Attitudes toward mobile advertising among users ... - Semantic Scholar

Telematics and Informatics 32 (2015) 355–366

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Telematics and Informatics journal homepage: www.elsevier.com/locate/tele

Attitudes toward mobile advertising among users versus non-users of the mobile Internet Alicia Izquierdo-Yusta a,⇑, Cristina Olarte-Pascual b, Eva Reinares-Lara c a

Department of Business Administration, Facultad de Ciencias Económicas y Empresariales, Universidad de Burgos, Plaza Infanta Elena s/n, 09001 Burgos, Spain Department of Economy and Enterprise, Facultad de Ciencias Empresariales, Universidad de La Rioja, Ed. Quintiliano, c/La Cigüeña 60, 26004 Logroño, Spain c Department of Business Administration, Facultad de Ciencias Jurídicas y Sociales, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n, Campus de Vicalvaro, 28932 Madrid, Spain b

a r t i c l e

i n f o

Article history: Received 19 May 2014 Received in revised form 18 August 2014 Accepted 14 October 2014 Available online 28 October 2014 Keywords: Mobile advertising Consumer attitudes Reference group Value perceptions

a b s t r a c t Marketing communication strategy via mobile phone constitutes a promising approach to companies because it enables to reach appropriate audiences at the right time and place. The main aim of this research is to determine key factors that create new opportunities for commercial communications by considering consumer attitudes toward mobile advertising. The proposed causal model of attitudes and intentions toward mobile advertising highlights potential differences between users of smartphones with Internet capabilities and non-users. The theoretical model integrates the influences of control, reference groups, perceived added value, and ease of use on attitudes toward mobile advertising, as well as the relationship of these effects with intentions toward advertising, mediated by mobile Internet usage. The sample is 612 respondents who receive advertising by their mobile phones. Our results reveal, similar to other research, that attitudes exert positive influences on intentions to receive advertising, especially among those who already have access to the Internet on their mobile phones. However, regarding these variables, the experience does not contribute to positive attitudes. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction With the emergence of mobile communications, mobile advertisement enables advertisers to deliver personalized advertising information to consumers at the right time and place. Consumer can not only receive advertisement information through the push-type delivery method but can also proactively retrieve advertisement information via the pull-type method. Mobile carries typically have personal information on their subscribers; therefore, designing a personalized advertisement based on a subscriber’s profile and preferences is deemed conceivable. This personalized advertisement can then be delivered to the personal handset device to maximize the advertisement and marketing effects. Modern developments in advertising through mobile devices creates high expectations for improved communications and competitiveness in most business sectors, both consumer and industrial markets (Salo, 2012), in parallel with a high degree of skepticism and defensiveness among consumers toward the arrival of such commercial messages. Even with these developments, the effectiveness of advertising in mass media continues to decline, particularly in the face of the modern global recession. Yet three signals suggest the likely success of advertising through mobile devices.

⇑ Corresponding author. Tel.: +34 (9)47259036; fax: +34 (9) 472542110. E-mail addresses: [email protected] (A. Izquierdo-Yusta), [email protected] (C. Olarte-Pascual), [email protected] (E. Reinares-Lara). http://dx.doi.org/10.1016/j.tele.2014.10.001 0736-5853/Ó 2014 Elsevier Ltd. All rights reserved.

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A. Izquierdo-Yusta et al. / Telematics and Informatics 32 (2015) 355–366

