17 Mar Purchase Intention
How smartphone advertising inﬂuences consumers' purchase intention
In the last decade, the use of smartphones has grown steadily. The way consumers interact with brands has changed owing to the accessibility of internet connection on smartphones, and ubiquitous mobility. It is crucial to understand the factors that motivate consumers to interact with smartphone advertisements and therefore what stimulates their decision to purchase. To achieve this goal, we proposed a conceptual model that combines Ducoﬀe’s web advertising model and ﬂow experience theory. Based on the data collected from 303 Portuguese respondents we empirically tested the conceptual model using a partial least squares (PLS) estimation. The results showed that advertising value, ﬂow experience, web design quality, and brand awareness explain pur- chase intention. The study provides results that allow marketers and advertisers to understand how smartphone advertisements contribute to consumer purchase intention.
The number of smartphone users has been increasing signiﬁcantly because of the growth of the smartphone industry, which develops new operating systems and a proliferation of applications. According to Gartner (2016) global sales of smartphones to end users totalled 349 million units in Q1 2016, a 3.9% increase over the same period in 2015. Moreover, smartphone sales represented 78% of total mobile phone sales in Q1 2016. Smartphones have been inﬂuencing the way people communicate with each other, becoming a near necessity in both private and professional lives (Derks, Bakker, Peters, & van Wingerden, 2016). The unprecedented growth of smartphones has attracted academic attention, hoping to determine the motivations that explain smartphone use (Park, Kim, Shon, & Shim, 2013; Yeh, Wang, & Yieh, 2016).
Earlier studies focused mainly on antecedents of advertising value and ﬂow experience on mobile advertising, to study attitude toward mobile advertising or intention to read or click (Liu, Sinkovics, Pezderka, & Haghirian, 2012; Yang, Kim, & Yoo, 2013). There is little research about what leads to advertising value, ﬂow experience, and purchase intention on smartphone advertising (Kim & Han, 2014). Therefore, the aim of this study is to analyse the factors that inﬂuence consumers’ purchase intention after seeing smartphone advertisements. To do so, we developed a model that combines Ducoﬀe’s web advertising model, ﬂow experience theory and three additional variables (emotional value, web design quality, and brand awareness) to under- stand the antecedents of purchase intention on smartphone advertising. The research questions (RQs) that emerged are as follows:
RQ1 – What are the factors that inﬂuence advertising value and ﬂow experience?
RQ2 – Do emotions add signiﬁcance to advertising value in smart- phone advertisements?
RQ3 – Does web design quality inﬂuence ﬂow experience in smartphone advertisements?
RQ4 – Does brand awareness play an important role in forming purchase intention in smartphone advertisements?
The contributions of this research are threefold. Firstly, it will be a guideline for marketers and advertisers to understand the factors that play an important role in smartphone advertising. Secondly, it provides valuable insights on how smartphone advertisements contribute to forming consumer purchase intention. Thirdly, we investigate the elements that inﬂuence best communication strategies for brands in the smartphone advertising market.
This article is structured as follows: Section 2 contains the theoretical background, i.e., the concept of mobile advertising, smartphone advertising and purchase intention, and theoretical foundation. Then, in Section 3 it presents the conceptual model, followed by Section 4 which covers the method used in the research. Sections 5 and 6 contain data analysis and discussion, respectively. Conclusions are in Section 7.
2.1-The concepts of mobile advertising, and smartphone advertising
Mobile advertising is deﬁned by The Mobile Marketing Association as “a form of advertising that transmits advertisement messages to users via mobile phones or other wireless communication devices” (Chen & Hsieh, 2012). By incorporating mobile advertising techniques in their communication strategies, retailors, services providers and manufacturers can create more dynamic oﬀers and campaigns. From a theoretical perspective, in order to understand how campaigns can reach successful levels, one must know how to ensure alignment between all context variables, the advertising goals, the stakeholders, market conditions, and the chosen mobile ad elements (Grewal, Bart, Spann, & Zubcsek, 2016).
