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The purpose of this paper is to examine the impact of a customer engagement-enabling platform on a value captured by the firm and value acquired by the customer. It explores the relevance of relational and expertise value for customers during the engagement process so as to ensure positive referrals about the service provider.

Using a sample of 482 students, the study examines the customer engagement efforts of professional institutes that provide training to prepare for the civil service examinations of the Union Public Service Commission. The survey is confined to central areas of New Delhi, India. Statistical techniques including confirmatory factor analysis and structural equation modelling are used to analyse the data, and reliability and validity tests are performed.

The findings reveal the indispensable role of service providers as creators of a meaningful effective learning process and of interpersonal relations with customers for generating more business through customer referrals.

The study validated the moderating role of relational value between customers’ expertise value and their referrals on the basis of motivation theory, which asserts that customers’ motivation to contribute to the organisation is driven by the individuals’ extrinsic relational need for belongingness, acceptance by like-minded individuals, and feedback, recognition and respect from employees of the organisation.

The study contributes to the existing literature by integrating the well-developed social exchange and motivation theory so as to investigate the factors that propel customers’ positive word of mouth for the service provider.

Word‐of‐mouth is a rarely quantified phenomenon, in spite of its importance for service firms. Therefore, referrals remain a neglected determinant of customer lifetime valuation, although some authors claim them to be the astronomical part of customer equity. The paper discusses different approaches to the calculation of positive word‐of‐mouth, leading to a monetary referral value of a company’s customers.

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.

The purpose of this paper is to address a critical problem for health-care organizations: patient referral leakage. This paper explores the nature of patient referrals by examining how health-care providers’ breadth and depth of connectivity within a hospital network and identification with each other influence the likelihood of future patient referrals.

The data was collected by using a multi-sourced data set from the health-care industry. The proposed model was tested by using logistic regression to determine the likelihood of a primary care physician’s (PCP) referral to a specialist within a hospital network.

A model linking provider connectivity to examine co-creation practices in the form of patient referrals is tested. Results indicate that patient referrals are multidimensional. A PCP’s likelihood to refer to a specialist within the hospital network is influenced by the breadth and depth of connectivity of each provider.

This investigation extends service ecosystems to patients, health-care providers and hospital organizations, making it the first to explore how different degrees of connectivity (breadth of referral partners and depth of exchange) between and among health-care providers influence the likelihood of future patient referrals. Findings complement extant literature on service ecosystems by empirically showing that provider relationships are interdependent and rely on the mutual coordination of benefits within the entire health-care organization and network.

Managers and health-care professionals can use the framework to build and strengthen relational ties/alliances within a service organization. An ecosystems perspective reduces patient referral leakage through enhanced organizational performance, competitive advantage and continuity of care.

The authors offer a novel view of referral relationships using hard-to-access proprietary data. Moreover, this study responds to the need for transformative service research by offering service researchers and policymakers a means to enhance consumer well-being. The main contribution of this study is a framework to gain a better understanding of patient referral relationships between employees (i.e., health-care providers) in an organization, thereby affording an opportunity to bolster operational efficiencies, improve clinical outcomes and strengthen referral pathways. By viewing health-care networks through a service ecosystems perspective, contextual boundaries and the relative power of relationships are also identified. The novel use of rarely available hospital data in this setting helps explain how patient leakage compromises the health of the ecosystem and its members.

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 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).

The purpose of this study is to examine how customers’ perceptions of brands’ social media interactivity impact customer engagement behaviors (CEBs) (e.g. customer purchases, referrals, influence and knowledge) while accounting for the moderating role of brand type and social media platform.

Two separate online surveys (Study 1 (N1) = 341, Study 2 (N2) = 183) were conducted to measure the constructs of interest. Regression analyzes tests research hypotheses; PROCESS Model 1 was used to test the moderating roles of brand type and platform. Further, the pick-a-point approach (i.e. spotlight analysis) was used to probe the interaction terms.

The results indicate that when customers perceive a brand to be highly interactive on social media (vs inactive), they are more willing to buy brand offerings, refer the brand in exchange for monetary incentives, inform their family and friends about the brand on social media and provide feedback and suggestions for improving the brand. Furthermore, the positive impact of perceived social media interactivity on customer purchases, referrals, influence and knowledge varies across brand and social media platform types.

Online surveys using convenience samples were conducted to assess the constructs of interest. Archival data may provide an avenue for further insight. Future research may be able to track actual online customer behavior using such data. Further, researchers are encouraged to corroborate the results found here over time as the winds of social media shift to new platforms.