First, the growth of mobile devices has been substantial in the past two decades (Khalifa and Shen, 2008; Zhang and Mao, 2008). The mobile phone has achieved the 6.8 million subscribers with a global penetration rate of 96% (ITU, 2013), showing the number of users a growing trend. A Spanish level, there is a penetration rate of 128.4%, with 20.8 million smartphones, standing in the middle of the European countries (Netsize, 2012). Second, from the point of view of advertising investment is expected that mobile advertising has the second investment and is the medium that will grow in the next five years (IAB Europe, 2011). By way of illustration, it should be noted that even though we are immersed in a crisis in the advertising industry, advertising investment on mobile marketing in Spain during 2012 exceeded 90 million, an increase of 45% over 2011 to all activities of mobile marketing (Mobile Marketing Association, 2012). Third, the development of increasingly sophisticated mobile devices offers new alternatives for interacting with consumers, including through enriched communication options. In this sense, mobile advertising constitutes a second phase of digital advertising, penetrating various aspects of people’s lives through improved wireless Internet services and the latest generations of mobile technology (e.g., 2G, 2.5G, 3G, 4G). In their efforts, the firms seek to segment consumers according to various criteria, such as demographics, location, or the type of mobile terminal being used. In this sense it is important to take account of several advantages, including the personal nature of mobile advertising, which should result in high response rates (Barutcu, 2007), as well as the ubiquity of mobile phones (Olla and Atkinson, 2003; Pagani, 2004), which support communications that reflect the location of the user (Rodríguez Perlado and Barwise, 2005). By providing information on products that interest consumers, including price, promotional activity and brand information, properly formatted for each consumer’s mobile device, advertisers allow consumers to obtain detailed product information quickly. If consumer can properly receive and view advertising content customtailored for them, it will leave a good impression and increase their desire to purchase the product. That is, mobile advertising can reach appropriate audiences at the right time and in the right place at a relatively low cost (Facchetti et al., 2005). Despite these favorable conditions and market analyses that indicate consumers are aware of advanced mobile services, many people remain reluctant to use their mobile phones to receive marketing communications (Fogelgren-Pedersen et al., 2003; López et al., 2008). In such a scenario, we need additional analyses to specify and clarify the implications of mobile marketing and commercial communication for different audiences, such as those who use versus do not use mobile Internet access. Prior research on mobile advertising has sought some insight into the relationships between marketing stimuli and consumer responses (Barwise and Strong, 2002; Bauer et al., 2005; Lee, 2003; Soroa-Koury and Yang, 2010; Xu, 2006/2007). Communication is the key link; the audience’s interest in proposals offered by suppliers depends largely on several variables. For example, consumers’ attitudes toward advertising have been widely studied (Dutta-Bergman, 2006; Shavitt et al., 1998), largely on the basis of theories derived from studies on attitudes toward brands (Bauer and Greyser, 1968; Durvasula et al., 1993; Lutz, 1985; Muehling, 1987). In order to provide a solid theoretical basis for examining the adoption of mobile advertising, this paper draws on two schools of thought regarding the nomological structure (Lee, 2009) of the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975): (i) the Technology Acceptance Model (TAM) (Davis, 1989) and (ii) the Theory of Planned Behavior (TPB) (Ajzen, 1991). Since TAM and TPB have been used in many studies to predict and understand user perceptions of systems use and the probability of adopting an online system (Gefen et al., 2003; Hsu et al., 2006; Wu and Chen, 2005), they are the most appropriate tools for understanding mobile advertising adoption. This investigation, similar to others, (Lee, 2009; Igbaria et al., 1995; Mathieson, 1991; Taylor and Todd, 1995) proposes to integrate both models, TAM and TPB, in order to provide a more comprehensive model of mobile advertising. Empirical evidence in support of these models, applied to mobile advertising, highlights the usefulness of the attitude variable as decisive for mobile phone users’ intentions and behavior in terms of accepting mobile advertising (Karjaluoto et al., 2008; Lee et al., 2006; Tsang et al., 2004), as well as mobile phone adoption (Lee, 2003) and its use as a promotional communication medium (Bauer et al., 2005). However, analyses of the factors that determine attitudes toward mobile advertising and the relationships among attitudes, intentions, and behaviors remain somewhat contradictory. In response, we investigate in depth the antecedents and consequences of attitudes toward mobile advertising, using a causal measurement model that considers the influence of control, reference groups, perceptions of added value, and perceived ease of use on attitudes towards mobile advertising, as well as the relationships of these effects with intentions toward such advertising, as mediated by people’s use of the mobile Internet. Accordingly, we structure this article in three parts: First, we review our theoretical background, including extant literature on the Internet and mobile advertising, to establish the relationships we propose in our theoretical model. We also provide some research hypotheses. Second, we detail our research methodology and conduct an empirical study with a representative sample of 612 recipients of mobile advertising in Spain, featuring both users of mobile Internet and consumers who choose not to access this service. Third, we present the study results and findings, along with their implications and a few limitations.

2. Background and assumptions 2.1. Adoption of wireless Internet services through mobile technology Although technology use determinants have been studied for years (e.g., Davis, 1989; Mathieson, 1991; Moore and Benbasat, 1991; Thompson et al., 1994; Taylor and Todd, 1995; Venkatesh and Davis, 2000), as Karahanna and Straub


