Referral Bonus Research Articles

Optimal Mechanism Design with Referral

This paper establishes the optimal selling mechanism when a seller can incentivize an existing buyer to refer his privately known potential buyer to participate. We identify three optimal channels for providing referral incentives. First, if the existing buyer declares that no potential buyer exists, his virtual value is penalized. Second, if the existing buyer refers the potential buyer to the seller, his virtual value is boosted. Third, in some scenarios where this carrots-and-sticks-via-virtual-value approach is insufficient for creating proper referral incentives, the existing buyer is then given a constant referral bonus for referring the potential buyer. We also provide conditions under which the optimal mechanism can be implemented using simple mechanisms. Finally, we demonstrate that the conventional resale mechanism is suboptimal. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: H. Zhou’s research is supported by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission [Grant 22CGA77]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.01540 .

Referral bonuses in global talent acquisition: the role of social networks in China and the US

Referral bonuses in global talent acquisition: the role of social networks in China and the US

A Study on the Employee Referral Program with Reference to Alloysys Extrusion

The main objective of this project is to study the employee referral program in “Alloysys Extrusion”. Employee Referral is one of the external sources of recruitment and an internal recruitment method employed by organizations to identify potential candidates from their existing employees' social networks. An employee referral scheme encourages a company's existing employees to select and recruit the suitable candidates from their social networks. As a reward, the employer typically pays the referring employee a referral bonus. The purpose of this study is based on encouraging employees to help the recruitment team in recruiting by referring right candidates.

A Study on Satisfaction Level of Employees at Eastman Spinning Mills (P) Ltd., to Dindigul

The main objective of this project is to study the employee referral program in “Alloysys Extrusion”. Employee Referral is one of the external sources of recruitment and an internal recruitment method employed by organizations to identify potential candidates from their existing employees' social networks. An employee referral scheme encourages a company's existing employees to select and recruit the suitable candidates from their social networks. As a reward, the employer typically pays the referring employee a referral bonus. The purpose of this study is based on encouraging employees to help the recruitment team in recruiting by referring right candidates.

What Do Employee Referral Programs Do? Measuring the Direct and Overall Effects of a Management Practice

Employee referral programs (ERPs) are randomly introduced in a grocery chain. On direct effects, larger referral bonuses increase referral quantity but decrease quality, though the increase in referrals from ERPs is modest. However, the overall effect of having an ERP is substantial, reducing attrition by 15% and significantly decreasing labor costs. This occurs, partly, because referrals stay longer than nonreferrals, but, mainly, from indirect effects: nonreferrals stay longer in treated than in control stores. The most supported mechanism for these indirect effects is workers value being involved in hiring. Attrition impacts are larger in higher performing stores and better local labor markets.

How can hospitals engage their current employees in the recruitment of qualified nurses? A referral bonus and self-determination perspective.

To investigate the impact of promising a referral bonus and an autonomous referral request on nurses' referral likelihood and the quality of their referrals. We applied a 2×2 between-participants factorial design with referral bonus and autonomous referral request as experimental variables. In May 2019, 110 nurses working in Belgian hospitals were shown a fictitious e-mail with a request from their employer to look for potential new-hires and filled out an online survey measuring referral likelihood and quality. Promising a referral bonus did not affect nurses' referral likelihood and quality. Instead supporting self-determination theory, nurses exposed to the autonomous request were more likely to refer and assure referral quality than those exposed to the controlling request. Hospitals can increase nurses' referral likelihood and quality by framing their referral request in an autonomy-supportive way. Recruiting nurses are more important than ever in the current Covid-19 crisis. Our findings offer practical insights on how hospitals can engage their employees in the recruitment of nurses (i.e. through framing referral requests in an autonomy-supportive way).

Market Transition and Network-Based Job Matching in China: The Referrer Perspective

To better understand how network-based job matching responds to market development, the authors investigate network matching in China. They examine this question from the perspective of referrers, those who share information about job opportunities with potential job candidates. Using unique data from a population survey and leveraging interprovincial differences in market development, the authors show that market development has a negative association with individuals’ propensity to share job information. People who work at firms that offer a referral bonus and people who work at private firms, however, are more likely to share information and share it with more people, and the number of such employers increases with market transition. This increase can produce a positive association between market development and overall prevalence of job information-sharing. Results clarify the role employer-side processes play in job information-sharing and carry important implications for understanding network matching.