Smartphones, diﬀerent from standard mobile phones in terms of the operating system, have been attracting a substantial number of users and have become a perceived necessity in personal and work lives. People use them for social networking purposes, for features and functions like reading e-books, answering e-mails, sending messages, and playing games. The Smartphone is a quite new technology and it has received minor attention in academic research in terms of under- standing users’ mind-sets about the adoption of smartphones (Joo & Sang, 2013). Nevertheless, smartphone advertisements play an in- creasing role in the decision-making process in supporting consumer purchases (Kim & Han, 2014).
Advertisements on smartphones have become more sophisticated, adapting to device screens that are not suitable for showing traditional online advertising (pop up, pop under, video, and display ads).
2.2-The concept of purchase intention
Purchase intention indicates likelihood that consumers will plan or be willing to purchase a certain product or service in the future (Wu, Yeh, & Hsiao, 2011). Past research has demonstrated that an increase in purchase intention reﬂects an increase in the chance of purchasing. If consumers have a positive purchase intention, then a positive brand engagement will promote that purchase. Regarding the context of smartphones, one needs to consider purchase intention as the desire of consumers to make a purchase through the mobile application (Chen, Hsu, & Lin, 2010). Some of the most relevant research on mobile purchase intention is summarized in Table 1.
In their most recent research, Zubcsek, Katona, and Sarvary (2017) present several arguments supporting the assumption that consumers’ movement patterns tend to represent their product preferences, which should be used by marketers to improve the provided commercial oﬀer. In line with this, Shen (2015) argues that not only is mobile shopping increasing to the point of becoming part of many people’s routine, but there is also a set of determinants, such as attitudes, subjective norms, and perceived behavioral control that tend to impact the customer intention to purchase. Hence, product information in mobile advertising should take into consideration these determinants to be well accepted by customers and to have the desired trigger eﬀect.
2.3.1-Ducoﬀe’s web advertising model
Ducoﬀe (1995) developed an approach to study the eﬀectiveness of attitude toward web advertising, focusing on advertising value. In order to understand what makes an advertisement valuable, Ducoﬀe (1995) found the antecedents (i.e., informativeness, irritation, and entertainment) of advertising value on the World Wide Web. Firstly, informativeness, described as the ability of advertising to inform consumers of product types. Secondly, irritation reﬂects the techniques employed by advertisers that annoy, oﬀend, insult, or manipulate consumers. Consequently, techniques are perceived as unwanted, irritating consumers. Thirdly, entertainment is perceived as pleasant or likeable advertising and has a positive impact on brand attitudes. These three determinants were the starting point to justify how consumers evaluate the value of advertising. The addition of credibility by Brackett and Carr (2001) and incentives by Kim and Han (2014) as antecedents of advertising value came later. Varnali, Yilmaz, and Toker (2012) describe incentive as generic monetary gains (lotteries, discounts, prepaid credits, and gifts).
2.3.2-Flow experience theory
Csikszentmihalyi (1975) pioneered ﬂow construct. Flow illustrates the best feelings and the most enjoyable experiences possible in human lives as “the bottom line of existence”. By deﬁnition, ﬂow is a psychological state in which an individual feels cognitively eﬃcient, motivated, and happy. Researchers have started to recognize the value of this theory in understanding people’s behaviour while using the web (Hoﬀman & Novak, 2009; Novak, Hoﬀman, & Yung, 2000). The concept of ﬂow was ﬁrst applied to the experiences of web users by Hoﬀman and Novak (1996) in an examination of online marketing activities.
3.1-The conceptual model
The conceptual model, as shown in Fig. 1, is based on Ducoﬀe’s web advertising model and ﬂow experience. The goal of this research is to determine how consumers perceive the antecedents of the interaction with smartphone advertisements, and consequently how this inﬂuences their purchase intention. The constructs, advertising value, and ﬂow experience have ﬁve common variables: (1) informativeness; (2) cred- ibility; (3) entertainment; (4) irritation; and (5) incentives. A new variable was added to advertising value, i.e., emotional value. Simi- larly, the web design quality variable was added to ﬂow experience. We added brand awareness and the antecedent emotional value. Purchase intention is depicted as the consequence of advertising value, ﬂow experience, web design quality, and brand awareness. Each of these constructs is discussed in the following sections.
