THE ROLE OF INDIVIDUAL VALUES IN THE BEHAVIOR OF USING DISCOUNT COUPONS

O PAPEL DOS VALORES INDIVIDUAIS NO COMPORTAMENTO DO USO DE CUPONS DE DESCONTO

ABSTRACT

Purpose

The research analyzed the determinants (Performance Expectation, Expectation of Effort, Social Influence, Facilitating Conditions, Hedonic Motivation, Value and Habit) of the intention and the behavior of using online discount coupons, through UTAUT2 in the Brazilian context.

Design/methodology/approach

The survey was adopted with an instrument adapted from Yang (2010) and Christino et al. (2019) validated by experts. Made available online, the instrument collected 309 responses for analysis using the structural equation modeling technique.

Findings

The results validated the positive relationships for Facilitating Conditions, Hedonic Motivation, Perceived Value, Habit and Performance Expectation - the highest’s coefficients. The influence of Expectation on Effort and Social Influence has not been validated.

Research limitations/implications

The results cannot be generalized to all Brazilian individuals, in addition to considering recognized determinants of international literature. For this reason, suggestions are made for continuing and deepening the research.

Practical implications

The results contribute by indicating the main perceptions that lead to the intention and use of discount coupons, which are the performance expectation and the habit. Thus, managers can develop their sales strategies considering such factors while society can establish strategies to more sustainable purchases.

Originality/value

The research discusses the determinants of UTAUT2 in the Brazilian context to explain the intention and behavior of using online discount coupons, which are grouped together are unprecedented in Brazilian literature.

Keywords:
Discount Coupon; UTAUT2; Technologies; Loyalty; Utility

RESUMO

Objetivo

A pesquisa analisou os determinantes (Expectativa de Desempenho, Expectativa de Esforço, Influência Social, Condições Facilitadoras, Motivação Hedônica, Valor e Hábito) da intenção e comportamento de uso de cupons de desconto on-line, por meio da UTAUT2 no contexto brasileiro.

Metodologia

Adotou-se a survey com instrumento adaptado de Yang (2010) e Christino et al. (2019) validado por especialistas. Disponibilizado por meio on-line, o instrumento coletou para análise 309 respostas, analisadas a partir da técnica de modelagem de equações estruturais.

Resultados

Os resultados validaram as relações positivas para Condições Facilitadoras, Desempenho Hedônico, Valor Percebido, Hábito e Expectativa de Desempenho - a de maior coeficiente. A influência da Expectativa de Esforço e da Influência Social não foram validadas.

Limitações Teóricas/Implicações

Os resultados não podem ser generalizados a todos os indivíduos brasileiros, além de considerar determinantes reconhecidos da literatura internacional. Por isso, são apresentadas sugestões para continuidade e aprofundamento da pesquisa.

Implicações Práticas

Os resultados contribuem a medida que indicam as principais percepções que levam à intenção e ao uso de cupons de desconto, quais sejam o desempenho utilitário e o hábito. Assim, gestores podem desenvolver suas estratégias de vendas considerando tais fatores enquanto a sociedade pode estabelecer estratégias para compras mais sustentáveis.

Originalidade

A pesquisa discute os determinantes da UTAUT2 no contexto brasileiro para explicar a intenção e o comportamento de uso de cupons de desconto, o que agrupados são inéditos na literatura brasileira.

Palavras-Chave:
Cupom de Desconto; UTAUT2; Tecnologias; Fidelização; Utilidade

1 INTRODUCTION

Electronic commerce every year attracts more Brazilians to the web shopping, new stores are launched and traditional players in the physical retail invest in increasing the online market. The online shopping is longer a promising prospect to become a reality in Brazil. In 2014, 51.5 million people have made at least one purchase online in Brazil (E-bit, 2014), while in 2018, the survey ‘TIC Domicílios’ revealed that 82 per cent of Brazilian consumers with internet access have already opted for online shopping, that is, more than 130 million people (Cetic, 2020). During social isolation due to the Covid-19 pandemic, revenue from online sales increased by 47 percent in the first half of 2020 alone (Exame, 2020).

Increasing sales and access to the website is the first strategy that an e-commerce seeks when working with more active marketing strategies, as with online discount coupons (Rodrigues & Martins, 2020). By offering a discount coupon for your customers, the company stands out from its competitors and awakens an interest in purchasing their products in the consumer, who can identify immediate savings opportunity for financial discount.

Unlike traditional business models, economies based on virtual access have dependency characteristics of the internet mechanisms, lower priced products/services more personal, ability to access inactive resources and unconventional workforce (Mao & Lyu, 2017). An efficient strategy to improve sales, from the consumer’s perspective, is the use of discount coupons with a percentage for use in future purchases, which can result in increased sales in the short and long term, and loyalty (Anauate et al., 2020).

Coupons promotional discounts facilitate the decision to purchase, reducing the frequent abandonment of products in the cart. In addition to increasing the conversion rate of companies, reduces investment in remarketing, since the number of consumers who do not complete the purchase will be greatly reduced. On this point, when it adopts a consumer behavior perspective on consumption based on access, hereof by using discount coupons, it is especially important to understand and deepen the influential factors in the intended use (Mao & Lyu, 2017). The aim of this study is to analyze the determining factors in the process of intended use and behavior use online discount coupons from the unified theory of acceptance and use of technology (UTAUT2).

In the literature, however, little is discussed comprehensively about the determinants of intention to use behavior of the individuals involved in the use of discount coupons, which have remained in their role as a determinant of sales, experimentation, in products and services, and in their operationalization (Alvarez & Casielles, 2005; Bawa & Shoemaker, 1987; Ndubisi & Moi, 2005). Even recent research shows that it has advanced towards popularization in specific sectors and businesses (Agarwal & Dham, 2018).