Multi-objective optimization model in a heterogeneous weighted network through key nodes identification in overlapping communities

Nowadays, it is possible to easily utilize positive and negative effects of neighbors on a social network to maximize diffusion of a novel product and profit of the seller. Hence, this paper aims to introduce a new mathematical model for a product pricing in non-competitive environment having multiple goals. The proposed model is designed while there are a monopole seller and several heterogeneous customers for a novel product. Considering various criteria, these customers are able to purchase the novel product including price, product quality, urgent need to have the product, and positive/negative externalities received from the neighbors. Moreover, they are able to comment in case of satisfaction or dissatisfaction with the product. However, the extent of influence depends on strength of the relations with neighbors that is considered in the proposed model with complete information and quantitative values. Proportionate to activating the neighbors, referral bonus is considered from the seller. To find influential nodes for the influence and exploit strategy implementation we propose a new overlapping community detection algorithm. In this algorithm, a new overlapping score based on non-member neighbor nodes connectivity is introduced to identify overlapping communities. Finally, we evaluate the efficiency of the proposed model, by implementing the proposed community detection algorithm in a real-world dataset. The results show that it is possible to obtain desired selling price in a fashion that maximum diffusion in the network happens and the seller achieves his desired profit under various management viewpoints.

Screening with Network Externalities

Network externality is a prominent feature of increasingly many products: the marginal payoff of one’s consumption increases as his neighbors consume more. In- formation of network structure is important to the seller, but is often privately known to the buyers. We model a monopoly’s optimal pricing strategy to “screen” buyers’ network information: their susceptibility (out-degree) and influence (in-degree). Our main result is that susceptibility and influence have different effects on the optimal allocations and can be separated out in the optimal screening contracts. Specifically, we characterize the optimal allocations for both directed networks where each buyer’s susceptibility and influence are independent, and undirected networks where the two are identical. For directed networks, we show the optimal allocation only depends on a buyer’s susceptibility and exhibits simple forms with quadratic intrinsic value. We further contrast the analysis with two benchmarks – complete information pricing and uniform pricing – to shed light on the value of screening and the value of network in- formation. For undirected networks, we show the seller directly screens buyers on their susceptibility and indirectly condition the allocations on their inferred influence. We also extend the model to accommodate for (1) weak affiliation between a buyer’s susceptibility and influence, (2) the case in which the monopoly can incentivize influence with contingent contracts (referral bonuses), and (3) the situation in which susceptibility and influence are endogenous to the optimal allocations.

At the Expense of Quality?

Many organizations use employee referral programs to incentivize employees toward referring potential applicants from their social network. Employers frequently offer a monetary bonus to employees who refer an applicant who is then hired and retained for a given length of time. In deciding whether to refer someone, referrers balance their motivations for helping connections find job opportunities with concerns regarding their reputations with their employer. To the extent that monetary incentives shift an employee’s considerations away from finding the best matches for their employer, referral bonuses may increase the chances that lower quality candidates are referred. Using a survey vignette experiment, we find that even a small referral bonus increases the likelihood that referrers will refer lower quality candidates and are more likely to refer people they do not know well. We conclude with a discussion of the study’s theoretical and practical implications regarding the efficiency of incentivized referral programs in producing quality applicant pools for employers.

Development of a monopoly pricing model for diffusion maximization in fuzzy weighted social networks with negative externalities of heterogeneous nodes using a case study

Today, informational structure is organized in such a way that sellers can easily employ the various capabilities of social networks, such as the analysis of positive and negative tendencies of neighbours, to maximize diffusion in the network. Therefore, in this paper we employ this approach to introduce a novel mathematical product pricing model for a monopoly product in a non-competitive environment and in the presence of heterogeneous customers. In this model, all customers are divided into various groups based on their preferences for the price, quality and need time for the product demand and also the positive and negative influences of neighbours. So, it seems customers utilize a multi-criteria decision-making model for buying a product. When a customer buys a product and additionally, persuades its neighbours to also buy the product they will receive a referral bonus from the seller. Meanwhile, the intensity of relations between neighbours in the network is incorporated into the model qualitatively. Finally, hardness of the problem justifies application of a genetic algorithm for solving the proposed pricing model and real-world dataset is used to conduct a case study that verifies its applicability.