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(1999) note, most studies consider the attitudes of end users only after they have adopted the systems. Initial adoption is a first step toward long-term use, but the factors affecting such extended usage may not be the same or exert the same degree of effects as do the factors that lead to initial acceptance. In particular, research into Internet usage often focuses on pre-adoption criteria. Similar criteria likely have strong influences on the adoption of wireless and mobile systems and the use of mobile marketing, in the sense that these activities are relatively new and risky. Foxall (2002, 2003) argues that people who have never used mobile marketing are more risk averse than those who have. TRA proposes that human behavior is preceded by intentions; subjective norms and individual attitudes are the antecedents of behavioral intention (Fishbein and Ajzen, 1975). TRA is very general in nature and attempts to explain almost any human behavior. Warshaw (1980) argued that TRA has a weak predictive power in marketing applications and suggested an intention model specialized in product purchase situations The TAM is an adaptation of the TRA by Fishbein and Ajzen (1975) and was developed by Davis (1989) to explain acceptance of information technology for different tasks. This model hypothesizes that system use is directly determined by behavioral intention to use, which is turn influenced by users´ attitudes toward using the system and the perceived usefulness of the system. Attitudes and perceived usefulness are also affected by perceived ease of use. A critical review of TAM has revealed that there is a need to include other components in order to provide a broader view and a better explanation of Information Technology (IT) adoption. Specifically, factors related to human and social change processes should be incorporated. In Information System (IS) literature, the Technology Acceptance Model (TAM) (Davis, 1989), the extended Technology Acceptance Model (TAM2) (Venkatesh and Davis, 2000) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) are used to explain possible adoption and acceptance patterns of new technologies among consumers. Concepts like relative advantage, compatibility, complexity, and observability, as well as perceived risk, perceived usefulness, subjective norm and perceived ease of use play a key role in these approaches. The TPB underlying the effort of TRA has been proven successful in predicting and explaining human behavior across various information technologies (Ajzen, 1991, 2002). According to TPB a person’s actual behavior in performing certain actions is directly influenced by his or her behavioral intention and, in turn, is jointly determined by his or her behavioral attitude, subjective norm and perceived behavioral control toward performing the behavior. The TAM and TPB has been successfully applied to a range of computer – based technology (e-mail, office suite applications, online shopping mobile phone). The TAM and its extended versions provide some insights into which factors influence mobile Internet adoption (Fogelgren-Pedersen et al., 2003) and the antecedents of the pre-adoption criteria for the mobile Internet. Lu et al. (2005) evaluate a model that integrates the TAM elements, in which pre-adoption combines the usefulness and perceived ease of use of the mobile Internet together with the user’s personal ability to adopt information technologies and social influences, in the form of subjective norms and images (Taylor and Todd, 1995). Both social and personal influences also have direct positive impacts on perceptions of usefulness and ease of use. Lu et al. (2003) conclude that personal abilities to adopt information technology, along with other factors, determine short- and long-term perception of the usefulness and ease of use of the mobile Internet, as well as attitudes and intentions to adopt it. Finally, López et al. (2008) integrate the TAM with Innovation Diffusion Theory (IDT) (Rogers, 2003) and show that the traditional antecedents (behavioral intentions, ease of use, and usefulness) of mobile Internet usage relate to variables such as social influence and perceived benefits. The great majority of the research has studied adoption behavior of different consumer segments based on adopter and non-adopter classification (Dickerson and Gentry, 1983). The IDT suggested by Rogers (2003) provides a useful base to view different segments of adopters. Rogers (2003) identified five different groups of adopters on the basis of their relative time of adoption namely, innovators, early adopters, early majority, late majority and laggards. According to this classification, early adopters perceive innovations as less risky and complex, and more advantageous, compatible and observable. These people are also characterized as younger wealthier, better educated individuals who are perceived to be opinion leaders by others. They are also perceived as the individuals who have more experience with technology. Bass (1969) distinguished adopters segments as innovators and imitators. He assumed that innovators adopt an innovation independent of others whereas imitators heavily rely on interpersonal communication before making their decision. Although adoption research has often focused on explaining the factors affecting the adoption behavior, some of them have integrated Adoption Theories and Innovation Diffusion Theories to identify different adopters segments and their characteristics. There are very few investigations (Verkasalo et al., 2010) that have attempted to integrate IDT with TAM to profile different categories of adopters. However, neither TAM nor TPB have been found to provide consistently superior explanations or behavioral predictions (Chen et al., 2007). Recently, a growing body of research has focused on integrating them to examine IT usage and e-service acceptance because the two models are complementary, and the results have showed that the integration model had better exploratory power that the individual use of TAM and TPB (Bosnjak et al., 2006; Chen et al., 2007; Wu and Chen, 2005). However, nearly none of these studies have attempted to integrate TPB with TAM to profile different categories of user vs nonusers.

