Customer-to-Customer Communication: Referral of High and Low Involvement Products through Stimulated Word-of-Mouth

Abstract

Referral reward programs (RRPs), considered as a form of stimulated word-of-mouth (WOM), provide incentives to existing customers to bring in new customers. The research here adds to previous knowledge by exploring the usage of referral codes for high and low-involvement products in three stages of the consumer decision journey, on a sample of 218 consumers analyzed by regression analysis. Results show that components of the Theory of Planned Behavior influence the behavioral intention toward participating in an RRP, with perceived behavioral control having the strongest effect, followed by subjective norm and Attitude. Referral codes have a significant effect on respondents’ behavior; high conformity of high-involvement products and low conformity of low-involvement products was found, with referral programs having a weaker effect on high-involvement products. Customers tend to follow all steps of the traditional consumer journey when buying a high-involvement product; in the case of low involvement products, low conformity was even lower when using a referral code. Low-involvement products at the need recognition stage, and high-involvement products at the active research stage, are the least affected by the RRP. Results provide insights for companies to optimize their marketing strategy through stimulated WOM, and with the usage of RRPs.

Keywords:

• customer-to-customer communication
• referral reward programs
• consumer decision journey
• product involvement
• word-of-mouth

Introduction

Accelerating the spread of information exchange and access to more information has also changed the way consumers make purchase decisions. Tie strength plays an essential role in making more informed and accurate purchasing decisions, as it relates to the interaction frequency and closeness of a relationship. Tie strength also plays a crucial role in marketing strategy and customer acquisition, for example, through referral reward programs (RRPs), that are a form of stimulated word-of-mouth (WOM), providing incentives to existing customers to bring in new customers (P. Schmitt et al., 2011).

Results show that people are four times more likely to buy a product when referred to it by a friend—or in this study—a strong tie; plus, referral leads are believed to be 25% more valuable in comparison to a lead coming from traditional marketing sources (Berman, 2016). RRPs are a cost-effective strategy for gaining new customers with superior profitability to the firm as referred customers are also known for staying longer with the company, with lower acquisition costs while generating 16% more profits. Customers acquired through RRPs (who remained customers after the first purchase) are 13% less likely to leave the company than non-referred customers (P. Schmitt et al., 2011).

Referral Marketing has been studied in many different research areas, focusing on referral likelihood, analyzing factors that can influence the referrer to take part in an RRP, like tie-strength, motivations, brand strength and size of the incentive associated with the program, which is the most dominant research area in the field (Orsingher & Wirtz, 2018; Ryu & Feick, 2007; Wang & Chen, 2022; Wirtz & Chew, 2002; Xu et al., 2023). Azmat et al. (2022) investigated the impact of personality traits on consumer WOM and found that openness to experience, and agreeableness have an indirect impact on WOM through knowledge-sharing behavior and figurative language.

As a majority of the research conducted in referral marketing focused on the incentive structures of the RRPs, the focus of our research is on a less researched area of customer behavior relating to RRPs. With this research, our aim is to extend the existing knowledge in referral marketing by analyzing the effects of an RRP on customer’s behavior, and the consumer journey in the case of high and low-involvement products. No study has previously analyzed the actual usage of a referral code by the customer in the context of a decision-making journey, nor if their purchasing behavior would be different depending on the product type. In our study, the effects of the reward program on the customers’ behavior will be analyzed in the context of Ajzen’s (1991) Theory of Planned Behavior (TPB) and of the Consumer journey scheme presented by Court et al. (2009).

The current research addresses important research gaps in referral marketing as our study seeks to answer the following research questions:

RQ1: Can the Theory of Planned Behavior model be used to indicate a person’s behavior intention to participate in referral reward programs?

RQ2: What are the effects of using a referral reward program on the Consumer Decision-Making Journey in case of high- and low-involvement products?

Research Question 1 addresses the research question with a focus on behavior intention. According to previous studies, referrals have a significant effect on buying behavior (e.g. Podnar & Javernik, 2012; Rejón-Guardia & Martínez-López, 2014), but RRPs’ effect on behavior intention has not been studied before. The research question aims to show that attitude, social norms and perceived behavioral control (PBC) indicate behavior intention (Ajzen, 1991) in the context of RRPs. Through the TPB, we investigate the customers’ relationships and perceptions toward an RRP, trying to predict behavioral intention of participation in a rewarded referral program.

Research Question 2 defines the relationship between the effect of RRPs and the Consumer Decision-Making Journey. If customers are willing to use a code in one of their purchases, it is interesting to understand how it could affect each step of their decision-making process, as marketing actions are aiming to influence customers in the right moment (Court et al., 2009). Guo (2012) also suggested that the perspective of the referred customer toward RRPs should be further analyzed and also, studying how their journey would look like while participating in such program needs more research. In our research we expected that the introduction of an RRP influences the referred customer’s consumer journey stages as strong ties who know their preferences (Chen, 2022; Granovetter, 1973), and thus, may lead to changes in it. Also, the importance of involvement (Zaichkowsky, 1994) is introduced along with the high and low-involvement products, which has been used in the research of advertising (e.g. Han & Kim, 2017; S. Kim et al., 2017; Mainardes et al., 2023; Xue & Muralidharan, 2015).