In a mobile devices context, information is considered as a valuable incentive because consumers react very positively to advertising (Aitken, Gray, & Lawson, 2008). Consumers do not feel annoyed if mobile advertisements provide appropriate information. Scharl, Dickinger, and Murphy (2005) concluded that consumers are likely to purchase advertised products if advertisers provide funny and entertaining SMS messages that are informative and relevant. Thus, in- formativeness is strongly related to perceived advertising value (Ducoﬀe, 1996). In addition, informativeness positively inﬂuences ﬂow experience because it will aﬀect consumer attention. The consumer focuses on product information messages, concentrating on their de- tails, excluding irrelevant thoughts (Hoﬀman & Novak, 1996; Li & Browne, 2006). Thus:
H1. Perceived informativeness of smartphone advertisements is (H1a) positively associated with perceived advertising value and (H1b) positively associated with ﬂow experience.
“The extent to which the consumer perceives claims made about the brand in ads to be truthful and believable”, deﬁnes credibility (Mackenzie & Lutz, 1989). Several empirical studies have demonstrated that advertisement credibility has a signiﬁcant eﬀect on attitudes toward advertising and behavioral intentions (Tsang, Ho, & Liang, 2004; Zhang & Mao, 2008). Advertising credibility is evaluated through the content of advertisements, being further inﬂuenced by a company’s credibility and the holder of the message (Balasubraman, Peterson, & Jarvenpaa, 2002). Thus, advertising credibility positively aﬀects the perceived value of advertising. According to Yang et al. (2013) a consumer may avoid or not respond to advertising if they do not think mobile advertisements are trustworthy, not paying attention to the message. Therefore, the reliability of a mobile message is critical and consumers are able to experience ﬂow state with a credible message (Choi, Hwang, & McMillan, 2008). Thus:
H2. Perceived credibility of smartphone advertisements is (H2a) positively associated with perceived advertising value and (H2b) positively associated with ﬂow experience.
Ducoﬀe (1995) conﬁrmed that entertainment of advertising in- formation is positively related to advertising value. Entertainment is the ability of an advertisement to promote enjoyment and create positive consumer attitudes by providing a form of escapism, diversion, aesthetic enjoyment, or emotional release (Elliott & Speck, 1998; Shavitt, Lowrey, & Haefner, 1998). In the advertising context, entertainment is pleasurable, enjoyable, and fun to watch (Schlinger, 1979). According to Sternthal and Craig (1973) entertaining advertisements attract consumers’ attention, consequently the eﬀectiveness of the advertisement increases. Coulter, Zaltman, and Coulter (2001) found that entertainment is an important value that consumers look for in advertising. Moreover, entertainment has recently become a factor that consumers expect when they view advertising. Entertainment positively inﬂuences consumer ﬂow experience. Hence:
H3. Perceived entertainment of smartphone advertisements is (H3a) positively associated with perceived advertising value and (H3b) positively associated with ﬂow experience.
Irritation refers to the extent to which consumers perceive that mobile advertisements are irritating or annoying, involving negative feelings toward the advertisements (Yang et al., 2013). Past research examined irritation as being negatively related to advertising value, reducing advertising eﬀectiveness and the value perceived by consumers (Korgaonkar & Wolin, 1999; Okazaki, 2004). Mobile advertising may provide information that is distracting and that overwhelms the consumer (Stewart & Pavlou, 2002) and this can be perceived as an intrusion into the mobile consumer’s privacy. According to Liu et al. (2012) consumers then feel confused about the advertising and react negatively to it, and irritation caused by incomprehensible or unwanted mobile advertising messages may reﬂect negatively on the perceived value of mobile advertising. Hence:
H4. Perceived irritation of smartphone advertisements is (H4a) negatively associated with perceived advertising value and (H4b) negatively associated with ﬂow experience.