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2.2. Attitude toward mobile advertising Attitude is critical for explaining people’s behaviors (Stevenson et al., 2000; Luna et al., 2002; Richard and Chandra, 2005; Sicilia et al., 2006). Fishbein and Ajzen (1975, p. 6) define attitude as ‘‘a learned predisposition of human beings’’ that leads them to respond ‘‘to an object, idea or opinion.’’ Predicting how attitudes affect consumer behavior is the most important concern for most people interested in consumer behavior. Various situational and dispositional factors enhance the consistency of attitudes with behavior and researchers have found that attitude is more predictive of behavior in certain situations (Wen, 2009). Perhaps, the three best known are TRA, TAM and TPB. Barutcu (2007) proposes a model to determine attitudes toward mobile marketing tools and identify users of mobile phones with more positive attitudes. His findings show that mobile phone users’ adoption of mobile shopping is low, despite their positive attitudes toward various types of mobile marketing. Tsang et al. (2004) also reveal negative aspects of attitudes toward mobile advertising, unless consumers have given their consent to receive it. Regarding the determinants of attitudes towards mobile advertising, Tsang et al. (2004) suggest distinguishing attitudes toward mobile advertising and consumers’ perception of the entertainment, information, irritation, or credibility provided by mobile adverts. They find that entertainment is the attribute with the greatest effect on attitudes toward mobile advertising. Xu (2006/2007), citing Dickinger et al. (2005), adds advertising personalization as a strong effect on attitudes toward mobile advertising, especially among female users. In the same vein, and in addition to the factors already mentioned by Tsang et al. (2004), Choi et al. (2008) introduce the value of mobile advertising and perceived interactivity as antecedents of attitudes and intentions to purchase. These authors conclude that entertainment and credibility are key factors for determining a positive attitude and intentions to purchase. Moreover, they identify cultural differences between countries with regard to the informative, interactive perception and value of mobile advertising. For Karjaluoto et al. (2008), ease of use, trust, and perceived utility emerge as the factors that most affect attitudes toward mobile advertising. Using the extension of the TAM2, Soroa-Koury and Yang (2010) conclude that perceptions of utility predict attitudes toward mobile advertising, but perception of ease of use does not. 2.3. Attitude–intention relationship regarding mobile advertising We define intentions to receive messages as a person’s willingness to receive commercial communications via mobile devices, referred to as opt-in communications, which excludes junk e-mail. Many studies consider attitude as one of the determinants of mobile users’ behavior and intentions to accept mobile advertising. For Lee et al. (2006), favorable attitudes toward mobile advertising, correlated with strong reasons, lead to positive actions and intentions. Identical results were obtained by Soroa-Koury and Yang (2010). Tsang et al. (2004) and Xu (2006/2007) reveal a direct relationship between consumer attitudes and the incentives offered, as well as their intentions and behavior. On this basis, we propose the following hypothesis: H1. Attitudes toward mobile advertising have positive influences on intentions to receive mobile advertising, and the effect is greater among mobile Internet users.

2.4. Perceiver ease of use Perceived ease of use is an individual’s assessment of the extent to which interaction with a specific information system or technology is free of mental effort (Davis, 1989). It is one of the major behavioral beliefs influencing user intention to technology acceptance in the original and the revised TAM models. In this research context, we define ease of use as mobile phone users’ expectations of the effort required to use mobile advertising messages (Zhang and Mao, 2008). The technical and interactive characteristics of mobile media, in comparison with other media such as television or magazines, mean that consumers’ experiences of mobile advertising differ from those of advertising in other media. Literature regarding the acceptance and adoption of technology highlights the importance of perceived ease of use, particularly for computers (Davis and Venkatesh, 1996; Taylor and Todd, 1995). Regarding the use of the mobile Internet, Lu et al. (2005) contrast hypotheses in which perceived ease of use has a direct positive effect on perceived usefulness, as well as on the intention to adopt mobile Internet. Similarly, various empirical studies distinguish ease of use as the main determinant of intentions to adopt (e.g., Agarwal and Karahanna, 2000; Lowry, 2002; Warren, 2002). Although other authors find a mediating effect (e.g., Henderson and Divett, 2003), Clark (2000) reveals that ease of use is one of the five factors that determines the use of wireless handheld devices, as confirmed in research related to data and mobile commerce services (e.g., Massey et al., 2004; Siau et al., 2004; Ziefle, 2002). Karjaluoto et al. (2008) demonstrate how perceived ease of use affects attitudes toward mobile advertising. In contrast, for Soroa-Koury and Yang (2010), the perception of ease of use does not predict attitudes toward mobile advertising. Regarding the factors that determine the acceptance of mobile advertising, Zhang and Mao (2008) propose a model that includes, among other key factors, perceptions of ease of use, highlighting the relationship between intentions to use mobile advertising and to act in the way the message suggests. Contradictory results in prior literature relate to the importance of per-