Although other studies are useful and point contributions to the understanding of the behavior associated with the technology, few studies consider models and psychological theories and acceptance of dominant technologies in consumer behavior research flow (Christino et al., 2019). Without greater knowledge of the determinants of the use of these technologies, strategies aimed at increasing sales with the help of discount coupons may be ineffective, as comprehensive solutions applied to everyone regardless of their behavioral perceptions (Kadoya et al., 2020).

This study has the potential to contribute to the discussions of the theory applied to consumer behavior with respect to different variables that may have different characteristics in the field. The way in which consumers are sensitive to aspects of local culture makes it relevant to know the aspects of each country, including the Brazilian one (Ibiapina et al., 2020; Singelis & Sharkey, 1995). Moreover, it can result in practical contributions to the market since the adoption of the most appropriate strategy involves reducing costs and boost sales.

2 THEORETICAL FRAMEWORK

2.1 Consumer Behavior

Consumer behavior analysis assumes since the 1970s, the emerging field of status in various sciences, the optics of the motivations that lead consumers to buy attitude is constantly evaluated by economics, sociology, anthropology, psychology, management, philosophy, law and for communicating with different objects and methods from specific readings that area (Sampaio et al., 2013). Companies in the desire to offer the best products are taken to investigate the causes that attract or away consumers buying decision became the subject of discussion, plus a desire to customer satisfaction that has brought a new international literature format.

From the perspective of Sheth et al. (1999), the study of the consumption behavior requires an understanding of all the elements that make the purchase, and bring the discussion to the motivations that lead individuals to discontinue the use of such services. In optical Blackwell et al. (2005) various elements influence the buying behavior, not only cultural, but also elements linked to other fundamental aspects for discussion, such as ethnicity, personality of the individual, family, stages or cycles of life, values acquired over the years, income, buying attitudes, opinions of influencers, individual motivations, previous experience of buying and many other elements.

Initial research on consumer behavior were based on economic concepts in the individual purchase rationally to maximize their benefits. Subsequent research showed that consumers can buy impulsively and may be influenced by family, friends, advertising for his state of mind, for their situation and the emotion (Garcia et al., 2010). Considering the relationship between service quality and behavioral intention, Zeithaml et al. (1996) proposed a model of behavioral and financial consequences.

Shimp (2003) argues that the sales promotion techniques, whether monetary or not, reward the customer with some incentive or benefit, encouraging him to purchase. The author adds that the price reduction (discount) should be used when the manager thinks of reward users of the brand, cause consumers to buy more, stop the competition, obtaining various spaces and provide incentives to retailers. Kumar and Swaminathan (2005) argue that the higher the discount percentage, the greater the influence of this type of promotion in the shop.

Among the promotional activities found, there are different modalities, such as an immediate percentage discount applied to the purchase or its issue (Alvarez & Casielles, 2005; Anauate et al., 2020). Mobile coupons (M-coupons) became a third marketing channel, after digital and print media (Gonzales, 2016). Text messages, electronic mail or online platforms redeemed within stores to obtain a discount on a product or service are known as m-coupons (Andrews et al., 2016; Hsu et al., 2006).

The discount coupon can be considered a well-known item in the management and sales promotion. Set as a promotional mechanism that offers a discount to the consumer at the time it is presented, contributing to the loyalty increase in revenues. Burns (2009) found that 99 per cent of US consumers have used some type of coupon and that the assessment of good or very good was 93 per cent. Shimp (2003) mentions that in Canada and Europe coupons are also widely used while in Brazil this type of sales promotion is still little used (Mondo & Costa, 2013).

Discount coupons have long-term effects, generating a future purchase, due to the advantage offered or their redemption condition (Bawa & Shoemaker, 1987; Ndubisi & Moi, 2005). Empirical evidence indicates that discount coupons mediate the relationship between promotions and repurchases (Ndubisi & Moi, 2005).

Barat et al. (2013) highlight the need for the evolution of research on discount coupons in the psychological aspect, comparing previous research that examined the redemption of coupons from a perspective of rational decision-making, whose support provided sequential steps since the research of coupons, clipping and storage of the coupon. More recent research adds to affiliate marketing, whereby discount coupons are issued on a specific website for such high-traffic coupons, and which becomes a business partner, usually through revenue commissions (Agarwal & Dham, 2018).

These strategies are closer to collaborative consumption by group businesses, where consumers help each other to achieve consumption benefits, mediated by specific discount redemption coupons websites (Chen et al., 2016; Hamari et al. 2016). More punctually, it is also possible to perceive discount coupons as a tool for implementing economic stimulus policies, as in 2015, when the Japanese government implemented a discount shopping coupon scheme, offering a 20 per cent discount on purchases (Kadoya et al., 2020).

2.2 Unified Theory of Acceptance and Use of Technology (UTAUT2)

For the theoretical basis of the research, it was applied the instrument of Unified Theory of Acceptance and Use of Technology (UTAUT2). In its essence, this model analyzes the determinants of behavior intention and use of products or services on technological platforms (Miles, 2012). The proposed theoretical model assumed concepts and set seven determinants: performance utility, hedonic, performance expectation, expectancy of efforts, social influence, value, habit and facilitating conditions (Venkatesh et al., 2012).

The model of the Unified Theory of Technology Acceptance (UTAUT) was developed in 2003 from the review and consolidation of eight theories and models used to explain the behavior and the use of information systems: the Theory of Reasoned Action (TRA) the Model Technology Acceptance (TAM and TAM 2), the Motivational Model (MM), the Theory of Planned Behavior (TPB), the combined model TAM and TPB (C-TAM- TPB), the use of model PC (MPCU), the Innovation Diffusion (IDT) and the Social Cognitive (SCT) (Miles, 2012).

Since its inception, the UTAUT has been used in various contexts, applied to various technologies and to use individual or organizational behavior (Baptista & Oliveira, 2015). Despite its widespread use, and their detailed explanation power in the intended use, the model presented limitations that have led to the development of UTAUT 2, which extends and improves the theory applying it to the user context (Christino et al., 2019).