Incentives are major predictors of consumers’ responses and entail monetary beneﬁts such as discounts, coupons, gifts, and non-monetary beneﬁts (Varnali et al., 2012). Incentives are considered to have an impact on consumer intentions to receive mobile advertising and pro- vide speciﬁc ﬁnancial rewards to consumers who agree to receive an advertisement (Tsang et al., 2004). Y. Kim and Han (2014) introduced the incentives in the Ducoﬀe (1995) model. They suggest increasing
incentives for consumers receiving smartphone advertisements, affecting consumer ﬂow experience. Their study reported that consumers are interested in tangible beneﬁts and pay more attention to an advertising message for ﬁnancial advantage. Thus, consumers perceive value in an advertisement with incentives. Consequently:
H5. Perceived incentives of smartphone advertisements is (H5a) positively associated with perceived advertising value and (H5b) positively associated with ﬂow experience.
Past research studied emotion in the advertising ﬁeld (Edell & Burke, 1987). The utility derived from the feelings or aﬀective states (i.e. enjoyment or pleasure) that a product generates deﬁnes emotional value. Emotional value toward a brand relates to positive feelings upon using the brand, which increases consumer loyalty toward the brand (Sweeney & Soutar, 2001). When consumers view advertising, the in- formation contained in it induces emotional responses and creates an attitude toward the brand. Hyun, Kim, and Lee (2011) deﬁned emotional responses toward advertising as the set of emotional responses elicited during advertising viewing. We suggest the addition of emotional value to explain perceived adverting value and increasing brand awareness. Therefore:
H6. Perceived emotional value is (H6a) positively associated with advertising value and (H6b) positively associated with brand awareness.
Advertising value is a measure of advertising eﬀectiveness, being deﬁned as a “subjective evaluation of the relative worth or utility of advertising to consumers” (Ducoﬀe, 1995, p. 1). Perceived advertising value contributes to the growth of ﬂow experience because consumers focus totally on the messages received, eliminating irrelevant thoughts (Hoﬀman & Novak, 1996). Consumers evaluate the received messages as being worthy if they match their needs or include valuable in- formation to purchase. Past research studied the relationship between advertising attitude and purchase intention (Tsang et al., 2004). How- ever, there are few studies investigating the relationship between advertising value and purchase intention. Consumers show a favorable attitude to products or services when purchase intention increases (Ko, Cho, & Roberts, 2005). Thus:
H7. Perceived advertising value is (H7a) positively associated with ﬂow experience and (H7b) positively associated with purchase intention.
Web design is the set of elements that a consumer experiences on a web site – information search, product selection (Ha & Stoel, 2009). Design factors – size of the advertisement, use of colour, music eﬀects, presence of animation, and the length of the commercial are related to how eﬀectively the advertisement is designed. Web site design aﬀects online purchase intention. A poorly designed interface can disrupt a ﬂow experience by demanding an excessive amount of attention, or contrarily, distracting the users. H. Kim and Niehm (2009) reported that web design quality positively inﬂuences consumer perception regarding the quality of information shown on the web site, and consequently aﬀects brand perception as reliable. We include web design quality due to the lack of study about designing mobile advertisements. Accordingly:
H8. Perceived web design quality is (H8a) positively associated with ﬂow experience, (H8b) positively associated with purchase intention, and (H8c) positively associated with brand awareness.
The concept of ﬂow refers to optimal and enjoyable experiences when an individual engages in an activity with total involvement, concentration, and enjoyment. When consumers become absorbed in their activities, irrelevant thoughts and perceptions are ﬁltered out. Researchers concluded that surﬁng the web is an activity that can facilitate the occurrence of ﬂow (Chen, Wigand, & Nilan, 1998; Hoﬀman & Novak, 1996). The decision to interact with smartphone advertisements and whether to purchase advertised products or services or not is crucial for ﬂow experience (Kim & Han, 2014). Thus, consumers’ ﬂow experience positively inﬂuences purchase intention. Hence:
H9. Flow experience is positively associated with purchase intention.
Brand awareness is related to the strength of the brand node or trace in memory as reﬂected by consumers’ ability to recall or recognize the brand under diﬀerent conditions. Hence, only brands that consumers recognize can be identiﬁed, categorized, and ultimately purchased. The importance of brand awareness resides in the fact that consumers include it in their decision to purchase and evaluate the product. Regarding purchase intention, consumers’ choice of a more familiar brand is usually higher than that of a less familiar brand (Hoyer & Brown, 1990). We add brand awareness because past research has demonstrated that raising it increases the chance of the brand being considered for purchase (Washburn & Plank, 2002). Thus: H10. Brand awareness is positively associated with purchase intention.