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ceptions of ease of use as an antecedent of consumers’ attitudes toward mobile advertising. In accordance with TAM literature, we propose the following hypothesis: H2. Perceived ease of use exerts a positive effect on attitudes toward mobile advertising, and this effect is greater among mobile Internet users. 2.5. Perceived added value Consumer value has been recognized as one of the most important factor to successful business (Zeithaml, 1988) since consumers choose products or service based on their multiple values. However, defining and measuring perceived value is not as simple as it might seem (Woodruff, 1997; Zeithaml, 1998). For this reason, there have been various definitions of consumer value. Consumer value is the result of the overall assessment of the perceived utility of a product or service based on the comparison between what is received and what is expected (Zeithaml, 1998, p. 14). According to Woodruff (1997, p. 42) customer value is ‘‘a customer’s perceived preference for and evaluation of those product or service attributes, attribute performance, and consequences arising from use that facilitate (or block) achieving the customer’s goal and purposes in use situations’’. Applications of the perceived value model proposed by Zeithaml (1998) indicate effects on consumer behavior intentions (Dodds et al., 1991; Grewal et al., 1998; Sweeney and Soutar, 2001). In particular, they emphasize the important role of perceived value in the decision processes of individuals do. In this sense, people buy or use a product or service over others with similar characteristics, in terms of the added value they perceive in the former. More specifically, if consumers perceive additional benefits in the use of a product or service, they will show a special interest in it, to the detriment of other options available in the market. With the development of mobile phones and the Internet, this variable has a prominent role in understanding the behavior of consumers in the network (Alba et al., 1997; Keeney, 1999; Teo et al., 2003). The TAM model includes the variable called perceived usefulness, as an antecedent of attitude towards using technology. This concept is very close in its definition to value added. In fact, it refers to the extent to which an individual believes that using a technology will enable him to improve the development of an activity. In this vein, research by Chang et al. (2005), Chen and Dubinsky (2003), Lee et al. (2005) and Shih (2004) and emphasize that the additional benefits have been considered one of the major antecedent factors of Internet adoption. Without any doubt, these advantages imply an increase in the perceived value or perceived usefulness in the use of Internet. Both perceived usefulness and added value, in reference to mobile technology and the application of the TAM model, continue to have importance, as pointed out by Karjaluoto et al. (2008), Soroa-Koury and Yang (2010) and Zhang and Mao (2008). Choi et al. (2008) further identify factors that influence the value and effectiveness of mobile advertising by comparing the various factors that influence perceptions of value, attitudes, and intentions among Korean and U.S. consumers. Beyond cultural differences, they find unique relations between the value and the characteristics of mobile advertising, such as entertainment, information, credibility, and interactivity, with largely positive effects on the value of advertising. Thus, in accordance with TAM literature, we predict: H3. El formato de H3 es diferente de H1 y H2. H3 no tiene punto y aparte. 2.6. Reference groups and subjective norms One of the variables previously used to measure the influence of reference groups on consumers is subjective norms, which reflect the focal consumer’s beliefs about what significant others (i.e., reference group members) will think about his or her conduct (e.g., purchasing online, mobile advertising) and which in turn influence intentions to conform to these opinions. Subjective norms include both normative beliefs and the motivation to adapt to the reference group’s beliefs. Mobile devices and services allow people to move around while maintaining access to services and staying (socially) connected. Mobility, availability and personalization, may also be important perceived benefits of (multimedia) mobile services (Pagani, 2004). Individual potential adopters of mobile technologies are exposed to informal social networks in which the benefits of mobile services are discussed. Opinions, decisions, and behavior are affected by these interactions. From this perspective, mobile advertising might create a feeling that consumers form a social group that defines appropriate attitudes, behaviors, life styles, trends and ways of relating. Mobile advertising thus might offer a promise of incorporating consumers into social groups to which they aspire to pertain, in line with the general human tendency to aspire to higher levels in society (Rouchy, 2002). Few studies consider the influence of subjective norms on intentions and conduct related to mobile marketing communications though. Bauer et al. (2005), Muk and Babin (2006), and Rohm and Sultan (2006) contrast the positive influence of reference groups with intentions to participate in mobile marketing, yet research into the influence of subjective norms on attitudes, as antecedents of intentions and behaviors, is truly scarce (Malhotra and Galletta, 1999; Vijayasarathy, 2004). According to Rogers’s (2003) definition, innovation diffusion is a process by which members of a social system, provide information that individuals evaluate when developing positive or negative attitudes towards the innovation. Moreover, as

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individual process information gathered from the various communication sources available in their society, they may model or imitate the behaviors of others who have interacted with the innovation. Empirically, researches have found that normative pressure of consumers´ relevant others exerts considerable influence over consumers´ decision to purchase or use (or not) new products or services. Assumptions about the influence of subjective norms on attitudes toward mobile advertising, mediated by the use of mobile Internet, stem from an empirical study by Nysveen et al. (2005), who show that normative pressures affect the rapid adoption of different mobile phone services. In contrast, López et al. (2008) predict that social influence has a positive effect on a person’s attitude toward mobile innovations, including a significant influence on the decision to adopt the mobile Internet. On the basis of the TPB (Ajzen, 1991), and the TAM (Davis, 1989), we analyze this relationship with the following hypothesis: H4. Subjective norms have a positive influence on attitudes toward mobile advertising, and this effect is greater on mobile Internet users.

2.7. Perceived control Perceived behavioral control (PBC) reflects a person’s perception of the ease or difficulty with which they can perform a certain task (Ajzen, 1991). Within the context of IS research, perceived behavioral control consists of ‘‘perceptions of internal and external constraints on behavior’’ (Taylor and Todd, 1995, p. 149). It concerns beliefs about the presence of control factors that may facilitate or hider their performing the behavior. Thus, control beliefs about resources and opportunities are the underlying determinant of PBC and can be depicted as control beliefs weighted by perceived power of the control factor in question (Verkasalo et al., 2010). In sum, grounded on the effort of TRA, TPB is proposed to eliminate the limitations of the original model in dealing with behavior over which people have incomplete volitional control (Ajzen, 1991). In essence TPB differs from TRA in that it has the additional component of PBC. Furthermore, perceived control can, and usually does, vary with different situations and actions (Ajzen, 1991). It has been often associated with the concept of computer self-efficacy, which appears in TAMs (Mathieson, 1991; Taylor and Todd, 1995). Its application to mobile technology has been limited though; preliminary results suggest that control has little association with intentions to receive mobile marketing communications (Karjaluoto and Alatalo, 2007; Merisavo et al., 2007; Venkatesh et al., 2003). Accordingly, we hypothesize: H5. Perceived control based on permission has a positive influence on attitudes toward mobile advertising, and this effect is greater among mobile Internet users. These hypotheses combine to form an inclusive theoretical model of the influences on attitudes and intentions toward mobile advertising (see Fig. 1). 3. Methodology For our empirical research, we collected a sample of Spanish adults who have received advertising on their mobile phones, with the technical support of the Cint Panel Exchange. We thus gained access to a wide range of consumers, representative of the Spanish market, with high guarantees of quality. Table 1 lists the technical specifications and Appendix A list study items.