The model of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was developed by Venkatesh et al. (2012), as an extension and adaptation of the original model UTAUT. Existing key constructs were adapted the organizational context for consumption context.

Furthermore, the constructs performance expectancy, effort expectancy, social influence and Facilitating Conditions continued as influencers of Behavioral Intent along with Hedonic Motivation, the price value and habit. The construct Conditions Facilitators gained a new relationship and the perception of influence on the behavior of use, together with the habit and Behavioral Intention. From the basis of the theory and the recognition of the various levels of sensitivity and propensity to use by consumers, the hypotheses for verification are proposed.

The construct denominated Performance Expectation or Utility Performance is defined as the degree to which the use of a technology will provide benefits to consumers in carrying out certain activities (Venkatesh et al., 2012). Within of original template, this construct was found to predict Intention more strongly and remained significant in all measurement points performed by Christino et al. (2019).

According to Barat et al. (2013), the benefits of coupon redemption can also be classified as utilities, exemplified by the economy, better product quality or the improved shopping experience with these coupons. The individual, when receiving a coupon, performs a cognitive cost-benefit analysis, which answers whether the coupon should be redeemed (Alvarez & Casielles, 2005). Thus, we propose the following Hypothesis 1.

• Hypothesis 1: The Utility Performance impacts positively on the intention to use discount coupons.

The ease associated with the use of technology by consumers, is known as the Effort Expectancy (Venkatesh et al., 2012). Venkatesh et al. (2003) state that the ease of use has a positive effect on the acceptance of new technologies. In the model, effort expectancy positively influences Behavioral Intent, a result that is supported by other studies (Macedo, 2017). In the present study assessed the ease associated with the use of discount coupons.

As stated in Godoe and Johansen (2012), the perceived ease of use of a technological tool favors the perception of usefulness. Some applications easy to use can be perceived as useful, but not all useful applications are easy to use. Consumers prefer the easy to use and understand technologies that have the highest efficiency (Gupta et al. 2018). The ease of use is critical in situations of purchase discount coupons.

Ease of use is essential in situations of acquisition and redemption of discount coupons, since the individual can redeem the coupon immediately on impulse without necessarily going through the effort of an entire research, generation, and custody process (Barat et al., 2013). So, it is possible to formulate the Hypothesis 2.

• Hypothesis 2: The effort expectancy negatively impacts the intention of using discount coupons.

The construct Social Influence is defined as the extent to which consumers perceive that people in their social environment, as friends and family, consider important to use certain technology. Social influence is composed of three variables: the subjective norm, the social factor and the image (Huang & Kao, 2015).

The subjective norm is related to the pressure perceived to use any tool, product or service (Ajzen, 1991). The social factor is the internalization of the individual from the subjective culture of the social system, interpersonal agreements with others; and the image is defined as the degree to which an individual identifies the use of innovative technology can improve your status in social organization (Huang & Kao, 2015). Venkatesh et al. (2003) and Venkatesh et al. (2012) theorized that social influence is crucial in the Behavioral Intent, which can be seen in numerous empirical studies, such as that by Christino et al. (2019).

Aspects of local culture affect the effects of social influence on consumer behavior, as is the case with collectivist cultures, which are particularly sensitive to how other people view certain behavior (Hofstede et al., 2010; Singelis & Sharkey, 1995). Even so, some local cultures may treat cognitively differently, such as the citizens of Japan admittedly collectivist but who are ashamed to redeem coupons (Kashani & Quelch, 1990).

The weight of the opinion of important people in consumer behavior was reinforced by studies investigating the adoption of mobile shopping services (Yang, 2010), adoption of banking online (Luo et al., 2010) adoption of mobile applications (Hew et al., 2015). Social influence also demonstrated a positive relationship with the intention and use of coupons, as in Wu and Lee (2017) for redemption coupons, and Prabhakaran and Vasantha (2020), Jayasingh and Eze (2012) and Phan et al. (2020) for mobile coupons.

In this work, the Social Influence is measured in how respondents perceive opinion of the people important to them about the use of discount coupons. Therefore, emerges the following Hypothesis 3.

• Hypothesis 3: The Social Influence affects positively the intention of using discount coupons.

The variable Facilitating Conditions refers to the perceptions of consumers about the resources and supports available to use technology (Venkatesh et al., 2012). The Facilitating Conditions are environmental factors and can vary significantly between different platforms. The involvement of consumers in certain tasks will depend on an infrastructure conditions can facilitate the necessary interaction.

According to Venkatesh et al. (2012), the Facilitating Conditions will act more as a perceived behavioral control and will influence the intention and behavior (Ajzen, 1991). A consumer who has access to a favorable set of Facilitating Conditions is more likely to have a greater intention to use certain technology (Venkatesh et al., 2012), validated by previous studies (Dwivedi et al., 2016).

In the case of discount coupons, the individual can redeem a coupon immediately, on impulse, without necessarily going through an effort to produce or win it (Barat et al., 2013; Heilman et al., 2002). Thus, the Facilitating Conditions are related to both the Behavioral Intent and for the behavior of Use and are measured in this research as the perception of individuals regarding the availability of resources and supports for the use of discount coupons.

The positive relationship between facilitating conditions and intention and use behavior, as demonstrated in recent research such as Jun et al. (2018) for payment apps, Madan and Yadav (2018) for e-shopping apps with discount coupons, Wu and Lee (2017) for redemption coupons, and Phan et al. (2020) and Jayasingh and Eze (2012) for ‘mobile coupons’ directly and indirectly, respectively (see Hypothesis 4a and 4b).

• Hypothesis 4a: The Facilitating Conditions influence positively the intention of using discount coupons.

• Hypothesis 4b: Facilitating Conditions influence positively the behavior of using discount coupons.