The most important concepts, the review of literature on WOM and RRPs are discussed in the following section, along with the introduction of the TPB and the Consumer Decision-Making Journey. Along with the introduction of the constructs, the hypotheses are formulated in the following section, which is followed by a section where the methods applied in the current research are described. The section after covers the results of the study, which is followed by the discussion and implications. The final section describes the limitations of the research and proposes future research directions. Results and conclusions of this study will allow marketers to better understand if creating an RRP is the right strategy to acquire more customers, based on the type of product they offer.

Literature review and hypotheses

Word-of-mouth and referral reward programs (RRPs)

The concept of WOM can be summarized as one of the most effective forms of advertising that happen through natural customer-to-customer communication about a product, service, or firm (Brown et al., 2007; López & Sicilia, 2014; Plummer, 2007).

WOM has an unremitting impact on consumer attitudes and how they behave (Wirtz & Chew, 2002), it provides psychological benefits to customers, like relieving anxiety in the decision-making process (Furrer et al., 2021) and reducing cognitive dissonance (Wangenheim & Bayón, 2007), as the referred person may rely on the peer’s previous experiences and information research. Podnar and Javernik (2012) investigated the effects of WOM on consumers’ attitudes toward purchase probability and found that negative WOM has an impact on consumers’ attitudes and purchase probability, while the influence of positive WOM was not significant.

As a form of stimulated WOM, existing customers are used as brand advocates to further expand the company’s customer base (Guo, 2012).

People tend have more knowledge about other people who are socially close to them (Liviatan et al., 2008) which means they have a strong tie. As knowledge is often informal, and diffused by WOM, consumers might get information from nonexperts, thus identity and credibility becomes a key issue (X. Lin & Spence, 2018).

Tie strength ranges from weak to strong and relates to the interaction frequency and closeness of a relationship (Marsden & Campbell, 1984). WOM was found to be more common between strong ties than between weak ties (Frenzen & Nakamoto, 1993), as people know more about the preferences of the ones close to them (Granovetter, 1973). Because of their accessibility and predisposition to be helpful, strong ties lead to perceived efficiency (Levin & Cross, 2004), and referred people are more likely to buy the recommended product. Customers are motivated by the referral program to seek new potential clients (Berman, 2016), which is likely to be a strong tie (Frenzen & Nakamoto, 1993; Ryu & Feick, 2007). Ryu and Feick (2007) reinforce the altruist aspect of strong tie relationships, where one cares about the welfare of the other without the need to reciprocate. Contrary to that, weak ties have exchange-like interactions, driven primarily by self-interest, seeking to maximize their outcomes and minimize their costs regardless of the other (Ryu & Feick, 2007). Ryu and Feick (2007) discovered that the referral likelihood is greater with strong ties.

As the costs and benefits of taking part in a referral program are evaluated by the customer, the reward has been identified as an important driver in participation (Ryu & Feick, 2007; Wirtz & Chew, 2002; Wirtz et al., 2013). The compensation is not necessarily monetary: loyalty points, vouchers, free goods, cash, and charitable donations are all included in what could be earned through the program (Berman, 2016). Referral likelihood is influenced by reward sizes and schemes, and connected to the type of referrer-referred relationship. In the case of weak and strong ties, the referral likelihood without a reward is much smaller than when a reward is offered (Ryu & Feick, 2007).

Contradictory results have been presented about the reward in the RRP, as the incentive shows a significantly positive effect on referral likelihood (Ryu & Feick, 2007), but a negative one toward the referred person’s perception of the program (Verlegh et al., 2013). In cases when the referrer’s objective is to be seen as knowledgeable and the referral effectiveness is sufficiently high, it is advised not to provide a reward and rely solely on organic WOM (Xiao et al., 2011). These tradeoffs are inherent to RRPs, and its worth must be decided based on internal customer knowledge (Orsingher & Wirtz, 2018).

Studies were conducted on the perception of ulterior motives by customers that might be suspicious about the real driver of a recommendation by the referrer (Jin & Huang, 2014; Tuk et al., 2009; Verlegh et al., 2013). According to Verlegh et al. (2013), it is less likely to happen if the successful recommendations are not rewarded, or if the referred requests the recommendation. A less negative response was found when the program rewarded both parties with a symbolic reward (e.g. a donation to a charity institution). Tie strength was also connected to the ulterior motives’ perception in case of a weak relationship between the referrer and referred customers (Verlegh et al., 2013).