Fig. 1. Behavior toward mobile advertising.

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A. Izquierdo-Yusta et al. / Telematics and Informatics 32 (2015) 355–366 Table 1 Technical information of the research. Universe Sampling procedure Scope Real sample Sampling error Trust level Supported maximum variance Date of fieldwork

Consumers who use mobile phones and receive mobile advertising Random, self-administered survey, using a structured questionnaire through the Internet (Cint Panel Exchange) Spain 612 ±4.04% 95% (Z = 1.96) P = q = 50% June 2011

The 612 mobile users in the sample include a slight majority of women (51.8%), ranging in age mainly between 35 and 44 years (33.8%), with a university education (36.6%), who were married (60.3%) and earned a maximum income of 1201– 1800 Euros. To uncover any significant differences between consumers with Internet access through their mobile phones and those without it, we identified the 456 people without Internet access and the 156 who have it. In their sociodemographic profiles, we found no significant differences between these two samples.

4. Results We validated the structural model (Fig. 1) using a partial least squares (PLS) regression technique. The model was estimated using SmartPLS 2.0; to establish the significance of the parameters, we used a bootstrap resampling procedure with 456 (without Internet) and 156 (with Internet) entries. The results of this validation procedure appear in Tables 2 and 3. To ensure convergent validity, we eliminated any indicators whose factor loadings were not significant at 0.7; the resulting model thus suffered no reliability problems according to any of the established criteria (Cronbach’s alpha, composite reliability, average extracted variance). To evaluate discriminant validity, we used the only criterion applicable for PLS estimations and tested whether the average variance extracted for each factor was greater than the square of the correlation between each pair of factors (Fornell and Larcker, 1981). We provide these results in Tables 4 and 5. Similarly, to assess the predictive capacity of the structural model, we used the criterion proposed by Falk and Miller (1992): The R-square of each dependent construct must be greater than 0.1. Table 6 shows the corresponding values, as well as the results obtained from contrasting the model in Fig. 1. After evaluating the psychometric properties of the measurement instrument, we can estimate the structural model in Fig. 1, which synthesizes the hypotheses, again using PLS and the same criteria for the significance of the parameters (bootstrapping of 612 subsamples). To assess the predictive ability of the structural model, we required the R-square of each dependent construct to be greater than 0.1 (Falk and Miller, 1992). Lower values, even if they are significant, are not accept-

Table 2 Reliability and convergent validity: no Internet access on mobile phones. Factor

Indicator

Loading

t-Value

Cronbach’s a

Compound reliability

Average variance extracted

Perceived ease of use (PEOU)

PEOU1 PEOU2 PEOU3 PEOU4 CR1 CR2 CR3 CR4 RG1 RG2 RG3 KG4 PV1 PV2 PV3 PV4 ATT1 ATT ATT3 ATT4

0.720*** 0.873*** 0.872*** 0.837*** 0.849NS 0.909*** 0.908*** 0.757 NS 0.868*** 0.785*** 0.904*** 0.886*** 0.836*** 0.843*** 0.905*** 0.893*** 0.884*** 0.684*** 0.861*** 0.915***

3.94 9.70 10.12 8.73 1.80 3.81 3.18 1.57 23.91 19.75 29.90 31.01 27.54 28.00 30.44 30.44 36.05 31.59 27.92 35.91

0.8503

0.864

0.6851

0.8824

0.9175

0.7365

0.8838

0.9201

0.7427

0.8949

0.9255

0.7566

0.8778

0.9162

0.7324

Control (CR)

Reference groups (RG)

Perceived value (PV)

Attitude (ATT)

⁄

p < .10. p < .05. p < .01. NS = not significant. ⁄⁄

***

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Table 3 Reliability and convergent validity: Internet access on mobile phones. Factor

Indicator

Loading

Perceived ease of use (PEOU)

PEOU1 PEOU2 PEOU3 PEOU4 CR1 CR2 CR3 CR4 RG1 RG2 RG3 KG4 PV1 PV2 PV3 IPV4 IPV4 ATT1 ATT ATT3 ATT4

0.601NS 0.801NS 0.919*** 0.698*** 0.850NS 0.985NS 0.891NS Eliminated 0.617*** 0.827*** 0.927*** 0.869*** 0.814*** 0.851*** 0.859*** 0.884** 0.698*** 0.924*** 0.680*** 0.897*** 0.928***

Control (CR)

Reference groups (RG)

Perceived value (PV)

Attitude (ATT)

⁄ ** *** NS

t-Value 0.343 0.941 2.697 2.358 0.752 1.360 0.57 11.05 14.39 18.86 15.51 13.77 14.02 17.80 14.06 14.22 26.20 18.83 26.57 28.31

Cronbach’s a

Compound reliability

Average variance extracted

0.8574

0.8851

0.6637

0.8794

0.9216

0.7969

0.8841

0.9196

0.7413

0.8967

0.9224

0.7043

0.9287

0.9492

0.8238

p < .10. p < .05. p < .01. = not significant.