All constructs were adapted, with slight modiﬁcations, from the literature (see Appendix A). All the constructs were measured by using seven-point range scales in each item, ranging from “strongly disagree” (1) to “strongly agree” (7). The language of the constructs was modiﬁed to be suitable in the smartphone ad context. We also included four demographic questions relating to age, gender, education, and job. The questionnaire was uploaded to the web, to be divulged online, through surveymonkey.com.
In July 2016 a pilot survey was conducted with 44 responses to reﬁne the questions, obtain additional comments on the content and structure in order to decide which would be the ﬁnal items to analyse. Respondents of the pilot test were asked to provide feedback and suggestions for improvement when instructions or questions were not clear. Respondents also answered all questions by following the instructions. The most important changes were in the items of emotion value (EV), web design quality (WDQ), incentives (INC), and purchase intention (PI), as they generated misunderstandings and users did not clearly understand the questions. For this reason and regarding the smartphone context, the items were modiﬁed by many suggestions about the phrasing and the overall structure of the questionnaire. The data from the pilot survey was not included in the main survey. A survey was conducted to examine the hypotheses in this study.
Respondents were those who have a smartphone and have had an ex- perience viewing smartphone advertisements. The data were collected from smartphone consumers who had experienced SMS, MMS, keyword search, display, and rich media advertising. We carefully scrutinized the responses for each question. Improper responses such as having the same answers to all questions and incomplete responses were excluded from our sample. In total, 303 respondents successfully completed the questionnaire, which can be considered an adequate sample for a re- search of this kind (Baptista & Oliveira, 2015; Hossein, 2015; Hsia, Chang, & Tseng, 2014; Zhu, Chang, & Luo, 2016). These valid responses were analysed to assess reliability, validity, and appropriateness for hypotheses testing.
We administered the questionnaires to people residing in Portugal, the 18th country in the World regarding smartphone penetration rate (Newzoo, 2017). The ﬁnal sample comprised 303 individuals (see Table 2), in which 49% (151) are male and 51% (152) are female. The average age is 33, the youngest respondent being 15 and the oldest 63.
To examine the causal relationships and estimate the conceptual model, we used structured equation modeling (SEM). SEM has changed the nature of research in international marketing and management. It is a statistical technique for testing and estimating causal relationships using a combination of statistical data and qualitative causal assump- tions (Henseler, Ringle, & Sinkovics, 2009). The use of Partial Least Squares (PLS) is suitable and was considered the most appropriate method due to: (a) the early stage of theoretical development; (b) this conceptual model has not been tested in the literature and; (c) the conceptual model is considered to be complex. In the next two sub- sections we ﬁrstly examine the measurement model in order to assess indicator reliability, construct reliability, convergent validity, and dis- criminant validity. Secondly, we test the structural model. The software used for applying the method was PLS Smart 3.0 Software (Ringle, Wende, & Will, 2005).
Firstly, in order to analyse the indicator reliability, the loadings should be higher than 0.7 (Chin, 1998; Hair & Anderson, 2010; Henseler et al., 2009). All the items have loadings > 0.7 (Table 3), conﬁrming that the indicator reliability is achieved. Secondly, two criteria were used to ex- amine the construct’s reliability – Cronbach’s alpha (CA) and composite reliability (CR). As seen in Table 3, all constructs have CR and CA > 0.7, approving construct reliability (J. Henseler et al., 2009). Thirdly, in order to assess convergent validity, the average variance extracted (AVE) should be at least 0.5 to be considered suﬃcient and explain more than half of the variance of its indicators on average (Hair & Anderson, 2010; Henseler et al., 2009). As seen in Table 3, AVE for all the constructs are above 0.5, guaranteeing convergent validity.