The relation between RRPs and product involvement is defined based on the personal relevance of a product to the customer (Sundaram et al., 1998; Zaichkowsky, 1994). In their study, Wirtz et al. (2019) rejected that the referrer’s likelihood of making a successful referral was positively related to the referrer’s involvement in the product. This means that the likelihood of a successful referral could be dependent on the referred customer instead. Fan et al. (2014) have shown that for high-involvement products, rewarding only either the referrer or the referred will create a higher purchase intention than rewarding both parties. The highest purchase intention for low-involvement products came from rewarding the referred customer instead of rewarding the referrer, or even both parties (Fan et al., 2014). Based on previous studies, benefits that come from RRPs are more significant than the possible harm created by opportunistic customers (P. Schmitt et al., 2011).

Theory of Planned Behavior (TPB)

TPB (Ajzen, 1991) establishes a connection between intention and behavior through analyzing attitude, subjective norms, and PBC. As consumers tend to lack cognitive ability to integrate eventualities into the process of anticipating behaviors (Chandon et al., 2005), respondents provide inaccurate predictions when asked about their future behaviors.

These three components have a significant impact on behavioral intention. Attitude relates to the perception of carrying out specific behavior (Cheng & Huang, 2013), either positive, neutral, or negative, and is connected to the result of performing an action (Ajzen, 1991).

Subjective norm is related to meta perception, which refers to the process of determining what third parties might form of them and the behavior in question (Ajzen, 1991). As people strive to be seen in a socially positive light (Schlenker & Leary, 1982), they might not take an unfavorably assessed action. Social pressure to engage in eWOM is also created by subjective norms (Gunawan & Huarng, 2015; Park, 2000) as the norms play a critical role in shaping behaviors (Shan & King, 2015).

PBC refers to the certainty of being able to execute an action required to reach a specific goal. Believing that they have the capacity, knowledge, and tools needed to do it, they are more likely to perform an action, concluding that PBC is positively correlated with behavioral intention. This intention is believed to be the best predictor of actual behavior (Ajzen, 1991), and represents the effort one is willing to put into performing the indicated behavior: if it is high, it is expected that one will complete the action.

RRP, as a form of eWOM (Guo, 2012) influences consumer behavior (Wirtz & Chew, 2002), and the TPB has been successfully applied in different advertising areas as an indicator to behavior intention (e.g. E. Kim & Park, 2023; Raza et al., 2019; Sanne & Wiese, 2018). It is important to investigate not only one’s perception of taking part in an RRP though their attitude, but also the subjective norm which includes the influence of reference groups. The perceptions of the norm can be selective in their social environment (Prentice & Miller, 1993) and their norms and behaviors represent the group’s where they want to belong to (Leung & Morris, 2015). These reference groups have an impact on one’s behavior (Kotler & Armstrong, 2012; Kotler & Keller, 2009; Schiffman & Kanuk, 2008) which can result in taking part in an RRP. One’s ability to join an RRP may be limited or even be out of reach depending on the characteristics of the program (e.g. need for invitation), which emphasizes the importance of investigating the PBC which represents self-efficacy and controllability (Ajzen, 2002). By using TPB, we seek answer to the following hypotheses, where the behavior in question is the intention to participate in a RRP.

H1a: The attitude of the referred towards RRPs positively influences the Behavior Intention to participate in a RRP ceteris paribus.

H1b: The subjective norm relating to RRPs positively influences the Behavior Intention to participate in a RRP ceteris paribus.

H1c: The perceived behavioral control while taking part in a RRP positively influences the Behavior Intention to participate in a RRP ceteris paribus.

The consumer decision-making journey

The consumer journey is the customer’s process before purchasing a product or service: from need recognition until the final decision. Howard and Sheth (1969) renowned model, that decomposes customer’s information to analyze motives, inhibitors, predispositions, and decision mediators that lead to purchase behavior, was applied in multiple pieces of research recently (e.g. S.-C. Lin et al., 2022; Sivakumar, 2021).

Customer experience tracking grants a deeper understanding of customer touchpoints (B. Schmitt & Zarantonello, 2013). In this research, the focus is on the brand-owned touchpoints designed and managed by the company to influence customer attitudes and preferences (Hanssens et al., 2014). RRPs built on that can induce the customer to establish higher initial confidence levels in a brand, build an emotional bond (Van den Bulte & Wuyts, 2007) and become a brand advocate (Leboff, 2014).

The consumer decision-making model (Court et al., 2009) was constructed after examining almost 20,000 consumers across five industries and three continents. They present the decision-making process as a circular journey with four phases: initial consideration, active evaluation, moment of purchase and post-purchase experience. Customers first consider an initial set of brands based on brand perceptions and exposure to recent touchpoints, then add or subtract brands as they evaluate them. Finally, the consumer selects a brand at the moment of purchase to create their judgment based on their own experience to inform the next decision journey.