The variable Hedonic Motivation defined by Venkatesh et al. (2012) as pleasure derived from use of a technology, also known as intrinsic motivation (Vallerand, 1997) was added to the extended model, acting as a precursor of behavioral intention. Hedonic consumption is the facet of consumption that relates to the multisensory, emotional and fantasy aspects of using the product (Gerhardt et al, 2020; Leitinho & Farias, 2018).

Hedonism has more subjective and personal and is related to the essence of the psychological experience of the individual (Huang & Kao, 2015; Ozturk et al., 2016). Thus, for a hedonic perspective, consumers are seeking pleasure, satisfaction and enjoyment with the use of a product or service, considering the purchasing process as a nice practice (Anderson et al., 2014; Gasimov et al., 2010; Mittal, 1994). Monetary and non-monetary sales promotions provide different levels of hedonic benefits to the consumer, such as expression of value, entertainment and exploration (Barat et al., 2013; Chandon et al., 2000).

In this research, it is understood as pleasure perceived in the use of discount coupons for shopping, which the use of the coupon at the time of purchase provides. The literature supports the positive relationship between the hedonic motivation and behavior technology adoption (Baptista & Oliveira, 2015).

Ozturk et al. (2016) emphasize the utility, and hedonic scale as critical dimension determining the influence to the continuous use of tools. Previous research has shown that hedonic motivation has a positive relationship with the behavior of using financial technologies, such as Madan and Yadav (2018) for electronic shopping applications with discount coupons, and Liu et al. (2015) and Souiden et al. (2019) for mobile coupons. Thus, it is proposed the Hypothesis 5.

• Hypothesis 5: The Hedonic Motivation positive impact on intention to use discount coupons.

The object’s Value is defined as the exchange process (tradeoff) between the perceived benefits of the product and/or service and the monetary cost to use them. There is an important difference between consumer and organizational contexts, which led to the inclusion of the value construct the model, is that consumers tend to support the monetary cost of using technology, while employees do not (Venkatesh et al., 2012).

The price value reflects the conviction of the respondent that the use of technology will be valuable in terms of cost for example, cost/benefit (Frank & Milkovic, 2018). If the economic benefit is low, consumers are less likely to use a service (Jun et al., 2018). Among the benefits of coupon redemption are the perception of savings or savings (Barat et al., 2013; Milkman & Beshears, 2009). Consumers, as highlighted Alalwan et al. (2017), are predisposed to adopt certain technology, based on their budget constraints. Thus, intelligently analyze the utilities included in the use of new systems to the financial cost, which should be paid for this system (Alalwan et al., 2017).

Baptista and Oliveira (2015) stress that, in the context of adoption of Internet technologies, the construct Value integrates elements such as the cost of the device, the cost of data service providers and transaction fees (Christino et al., 2019). Empirical evidence indicates that there is a positive relationship between value or benefit to the consumer and adherence to the corresponding technology, as evidenced by Madan and Yadav (2018) for electronic shopping applications with discount coupons, Jun et al. (2018) for payment applications, and Souiden et al. (2019) and Liu et al. (2015) for ‘mobile coupons’ for the economic discount and the inverse reason to its cost. Therefore, it is proposed the Hypothesis 6.

• Hypothesis 6: The value influences positively the Behavioral Intention of use discount coupons.

The construct Habit is defined by Limayem et al. (2007) as the extent to which people tend to perform behaviors automatically because of learning, ie, the degree to which the individual believes that the behavior is automatic. Unlike reflections, that certain behavior becomes a habit learning is required. This is a short-term repetition of composition, reinforcement, clarity of the situation, interest and ability to learn (Pahnila et al., 2011).

As stated in Lankton and Wilson (2013), past behavior and habit, although intimately connected, are not identical constructions. The repeated occurrence is critical for the formation of habit, but do not make up the habit itself. The habits tend to be formed when the behaviors are repeated in a stable environment (Wilson & Lankton, 2013). Venkatesh et al. (2012) posit that the habit has a direct effect on Behavioral Intention. This relationship is confirmed by Gutierrez et al. (2017). Existing studies also highlight the effects on the use (Gupta et al., 2018).

For Frank and Milkovic (2018) the habit determines the level of certainty of the individual that, according to their experiences, the use of new information technologies will become a routine. Aarts et al. (1998) found that the force of habit attenuates the amount of information being acquired before the decision is made. Limayem et al. (2007) and Venkatesh et al. (2012) define a habit as the degree to which consumers tend to carry out the use of technologies or the use of technology products automatically because of learning.

Mills and Zamudio (2018) highlight a controversial point between habit and loyalty to a brand or company in contrast to the incentive and insensitivity of discount coupons. These coupons lead consumers to repeat purchases, capturing consumer loyalty to a brand, but from repeated purchases from the same company, the effect of the coupons may become less effective. Empirical evidence indicates that coupon use behavior was previously indicated as a determinant of future intentions and actions (Barat et al., 2013).

The Habit is understood as the degree to which people tend to perform automatic behaviors through learning (Limayem et al., 2007). The practice has been noted as significant predictor of behavioral intention (Gutierrez et al., 2017). Existing studies also highlight the effects of habit on the use of behavior (Gupta et al., 2018). So, it is possible to formulate the following Hypotheses 7a and 7b.

• Hypothesis 7a: Habit impacts positively the intention of using discount coupons.

• Hypothesis 7b: Habit impacts positively the behavior of using discount coupons.

Finally, the construct Behavioral Intent remains of the original theory UTAUT in which is considered mediator variable of Use, and the degree to which the individual feels motivated to adopt certain behavior. That is, the intention to use or continue to use technology in the future. In this research, it refers to the intention of the respondent to become a user of discount coupons.

The intended use was observed by social psychologists, who widely explored behavioral intentions and relations with the future behavior (Aarts et al., 1998). The behavioral intention, or intended use, refers to the degree to which a person has formulated plans to make conscious or not some specified future behavior. Behavioral intention was often measured as the loyalty, which is an important goal in marketing (Giovanis et al., 2013). In the context of marketing, loyalty is defined as the degree to which customers are willing to buy again a product and support the company with mouth communications to positive mouth (Webb et al., 2009). Therefore, we assume the following Hypothesis 9.