Finally, the discriminant validity has three criteria. The ﬁrst criterion is the Fornell-Larcker criterion, which demands that the root square of AVE (Table 4 in bold) for each latent variable should be greater than the correlation with any other latent variable (Fornell & Larcker, 1981). In Table 4, we see that these criteria are achieved. The second criterion, the loading of each indicator is expected to be greater than all of its cross-loadings (Chin, 1998). This was also analysed and each construct has loadings with higher values than their cross loadings (Hair & Anderson, 2010); this result is available from the author upon request. The Hetrotrait-Monotrait ratio (HTMT) table is available upon request, and all values are below the threshold of 0.9 (Jörg Henseler, Ringle, & Sarstedt, 2015).
Therefore, all the measures satisfy the discriminant validity of the constructs. The assessment of the construct reliability, convergent validity and indicator reliability, produce satisfactory results, indicating that the constructs can be used to test the conceptual model.
We demonstrated above that the measurement model is satisfactory. Now, it is possible to test the structural model. This article used a bootstrapping of 5000 resamples to estimate the statistical signiﬁcance of path coeﬃcients (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005). Ac- cording to Chin (1998), the crucial criterion for assessing the structural model is the coeﬃcient of determination (R2) of the endogenous latent variables. R2 should be above 0.2 to be considered moderate. The results of the hypotheses of structural model are illustrated in Fig. 2.
First, the research explains 71.7% of variation in advertising value in the conceptual model. The hypotheses of informativeness (β‸ = 0.133; p < 0.05), credibility (β‸ = 0.334; p < 0.01), entertainment (β‸ = 0.205; p < 0.01), irritation (β‸ = −0.071; p < 0.10), and incentives (β‸ = 0.260; p < 0.01) are statistically signiﬁcant. However, emotional value (β‸ = 0.011; p > 0.10) is not statistically signiﬁcant. Therefore, hypotheses H1a, H2a, H3a, H4a, and H5a are supported, but H6a is not supported to explain advertising value.
Second, ﬂow experience is explained by 67.4% of the variation in the conceptual model. The hypotheses that are statistically signiﬁcant to explain ﬂow experience are credibility (β‸ = 0.208; p < 0.01), entertainment (β‸ = 0.164; p < 0.05), irritation (β‸ = −0.084; p < 0.10), incentives (β‸ = 0.321; p < 0.01), and advertising value (β‸ = 0.288; p < 0.01). However, informativeness (β‸ = −0.156;
p < 0.01) and web design quality (β‸ = 0.035; p > 0.10) are not statistically signiﬁcant. Therefore, hypotheses H2b, H3b, H4b, H5b, and H7a are supported, while hypotheses H1b, and H8a are not supported. Third, brand awareness is not explained by 26% of the variation in the
conceptual model. The hypotheses emotional value (β‸ = 0.231; p < 0.01)
Finally, the model explains 68.3% of variance in purchase intention. The hypotheses of advertising value (β‸ = 0.228; p < 0.01), web de- sign quality (β‸ = 0.099; p < 0.05), ﬂow experience (β‸ = 0.516; p < 0.01), and brand awareness (β‸ = 0.109; p < 0.01) are statistically signiﬁcant to explain the purchase intention. Therefore, H7b, H8b, H9 and H10 and are supported.
In summary, out of a total of 19 hypotheses in the model, 16 are supported and 3 are not.
This research has three theoretical implications. First, advertising value was positively inﬂuenced by informativeness, credibility, entertainment,
and incentives, which is consistent with previous ﬁndings (Ducoﬀe, 1995; Kim & Han, 2014; Liu et al., 2012). Credibility was the strongest positive factor, followed by entertainment and informativeness. These results show that consumers perceive smartphone advertisements as a good source of product information and tend to consider it as being somewhat useful and enjoyable. In contrast, irritation negatively inﬂuences advertising value, meaning that consumers avoid irritating or annoying smartphone adver- tisements (Kim & Han, 2014). In addition, this research failed to predict the eﬀect of emotional value. That is, consumers do not have positive feelings about the brand advertised, and do not derive any beneﬁt from the ex- perience of smartphone advertisements.
Second, ﬂow experience is positively inﬂuenced by credibility, en-
tertainment, incentives, and advertising value. Informativeness and irritation had a negative inﬂuence, which is consistent with earlier research (Kim & Han, 2014). Incentives are the strongest factor, followed by credibility and entertainment. To the contrary, the addition of web design quality did not have a signiﬁcant impact, the eﬀect of web design experience is not relevant for consumers while they are interacting with smartphone advertisements.
