The relationship between e-lifestyle and Internet advertising avoidance

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Australasian Marketing Journal j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / a m j

The relationship between e-lifestyle and Internet advertising avoidance Amir Abedini Koshksaray a,*, Drew Franklin b, Kambiz Heidarzadeh Hanzaee c a

Department of Business Management, Qazvin branch, Islamic Azad University, Qazvin, Iran. Department of Marketing, Advertising, Retailing and Sales, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand Department of Business Management, Tehran Science & Research Branch, Islamic Azad University, Ashrafee-e-Esfahani Highway, Hesarak Road, 1477893855, Iran b c

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Article history: Received 11 January 2015 Accepted 11 January 2015 Available online Keywords: Internet E-lifestyle Advertising avoidance Internet advertising

A B S T R A C T

This study provides insights into e-lifestyle of Internet users and their avoidance of Internet advertising. Determining the type of avoidance of each e-lifestyle aids the development of strategies for designing and publishing advertisements on the Internet, so that their effectiveness is enhanced and the negative trend of clicks on Internet advertisements is reduced. A survey was conducted with a group of 412 participants. The data were analysed through structural equation modelling (SEM) and multiple regression analysis both before and after adjusting the data by introducing the effect of average hours of Internet use on participant responses. The results obtained by analysing the main data reveal that e-lifestyle does not have a significant effect on Internet advertising avoidance (IAA). However, analysis of the modified data does indicate a significant effect. Also, in the analysis of the main and modified data, the type of avoidance from Internet advertising (cognitive, affective, and behavioural) varies according to each e-lifestyle. To the authors’ belief the present study is the first reporting an investigation of the effect of e-lifestyle on avoidance of Internet advertising adjusted by average hours spent online. © 2015 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved. C H I N E S E

A B S T R A C T

本研究就网民们的电子生活方式和逃避互联网广告行为之间关系提出了见解。本研究认为明确每种电子生活方式 中存在的逃避行为，有利于推动互联网广告设计和发布策略的形成，增强广告效果，提高互联网广告的点击率。 本调查共有412名参与者。研究依据互联网平均使用时数对参与者回应的影响，分别在利用结构方程模型（SEM） 和多元回归分析法进行数据分析之前后对数据进行适当调整。通过分析主数据得到的结果表明，电子生活方式不 会对互联网广告逃避（IAA）产生显著效果。但是，修改后的数据分析结果恰恰相反。此外，在分析主数据和修 改后的数据时，逃避互联网广告行为的类型（认知、情感和行为）因电子生活方式的不同而变化。笔者认为，本 报告首次研究了平均上网时数对电子生活方式中逃避网络广告行为的影响。 © 2015 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.

1. Introduction The Internet is a pervasive domain that has become an everincreasing part of our daily lives since its inception and commercialisation in the mid-1990s. As with many choice exercises, users employ the Internet for many different reasons – from simple information search to building and developing social networks that function as electronic proxies to that of real-life. These behaviours can be described as the characteristics of an Internet user’s e-lifestyle and serve as a foundation upon which to build effective and engaging Internet advertising.

* Corresponding author. Tel.: +989141937587; fax: +2634423090. E-mail address: [email protected] (A.A. Koshksaray).

The Internet has been described as a convergent medium that covers other media such as TV, radio, newspapers, magazines, billboard, and direct mail (Cho and Cheon, 2004). In effect, users attend to the Internet to investigate the claims within these media, and seek a measure of support from other Internet users. The Internet has provided a space for producers and advertisers to access their consumers rapidly and directly, relative to more traditional media channels. According to Internetworldstats.com (2011) the number of Iranian Internet users reached almost 34 million by 2010 and the country was ranked first in the Middle East and fourth in Asia for Internet usage. As of 2012, mobile penetration in Iran stood at 75.82 per 100 people and the number of mobile users has grown at an average of more than 49.05% during 2000–12 (Marketline Advantage, 2014). In 2012, Internet users as a percentage of the total population stood at 25.59%, up from 20.67% in 2011. The liberalisation of the telecommunications

http://dx.doi.org/10.1016/j.ausmj.2015.01.002 1441-3582/© 2015 Australian and New Zealand Marketing Academy. Published by Elsevier Ltd. All rights reserved.