At the Expense of Quality

Many organizations use employee referral programs to incentivize employees to refer potential applicants from their social networks. Employers frequently offer a monetary bonus to employees who refer an applicant, and this is often contingent on whether the person is then hired and retained for a given length of time. In deciding whether to refer someone, referrers face a potential role conflict, as they need to balance their motivations for helping connections find job opportunities with concerns regarding their reputations with their employers. To the extent that monetary incentives shift an employee's considerations away from finding the best matches for the employer, referral bonuses may increase the chances that lower-quality candidates are referred. Using a survey vignette experiment, we find that even a small referral bonus increases the likelihood that referrers will refer lower-quality candidates, and they are more likely to refer people they do not know well. We further discuss theoretical and practical implications regarding the efficiency of incentivized referral programs in producing quality applicant pools for employers.

Motivating employee referrals: The interactive effects of the referral bonus, perceived risk in referring, and affective commitment

Research has provided compelling evidence that employee referrals result in positive outcomes for organizations and job seekers, but it has been limited on how organizations can increase the likelihood of obtaining employee referrals. Using the theoretical lens of social exchange theory and tenets from expectancy theory, we tested two common assumptions of most employers: A referral bonus motivates employees to refer, and higher bonus amounts incite greater likelihood of referring. We theoretically developed and tested a model integrating the effects of perceived risk in referring and affective commitment and their interactions with the referral bonus to better explain the likelihood of referring. Results largely supported our predictions. Referral bonus presence, referral bonus amount, and affective commitment positively related to likelihood of referring, while perceived risk in referring negatively related to likelihood of referring. The findings also suggest that larger referral bonuses can help offset perceived risk in referring and low affective commitment levels. We contribute to the literature by developing theory, expanding the scope of the current referral literature, and offering a quantitative examination of previously theorized variables in the referring process. We conclude with suggestions to practicing managers on ways to improve the motivating potential of their employee referral programs.

Effects of Referral Bonus Characteristics, Risk in Referring, and Commitment on Referring Behavior

Research has provided strong evidence that employee job referrals result in positive organizational and job seeker outcomes. Yet research has been limited on how organizations can actually increase the likelihood of obtaining such referrals. Using the theoretical lens of expectancy and prospect theory, we start by testing the common assumption of most employers: that a referral bonus motivates employees to refer and that higher bonus amounts incite greater likelihood of referring. Additionally, we explore the effect of a payout contingency, or a delay in payment, in relation to referring likelihood. Our study also theoretically develops and quantitatively investigates the effect of risk in referring, or the generalized threat individuals have to their organizational reputation and well-being if the referral falters. Finally, we investigate affective commitment’s impact on referring likelihood and its interaction with the referral bonus. Results largely supported our predictions regarding bonus presence, bonus size, risk in referring, and the role of affective commitment. We contribute to the literature by developing theory, expanding the scope of current referral literature, and offering a quantitative examination of previously theorized variables in the referring process. We conclude by offering suggestions to practicing managers on how to best benefit from their referral program.

The Dark Side of Employee Referral Bonus Programs: Potential Applicants’ Awareness of a Referral Bonus and Perceptions of Organisational Attractiveness

The purpose of this study is to examine the effects of potential applicants’ awareness of employees being rewarded for referrals on organisational attractiveness, based on credibility theory and the multiple inference model. In a first study (N = 450), final‐year students were less attracted to the organisation when they knew employee referrals were rewarded, which was partially explained by lower credibility perceptions. Moreover, varying the specific characteristics of the referral bonus program (i.e. timing, size, type, recipient) did not improve potential applicants’ perceptions of credibility and attractiveness. A second study (N = 127) replicated the negative effect of referral bonuses on organisational attractiveness and found that it could be explained by both potential applicants’ inferences about the referrer's other‐oriented motive and lower referrer credibility. Whether employees explicitly stated that their referral reason was bonus‐driven or not did not affect these results.

Does Money Always Talk? Social Distance and Fairness in Online Referral Systems

Online referral incentive systems help attract new customers to firms by leveraging existing customers' social networks. Designing an appropriate referral incentive system by leveraging social commerce allows firms to enhance their customer base and increase sales. This study integrates the ultimatum game theory (fairness theory) with construal-level theory to hypothesize the role of monetary incentives (referral bonus) and social distance (friends versus acquaintances) in the performance of three competing designs for online referral incentive systems (i.e., rewarding only the proposer, rewarding only the responder, or dividing the reward equally between the two parties). A randomized field experiment was conducted in cooperation with a major online ticketing company to test the effects of the fairness of the split of the monetary incentive (equal split versus unequal split of the referral bonus) and social distance (friends versus acquaintances) between the proposer and the responder on the ultimate performance of online referral incentive systems (responder's acceptance of the proposer’s offer). Interestingly, our results show that for small social distance (friends), a "U" shaped relationship was observed between monetary incentive and referral performance, with referral performance not being affected by the referral bonus. In contrast, for a large social distance (mere acquaintances), an inverted "U" shaped relationship was observed between monetary incentives and referral performance, notably that a fair split of the referral bonus led to the highest referral performance. By studying the interaction effects of monetary incentives relative to social norms, our research contributes and has theoretical and practical implications for the ultimatum game and social commerce by showing that the pursuit of fairness is either useful or harmful for dyadic relationships that differ on their social distance.