Table 4 Discriminant validity: no Internet access on mobile phones. ATT ATT CR INT SN PEOU PV

CR

0.8558 0.1415 0.7368 0.7112 0.2600 0.7256

0.8581 0.1041 0.2878 0.0267 0.0262

INT

SN

PEOU

PV

NA 0.4359 0.2162 0.5599

0.8618 0.1950 0.5501

0.8277 0.3603

0.8698

INT

SN

PEOU

PV

NA 0.5676 0.2966 0.6994

0.8609 0.1571 0.6620

0.8146 0.2770

0.8801

Table 5 Discriminant validity: Internet access on mobile phones. ATT ATT CR INT SN PEOU PV

0.9076 0.0326 0.7644 0.7679 0.2507 0.7747

CR 0.8926 0.0878 0.1361 0.0752 0.1758

Table 6 Hypotheses contrasts. Hypothesis

H1 Attitude ? Intention H2 PEOU ? Attitude H3 Perceived value ? Attitude H4 Reference groups ? Attitude H5 Perceived control ? Attitude Predictive ability

*** NS

p < .01. = not significant.

No Internet access

Internet access

(b) Standardized

T Bootstrap value

(b) Standardized

T Bootstrap value

0.711⁄⁄⁄ 0.001NS 0.523⁄⁄⁄ 0.366⁄⁄⁄ 0.023NS R2 Attitude = 0.664 R2 Intention = 0.543

26.88 0.0469 13.95 9.554 0.817

0.764⁄⁄⁄ 0.052NS 0.452⁄⁄⁄ 0.462⁄⁄⁄ 0.012NS R2 Attitude = 0.719 R2 Intention = 0.584

23.852 0.928 6.755 7.009 0.1894


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able. As Table 6 shows, all the R-square values for the dependent factors exceed that level, so we can evaluate the hypotheses by accounting for the significance of the estimated standardized regression coefficients. The results confirm the central role of attitude toward mobile advertising in both models. That is, this variable has a direct, intense effect on usage intentions (no Internet access b = 0.711, p < 0.01; Internet access b = 0.764; p < 0.01), predisposing a person toward intentions to use mobile advertising. These results confirm H1 and are consistent with previous research by Tsang et al. (2004), Lee et al. (2006), Karjaluoto et al. (2008), and Soroa-Koury and Yang (2010). Attitude toward advertising received through mobile phones thus is a necessary and sufficient condition for its use. In addition, these effects are more intense among consumers who have mobile access to the Internet. Regarding the antecedent factors of attitudes toward mobile advertising, we find that perceived value has greater influences on attitude, with more intense effects among those without Internet access (b = 0.523, p < 0.01) than among consumers with such access (b = 0.452, p < 0.01), in support of H3 and coincident with results published by Karjaluoto et al. (2008) and Soroa-Koury and Yang (2010). The next greatest influence comes from social norms (no Internet access b = 0.366, p < 0.01; Internet access b = 0.462, p < 0.01), as we predicted in H4. These findings suggest the value of advertising as a source of information, as well as the influence of reference groups on a person’s attitudes toward advertising. Finally, ease of use and perceived control are not significant in either model; their effects even are contrary to our predictions for consumers who lack, versus have, Internet access (perceived ease of use: b = 0.001, p < 0.01; b = 0.052, p < 0.01; perceived control b = 0.023, p < 0.01; b = 0.012, p < 0.01). In terms of perceived control, this result may reflect the need for the individual user to authorize his or her receipt of advertising through mobile phones. For ease of use, we posit that most Spanish adults likely have extensive experience with mobile phone use. The results thus reveal distinct aspects in our research. A favorable attitude toward advertising also exerts a favorable influence on intentions to receive mobile advertising. In addition, for there to be favorable attitudes, the role of perceived value must be emphasized, as should the influence of reference groups on the consumer. 5. Conclusions and implications This research sought to determine key factors with a bearing on the investigation and identification of new possibilities for commercial communication about goods and services, especially considering the importance and high level of penetration of mobile phones as communication channels. As a starting point, we have provided an in-depth examination of the behaviors exhibited by mobile advertising recipients. Our analysis addresses factors that might influence their attitudes toward such advertising and the resulting influence on experiences, emotions, and intentions. We validate our proposed conceptual model, which integrates differences between users of 3G technology and people less predisposed to this innovation. In this analysis context, we contrast several assumptions using a causal model of their relations, through the application of PLS. The results show, similar to other research, that attitude has a positive influence on people’s intention to receive advertising. However, with respect to the variables involved in establishing attitudes toward mobile advertising, the experience effect does not contribute to positive attitudes but rather exerts an opposite effect. Perhaps the misuse of this medium by companies—such as sending out advertising on a massive scale, without prior segmentation—leads consumers, and particularly experienced ones, to refuse to even open the message, which they consider ‘‘junk.’’ This negative relationship between frequency and attitude must be considered by companies, especially those that use cross-databases of mobile advertising users to optimize their contacts with their target markets. In the same vein, prior to now, users have not had much control over the receipt of advertising messages. In a mobile setting though, data privacy is vulnerable to advertising campaigns, such that people who receive marketing messages without their consent likely grow more annoyed. As the proposed model reveals, only when certain value exists, such that the user is aware of the benefits of advertising, and those benefits are supported by third-party influences or reference groups, do the benefits of having information available at any time and in any place seem evident. This finding corresponds to the principle of utility maximization; consumers want to have as much information as possible at any time, but at the lowest cost and without having to travel to find it. Companies thus should emphasize how consumers can access advertising on the go, which means a more efficient use of their time. Similarly, these results confirm the important role of reference groups and leaders for defining acceptance of mobile advertising. Companies need to promote advertising, such that reference groups for their target customers perceive and convey advertising through mobile devices as valuable, mainly because they can read it in real time. With regard to perceived control, our study results are similar to some preliminary findings that control has little bearing on intentions to receive mobile communications (Karjaluoto and Alatalo, 2007; Merisavo et al., 2007; Venkatesh et al., 2003). This empirical evidence helps establish how little influence perceived control has on attitudes toward advertising. In our study context (i.e., Spain), prior authorization is required from the customer to receive advertising through mobile devices, so perhaps these users do not consider such control necessary. This effect is consistent across the two groups (users and nonusers) we considered. The nature of the communication means and their strong technology links, as well as the interactive nature of advertising, leads to a result that indicates a negligible influence of ease of use on attitudes. We included this variable in our model because of the contradictory results in prior academic literature though. Karjaluoto et al. (2008) concur with our findings

