2.3. Hypotheses Development
UTAUT-2 identifies three antecedents of consumer acceptance and use of technology: (1) intention to use, which captures the consumer’s main motivations to use the technology; (2) facilitating conditions; and (3) habit, where the other two antecedents have a direct influence on technology use. This conceptualization of the intention to use is consistent with social psychology theories, such as TRA [
37] and TPB [
40]. According to Fishbein and Ajzen [
37] and Ajzen [
40], intention describes the motivational factors that influence whether or not an individual will perform a given action in a particular situation. Fishbein and Ajzen [
37] verified the relation between intentions to use and use in social psychology, based on TRA, stating that the intention of carrying out a behavior is the greatest predictor of the performance of such conduct. Abroud et al. [
47] found that intentions to use had a direct positive impact on the use of Internet stock trading systems, according to the TAM. Authors such as Venkatesh et al. [
20], Im et al. [
45], and Yu [
24] supported the idea that the intention to use has a direct positive influence on technology usage. Accordingly, this research hypothesizes the following:
Hypothesis 1 (H1). Intention to use directly and positively affects the individual use of MARSR.
Venkatesh et al. [
20] defined performance expectancy as the degree to which using a technology will provide consumers with benefits when performing certain activities. Performance expectancy is very similar to the perceived usefulness variable within TAM, which has become the most commonly used instrument for predicting technology usage, because it is robust, powerful, and parsimonious [
48].
The performance expectancy construct is considered to be one of the core predictors of the intention to adopt a technology [
20,
49]. Previous research has tested this relationship, and has obtained positive results in the mobile payment context [
27], app-based tour guides [
17], and diet food apps [
19]. However, there is no evidence of this relationship in the context of MARSR. Since MARSR enable consumers to find a desired or convenient restaurant more quickly and/or efficiently, such expectations can influence the intentions to use. Therefore, it is proposed that:
Hypothesis 2 (H2). Performance expectancy directly and positively affects the intention to use MARSR.
Effort expectancy is the degree of ease that is associated with consumers’ use of technology [
20]. There is a substantial similarity between effort expectancy in UTAUT-2, and the perceived ease-of-use in TAM. Previous empirical studies on mobile app adoption have supported the idea that effort expectancy influences intentions to use [
17,
19,
24], but not in the MARSR environment. Since MARSR are easy to use, understandable, and easy to interact with, minor efforts that are made by consumers in their restaurant searches and/or reservations via MARSR will affect consumers’ intentions to use. Thus, the following hypothesis is proposed:
Hypothesis 3 (H3). Effort expectancy positively and directly affects the intention to use MARSR.
Facilitating conditions refer to consumers’ perceptions of the resources and the support that are available to perform a behavior. UTAUT identifies the facilitating conditions as a construct that reflects a person’s perception of their control over their behavior [
50]. This definition captures the external facets of the concept represented by TPB, as proposed by Ajzen [
40], in the form of the perceived behavioral control constructs. Venkatesh et al. [
21] added a direct relationship between the facilitating conditions and the intentions to use in UTAUT-2, and Lai [
17] confirmed this relationship among users of app-based mobile tour guides. Yet, these relationships have not been found in other studies, such as San Martin and Herrero [
23], among users of rural online purchase systems, and from Okumus et al. [
19], among users of diet food apps. However, in accordance with Gallivan et al. [
51] and Venkatesh et al. [
50], appropriate facilitating conditions are essential for the acceptance of ICTs. In the literature on mobile banking adoption, Joshua and Koshy [
52] showed that easier access to computers and the Internet results in a higher adoption rate. Hence, higher facilitating conditions are expected to lead to higher intention to use, and a higher rate of use of MARSR. Based on the above discussions, and the paucity of evidence on mobile applications, the following hypotheses are proposed:
Hypothesis 4 (H4). Facilitating conditions directly and positively affect the use of MARSR.
Hypothesis 5 (H5). Facilitating conditions directly and positively affect the intention to use MARSR.
Brown and Venkatesh [
53] defined hedonic motivation as the fun or enjoyment that is derived from using a technology, and this construct has been shown to play a substantial role in determining technology acceptance and use [
21,
54]. Previous studies have subsequently proposed and tested the positive influence of perceived enjoyment on mobile internet adoption [
55] and mobile banking use [
56]. The positive influence of hedonic motivation on behavioral intentions has been tested by Venkatesh et al. [
21] in the mobile context, although other authors found it to be weak among users of online services for purchasing tickets for low-cost carriers [
16]. To test this relationship within the context of MARSR, the following hypothesis is proposed:
Hypothesis 6 (H6). Hedonic motivation directly and positively affects the intentions to use MARSR.
