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The purpose of this paper is to explore how the product type (hedonic product and utilitarian product) and reward type (hedonic gift and utilitarian gift) influence customer referral likelihood in referral reward program.

The authors test the effect of the product type and reward type on referral likelihood through two studies. Study 1 produces a 2 (product type: hedonic product and utilitarian product) × 2 (reward type: hedonic gift and utilitarian gift) factorial design to test H1, H2 and H3, that is, the effect of the product type and reward type on referral likelihood and their interaction effect. On the basis of study 1, study 2 will select different subjects, different products and different incentive allocation schemes to test H1, H2 and H3 again.

The results are as follow: first, the product type has significant influences on referral likelihood. Compared with a utilitarian product, customers are more likely to make referrals when consuming a hedonic product. Second, the product type and reward type have significant interactions to referral likelihood. When rewarded a hedonic gift, customers who consumed the hedonic product have great willing to make referrals; however, when rewarded the utilitarian gift, customer who consumed the utilitarian product have great willing to make referrals.

The authors’ findings contribute to the literature of consumers’ recommendation in the following aspects. First, from the perspective of enterprises which launch referral reward program, the present research demonstrates the product type (hedonic product and utilitarian product) and reward type (hedonic gift and utilitarian gift) influence customer referral likelihood. Previous studies discuss attributes of product that influence consumers’ referral likelihood, such as product sensitivity (Kornish and Li, 2010), product involvement (Zhu et al., 2011), brand strength (Ryu and Feick, 2007) and price (Xiao et al., 2011). However, few studies focus on the hedonic and utilitarian attributes of products and explore their impact on the willingness to recommend. This paper makes a useful supplement to the research gap. Most previous studies simply divide the type of reward into tangible and intangible (Shi and Wojnicki, 2007), cash and coupon (Wang, 2010) or cash and gift (Huang et al., 2013). This paper enriches the research on reward types and refines the types of gifts in a referral reward program. The present research divides the type of reward into hedonic gifts and utilitarian gifts, and applies benefit congruency frameworks (Chandon et al., 2000), attitude theory (Eagly and Chaiken, 1993) and over-justification effect (Deci and Ryan, 1985).

Referral reward programs (RRPs) incentivize existing customers (inductors) to refer new customers (inductees). The effectiveness of RRPs is not well understood as previous studies either focused on referral intent and/or ignored inductee responses. However, an RRP is only effective if inductors recommend and inductees respond with buying the service. The purpose of this paper is to examine the drivers of existing customers’ successful referral behavior.

This study combines a bank’s customer relationship management (CRM) data which were used to identify successful inductors and non-inductors. Then, observed behavioral and customer background data from the CRM database (including successful referrals, deposits in euros, number of products held, relationship duration, income, age, and gender) were combined with survey data capturing attitudinal variables (i.e. perceived relationship quality, reward attractiveness, referral metaperception, opportunism, and involvement). This approach allowed for the simultaneous testing of all hypothesized drivers of successful referral behavior.

Metaperception (i.e. the process by which individuals determine the impressions other might form of them and their behavior) was the strongest and most significant driver of successful RRP participation, followed by attractiveness of the reward. That is, inductors recommended successfully when they believed that their incentivized referral did not look bad (or even looked good) and incentives were perceived as attractive. This finding is important as metaperception so far has only been examined in theoretical and experimental studies with intent as dependent variables. Second, latent class analysis (LCA) revealed that there were two segments of inductors of which one was opportunistic. Opportunism as a driver of referral behavior has not been shown in past research using more traditional analyses, whereas LCA uncovered it as a driver for one-third of all respondents.

The findings offer managers a better understanding of the key determinants of successful referral behavior with important RRP design implications that counter frequent practice (e.g. designing RRPs with high face value but then reducing its usefulness through terms and conditions). Furthermore, managers may consider segment-specific reward structures to improve the effectiveness of their RRPs.

This study is the first to examine inductor determinants of successful referral behavior and identify inductor segments.

This study seeks to examine the effect of pricing as a marketing instrument to stimulate word‐of‐mouth (WOM) by comparing the influence of two pricing strategies (i.e. a low‐complexity vs a network‐effects tariff) on the referral behaviour.

Using customer data from a German mobile network operator (including information on customer characteristics, referral behaviour, and service usage), the authors develop a logit model.

Surprisingly, the results indicate that it is the low‐complexity tariff that increases the likelihood of referrals and leads to an overall higher referral activity. Despite the lower referral activity, however, the network‐effects tariff generates higher revenues.

The results show that companies can use pricing schemes to influence referral behaviour and strongly indicate the need of further research on manageable tools to stimulate word‐of‐mouth marketing.

The findings show not only that pricing has an impact on customers' referral behaviour but also that it is the low‐complexity tariffs that trigger referrals. Furthermore, the results underline the importance of considering the monetary value of referrals.

In contrast with many previously conducted studies on customer referrals, the paper explicitly analyses the impact of pricing on referral behaviour and empirically shows that firms are able to actively manage WOM among customers.

Addressing the significant differences between referral programs and traditional promotional marketing, this paper aims to investigate and examine the impact of how reward-related information is presented within referral programs and how it interacts with reward size and reward allocation.

This study adopts framing effect and equity theory to build the relationship between reward presentation, reward size and reward allocation. Then, two scenario-based experimental studies are designed and conducted on Amazon Mechanical Turk.