Due to the research’s reliability, the model created by Court et al. (2009) will be used as the basis of the analyses of this study, with the addition of need recognition before the initial consideration of brands. The same standard consumer journey will be used for low and high-involvement products, and the results will show if there is a difference in how the products approach the standard journey and if there is one type that takes a more considerable distance from it. The strength and importance of each stage can vary, influencing a decisive part of the journey, according to the product, the customer’s individual journey (Lemon & Verhoef, 2016) and the presence of unexpected factors in the standard journey.

The tendency to comply with group norms which may result in changes in consumption behavior is referred to as consumer conformity (Lascu & Zinkhan, 1999). Two levels of conformity were defined by Bearden et al. (1989): a higher level of conformity and a lower level of conformity. A higher level of conformity means a tendency to take actions which conform to expectations; in our case, being aligned to the classic consumer decision journey. In our research, we expected that the introduction of an RRP influences the referred customer’s consumer journey stages, as strong ties who know their preferences may lead to changes in it.

The WOM was found to influence the consumer journey (Ngarmwongnoi et al., 2020), in which case, the consumer journey conformity (Lascu & Zinkhan, 1999) can be higher or lower (Bearden et al., 1989). In advertising research, product involvement was found to be a relevant differentiator (Han & Kim, 2017; S. Kim et al., 2017; Mainardes et al., 2023; Xue & Muralidharan, 2015). Investigating the effect of involvement is highly relevant in case of consumers journeys, as the usage of a complex product may require higher cognitive effort (Melumad et al., 2019) to proceed through subsequent stages of the consumer decision-making journey (Browne & Kaldenberg, 1997), which results in spending more effort and time in information seeking and research (Tong et al., 2020). In the case of low-involvement products, consumers do not exert great effort to process information (Chung & Zhao, 2003) which shortens their decision-making journey. These differences in the stages of the consumer journey justified the usage of low and high-involvement products in our research conducted in the context of RRPs. With those insights, two final hypotheses were created to understand better the effects of product type and RRPs in the classic consumer journey once the customer has shown the behavioral intention to participate.

H2: Consumer journey conformity is lower with than without a referral code in both involvement scenarios in all three (need, consideration, and evaluation) stages.

H3: Consumer journey conformity is lower for low-involvement products than high-involvement products when a RRP is introduced.

Method

Sample

This study investigates consumers’ relationship with referral codes, for two different product types and in 3 different stages of the consumer journey.

A qualtrics survey was distributed online via social media (Instagram and WhatsApp), using convenience, voluntary response, and planned snowball sampling techniques, and the respondents’ consent was obtained when answering the survey. Although non-probability sampling has a higher risk of bringing biased respondents and more limited conclusions (McCombes, 2019), sample size (n = 218) and diversity of answers (28 countries) contributes to the representation of the population. The SPSS program was used for data analysis.

In the sample, the age mean was 31.96 years old, with a standard deviation of 12.85. Forty-two point two percent of respondents are men, ranging from 21 to 64 years old; 57.8% are women, ranging from 17 to 70 years old. Country diversity was high: people from Brazil contributed 47.7% of the answers, followed by Portuguese at 16.1%, and Germans at 14.7%. The remaining 21.5% of the respondents came from 25 different countries.

Sixty-one percent of the respondents work full-time; others study full-time (12.8%), study and work (14.7%), or work part-time/intern (2.3%), are retired (4.1%) or unemployed (5%).

Measures

Behavioral intention

The behavioral intention study aimed at understanding the relationship and perception of respondents toward RRPs and analyzing consumer behavior when a referral code is present. The behavior in question, defined according to the elements and guidelines of Terry and O’Leary (1995), was whether the person would participate in an RRP when presented with the opportunity. Through four different sets of statements, the questionnaire investigated the interaction using the components of the TPB (attitude, subjective norm, PBC, behavioral intention) and aspects of decision-making and purchase incentive. To ensure mutual understanding, the referral code’s definition was presented, using the RRP of Airbnb as a tangible example. Built on Davis et al.’s (2002) instruments for TPB Questionnaire Construction, 18 affirmations evaluated these aspects using a seven-point Likert scale, ranging from 1 = Strongly Disagree to 7 = Strongly Agree.

Consumer journey conformity (CJC)

This measurement evaluates if the consumer behavior is aligned to the standard journey throughout the three first stages of the process, both for low and high-involvement products. It was used to measure and analyze the differences expected and pointed out in the hypotheses. To ensure the understanding of product types, a brief definition and examples were presented, the same used by Fan et al. (2014), before a set of three affirmations that would measure the consumer behavior for each product type, during each phase of the journey. In the survey, high-involvement products were defined as typically complex in functions or content (e.g. smart phones, cars, and tourism) and low-involvement products were described as usually inexpensive products which possess single functions or simple content (e.g. books, consumer goods, and Uber rides). We have chosen typical examples of generally low and high-involvement products, so there was no need for a manipulation check later. The phases of the journey addressed are: recognizing the need for the product, initial consideration, and active evaluation. These measurements were also made using the same seven-point Likert scale and classified as high conformity (4–7) or low conformity (1–3).