The results suggest that interacting on social media encourages customers to contribute to brand value directly (through purchasing) and/or indirectly (through referring, influencing and suggesting). While all brands may leverage social media activity for success, the positive impact of perceived social media interactivity on CEBs is particularly impactful for non-global 500 brands. The results also indicate that customers are more willing to add value to the brand through purchases and suggestions when they perceive the brand to be highly interactive on both social media networking sites and the brand’s website. However, they are more willing to promote this brand and influence their social networks about it only when they perceive the brand to be highly (vs less) interactive on its own website.

This study examines the novel issue of the impact of perceived social media interactivity on different CEBs while accounting for the moderating role of the brand and platform used by customers. The results provide value in better understanding the levers through which social media affects performance.

The purpose of this paper is to close the gap between the theoretical nature of existing contributions in customer engagement value (CEV) and its need to practically empower business decisions. This is done by proposing a framework that consists of three techniques, each of which combines the components of CEV to make it more comprehensive and applicable. The paper also reviews and analyzes the work that has been done so far in the area of CEV whether in business to business (B2B), business to consumer (B2C) or consumer to consumer (C2C) markets.

CEV is a comprehensive term that measures the total value of the customer through capturing his transactional and non-transactional behaviors. Hence, it is an essential term for measuring the value of the customer in direct marketing. This motivates researchers to compete in developing models to maximize CEV. Meanwhile, most of the existing models are conceptual and the majority of them lack applicability due to many reasons. First, these models relied on a linear version of the CEV model, hence double-counting the value of the customer; also they weighted the components of CEV equally, which is unrealistic. Finally, the effect of the environmental components in determining the engagement level of each customer was almost ignored. In this paper, two main contributions are presented. First, a summary and analysis of the contributions of the literature in the CEV field for different market types whether in B2C, B2B or C2C. Furthermore, three modifications are added to the existing models. The first model introduces a non-linear relationship of the components of CEV. The second model is a weighted linear model of these components. Finally, the third model adds the environmental factors to the CEV components. All the proposed models are theoretical in nature, however, these models are expected to show superiority when being applied to real data sets due to their ability to capture the complexity in the relationship between the firm and its customers in real-life situations. The proposed models are expected to attract the practitioners and other researchers and they both are encouraged to apply the proposed models on real-life data sets, test their performance, compare them against each other, to be able to apply each of them on the best suitable data set and business scenario.

Based on the review and analysis that has been done on about 87 papers, it is found that the majority of the contributions that have been done in the area of CEV are theoretical in nature, in spite of the effectiveness of CEV in empowering business decision. It is also found that few researchers proposed a set of theoretical comprehensive frameworks that combined CEV’s components together. Meanwhile, those frameworks are not practically applicable.

Although the contribution of the proposed models expected to attract both researchers and practitioners, these are not applied to real-life case studies to prove their effectiveness.

The research in this paper has many industrial and managerial implications. First, it helps managers and decision takers to treat the customers as assets and cost-free resources who can work with the firm to achieve what’s both aims to (i.e. increase customer satisfaction and firm’s profitability). Second, it helps the firm to determine the total value of each customer and treat its customers accordingly. Third, it empowers the managers to do target marketing, based on grouping the customers upon their total engagement. This would save time and cost and for sure increase the profitability and customer satisfaction. Forth, the proposed models take into consideration not only the transactional behavior of the customers but also the non-transactional factors that play a significant role in formulating the relationship between the firm and its customers.

This is hereby to certify that the paper is original, neither the paper nor a part of it is under consideration for publication anywhere else. Also, this study has no conflicts of interest to disclose.

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 address the following research question: What are the perceptions of professionals and consumers regarding the antecedents of client referrals in the financial advice sector?

A total of 61 qualitative interviews were conducted, with the following three key groups: independent financial advisers (IFAs; 20 interviews), clients of IFAs (26 interviews) and consumers who manage their own financial affairs and do not use the services of an IFA (15 interviews).

The financial advisers interviewed believe that client referrals are important to their business success, that they can influence clients to become ambassadors who will consciously seek out new clients and that excellent service will motivate clients to provide referrals. However, the interviews with the clients painted a different picture. While advisers believe that they can influence client referral behavior, the clients did not believe that they were influenced by the adviser to make referrals.

The sampling method was non-random and relied on the professional contacts of the principal researcher as a starting point, from which a network of contacts was established to identify interviewees. The study casts doubt on the ability of professional service providers to influence client referral behavior. This novel finding deserves further research investigation.

There is clearly scope for greater measurement in connection with referrals in professional service businesses. The propensity for clients to refer should be included as a metric in the performance measurement of professional service providers, in addition to standard financial measures. This would encourage the service provider to consider referrals during client interactions.

The study reports on a substantial qualitative study involving both professional service providers and their clients. While the providers believe that client referrals are critical to their business success, the evidence collected provides little or no support for this belief. Clients report they are not motivated to refer. Advisers do not explicitly measure referrals. The reality of referrals seems not to match the mythology.