Social welfare and wage inequality in search equilibrium with personal contacts

This paper incorporates job search through personal contacts into an equilibrium matching model with a segregated labor market. Firms can post wage offers in the regular job market, alternatively they can save on advertising costs and rely on word-of-mouth communication. Wages are then negotiated ex-post between the firm and the applicant, so the model can generate wage premiums or penalties depending on the parameter of bargaining power. Moreover, this paper shows that the traditional Hosios (1990) condition continues to hold in an economy with family contacts but it fails to provide efficiency in the economy with weak ties. There are two reasons for the inefficiency. First, workers bargaining over wages do not internalize the positive external effect on their contacts, originating from a higher probability of finding a job. This network externality puts an upward pressure on wages so the market tightness in the referral market is distorted downwards. Second, weak ties do not act in full interest of the unemployed worker so their search intensity is inefficiently low. Finally, this paper shows that a combination of a hiring subsidy and a referral bonus can decentralize the efficient allocation in the economy with weak ties.

Referral Bonus Effectiveness in Social Capital Activation: The Moderating Role of Referring Risk

Research has confirmed that employee job referrals result in positive job seeker and organizational outcomes. Yet little research has been conducted on the motivating forces behind a referrer’s dec...

The Effectiveness of Employee Referral as a Recruitment Source

An employee referral scheme encourages a company's existing employees to select and recruit the suitable candidates from their social networks. As a reward, the employer typically pays the referring employee a referral bonus. Recruiting candidates using employee referral is widely acknowledged as being the most cost effective and efficient recruitment method to recruit candidates and as such, employers of all sizes, across all industries are trying to increase the volumes they recruit through this channel. The present study has made a comparative assessment of various recruitment sources utilized by the organizations. The study attempts to evaluate the effectiveness of Employee Referral program in one of the esteemed organization dealing with IT and ITES. The data was analyzed using Correlation, Ratio analysis and Chi Square. Results indicated that the quality of applicants generated by E-referral is comparatively better or equivalent to other sources Research concluded that Employee Referral as a recruitment source in the organization has highest conversion rate, offer rate, and offer acceptance rate. Overall the research has provided evidence to support the need for organizations to develop recruitment strategy which incorporates a diverse range of sources to reach quality applicants in the desired target market. The organization should also improve the awareness and satisfaction level among the employees about the Employee Referral Program in order to gain maximum advantage from this source of recruitment.

Paying Human Subjects in Research: Where are We, How Did We Get Here, and Now What?

Both international and federal regulations exist to ensure that scientists perform research on human subjects in an environment free of coercion and in which the benefits of the research are commensurate with the risks involved. Ensuring that these conditions hold is difficult, and perhaps even more so when protocols include the issue of monetary compensation of research subjects. The morality of paying human research subjects has been hotly debated for over 40 years, and the grounds for this debate have ranged from discussion of legal rights, economic rights, philosophical principles of vulnerability and altruism to bioethical concepts of consent, best-interest determination, and justice theory. However, the thought surrounding these issues has evolved over time, and the way we think about the role of the human research subject today is markedly different than the way we thought in the past. Society first thought of the research subject as an altruist, necessarily giving of his time to benefit society as a whole. As time progressed, many suggested that the subject should not need to sacrifice himself for research: if something goes wrong, someone should compensate the subject for injuries. The concept of redress evolved into a system in which subjects were offered money as an inducement to participate in research, sometimes merely to offset the monetary costs of participation, but sometimes even to mitigate the risks of the study. This article examines ethical and legal conversations regarding compensation from the 1960s through today, examining theories of the ethics of compensation both comparatively and critically. In conclusion, we put forward an ethical framework for treating paid research subjects, with an attempt to use this framework as a means of resolving some of the more difficult problems with paying human subjects in research.