This last part replicated the same affirmations of the second part, with one single difference: the consumer’s possession of a referral code before entering the purchasing journey. The definition of referral codes was presented again, and respondents were asked to answer each affirmation considering they now had a referral code before making the purchase. For the presented example, characteristics of a well-developed RRP were used to maximize the program’s effects on the consumer journey.

“Best friend” was used as the referrer since customers are more likely to use a referral code given by a strong tie rather than by a weak tie (Ryu & Feick, 2007). Having a strong tie as a referrer also makes it less likely for the referred customer to perceive ulterior motives in the recommendation (Verlegh et al., 2013). In terms of reward size and type, since there were multiple examples of low and high-involvement products, and the evaluation of incentive attractiveness is subjective and personal (Orsingher & Wirtz, 2018), the term used was “attractive reward.” Every respondent could think what an attractive reward in their perception would be for the product they chose to use as the guide to their survey decisions.

As the goal was to maximize the chances of the customer engaging with the RRP, this higher reward attractiveness and meta perception would simulate an optimal program and increase the likelihood of the referrer being successful in it (Wirtz et al., 2019). This second part is considered as a new measurement of the same participants, just under a different condition (presence of a referral code), that will be used for comparison purposes.

Finally, demographics were asked about age, gender, nationality, and occupation, which helped to gather more information and context about the sample.

Considering that the four items in the study of behavioral intention part are relatively abstract, one method commonly used to analyze these types of results is grouping items into one unique scale using the mean or sum of the Likert scale values. Since one single item is less capable of making quality inferences about behavior (Rickards et al., 2012), the following analyses will be done based on four new scales created for each variable using the mean of the items.

Measure validation

A confirmatory factor analysis was conducted to assess the reliability and validity of all the constructs in the model as the responses were categorized. The method used to assess scale reliability was Cronbach’s Alpha (α) calculation, which measures the internal consistency and correlation of the items (Rickards et al., 2012). Since there is no clear consensus on the correct way of interpreting the values of α due to a wide range of scholars with different labels (Taber, 2018), K.S Taber’s approach was used, which represents a collective of all that has been used in the literature. Each category of the TPB (attitude, subjective norm, PBC, RRP behavioral intention) had an acceptable level of reliability ranging from .834 to .924. Please see Table 1 for the list of instruments used to analyze TPB.

Table 1. Instruments used to analyze TPB.

Instruments used to analyze TPB
Determinant	Item	Statement
Attitude	A1	I believe participating in a referral reward program is beneficial.
	A2	I believe participating in a referral reward program is positive.
	A3	I believe participating in a referral reward program is valuable.
	A4	I believe participating in a referral reward program is enjoyable.
Subjective norm	SN1	People who are important to me think I should participate in a referral reward program.
	SN2	People who influence my behavior think I participate in a referral reward program.
	SN3	It is expected of me to participate in a referral reward program.
	SN4	People who are important to me would participate in a referral reward program.
	SN5	People would like me to participate in a referral reward program.
Perceived behavioral control	PBC1	Participating in a referral reward program is entirely in my control.
	PBC2	I can choose whether or not to participate in a referral reward program
	PBC3	I am free to participate in a referral reward program as I want to.
	PBC4	I have the knowledge and ability to participate in a referral reward program.
	PBC5	I have the resources needed to participate in a referral reward program.
Behavioral intention	BI1	I have the intention to participate in a referral reward program.
	BI2	If I find it interesting, I will participate in a referral reward program.
	BI3	I want to participate in a referral reward program.
	BI4	I would participate in a referral reward program again.

Source: Davis et al. (2002).

Note: Constructs were measured using seven-point Likert-type scales anchored in 1 = strongly disagree, and 7 = strongly agree.

Results

The Bivariate Pearson Correlation test was used to investigate the correlation between the computed variables. Based on the correlation matrix of measures displayed in Table 2, there is a statistically significant linear relationship between all the pairs of variables. Only the correlations between PBC and RRP behavioral intention are of a high degree; all others are of a low degree, or almost a non-existent degree of correlation. That said, they all follow the same positive direction, as the analyzed variables tend to increase together.

Table 2. Statements used to analyze consumer journey conformity.