The price value construct for a technology whose use carries a monetary cost for the consumer was incorporated within UTAUT-2 by Venkatesh et al. [
21]. Price value refers to the consumer’s cognitive tradeoff between the perceived benefits, and their monetary cost [
57]. However, this variable is not applicable in the model when an app, such as MARSR, is free of charge. In fact, other studies have found that consumers who purchase products and services through websites can reduce prices and save time [
58], or can carry out comparisons of product prices [
59]. Both can be associated with price saving. In this research, the price-saving orientation refers to the economic benefits that are obtained by technology users; that is to say, the use of technologies allows consumers to acquire a product or service at a lower price [
16,
60]. Accordingly, in previous research on UTAUT-2, the price value construct has been replaced by the price-saving orientation [
16], and these authors found a positive relationship with intentions to use. Thus, in MARSR, information about prices and their comparison will allow for a price saving. Based on this theoretical review, the following hypothesis is proposed:
Hypothesis 7 (H7). Price-saving orientation directly and positively affects the intentions to use MARSR.
Habit is defined as the degree to which people tend to perform behaviors that are automatic, based on learning [
61]. It refers to a self-reported perception of a repeated behavioral pattern that occurs automatically outside of conscious awareness [
62]. Venkatesh et al. [
21] found two significant relationships: habit–intentions to use and habit–use. These relationships were confirmed by Escobar-Rodríguez and Carvajal-Trujillo [
16] in a study on low-cost carriers’ websites. Therefore, habit is expected to influence both the intentions to use and the actual usage of MARSR. Accordingly, this study posits the following hypotheses:
Hypothesis 8 (H8). Habit directly and positively affects the intentions to use MARSR.
Hypothesis 9 (H9). Habit directly and positively affects the use of MARSR.
2.4. The Expanded and Extended UTAUT-2
One of Venkatesh et al.’s [
21] proposals was for future research to identify factors that allow for the application of UTAUT to different technologies and use contexts, especially within the consumer behavior field. In this paper, the original UTAUT-2 has been expanded and extended by developing a new operationalization of the social influence factor, the substitution of the price value factor by price-saving orientation, and incorporating a new factor, perceived credibility.
Some studies on mobile marketing have identified the effect of social influence on intention to use, by the means of the subjective norm of TPB [
21,
24,
27]. Nevertheless, there is still a need for more in-depth research on the construct’s operationalization. Therefore, to expand the social influence construct, this study investigates the effect that is produced by perceived norms on consumers’ intention to use MARSR, according to the social norms approach (SNA) [
63,
64].
Social influence, in the context of technology, is defined as the extent to which consumers perceive that important others believe that they should use a particular technology [
21]. However, social influence is not a simple and unified concept [
65,
66], and it needs to be studied more thoroughly. Deutsch and Gerard [
65] studied social influence from the informational and normative perspectives. Kelman [
66] considered three different processes in social influence: conformity, internalization, and identification. Williams [
67] defined social norms as rules for conduct. The social norms approach (SNA) [
63,
64], which is the theoretical approach that will be used in this research, posits that the relevant norms can be categorized as being descriptive and injunctive [
68,
69]. Descriptive norms refer to the popularity of a norm or behavior [
70], while injunctive norms apply to social approval or the denial of a norm or act [
71]. Moreover, according to Smith and Park [
72], these norms exist at both the personal and the societal level. The personal level refers to significant norms among people who are relevant to the individual, whereas the societal level refers to beliefs that are based on legal, cultural, and religious norms.
In a study on mobile advertising, Soroa-Koury and Yang [
73] found evidence to support the relationship between social influence and intentions to use, through the social norms theory. Given SNA’s theoretical basis and prior research, the following hypothesis is proposed:
Hypothesis 10 (H10). Social influence directly and positively affects the intentions to use MARSR.
Perceived credibility in the mobile context is defined as the extent to which an individual believes that the use of mobile technology will not entail any security or privacy threats [
49,
74,
75]. In fact, it is well known that consumers are concerned about security, privacy, and transaction risks in the mobile context [
76], and that overcoming these issues is essential for the adoption of applications in the mobile ecosystem [
77]. Thus, identifying the influence of perceived credibility on the intentions to use MARSR is relevant.
In the mobile environment, perceived credibility has been measured with two dimensions: security and confidentiality/privacy [
78]. Additionally, in the context of MARSR, the user does not complete a sale, but only undertakes a search or booking. Therefore, the trustee’s characteristics of benevolence and integrity, as identified by Mayer et al. [
79], and which act as trust generators in the offline context, are not relevant in the mobile environment. In the MARSR framework, only the competence of the application’s managers [
79], which can be understood as security and privacy, is significant. Although perceived credibility has already been analyzed in the mobile banking context [
24], it has not been tested with respect to MARSR users, and it does not appear among the UTAUT-2 variables. Thus, this study includes perceived credibility, in order to better explain the intentions to use MARSR, as sustained by the direct positive relationship identified by Yu [
24] using UTAUT-1 in a sample of mobile banking users in Taiwan. However, perceived credibility has not been tested in UTAUT-2. Therefore, the following hypothesis is proposed:
Hypothesis 11 (H11). Perceived credibility directly and positively affects the intentions to use MARSR.
Previous research has suggested that age and gender are critical demographic variables that moderate the relationships between consumers’ perceptions of technology, and their behavioral intentions [
20,
80]. In addition to age and gender, in the ITC context, user experience has been considered as one of the main factors explaining an individual’s behavior, because users gain experience in using ITC as they utilize them over time [
81]. Experience is defined in terms of the time elapsed since the initial use of a technology by an individual, and it is usually operationalized by levels, based on the passage of time [
21].