The results show that there is no direct impact of reward presentation on referral likelihood, while the effect relies on reward size. As the levels of reward size increase, the referral likelihood gradually shifts from percentage form to dollar form as perceived size mediates the interaction effect on referral likelihood. Further, adding information about reward allocation also indicate the different impacts of equity and inequity on influencing the above findings.

The study contributes to the literature by introducing reward presentation and emphasizes its impact on individual’s behavior decisions in the context of referral programs. This study extends and broadens the scope and effectiveness of the framing effect on traditional promotional marketing strategies, while also bridging the gap in the literature by examining the combined role of information about rewards.

This 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.

The 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.

The 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.

This 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.

This analytical work can help platforms optimize referral reward strategies and referral reward allocation considering the influence of an inductee's altruism.

In 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.

The purpose of this paper is to investigate how referral reward programs (RRPs) with scarcity messages influence consumer’ recommendation behavioral intentions about a bank credit card.

In total, 1,599 consumers are accessed through Amazon’s Mechanical Turk worker panel. The authors use general linear models, analysis of variance and analysis of covariance to test the proposed hypotheses.

The results showed that offering RRPs with scarcity messages increases a consumer’s behavioral intentions to recommend. The limited-quantity message in RRPs has the highest positive impact on consumers’ behavioral intentions.

No prior studies have addressed the relationship between referral rewards and scarcity messages in the bank credit card context. The study contributes to the understanding of the effectiveness of RRPs with scarcity message in improving consumer’s referral.

The purpose of this paper is to investigate how the effects of referral rewards in referral reward programs (RRPs) are moderated through perceived social risk of a recommender.

A total of 717 consumers are accessed through Amazon's Mechanical Turk worker panel. The authors use t-test and analysis of variance to test the proposed hypotheses.

The findings show that consumers with high perceived social risk balance financial rewards with social risks, while low social risk consumers largely ignore these social risk elements surrounding a referral decision.

The inclusion of perceived social risk provides the opportunity to fully understand how a consumer goes about balancing social risk and referral rewards in making referral decisions. The concept of social risk has not been previously applied to this context.

The purpose of this paper is to research the effect of uncertain rewards on the recommendation intention in referral reward programs (RRPs) and investigate the interaction of tie strength and reward type on the recommendation intention.

The research adopts a quantitative exploratory approach through the use of experiments. Study 1 adopted a 2×2 between-participants design ((reward type: certain reward vs uncertain reward)×(tie strength: strong tie vs weak tie)). Respectively, by manipulating uncertain probabilities and expected value, Studies 2 and 3 further explore the effect of uncertain rewards and tie strength on customers’ referral intention.

This paper finds the following: compared to certain rewards, customers’ referral intention under uncertain rewards is higher and positive experience has a mediating effect between reward type and recommendation intention; when only the recommender is rewarded, the tie strength between the recommender and the receiver moderates the effect of reward type on the recommendation intention; for strong ties, customers’ recommendation intention is higher in uncertain reward condition, but for weak ties, customers’ willingness to recommend is almost the same in both reward types; when both the recommender and the receiver are rewarded, although certain rewards have a higher expected value than uncertain and random rewards, for strong ties, the participants have a higher referral intention under random rewards than that under uncertain rewards, which have a higher referral willingness than that under certain rewards. Additionally, for weak ties, the reverse is true.

The research has both theoretical implications for research on uncertain rewards and tie strength and practical implications for marketing managers designing and implementing RRPs.

The purpose of this paper is to investigate how referral reward programs (RRPs) utilizing scarcity messages influence bank credit holders’ referrals to and adoptions by close or distant friends.

A 2×2 experiment is implemented with 760 consumers solicited through Amazon’s Mechanical Turk worker panel. Logit transformation and general linear models are used to test the proposed hypotheses.

Results showed that offering RRPs with limited available referrals (quantity scarcity) increases the overall number of referrals to and adoptions by close and distant friends. The percent of strong ties also increases with RRPs. As quantity scarcity is relaxed, the percentages of referrals to and adoptions by close friends decrease.

The inclusion of tie strength with scarcity framing greatly enhances our understanding of the effectiveness of RRPs for bank credit cards. To the authors’ knowledge, this is the first research attempt on this topic.

The purpose of this paper is to extend prior research on referral reward programs (RRPs) by examining if and how the mode of customer acquisition (RRP-acquired customers vs non-RRP-acquired new customers) moderates the relationships between customer satisfaction and attitudinal loyalty, perceived switching costs and attitudinal loyalty, and attitudinal loyalty and behavioral loyalty (i.e. recommendations, cross-buying, and total spend).

Set in a banking context, this study is the first in an RRP context to link survey data with actual purchase data from a bank’s CRM records. Specifically, the survey captured customers’ satisfaction, perceived switching costs and attitudinal loyalty, whereas the CRM data provided actual loyalty behaviors (cross-buying and total spend).

The findings show that the effect of satisfaction on attitudinal loyalty, and the effects of attitudinal loyalty on recommendations, cross-buying, and total spend were stronger for RRP-acquired customers than for non-RRP-acquired new customers. Furthermore, perceived switching costs had a lower effect on attitudinal loyalty for RRP-acquired customers than for non-RRP-acquired new customers.

The findings offer managers a better understanding of how RRP-acquired customers differ from non-RRP-acquired new customers with regard to their satisfaction, perceived switching costs, and attitudinal and behavioral loyalty, thus enabling effective management of RRPs.

This is the first empirical study that explores the differences between RRP-acquired customers and non-RRP-acquired new customers with regard to the effects of satisfaction and perceived switching costs on attitudinal loyalty, and the effect of attitudinal loyalty on behavioral loyalty.