Product type	Phase	Statement
High-involvement	Need	I only purchase products/services of this type if there is a real need.
	Knowledge	When I buy products or services of this type, I consider an initial pool of brands based on my brand perceptions and market knowledge.
	Research	I usually research to gather information about the options available in the market that would attend my needs for this type of product or service before buying it.
Low-involvement	Need	I only purchase products/services of this type if there is a real need.
	Knowledge	When I buy products or services of this type, I consider an initial pool of brands based on my brand perceptions and market knowledge.
	Research	I usually research to gather information about the options available in the market that would attend my needs for this type of product or service before buying it.

Source: Fan et al. (2014).

Note: Constructs were measured using seven-point Likert-type scales anchored in 1 = strongly disagree, and 7 = strongly agree.

We employed multiple linear regression analyses to verify the relationship between the independent variables—attitude, subjective norm and PBC—and the dependent variable, behavioral intention, for participating in an RRP. To guarantee the validity of the results, some pre-requisites, like homoscedasticity, multicollinearity, sample size and linearity and normality of residuals, were studied and met numerically or graphically.

The three independent variables of the TPB are significant (Table 3) when explaining the dependent variable (p<.05), with their variance contributing to explain 42.1% of the BI_RRP variance (R2).

Table 3. Correlation between variables.

	Attitude	Subjective norm	PBC	RRP BI
Attitude	1	–	–	–
Subjective norm	.182**	1	–	–
Perceived behavioral control	.239**	.097	1	–
RRP behavioral intention	.161*	.146*	.531**	1

Source: Own research.

* p < .05; **p < .01.

With these results, H1a, H1b and H1c are supported, concluding that TPB components influence the behavioral intention toward participating in an RRP. Based on the analysis, the variable that has the most substantial effect on our results is PBC (β = 0.619), followed by subjective norm (β = 0.565) and then attitude (β = 0.416) (see Table 4).

Table 4. Coefficients table for behavioral intention for RRP.

	β	t	Sig.
Constant	1.905	4.011	.000
Attitude	.416	5.342	.000
Subjective norm	.565	6.566	.000
PBC	.619	8.703	.000

Source: Own research.

The standard consumer journey was investigated in the case of low and high-involvement products, without the influence of an RRP. For each stage of the journey, the closer the answers got to the “7—Strongly agree”, the closer the customer would be from acting as expected in the standard consumer journey (high conformity). Answers closer to the “1—Strongly disagree” represent a slight shift in the expected consumer behavior toward purchasing a product or a service, indicating low conformity. With the ordinal nature of data, it is possible to see by the modes of the sample that buyers react differently when purchasing high and low-involvement products.

Due to the presence of multiple factors, the Wilcoxon Signed Rank Test was conducted to determine the significance of the changes in the consumer journey model. Firstly, the conditions of normality, multicollinearity, and homoscedasticity between the two groups were studied, and all of them were violated—meaning the Cronbach’s Alpha Test could also not validate the reliability of the scale as a continuous variable. Secondly, the nature of the data is not interval or ratio, it is ordinal, which suggests the usage of a non-parametrical test; and lastly, the dependence of the samples considering that the two measurements were done with the sample participants.

Introducing an RRP scenario in the consumer journey has proven itself significant for both product types in all three stages of the journey, as p<.05 in all cases (Table 5). This confirms H2, supporting that the customer would be closer to the standard decision-making process going through all the decision steps in the scenario without a referral code in case of both types of products.

Table 5. Wilcoxon signed ranks test: effects of a referral.

Effects of a referral code
Pair	Z	Asym. sig. (two-tailed)
C_Need_H–Need_H	−10.867	.000
C_Knowledge_H–Knowledge_H	−10.579	.000
C_Research_H–Research_H	−5.596	.000
C_Need_L–Need_L	−2.254	.024
C_Knowledge_L–Knowledge_L	−10.693	.000
C_Research_L–Research_L	−10.310	.000

Source: own research.

Also, Table 6 shows that customers behave differently toward the different product types, regardless of the introduction of the RRP in the scenario, as p<.05 in all scenarios.

Table 6. Wilcoxon signed ranks test: difference among product types.

Effects on different product types
Pair	Z	Asym. sig. (two-tailed)
Need_H–Need_L	−11.705	.000
Knowledge_H–Knowledge_L	−9.906	.000
Research_H–Research_L	−8.877	.000
C_Need_H–C_Need_L	−8.819	.000
C_Knowledge_H–C_Knowledge_L	−10.722	.000
C_Research_H–C_Research_L	−11.400	.000

Source: own research.

This descriptive statistic (Table 7) shows that customers tend to follow all the steps of the traditional consumer journey when buying a high-involvement product, but this will not necessarily be the case in the case of their counterpart.

Table 7. Descriptions of variables without and with the influence of a referral code.