Third, the addition of emotional value and web design quality was re- vealed to explain brand awareness. These results show the importance of consumers developing an emotional bond with the brand they recognize in smartphone advertisements, and web design plays a crucial role in the perception of brand to consumers, a feeling that is reliable.
Finally, results indicate that advertising value, ﬂow experience, web design quality, and brand awareness are key factors to explain purchase intention in the context of smartphone advertisements. Table 5 illustrates the results demonstrated in this section.
Several practical implications can be drawn. First, while consumers view and engage with smartphone advertisements, valuable information that fulﬁls consumer needs should be delivered. Consumers enjoy focusing on the details of the product or service advertised. Marketers and advertisers can provide advertisements that meet consumer needs, and ensure they are part of the target communication.
Second, irritation is recognized by consumers as being annoying and intrusive with advertisements. Marketers and advertisers should con- sider if consumers are receptive to advertisements on smartphones, and allow the option for consumers to choose whether they want to receive them or not. This would contribute to making consumers feel less irritated, impatient, and advertisements being less intrusive.
Third, regarding the importance of emotional value on brand aware- ness, consumers become more engaged with the brand the more they are familiar with it. Advertisers should consider creating advertisements that arouse emotions. Emotions are representative of consumers’ feelings and the way they interact with the brand relies on the basis that smartphone advertisements’ connection with consumers arouse emotions, allowing for positive brand recognition, perceiving it as relevant and valuable.
Fourth, advertisers should develop smartphone advertisements that easily engage consumers’ attention. Brands should consider investing in better designed advertisements that make the experience of viewing advertisements more attractive. Web design makes a diﬀerence in consumer perception about the content and product or service in- formation. Improving web design quality in smartphone advertisements should induce pleasure and satisfaction among consumers.
6.3-Limitations and future research
Our study has several limitations. First, the study was conducted with consumers of only one European country. Therefore, in order to overcome cultural and economic disparities, it would be interesting to implement it in other countries, and compare the ﬁndings. Second, brand awareness conﬁrmed the inﬂuence on purchase intention and is one of the dimensions of brand equity. Thus, more eﬀort is required to theoretically and empirically test the antecedents of brand equity that inﬂuences purchase intention. Third, web design quality was un- supported to explain ﬂow experience and future studies should investigate the antecedents such as interactivity. Fourth, further research to understand the eﬀect of emotional value on purchase intention would be welcome.
The contribution of this research was to identify the strongest
factors inﬂuencing consumers’ willingness to purchase products or services, after viewing advertisements on smartphones. For this purpose, we developed a model based on Ducoﬀe’s web advertising model and ﬂow experience theory. This study was the ﬁrst to include emo- tional value, web design quality, and brand awareness. Based on a sample of 303 Portuguese respondents we empirically conﬁrmed that for advertising value the facilitators were informativeness, credibility, entertainment, and incentives, while irritation and emotional value were inhibitors. These ﬁndings revealed that consumers consider smartphone advertising as being credible, enjoyable, a good reference of information for purchasing products, and oﬀers the chance of obtain rewards. However, they may also perceive smartphone advertising as unwanted, intrusive, and annoying, and as a result, negative feelings arise toward the brand advertised. Flow experience was positively inﬂuenced by credibility, entertainment, incentives, and advertising value. Informativeness and irritation negatively inﬂuenced ﬂow experience. These results may be driven by the fact that, as argued in the literature, consumers are starting to develop positive attitudes toward smartphone advertisements, as they are useful, valuable, believable, entertaining, and correctly deliver the details of the products. Nevertheless, when consumers do not obtain proper information, they recognize smartphone advertisements as irritating. Brand awareness was successfully explained by emotional value and web design quality. Brand awareness was conﬁrmed to be crucial for consumers to recognize the brand, and consider purchasing of a brand’s products or services. Finally, we concluded that purchase intention was successfully explained by advertising value, ﬂow experience, web design quality, and brand awareness.