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market in 2009 and the installation of new fibre cables and modern switching and exchange systems by the state-owned telecom company has improved the ICT sector in Iran (Marketline Advantage, 2014) and is indicative of a market in high-growth mode. The number of Internet users in Iran is now in excess of 36 million and thus represents a very appropriate arena for Internet-based research. Reports suggest that the income earned from Internet advertising reached $2.6 billion dollars in the second quarter of 2010 (Iab.net, 2011). This indicates a 4.1% increase over the first quarter of 2010 and a 13.9% increase over the second quarter of 2009. However, this income is at risk of decline as recent statistics indicate that the clickthrough rate within Internet advertising is decreasing. According to Nielsen (2000), the click-through rate in 1995 was 2% whilst in 2008 the click-through rate had fallen to 0.3% (MediaPost, 2008; Cho and Cheon, 2004). Scholars have attributed this fall in click-through rate to the proliferation of Internet advertising and the “cluster-bomb” approach (Cho and Cheon, 2004). Other phenomenon symptomatic of the increasing clutter in the Internet advertising space includes Internet users’ “banner blindness” (Cho and Cheon, 2004) and the active blocking, or avoidance, of Internet content such as pop-up advertising. Avoidance refers to a state when the user consciously and intentionally seeks to avoid a stimulus (Tellis, 1997). Avoidance of advertising is defined as all actions performed by the users of a media that distinctively prevents the user from being exposed to the advertisements, and it can be achieved in different ways (Speck and Elliot, 1997). Advertisers need to understand all reasons, latent or otherwise, for Internet advertising avoidance so as to develop strategies to more efficiently transfer their message to the target market. To this end, many studies have been conducted to gain deeper insight into the reasons why Internet users seek to avoid Internet advertising (Cho and Cheon, 2004; Kelly et al., 2010). According to Cho and Cheon (2004), Internet advertising avoidance can be observed within three types, or modes, of avoidance; cognitive, affective and behavioural avoidance. The e-lifestyle of the Internet user is one factor that is expected to affect Internet advertising avoidance. Many studies have indicated that e-lifestyle is an important variable that influences the user’s means of employing the Internet for various activities or goals (Kim et al., 2001; Schiffman et al., 2003). These lifestyle features provide advertisers with practical, precise, information about consumers so that they can meet the needs of each user within competitive and complex markets (Kamakura and Wedel, 1995). This understanding becomes increasingly important within the Internet as the online domain penetrates many different layers of society and encounters numerous and varied lifestyles (Schiffman et al., 2003; Weiss, 2001). Classification of these various lifestyles serves to identify useful and important elements of each respective lifestyle so that advertisers are able to target appropriate consumers. The provision of more effective, targeted advertising will, therefore, serve to mitigate Internet advertising avoidance and generate more favourable click-through rates. Irrespective of the various persuasive opportunities that the Internet presents for individual advertisers, the basic challenge remains the same; the decrease in click-through rates, increased avoidance of Internet advertising and thus less effective online campaigns. These difficulties present an opportunity for researchers to explore these barriers to effective advertising. By identifying Internet advertising avoidance relative to each e-lifestyle and the type of avoidance (cognitive, affective, or behavioural), this study seeks to determine, firstly, if e-lifestyle does exhibit a significant relationship with Internet advertising avoidance as the literature suggests and, secondly, if individuals with different lifestyles show different kinds of avoidance. 2. Review of the literature A major stream of academic literature is dedicated to the effectiveness of Internet advertising. Many studies focus upon the medium