• Hypothesis 9: The Intent of Use impacts positively the behavior of using discount coupons.

Figure 1
Structural Equation Model

3 RESEARCH METHOD

To achieve the objective proposed in the research, we adopted a quantitative approach through survey data collection technique, and a questionnaire modeled from the constructs of UTAUT2 (Venkatesh et al., 2012), adapted to the context in question (Christino et al., 2019; Yang, 2010) and Brazilian individuals who possess knowledge about discount coupons, after discussion with experts.

The final instrument was based on nine constructs: Performance Expectation, Efforts Expectancy, Social Influence, Hedonic Motivation, Value, Habit, Facilitating Conditions, Behavioral Intention and Behavior of Use. The Table 1 presents a summary of the observable variables and the constructs used.

Table 1
Variables

Construct	Items
Performance Expectation (DU)	UPE1. I find it helpful to use discount coupons.
	UPE2. Use discount coupon allows me to spend less money on purchases.
	UPE3. Use discount coupons allow me to try new products and services.
Hedonic Motivation (DH)	HPE1. I enjoy shopping with discount coupons.
	HPE2. The discount coupons acquisition process is enjoyable.
	HPE3. Find discount coupons is interesting.
Efforts Expectancy (ESF)	EE1. My interaction with sites and discount apps is clear and understandable.
	EE 2. I find it easy to shop with coupons.
	EE3. It's easy for me to become skilled (a) in procurement mechanisms with discount coupons.
	EE4. Learning to use procurement mechanisms with discount coupons would be easy for me.
Social influence (SI)	SI1. People who influence my behavior think I use discount coupons.
	SI2. I would use discount coupons because of the proportion of my friends who use coupons.
	SI3. People who are important to me think I should use discount coupons.
Value (NPV)	FV1. Use discount coupons makes the value of goods and services better.
	FV2. Use discount coupons makes the best cost/benefit.
	FV3. The discount coupons offer a good discount.
Habit (BHA)	FH1. The use of discount coupons has become a habit for me.
	FH2. I am addicted to using discount coupons.
	FH3. I have need to use discount coupons.
	FH4. Use discount coupons became natural to me.
Facilitating Conditions (FAC)	FC1. I have access to the internet, computer or mobile phone to access websites and discount coupons applications.
	FC2. I have opportunity and knowledge to use sites and shopping apps with discount coupons.
	FC3. I have the knowledge needed for shopping discount coupons.
	FC4. I know sites that send discount coupons.
Intent (INT)	BI1. If there is opportunity, I intend to use discount coupons.
	BI2. I hope to continue using discount coupons.
	BI3. I plan to buy products with discount coupons.
Use Behavior (CPT)	UC1. Frequency of Use

• Source: Research Instrument

The items were measured using a Likert scale of seven points (1 “Strongly Disagree” and 7 “Strongly Agree”), however the variable Usage Behavior (UC) is formatted from the frequency range in interval variable 1 to 5 (Christino et al., 2019). The use of behavioral interval variable won the following weights: I have never used, 1; I rarely use, 2; I use monthly 3; I use biweekly 4; I use weekly 5.

The questionnaires were made available through Google Forms platform, from June 19 to August 30, 2019. The sample was collected by convenience and based on ease of access to individuals who had a total of 309 valid responses. Data analysis was performed from the modeling technique of structural equation modeling (SEM) estimated from the Partial Least Squares (PLS) developed by Wold (1982, cited in Ringle et al., 2015). The technique allows to estimate several multiple regression equations separate but interdependent, simultaneously, by specifying the structural model (Hair Jr. et al., 2016).

For the analysis, there was an evaluation of the measurement model and the structural model. Validations elapsed analysis in bootstrapping (PLS) consisting of the best alternative to test the hypotheses of mediation and provide a rigorous test of the importance of indirect effects, evaluating and comparing the indirect effects in models of mediation (MacKinnon et al., 2004).

4 ANALYSIS OF RESULTS

In this section, the results obtained from the processing and data analysis are presented. Initially, it performed a descriptive statistical treatment with the profile data of the respondents in the sample, and the frequency distribution of sociodemographic variables. Then the technique of structural equation modeling was used in order to validate the model of research, in addition to checking the veracity of the hypothetical relationships, allowing thus infer empirical implications for social reality.

4.1 Sample Characterization

For the characterization of the sample, the following variables, gender, age, marital status and family income, according to Table 2 in order to highlight the social context of the respondent sample, which can influence the interpretation of research findings.

Table 2
Profile of the respondents

Variable	Category	Qty	Percentage	Variable	Category	Qty	Percentage
Genre	Female	160	0.52	Education	High school	122	0.40
	Male	147	0.48		Bachelor	83	0.27
	Did not inform	2	0.01		Master / Doctorate	100	0.33
Total		309	1.00			305	1.00
Age Group	Up to 25 years	112	0.36	Family income	Less than 2.5 min. wage.	57	0.18
	26 to 35	91	0.29		2.5 to 5 min. wages.	101	0.33
	36 to 45	46	0.15		5 to 7 min. wages.	39	0.13
	46 to 55	37	0.12		7.5 min. wages or more	90	0.29
	Over 56 years	23	0.07		I prefer not to comment	22	0.07
Total		309	1.00			309	1.00

• Source: Own elaboration.

The sample is characterized by young respondents, 65 per cent under 35, however, the gender distribution is demonstrated equilibrated with female predominance of 52 per cent. With regard to training and income characteristics, the sample has the peculiarity that intermediate characteristics have fewer observations.