Without the influence of a referral code
Product involvement	Variable name	Consumer journey stage	Mode	CJ conformity
High	Need	Stage 0—need creation	6	High conformity
High	Knowledge	Stage 1—initial consideration	7	High conformity
High	Research	Stage 2—active evaluation	7	High conformity
Low	Need	Stage 0—need creation	3	Low conformity
Low	Knowledge	Stage 1—initial consideration	5	High conformity
Low	Research	Stage 2—active evaluation	5	High conformity
With the influence of a referral code
High	C_Need	Stage 0—need creation	5	High conformity
High	C_Knowledge	Stage 1—initial consideration	6	High conformity
High	C_Research	Stage 2—active evaluation	5	High conformity
Low	C_Need	Stage 0—need creation	2	Low conformity
Low	C_Knowledge	Stage 1—initial consideration	3	Low conformity
Low	C_Research	Stage 2—active evaluation	3	Low conformity

Source: own research.

Notes: The consumer journey conformity is indicated by the mode in the examined three stages of consumer journey, with or without the introduction of a referral code in both low- and high-involvement product scenarios. Low conformity: mode values 1–3, high conformity: mode values 4–7.

Changes can be seen after introducing the referral code for both product types. Regarding product type, customers still seem to have different buying procedures for the different types, being less careful with the low-involvement one. As to the journey steps, they all seem to be more distant from the traditional journey, with the numbers going further from “7 = Strongly agree.” This confirms H3, as after introducing a referral code in case of both types of products, the consumer journey conformity became lower. The results also imply that involving a referral code can decrease the consumer journey conformity more in case of low-involvement products in opposition to high-involvement products.

Discussion and implications

In our research, the TPB (Ajzen, 1991) was applied in the context of Referral Marketing, and the effects of RRPs on the Consumer journey model (Court et al., 2009) were evaluated for two product types.

The examination of the first hypothesis confirmed the belief that the TPB is an adequate instrument to evaluate the behavioral intention to participate in an RRP, as the attitude, subjective norm, and PBC components showed a positive correlation to the behavioral intention. This also supports previous findings in other fields (e.g. Raza et al., 2019 ; Sanne & Wiese, 2018); and answers our RQ1 by indicating the possibility to use the TPB model to indicate the intention to participate in an RRP. Regression analysis shows that the variable that best explains the behavioral intention is the PBC, meaning that if a person believes they have complete control of the process of Iparticipating in an RRP, they are more likely to do so. This also supports Perceived Behavior Control being the dominant factor, proposed by Ajzen (1991).

For the second and third hypotheses, focusing on consumer journey conformity with an RRP and different product types, results also went in the same direction, as expected. While answering our RQ2, we found that the consumer journey is not identical for low and high-involvement products, and has its differences to the traditional journey when the customer chooses to participate in a referral program. The consumers’ decision-making journey’s conformity was found to be high in case of high-involvement products, and low for low-involvement products. The fact that referral programs had a weaker effect on high-involvement products descriptively resonates with the study of Wirtz et al. (2019), in which they rejected that this factor would increase the likelihood of a successful referral.

In this study, the effects of two variables on the consumer journey were evaluated, and they already added a lot of complexity to how customers are willing to engage with RRPs and how they approach the decision-making process. This research adds value to the literature and provides insights for companies on building and targeting RRPs to have a more decisive influence in their customer’s decision journey.

The application of the TPB to the context of RRPs makes contributions to existing literature, mainly because the PBC was the strongest predictor to the behavioral intention, while it was often found to be the weakest in previous studies (Pelling & White, 2009; Sanne & Wiese, 2018), which calls for further investigation. The classifications created for consumer journey conformity can also add to what has been researched in the literature before. Differentiation between low and high-involvement products was not made in previous studies in the Referral Marketing context, therefore this aspect can also add to the referral marketing literature. The CJC shows that the journey of high-involvement products is more consistent to the standard process, meaning customers go through all the pre-defined steps before making a purchase, while the journey of low-involvement products tends to be simpler when taking part in an RRP.

With the presence of a Referral Rewards Program, customers buying low-involvement products tend to give less importance to the initial knowledge and active research phases, since they assume that the referrer already took care of these steps before referring it to them (Ryu & Feick, 2007). Our findings highlight that RRPs are more effective in the case of low-involvement products. For high-involvement products, the weight of these two phases is not as dominant as before but are still considered an essential part of their decision-making process.

The non-parametric tests provided statistically significant results about the existing differences between the pairs of product types. When comparing the two products, it is descriptively shown that without an RRP, the most prominent behavioral difference between them is in the first stage of the decision journey—need recognition, and when the RRP is in place, the most significant impact occurs in the last phase of the journey: research.

These findings expand the scope of the literature on product involvement, presenting a new connection between the RRP and consumer journey. Similar inferences can be made about the statistically significant differences between the standard journey and the journey with the introduction of an RRP. The need for differentiation between low and high-involvement products is also supported, as there are significant differences in customer behavior while using an RRP. With the results from the Wilcoxon Signed Rank Test, which stages are the least affected by the RPP can be determined: in the case of low-involvement products, the need recognition stage; in the case of high-involvement products, the stage of doing further research about the products.