and its nature, and attempt to increase the efficiency of Internet advertising by distinguishing and manipulating the individual elements within the medium itself (Ko et al., 2005; Shamdasani et al., 2001). Some studies, focusing on attributes of the message, seek to examine information-processing routes employed by consumers in order to raise the involvement level of Internet advertising (Rodgers and Thorson, 2000). Some researchers suggest that consumers avoid advertising on the Internet due to cognitive, behavioural, and mechanical factors (Speck and Elliot, 1997). Elliott and Speck (1998), in their early study, entitled “User’s perceptions of Internet clutter and its effect on different media,” address the role of demographic variables, variables related to media, and communication problems of advertising content to explain Internet advertising avoidance. Their findings indicate that perceived advertising clutter hinders search and disruption leads to less favourable attitudes and high levels of Internet advertising avoidance. These effects vary within different media. Demographic variables were also identified as significant, with a minor effect established for the variable of perceived advertising clutter. Li et al. (2002) introduced the notion of perceived goal impediment, or impeding purposeful activities of the user, as the main factor contributing to Internet advertising avoidance. These authors posit mechanisms of Internet advertising avoidance as behavioural, cognitive (inattention), and emotional (negative attitude) behaviours. Lee et al. (2003), by assembling the findings of previous studies on avoidance from advertising, argued that Internet advertising avoidance results from the general attitude of the users towards the advertisement. Kelly et al. (2010) later conducted an exploratory study, employing a qualitative methodology, on Internet advertising avoidance in an online social networking environment. The authors collected data using focus groups and in-depth interviews and proposed a model for avoidance of Internet advertising within the websites of online networks. The results indicated that advertisements in online social networking environments will be avoided more if consumers expect a negative experience, the advertisement is not related to the consumers, or consumers are sceptical about the advertising message or media. Kelly et al. further proposed four factors that contribute to Internet advertising avoidance within social networks; expectation of a negative experience, relevance of advertising message, scepticism regarding the advertising message, and scepticism of online social networking as an advertising medium. The authors also suggest that the websites of online networks are ineffective media for Internet advertising, and that there is a paucity of policies about advertising claims within the medium (Kelly et al., 2010). Cho and Cheon (2004) engaged a sample of students who use the Internet more than the societal average to identify and test their motivation to avoid Internet advertising, within the framework of the three factors of goal impediment, perceived advertising clutter, and prior negative experience. The findings revealed that these three factors explain cognitive, affective, and behavioural avoidance from advertising messages on the Internet, with goal impediment exhibiting the highest effect on Internet advertising avoidance. The main difference between the studies of Cho and Cheon (2004) and Kelly et al. (2010) is that the former presumed the Internet environment as a single, standard and unified environment within which to develop their research model, whilst the latter focused on the websites of social networks and developed their research model specifically for these sites. Kelly et al. (2010) employed Cho and Cheon’s (2004) model as a foundation to develop a model of Internet advertising avoidance within social networks. This represents an extension of the former study into a more specific Internet domain within which different motivations were deemed to exist. A burgeoning stream of advertising literature supports the contention that there are other important reasons for individuals engaging in active Internet advertising avoidance. Of particular interest to this research is that the e-lifestyle literature suggests that an understanding

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of, and sensitivity to, different e-lifestyle characteristics can help gain insights into the area of Internet advertising avoidance. Hence, the present study intends to investigate the effect of differences of e-lifestyle on Internet advertising avoidance with reference to three different types of avoidance – cognitive, affective, and behavioural. A number of studies has been undertaken on the influence of e-lifestyles on attitudes towards Internet advertising. Kim et al. (2001) investigated the e-lifestyles of Internet users and discovered that there are six main lifestyle types the authors named; fashion leader/ innovator, imitator/flatterer, considerable purchaser, social person, conservative/polite person, and family-oriented person. This study revealed that there is a strong relationship between the lifestyle of the user and their attitude towards Internet advertising. For example, fashion leader/innovator lifestyle types consider Internet advertising as useful and containing special information (Kim et al., 2001). Lee et al. (2009) conducted a study analysing the relationship between lifestyle and selection of meta-technology products. They introduced four interesting lifestyles in this area; fashion consciousness, leisure orientation, Internet involvement, and e-shopping preferences (Lee et al., 2009). The results indicate that these four lifestyle types are direct or indirect antecedents of tendency to adopt high-tech products. However, Yu (2011) proposed the most contemporary and significant findings into e-lifestyle. Yu designed an exploratory study, employing exploratory factor analysis, to identify e-lifestyles within seven groups and proposed and evaluated a scale for measuring these e-lifestyles. The seven e-lifestyles discovered are need-driven, interest-driven, entertainment-driven, sociability-driven, perceived importance-driven, uninterested or concern-driven and novelty-driven. These seven e-lifestyles, or factors, were grouped from an original 39 items to establish the personal e-lifestyle characteristics of Internet users. This study also suggests that by developing marketing and advertising strategies relative to the e-lifestyle of individual Internet users, the effectiveness of the strategies is enhanced. Thus attention to e-lifestyle can increase the efficiency of Internet advertising – the congruency of advertisement design with the e-lifestyle of the target user is expected to increase the click-through rate and decrease a user’s Internet advertising avoidance. The present study adopts these seven e-lifestyle types in order to investigate the type of Internet advertising avoidance each different e-lifestyle poses.