The formation of respondents is located on the high school level (40 per cent) and the titration of master and doctorate (33 per cent), leaving to the intermediate titration to bachelor level as only 27 per cent of the sample. In the national context the formation tends to be observed at lower levels (IBGE, 2019), however teachers and strict sense level researchers have greater sensitivity of the importance and difficulty of collecting data for scientific research, resulting in increased number of observations in the strata of more higher formation.

The distribution of family income follows the characteristics of the formation of the respondents, in which the income relates to the level of training (Dallabona et al., 2013). The major bands observed are between 2.5 and 5 minimum wages (33 per cent) and above 7.5 the minimum wage (29 per cent), this being the point with the highest denial response by 7 per cent. The sample peculiarities mitigate the usefulness of the findings, however the features can also enhance the interpretations in the context of the research, given that the respondents have strong availability of funds to make purchases, in addition to an early age, which is a factor of encouragement consumption.

4.2 Measurement Model

Data were analysed with structural equation modelling. The modeling of method PLS operated by Ringle et al. (2015) performs a sequence of regressions in terms of weighting vectors, in this case 300 iterations.

To assess the validity of the constructs were observed discriminant validity and convergent validity. The discriminant validity is to be checked first, with the initial 32 observable variables appearing in the search tool, all proved to be satisfactory with the highest load in the indicated construct. Later to model the significance of adjustment, they were excluded from the latent variables Effort Expectancy (ESF) and Social Influence (IS), with p-value greater than 0.05.

The convergent validity is the next step after the analysis of the factor loadings. This step is carried out analysis of AVE (Average Variance Extracted), Composite Reliability (CR) and the complementary Cronbach’s alpha and R². The convergence factors for AVE, CR, and Cronbach’s alpha, exhibited satisfactory values, respectively from 0.5, 0.7 and 0.7 (Fornell & Larcker, 1981) evidenced in Table 3.

Table 3
Analysis of AVE, CR, and Alpha

Variable	Cronbach's alpha	Reliability composed	Average Variance Extracted (AVE)
Parameter	0.7	0.7	0.5
DH	0.838	0.902	0.755
DU	0.843	0.905	0.762
ESF	0.892	0.925	0,756
FAC	0.863	0.917	0.786
HAB	0.920	0.943	0.806
INT	0.914	0.946	0.853
IS	0.876	0.924	0.802
VAL	0.906	0.941	0.842
UC	1.000	1.000	1.000

• Source: Research Data

The analysis of Table 3 shows that all loads for AVE were statistically significant, i.e., values greater than or equal to 0.5 (Fornell & Larcker, 1981). The indicator of the Composite Reliability met the threshold values satisfactory in the same way that the Cronbach’s alpha values above 0.7 for all the latent variables. Reliability indicators Composed and Cronbach Alfa satisfactory signal that the sample is theoretically free of bias and that the data collection instrument is reliable (Hair et al., 2016).

The calculation of Cronbach’s alpha is used to check the internal consistency of the results of each latent variable (VL), determining the expected error of measurement taken, and the closer to 1.00, the smaller the error the greater the expectation and reliability of the instrument (Hair et al., 2016). In the present sample, Cronbach’s alpha results showed an acceptable degree of fidelity.

The following analysis step of discriminant validity is verification of latent variables of the model, which according to Fornell and Larcker (1981), is confirmed when the value of the square root of the AVE is greater than the absolute values of correlations with other latent variables. Thus, it is expected that the main diagonal needs to have higher values than for the other latent variables, which is shown in Table 4.

Table 4
Discriminant Validity

VL	DH	DU	ESF	FAC	HAB	INT	IS	UC	VAL
DH	0.869
DU	0.642	0.873
ESF	0.654	0.620	0.869
FAC	0.330	0.416	0.654	0.887
HAB	0.550	0.471	0.529	0.403	0.898
INT	0.619	0.720	0.632	0.526	0.515	0.923
IS	0.586	0.477	0.420	0.266	0.509	0.448	0.895
UC	0.415	0.430	0.485	0.454	0.661	0.462	0.366	1,000
VAL	0.589	0.664	0.685	0.485	0.503	0.661	0.446	0.416	0.917

• Source: Research Data

Likewise, it is also shown the R² value to enrich the analysis and identification of the relevance of the model, and has no cutoff, while the largest possible is desired. Like this, R2 indicating how the model explains given variable. In the present study, it was demonstrated that the model explains 63.6 per cent of the use intention (INT), and in turn explains 48.3 per cent of the usage behavior (UC). The cause and influence of the independent variables dependent constructs is shown in Table 5 and Figure 2.

Figure 2
Structural Equation Model

4.3 Structural Model

The analysis of the structural model allows statistically validate the relationship between the constructs and the connections of a structure built according theoretical basis (Hair Jr. et al., 2016). To assess the validity of the structural model are analyzed criteria of (i) the size and significance of the path coefficients and (ii) determination coefficients of Pearson (R²) through the bootstrapping technique, and (iii) Relevance Predictive (Q²) blindfolding the platform, in addition to (iv) effect sizes (F²) (Hair Jr. et al., 2016).

In bootstrapping, sub samples are created with random observations taken from the original data set (with replacement) and then used to estimate the model PLS paths. In this case was generated under different samples N = 5,000, as recommended by Hair Jr. et al. (2016).

In bootstrapping, the structural relationships were validated, except the influence of Effort Expectancy and Social Influence on the intended use, causing new reanalysis of data, this time without the variables not validated.

Table 5
Relationship of the variables

Relationship	Original sample (O)	Sample mean (M)	Standard deviation	Statistics T (O/ STDEV|)	P-values
DH → INT	0.163	0.164	0.050	3.283	0.001
DU → INT	0.377	0.377	0.066	5.714	0.000
FAC → INT	0.197	0.198	0.051	3.841	0.000
FAC → UC	0.193	0.193	0.042	4.587	0.000
HAB → INT	0.080	0.079	0.037	2.147	0.032
HAB → UC	0.541	0.542	0.051	10.674	0.000
INT → UC	0.082	0.080	0.041	1.985	0.047
VAL → INT	0.179	0.178	0.067	2.674	0.008
ESF → INT *	-0,003	-0.001	0.068	0.038	0.970
IS → INT *	0,000	0.002	0.039	0.012	0.990

• * Model primer
• Source: Research Data

The Q² value of the Stone-Geisser (Geisser, 1974; Stone, 1974) is a criterion of predictive relevance. The Q² value of latent variables is obtained by blindfolding procedure. According to Ringle et al. (2015), the blindfolding is a sample reuse technique that systematically exclude data points and provides a prognosis of their original values, which requires the allocation of a default distance (D = 7).