This interpretation has not appeared in previous literature, and it can indicate further research directions in this area.

Managerial implications

These results emphasize how challenging it is for companies to understand the way their customers think through the consumer journey; many particularities challenge creating advertisement tools based on generalized personas. Based on the results of the TPB analysis, firms need to take the importance of creating a referral program that is easily accessible into consideration, giving the consumers the idea that they have control over this action of participating in RRPs—so that the likelihood of them doing so increases. It is important to highlight that the PBC can be influenced by external (e.g. price, availability) and internal factors, such as beliefs (Ajzen, 1991; Magnusson et al., 2001), which indicates the need for marketing practitioners to focus on both external and internal elements in the planning stage of promotion strategies. Nonetheless, attitude and subjective norm also impact the intention, so companies are advised to make sure that the communication of the RRP gives the customer a positive perception of the behavior, both from their own perspective, and others’ points of view.

Consumer journey conformity represents an opportunity for companies with mainly low-involvement products, given that the consumer journey seemed to react well to the influence of a referral code. This means that when using a referral code, customers may skip initial steps in the consumer journeys, leading to shorter customer journeys and prompter sales. Based on our findings, we can say that RRPs are most effective in the case of low-involvement products, which can provide insight for marketing managers. For high-involvement ones, even though they were still in the high conformity classification, their conformity to the standard journey also diminished as a response to the incentivized program. That means companies should see RRPs in general as a potential marketing strategy to be used to get new customers into their funnel.

Regarding RRPs, firms are advised to customize it for their target audience whenever possible (Wirtz et al., 2019). Product involvement should be a factor that is considered while planning, so for low-involvement products, the reward scheme should reward both customers (Fan et al., 2014) to have a bigger impact on consumer journey conformity. This perceived fairness will encourage customers to overlook their tendencies of going through the standard decision-making journey, stepping right into the program with the respective firm, as represented by the low conformity value modes.

Increasing the flexibility of the customer’s choice is an alternative to enhance the effectiveness of RRP in the case of high-involvement products (Fan et al., 2014). As purchasing these products involves risks, it is more difficult to find sufficient rewards for different customers, providing options regarding the rewarding scheme and the reward type could be beneficial. Based on our results, when buying high-involvement products, if the reward is perceived as attractive enough, consumers could be willing to give less importance to the research phase than before, which may shorten their customer journeys and may have an impact on sales’ timing.

Limitations and future research

Regarding the variables of the TPB that affect behavioral intention, future studies could try to understand why, in the context of an RRP, PBC was the strongest predictor when in past studies, and attitude and subject norm were the ones that best related to the intention (Pelling & White, 2009; Sanne & Wiese, 2018). This research suggests that this happens because of lower costs—e.g. time invested in the action—that are involved in the behavior if the individual perceives having complete control of it, instead of having to build a skill to perform it. In future research, the deviating effect of RRP on consumer decision journeys may be further examined.

Studying the post-purchase stage of the consumer journey, and focusing on the loyalty loop, can also contribute to previous knowledge. It would be relevant to understand if any factors within an RRP could influence customers to choose that brand, stay with it, and eventually become a loyal customer.

Carmack and Heiss (2018) state that additional research is needed to reveal the interaction effects among past behaviors, behavioral control, and intentions, validating the TPB scale in this respect, and comparing findings across TPB studies could be important contributions for this field.

Limitations of the study were present since a non-parametric test was conducted to examine the consumer journey conformity. There were also data violations that happened with the ordinal collected in the survey, which are proven to be less statistically accurate than parametric tests. It would be interesting to conduct the study while using a continuous response variable Y, taking as an example the “referral likelihood” variable used by Ryu and Feick (2007). Furthermore, the non-probability sampling method could have brought more biased answers into the study, limiting the generalization of the obtained results. Future studies should then try to use a less biased and more random sampling technique in an attempt to gather more reliable data.

In conclusion, RRPs has been previously explored, but this unprecedented connection with the consumer journey adds value to the field. This study reinforced the theoretical importance of adding the PBC as a variable in the Theory of Reasoned Action, then called TPB, as it was seen to be the best predictor of actual behavior. This finding is valuable for companies when creating their referral programs—making sure that it will be easy to understand, use, and share with friends. Moreover, companies need to focus on making their programs as customizable as possible regarding reward type, reward scheme, messaging (to enhance strong tie interactions), and product type based on involvement.

Ethical approval

This work was approved by the Research Ethics Committee of Corvinus University, KRH36/2023.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was funded by Fundação para a Ciência e a Tecnologia (UIDB/00124/2020, UIDP/00124/2020 and Social Sciences DataLab—PINFRA/22209/2016), POR Lisboa and POR Norte (Social Sciences DataLab, PINFRA/22209/2016).