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3. Theoretical framework Our conceptual research model is illustrated in Figure 1. Following the figure each relationship is discussed and an appropriate hypothesis expresses the research expectation. 3.1. E-lifestyle and Internet advertising avoidance According to the American Marketing Association (AMA), lifestyle, in the field of consumer behaviour, denotes a set of behaviours individuals exhibit in relation to their physical and mental environment. More specifically, it is used within theoretical sciences as a term describing values, attitudes, beliefs, and behavioural patterns of consumers. Classification of lifestyles serves to identify useful and important characteristics so that advertisers can target appropriate consumers and design more efficient Internet advertising. Also, awareness of lifestyle enables advertisers to perceive differences in attitudes of users (Yang, 2004). With this in mind, advertisers must seek information about different groups, or types, of Internet user to remain relevant and engaging. Advertisers must also obtain information about the attitude of users towards Internet advertising, relative to each respective e-lifestyle, in an effort to plan and present more purposeful advertisements (Yang, 2004). Some individuals exhibit positive attitudes towards advertising, whilst others exhibit negative attitudes; some individuals click on Internet advertisements, whereas others avoid Internet advertisements. According to Speck and Elliot (1997), avoidance of advertising involves all actions undertaken by the consumer of the media that prevents them from being exposed to the advertisement. Based on the definition of lifestyle, consumers with varying e-lifestyles are expected to exhibit different reactions to Internet advertising. Thus the following hypothesis is formed: H1. There is a direct relationship between e-lifestyle and Internet advertising avoidance. 3.2. Need-driven e-lifestyle and Internet advertising avoidance The characteristics of a need-driven e-lifestyle reflect a personal lifestyle largely developed through adopting the Internet, and Internet services, as a means of meeting job and life needs (Yu, 2011). This lifestyle reflects a central, and essential, adoption of the

Fig. 1. Conceptual framework.

Please cite this article in press as: Amir Abedini Koshksaray, Drew Franklin, Kambiz Heidarzadeh Hanzaee, The relationship between e-lifestyle and Internet advertising avoidance, Australasian Marketing Journal (2015), doi: 10.1016/j.ausmj.2015.01.002

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Internet and e-services to bring comfort, efficiency, and usefulness to daily and occupational tasks. Thus, in order to better interpret and analyse the characteristics of this group’s lifestyle, members have been designated as need-driven e-lifestyle (Yu, 2011). According to Kaye and Johnson (2001) and Papacharissi and Rubin (2000), there are four main motivations for using the Internet – information, convenience, entertainment, and social interaction. These motivations influence the time spent on the Internet and the type of website visited. Ko et al. (2005) suggest that users with information motivation are most probably involved in human–message interaction. Users who enter virtual spaces with comfort and social interaction motivation are most likely to be involved in human–human interaction. Therefore, individuals with different motivations possess different e-lifestyles. The motivated (Ko et al., 2005) or goal-oriented (Cho and Cheon, 2004) user attempts to meet their needs on the Internet with little regard to marginal issues. Yu (2011) also argues that this type of e-lifestyle corresponds to individuals who utilise the Internet more frequently to meet their daily and occupational needs. In other words, they perform all activities such as obtaining information, news, shopping, banking and financial services via the Internet and ICT tools. In general, these services greatly affect their daily and occupational lives. Needdriven individuals derive more benefit from increased time attending to the Internet, and each respective service, and report completing their jobs more readily. Thus, individuals utilising the Internet for a specific purpose will seek to avoid any factor, such as advertising, which impedes the use of the Internet. According to Cho and Cheon (2004), goal impediment is the main factor towards Internet advertising avoidance. Thus, when individuals utilise the Internet to meet their needs, they will ignore any advertising that prevents them from reaching their goals. Individuals with a need-driven e-lifestyle appear to show behaviourally based Internet advertising avoidance due to such high involvement with their Internet tasks. Upon this logic the following hypothesis is proposed: H2. Internet advertising avoidance is mostly behavioural among individuals with a need-driven e-lifestyle. 3.3. Interest-driven e-lifestyle and Internet advertising avoidance Individuals exhibiting this e-lifestyle spend more time within Internet services than other individuals, follow the latest advances within these services and are more predisposed towards learning and engaging with new Internet technologies. They enjoy learning about new services and actively seek information about these services. Hence, Internet services play an important role in their lives and they can be described as ICT influencers (Yu, 2011). Individuals exhibiting these characteristics tend to adopt interesting and novel ICT services, but can also passionately avoid less-than-acceptable services. Therefore, these individuals are capable of exhibiting both positive and negative reactions to stimuli such as Internet advertising. Thus: H3. Internet advertising avoidance is mostly affective among individuals with an interest-driven e-lifestyle. 3.4. Entertainment-driven e-lifestyle and Internet advertising avoidance Entertainment-driven individuals use the Internet for activities such as listening to music, watching movies and sports, playing computer games, making friends and having fun. They seek enjoyment from their Internet experience (Yu, 2011). Whilst individuals utilise the Internet for different reasons (Ko et al., 2005), it appears that users within this particular e-lifestyle participate on the Internet for more entertainment and hobby value (Ko et al., 2005). These individuals’ responses to stimuli depend heavily on how users feel about using the service, so it appears that their Internet ad-