The technique the PLS-SEM when there is stastical relevance, adequately predicts the indicator values. When Q² value is greater than zero for endogenous latent variable, it indicates that the model has predictive relevance to this construct. In this research the values have been validated for the intent with 0.508, and 0.466 for the behavior, therefore valid.

It was observed that values of path coefficients were considered significant at a level of 1 per cent. The illustration of the empirical model with path coefficients can be found in Figure 2.

The intended use (INT) is explained by variables Performance Utility (DU), hedonic performance expectancy (DH), Value (NPV), Habit (HAB) and Facilitating Conditions (FAC). In turn, the Behavior (UC) is explained by the Intention, Facilities and Habit. As briefly indicated in Table 6 were reject, with p value greater than 0.05, the variables Social Influence and Expected Efforts denoting a utilitarian behavior of individuals.

Table 6
Hypothesis

Hypothesis	Relationship	Original sample	P-values	Meaningfulness
H1	DU → INT	0.377	0,000	1 per cent
H2	ESF → INT*	-0,003	0.970	Not
H3	IS → INT*	0,000	0.990	Not
H4a	FAC → INT	0.197	0,000	1 per cent
H4B	FAC → UC	0.193	0,000	1 per cent
H5	DH → INT	0.163	0.001	1 per cent
H6	VAL → INT	0.179	0.008	1 per cent
H7a	HAB → INT	0.080	0.032	5 per cent
H7B	HAB → UC	0.541	0,000	1 per cent
H8	INT → UC	0.082	0.047	5 per cent

• * Model Home
• Source: Research Data

The results summarized in Table 6 demonstrate that the H2 hypothesis - Expectation effort and H3 - Social Influence not demonstrated statistically significant to influence the intention to use the discount coupons. Failure to validate the effort expectancy diverges from the propositions of Venkatesh et al. (2003) and Venkatesh et al. (2012), and the empirical evidence presented by Gupta et al. (2018) and Barat et al. (2013). Meantime, goes in line with the findings from Sallaberry et al. (2019) and from Yang (2010), that technologies are being incorporated into the individual’s daily life, no longer a differential that affects intention of the individual as demonstrated research for over a decade.

Regarding the non-validation of the relationship between Social Influence and the use of technology, the results differ from the proposals of Huang and Kao (2015), Ajzen (1991), Venkatesh et al. (2003) and Venkatesh et al. (2012). These results contradict the findings of Yang (2010), Luo et al. (2010), Wu and Lee (2017), Prabhakaran and Vasantha (2020), Jayasingh and Eze (2012) and Phan et al. (2020). This discrepancy may arise from the object of the technology, discount coupons, whose adoption or intention is individual and intimate, possibly failing to demonstrate to related people and consequently leading to the disregard of their opinion.

Regarding the behavior Use of coupons, three determining variables were statistically validated. While the main determinant of intention is the Performance Expectation, behavior is habit. The 1 per cent significance level, the hypothesis was validated Habit, valuing the investment in loyalty because corroborates the claim that people tend to perform behaviors automatically (Limayem et al., 2007). Although the variable habit has little influence on intention, it has the highest coefficient in relation to behavior, which may indicate that this behavior becomes indifferent to the intention - automatic.

The hypothesis of Facilitating Conditions was statistically validated the level of 1 per cent in relation to the behavior, with the same positive direction and intensity level that impacts intention. Finally, the intended use was also statistically validated, but the level of 5 per cent, and the low coefficient of ways but in relation to the behavior itself. While this result corroborates Ajzen (1991), reveals wear this theoretical relationship.

The hypotheses validated statistically to explain the intent of use Discount Coupons were H1 - Performance Expectation, H4a - Facilitating Conditions, H5 - Performance Hedonic, H6 - Perceived Value and H7a - Habit. These findings reinforce the results of Yang (2010) and partly results from Christino et al. (2019) for other technologies. The hypothesis H1 - Performance Expectation showed the highest coefficient of ways in relation to the intended use, ie, the main reason that encourages the individual to want to use.

The hypothesis Performance Expectation was statistically validated the significance level of 1 per cent, with a positive coefficient, confirming the theoretical model and indicating that the consumer has the perception that discount coupons bring benefits and influence their intended use (Venkatesh et al., 2012). Under this perception, it is revealed that the consumer clearly recognizes the usefulness of the technologies applied to the discount coupon and the benefit that will be provided in the purchase in relation to its use, that is, that the cost-benefit of its use is measured and satisfactory (Alvarez & Casielles, 2005; Barat et al., 2013).

The hypothesis tested the Facilitating Conditions on the intended use has been validated at the level of 1 per cent, indicating that consumers perceive favorable conditions for access to discount coupons, impacting positively the intention. These results corroborate the theoretical proposition of Ajzen (1991) and Venkatesh et al. (2012) and in the positive proportion of its ease, it reflects an increase in the intention to use discount coupons. The results follow the findings of Dwivedi et al. (2016), Jun et al. (2018), Madan and Yadav (2018), Wu and Lee (2017), Phan et al. (2020) and Jayasingh and Eze (2012).

