information propagation while accounting for competition for attention. The theory of exchange networks serves as the framework for identifying the optimal seeding strategy and recommends seeding people that have many friends, who, in turn, have only a few friends. There is little competition for the attention of those seeds’ friends, and these friends are therefore responsive to the messages they receive. Using a game-theoretic model, we show that it is optimal to seed people with the highest Bonacich centrality. Importantly, in contrast to previous seeding literature that assumed a fixed and non-negative connectivity parameter of the Bonacich measure, we demonstrate that this connectivity parameter is negative and needs to be estimated. Two independent empirical validations using a total of 34 social media campaigns on two different large online social networks show that the proposed seeding strategy can substantially increase a campaign’s reach. The second study uses the activity network of messages exchanged to confirm that the effects are driven by competition for attention