364


of an influence, but Soroa-Koury and Yang (2010) suggest that perceptions of ease of use do not predict attitudes toward mobile advertising. This study also suffers from several limitations. In particular, our survey included only users of mobile advertising, not consumers who lack any experience with this media channel. Further research should include people who have chosen not to receive mobile advertising and thus determine if the proposed model can explain their inaction too. Acknowledgments This work received partial financial support for Development of Research Projects (BOCM) of University of La Rioja and Santander Bank (Proyect 2009–2010). Appendix A. Study items

Variables PERCEIVED EASE OF USE (PEOU)

Items PEOU1 PEOU2 PEOU3 PEOU4

CONTROL (CR)

CR1 CR2 CR3 CR4

AGENT NORM (SN)

SN1 SN2 SN3

SN4 PERCEIVED VALUE (PV)

ATTITUDE (ATT)

PV1 PV2 PV3 PV4 PV5 ATT1 ATT2 ATT3 ATT4

It is easy for me to use the mobile phone I find it easy to use new mobile services I am familiar with all my mobile applications I find it easy to access the download services on my mobile I think it is important for me to be able to control who sends me advertising I think it is important for me to be able to control the type of message I think it is important for me to be able to control the number of messages I think it is important not to receive unrequested advertising I use advertising on mobile because my friends do I inform my friends about advertising sent to my mobile phone My reference group thinks I should receive advertising on my mobile phone I receive advertising on my mobile because my friends also receive it Reception in hours Ability to access it at the desired time Ability to access it in the desired location It brings additional benefits Additional advantages It captures my attention I find it entertaining It influences my shopping behavior I like it

References 1-5 Likert scale

Davis (1989); Davis and Venkatesh (1996); Karjaluoto et al. (2008); Soroa-Koury and Yang (2010); Taylor and Todd (1995); Venkatesh and Morris (2000); Zhang and Mao (2008) Ajzen (2002); Bamba and Barnes (2007); Jayawardhena et al. (2009); Karjaluoto and Alatalo (2007); Mathieson (1991); Merisavo et al. (2007); Taylor and Todd (1995); Venkatesh et al. (2003) Ajzen (1991); Bagozzi (2000); Bauer et al. (2005); Bearden and Etzel (1982); Childers and Rao (1992); Davis (1989); Fishbein and Ajzen (1975); Fitzgerald and Arndt (2002); Muk and Babin (2006); Rohm and Sultan (2006) Choi et al. (2008); Dodds et al. (1991); Flavián et al. (2009); Gallarza and Gil (2006); Grewal et al. (1998); Sweeney and Soutar (2001); Woodruff (1997); Zeithaml (1998) Ajzen (1991); Davis (1989); Durvasula et al. (1993); Dutta-Bergman (2006); Lee et al. (2006); Shavitt et al. (1998); Soroa-Koury and Yang (2010); Tsang et al. (2004); Xu (2006/2007)

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