vertising avoidance is of an affective nature. As such, the following hypothesis is proposed: H4. Internet advertising avoidance among individuals with an entertainment-driven e-lifestyle is mostly affective. 3.5. Sociability-driven e-lifestyle and Internet advertising avoidance A sociability-driven e-lifestyle exists within individuals who use the Internet and electronic services for creating an online conversational environment. With these individuals the Internet serves as a platform for expanding interpersonal interactions; they exchange information, beliefs and ideas through this medium and participate in social events (Yu, 2011). According to Ko et al. (2005), users who visit websites with the intention of comfort and social interaction are more likely to be involved in human–human interaction on a website. Users involved in human–message or human–human interaction evaluate the website content more positively. Human–human interaction has a significantly higher effect on attitude towards the website compared to human–message interaction (Ko et al., 2005). Research suggests that users possessing this e-lifestyle seek more word-of-mouth advertisements to gather information about various products and are less convinced by traditional advertising. These users exchange beliefs and ideas in chat rooms, feedback sites and blogs in an effort to obtain information about various products and services – shaping their own beliefs and attitudes in the process. As such, the following hypothesis is proposed: H5. Internet advertising avoidance among individuals with a sociability-driven e-lifestyle is mostly cognitive. 3.6. Perceived importance-driven e-lifestyle and Internet advertising avoidance Individuals with a perceived importance-driven e-lifestyle believe that the Internet has a positive impact on the economy, society, education-sector and individuals’ lives in general. This positive expectancy puts emphasis on the importance of the Internet for expanding the cycle of economic, social, and educational activities within society. Such people utilise such services and feel secure in the knowledge that gaining new information about electronic services provides them with a significant personal advantage (Yu, 2011). Individuals with an e-lifestyle also consider the Internet and other ICT technologies as a central mechanism towards progress, at a macro-economic level, and exhibit a positive attitude towards them. They are constantly seeking means of personal and professional advantage and are actively considering their own success (Yu, 2011). It is expected, then, within these individuals that Internet advertising is a barrier towards reaching their goals. The following hypothesis is proposed: H6. Internet advertising avoidance is mostly behavioural among individuals with a perceived importance-driven e-lifestyle. 3.7. Uninterested or concern-driven e-lifestyle and Internet advertising avoidance According to Yu (2011), individuals with this e-lifestyle do not have a positive attitude towards the Internet and believe that expansion of Internet services places more pressure on society. These users believe that such services have a negative impact on education and society; thus, they exhibit a negative opinion and projection towards these services within people’s lives. Since online communication and interactions between individuals are performed without body movements and emotions, they consider this space a communications barrier. Hence, they utilise Internet or ICT services only in emergencies (Yu, 2011). The negative attitude of these users is