The hypothesis of Hedonic Motivation is validated to the level of significance of 1 per cent, indicating a perception that the consumer tends to pleasure arising from the implementation of the discount advantage (Anderson et al., 2014), positively influencing behavioral intention. Despite being related to sensory and emotional aspects of the individual’s psychological experience, it represents a factor that generates satisfaction to individuals according to empirical evidence (Huang & Kao, 2015; Leitinho & Farias, 2018; Ozturk et al., 2016). These results corroborate the other findings of recent research (Madan & Yadav, 2018; Liu et al., 2015; Souiden et al., 2019).

The Value hypothesis was validated at the level of significance of 1 per cent, confirming the influence on behavioral intention of indicating a positive perception of the consumer on the cost/benefit of coupons (Frank & Milkovic, 2018). These results coincide with the theoretical proposal (Venkatesh et al., 2012), in addition to corroborating the evidence of recent research (Frank & Milkovic, 2018; Jun et al., 2018; Barat et al., 2013; Milkman & Beshears, 2009; Alalwan et al., Madan & Yadav, 2018; Souiden et al., 2019; Liu et al., 2015).

Habit is another variable validated at a significance level of 5 per cent relative to the intent, signaling a consumer loyalty perception. This result indicates characteristics of loyalty and automation of the cognitive purchasing process, based on repetitive behavior in the use of discount coupons proposed by Venkatesh et al. (2012) and corroborated by Gutierrez et al. (2017) and Gupta et al. (2018). In addition, it reveals the first stage highlighted by Mills and Zamudio (2018) in which coupons lead consumers to repeat purchases, capturing consumer loyalty. The results indicate how practical implications of the relevance of consumer loyalty, as the Habit was the variable with the greatest explanation of behavior coefficient. Individuals tend to continue in their routine and purchasing behavior, including need a greater after-sales care and post-use of coupon in order to value the experience.

On the other hand, is the main determinant of behavior is a habit, it is indicated the challenge of attracting new users, because the Intention little explains the behavior (Fishben & Ajzen, 1975; Venkatesh et al., 2003). Although validated and recognized in theory that the intention is the most traditional determinant of behavior, this research object in the corresponding sample, shows is less relevant.

Thus, there remains the perception of utility performance as determinant of intention and model, starting to signal that the promoter must make it clear to the user what the discount coupon provides, and how to use. Different strategies may cause the individual to experience the discount coupon, leaving the technological implementation and operationalization work properly to satisfy the customer and induce create the habit.

5 CONCLUSIONS

This search aimed to the analysis of the factors involved in the buy use with discount coupons process in the Brazilian context. From the modeling based on the UTAUT2 allowing the identification of the background and the level of influence of each in relation to the intention of the discount use behavior.

The survey findings reveal that the Intention of Use to Discount Coupons have as significant and positive determinants, Utility Performance, Facilitating Conditions, Hedonic Performance, Perceived Value and Habit. In addition to reinforcing the UTAUT2 model and its basic theory (Venkatesh et al., 2003), in its greater number of variables corroborate the empirical results of other research, such as results of Yang (2010) and partly accompany the results Christino et al. (2019), in related consumer mediation technologies. The main determinant of the use of intention is the Performance Expectation - Utility.

The high coefficient perceived for Utility Performance demonstrates that individuals recognize the characteristics and usefulness of discount coupons, therefore a popularized sales strategy instrument. The Facilitating Conditions demonstrated in the relationship between the variables demonstrate that consumers perceive conditions of easy access as positive elements to increase the purchase intention.

Satisfaction with the use of discount coupons on purchases positively influences the intention to purchase, which demonstrates the hedonic motivation of the individuals in the sample in such use, recovering sensory and emotional perceptions. The perceived value was also demonstrated as a positive relationship factor, which shows that the sample recognizes that discount coupons add savings, or decrease the cost of the purchase made.

Finally, the habit signaled a positive prospect of loyalty to the purchase instrument or to the organizations that provide it. This habit implies greater intention to use, but mainly reflects a greater effective use of coupons, which denotes characteristics of automation of behavior.

Thus, the use of appropriate technology results in a certain result, the use of discount coupons for purchases. Regarding the behavior of Use discount coupons, the three variables Facility, Habit and Intention were statistically validated.

Variables Effort Expectancy and Social Influence in the model did not show statistically significant to influence the intention to use the discount coupons, diverging from the propositions of Venkatesh et al. (2003) and Venkatesh et al. (2012), and empirical evidence (Gupta et al., 2018; Barat et al., 2013). The non-influence of these variables is in line with findings on the appropriation of recent technologies (Sallaberry et al., 2019; Yang, 2010) in addition to the individualistic nature of the use of this employment strategy of discount coupons at the time of payment.

The possibilities of practical implications of this research are relevant to show the loyalty of users as primary determinants beyond those little or no influence of others determinants, which may indicate to investors and managers which aspects need to receive investment and greater attention by the organization to operationalize the Discount coupons. Implicitly the results contribute to highlight the problem of capture new consumers and keeps them.

As a research limitation, indicates the multitude of discount coupons that have emerged in the market, which may lead to misinterpretation of the survey instrument. Moreover, inherent in the process, although the results with great sample, the results are restricted to the sample as they meet at a constant population group descriptive, as only.

The suggestions for future research are derived from the results of non-validated variables and greater integration of variables. Regarding non-validated variables, the results do not conform to theory, it is important to study them more depth, either under a qualitative or quantitative aspect. Although no theoretical support, it is relevant to verify the influence of determinants directly on usage behavior, given the difficulty of translating the intention of findings for the behavior, in this case. For further advances in the area of knowledge, other variables and moderating relationships can be tested, using other methodologies, such as experimental ones, that can provide us with theoretical and methodological innovations and also social and business contributions.

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• Plagiarism Check

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• FUNDING

  This work was supported by the CNPq - Conselho Nacional de Pesquisa under Grant 403073/2018-9.

Publication Dates

• Publication in this collection
  03 Nov 2021
• Date of issue
  Jul-Sep 2021

History

• Received
  24 Jan 2020
• Accepted
  07 Jan 2021