The moderating effects of gender, age, and experience were analyzed in UTAUT-1 and 2 between the independent variables and the dependent variable and behavioral intentions [
20,
21]. In the first model, Venkatesh et al. [
20] analyzed the moderating effects of gender, age, and experience (understood as the time elapsed since the initial use of MASRS apps) between the independent variables (performance expectancy, effort expectancy, social influence, and facilitating conditions) and the dependent variable (behavioral intentions), and also between facilitating conditions and technology use in a sample of individuals that had been introduced to a new technology in the workplace. They found that the effect between the performance expectancy and behavioral intentions was stronger for younger men, while the effect was stronger for older women with limited experience in the relationships between effort expectancy and social influence toward behavioral intentions. Additionally, they found that the effect was more significant for older workers with increasing experience in the relationship between facilitating conditions and technology use. A few years later, Venkatesh et al. (2012) [
21] analyzed the moderating effects of the new relationships added to the new UTAUT-2, in a sample of internet mobile users. They found that gender and age moderate the relationships between facilitating conditions and behavioral intentions, and between price value and behavioral intentions. Specifically, they observed that older women exercised a greater influence in both relationships. In addition, they found that gender, age, and experience moderate the relationship between hedonic motivation and behavioral intentions, and between habit and behavioral intentions. Specifically, they found that young men in the early stages of experience exercised the greatest influence of hedonic motivation toward behavioral intentions, while older men in the later stages of experience exercised the greatest influence of habit toward behavioral intentions, and of habit toward technology use. Some of these moderating effects were also found by Yu [
24]. This author identified that the effect of performance expectancy on behavioral intentions and facilitating conditions for use is higher in men than in women. In addition, they observed that the direct effect of the facilitating conditions for the use of mobile banking was higher among users aged below 30 or over 50, compared to other age groups, but no moderating effects were identified in other relationships within the model. Conversely, other authors have investigated the moderating effects of gender and age using UTAUT models, and they have not found any significant results among social media users [
35] or mobile banking users [
34].
Given that the above findings are inconsistent, it is necessary to ascertain the moderating effects of gender and age, and taking into account the scarcity of testing conducted on the effect of experience, the hypotheses of the moderating effects of UTAUT-2 must be reformulated. Bearing in mind the discussion above, the following hypotheses were proposed:
Hypothesis 12 (H12). Age and gender will moderate the effect of performance expectancy on intentions to use MARSR, so that the effect is stronger among younger men.
Hypothesis 13 (H13). Age, gender, and experience will moderate the effect of effort expectancy on intentions to use MARSR, so that the effect is stronger among older women with limited experience in using mobile applications.
Hypothesis 14 (H14). Age, gender, and experience will moderate the effect of social influence on intentions to use MARSR, so that the effect is stronger among older women with less experience in using mobile applications.
Hypothesis 15a (H15a). Age and gender will moderate the effect of facilitating conditions on intentions to use MARSR so that the effect is stronger among older women.
Hypothesis 15b (H15b). Age, gender, and experience will moderate the effect of facilitating conditions on the use of MARSR, so that the effect is stronger among older men with more experience in using mobile applications.
Hypothesis 16 (H16). Age, gender, and experience will moderate the effect of hedonic motivation on intentions to use MARSR so that the effect is stronger among younger men with less experience using mobile applications.
Hypothesis 17 (H17). Age and gender will moderate the effect of price-saving orientation on intentions to use MARSR, so that the effect is stronger among older women.
Hypothesis 18a (H18a). Age, gender, and experience will moderate the effect of habit on intentions to use MARSR, so that the effect is stronger among older men with experience in using mobile applications.
Hypothesis 18b (H18b). Age, gender, and experience will moderate the effect of habit on the use of MARSR, so that the effect is stronger among older men with experience in using mobile applications.
Hypothesis 19 (H19). Experience will moderate the effect of intentions to use MARSR on the use of MARSR, so that the effect is stronger among users with less experience in using mobile applications.
The additional factor, perceived credibility (security and privacy), that extends UTAUT-2 in this research, will be submitted to the same moderating effects as the rest of the constructs, based on the following arguments: i) previous research has highlighted the gender perspectives regarding confidence in technology, and, basically, the lower degree of confidence in technology activities identified in females [
82]. Compared to males, females have consistently higher levels of concerns about security [
83], and privacy-related and security-related policies are crucial for engaging females in the online market [
84] and, in particular, in mobile platforms [
85]. In addition, most young people are digital natives, who see technologies as part of their daily routine. They can thus be regarded as being pre-adopters of new technologies, since they are more familiar with the new innovations [
86]. Accordingly, older females with less experience in using mobile applications will have more influence in the relationships between perceived credibility and intentions to use MARSR. Therefore, the following hypothesis is proposed:
Hypothesis 20 (H20). Age, gender, and experience will moderate the effect of perceived credibility on the intentions of use of MARSR, so that the effect is stronger among older women with less experience in using mobile applications.