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well established within the study of Yu (2011) and leads to a confirmatory hypothesis: H7. Internet advertising avoidance among individuals with an uninterested or concern-driven e-lifestyle is mostly affective. 3.8. Novelty-driven e-lifestyle and Internet advertising avoidance Individuals exhibiting this e-lifestyle are readily willing to share information about new knowledge in the area of the Internet and ICT services with others. The acquisition and dissemination of knowledge about emerging ICT trends and developments is important to these users and they spend a great deal of time anticipating such trends and developments. The novelty of adopting new technologies, and the challenge they present, is of pronounced importance and they derive a great deal of pleasure when achieving the ability to use these new services (Yu, 2011). Users with this e-lifestyle are expected to show behavioural avoidance from Internet advertising. H8. Internet advertising avoidance is mostly behavioural among individuals with a novelty-driven e-lifestyle. 3.9. Internet advertising avoidance Cho and Cheon (2004), inter alia, have measured and used this variable before. These particular authors use items that they designed based around cognitive, affective and behavioural responses to Internet advertising. The items are reported in Tables 1 and 2. 4. Research method 4.1. Study one: confirmatory factor analysis 4.1.1. Sample Survey respondents were recruited from within the Computer and IT and the Management and Accounting faculties of Qazvin Islamic Azad University in Iran. A convenience sample was drawn from these faculties, screening for students who use the Internet, on average, between 2 and 3 hours a day. According to Davis (1989), students are the largest group of users of new technologies throughout the world. Thus, it can be expected that a higher percentage of students engage with the adoption and use of new, ICT-related products or services. Employing stratified random sampling, survey respondents from both faculties were invited to participate through systematic random sampling relative to each respective faculty class. For this study, 2–3 classes within each faculty were nominated for data collection with a total of 412 students participating in the study. 4.1.2. Instrument validity and reliability (measuring variables) As mentioned above, measurement indicators related to the variables of Internet advertising avoidance were extracted from Cho and Cheon (2004). Similarly, the items relating to e-lifestyle were extracted from Yu (2011). A back-translation method was adopted for questionnaire item translation and the syntax of some items was modified to reflect an Iranian context. All survey questions featured 5-point Likert scale responses (1 = strongly disagree; 5 = strongly agree). In order to investigate instrument validity, four types of validity were estimated – content, face, convergent and discriminant. For estimating content validity, the Lawshe method is employed. A questionnaire was administered among 12 marketing and e-commerce experts. The resulting coefficients were compared with the Lawshe content validity table to indicate the acceptability of instrument content. The Lawshe coefficient among the 12 marketing

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and e-commerce experts is 0.56 and the coefficients of all items exceed this value (Hanafizadeh et al., 2014). To estimate face validity, 30 questionnaires were administered within the main sample to investigate respondent views concerning the quality of questionnaire items. After necessary adjustments, such as providing examples in order to explore some items, the final survey instrument was developed for distribution. In order to verify the reliability of the instrument, internal consistency was measured using Cronbach’s Alpha. The instrument features a Cronbach Alpha score of 0.87 confirming the reliability of the instrument as a measure of a single, unidimensional construct. The alpha coefficients for each variable highlight the appropriate reliability of the instrument. Composite reliability (CR) and Average Variance Extracted (AVE), assessed to measure convergent validity of the instrument, are detailed in Table 1 and feature acceptable values. AVE estimates the variance extracted by the items in relation to measurement errors and must be more than 0.50 to justify using a construct (Barclay et al., 1995). The values of CR and AVE, 0.50 and 0.60 respectively, denote appropriate construct reliability and convergent validity (Fornell and Larcker, 1981). Entertainment-driven, societal-driven and importance-driven e-lifestyle constructs, however, do not feature acceptable CR and AVE values. According to Janz and Prasarnphanich (2003), since these items’ critical ratio (t-value) is more than +2 or −2 (and p-value < 0.05), and one item features an acceptable value, we can rely on the convergent validity to assess construct validity (Janz and Prasarnphanich, 2003). Discriminant validity was verified by examining the correlation of indicators within different variables in the covariance matrix. Previous research posits that discriminant validity is verified when the correlation between two constructs is not high. More contemporary research does not suggest a standard correlation value, however, