Reward the inductor or inductee? The effect of the inductee's altruism on a shopping guide platform

PurposeThis study highlights the effect of an inductee's altruism on referral reward programs (RRPs) on an online shopping guide platform to determine the optimal RRP and referral reward allocation under a Cashback and Referral RRP.Design/methodology/approachThe authors consider a Stackelberg game with a platform, seller, inductor and inductee, where the inductee's altruism plays a vital role in determining the optimal RRP in equilibrium.FindingsThe authors show that the conditions under which it is optimal to reward the inductor only or reward both inductor and inductee are equal or unequal depending on the degree of the inductee's altruism. Suppose the platform is unable to dynamically decide the commission fee. In that case, the platform may not always be involved in RRPs and will gradually reduce the rewards for inductees as the altruism increases.Research limitations/implicationsThis study focuses on a free-to-consumers model where sellers pay membership fees. Thus, this study has limitations regarding other pricing schemes such as a model in which consumers pay a fee while sellers do not or a model in which both types of users pay fees.Practical implicationsThis analytical work can help platforms optimize referral reward strategies and referral reward allocation considering the influence of an inductee's altruism.Originality/valueIn a Cashback and Referral RRP on a shopping guide platform, the authors provide applicable conditions for the platform to involve in the RRPs when rewarding an equal bonus for the inductor and inductee first. Further, the authors show the optimal referral reward strategy and referral reward allocation when giving the different bonuses to the inductor and inductee.

When to Lock the Volatile Input Price? Procurement of Commodity Components Under Different Pricing Schemes

Problem definition: We study a two-stage supply chain, where the supplier procures a key component to manufacture a product and the buyer orders from the supplier to meet a price-sensitive demand. As the input price is volatile, the two parties enter into either a standard contract, where the buyer orders just before the supplier starts production, or a time-flexible contract, where the buyer can lock a wholesale price in advance. Moreover, we consider three selling-price schemes: Market Driven, Cost Plus, and Profit Max. Academic/practical relevance: This problem is motivated by real practices in the cloud industry. Our model and optimization approach can address similar problems in other industries as well. Methodology: We assume that the input price follows a geometric Brownian motion. To determine the optimal ordering time, we propose an optimization approach that is different from the classic approach by Dixit et al. ( 1994 ) and Li and Kouvelis ( 1999 ). Our approach leads to deeper analytical results and more transparent ordering policy. Through a numerical experimentation, we compare profitability of different parties under different contracts, pricing schemes, and market conditions. Results: The buyer’s ordering policy is determined by a threshold policy based on the current time and input price; the optimal threshold depends on not only the drift and volatility of the input price but also, their relative magnitude. The supplier’s optimal procurement time should be determined by analyzing a trade-off between the holding cost of storing the components and the future input-price movement. Managerial implications: Under the Profit-Max and the Cost-Plus pricing schemes, the time-flexible contract is a Pareto improvement compared with the standard contract, whereas under the Market-Driven pricing scheme, the supplier may be better off under the standard contract. Moreover, although the most favorable scenario for the buyer is under the Profit-Max pricing scheme, the most favorable scenario for the supplier oftentimes is under the Cost-Plus pricing scheme. Furthermore, this study provides valuable insights into impacts of various characteristics of the component market, such as the trend and volatility of the input price, on the expected profit of the supply chain and its split between the two parties.

Driver Perceptions on Taxi-sharing and Dynamic Pricing in Taxi Services: Evidence from Athens, Greece.

The taxi industry has changed dramatically during the last decade, as ride-sourcing applications, ride-sharing and alternative pricing schemes have emerged, either as complementing or competitive services and strategies. After some years of familiarity with such trends, it is interesting to explore where the taxi industry stands with respect to possible service innovations. This paper explores behavioral patterns of drivers, focusing on issues such as their preferred way of conducting business, and their views on introducing taxi-sharing and dynamic pricing. Data collected from a face-to-face questionnaire survey in Athens, Greece are exploited, and appropriate econometric models are developed for the purposes of the study. The analysis shows that young and/or educated drivers, as well as those who are familiar with new technologies are more willing to accept innovations in taxi services. Results from a stated choice experiment show that on average 3.5 euros is the extra charge that the taxi market would accept to offer a taxi-sharing service. However, results reveal that the value of taxi-sharing varies across different groups of drivers. Overall, findings indicate that in the years to come, competition by other services, (e.g. ridesharing) will force the taxi industry to adopt new models of operation and pricing.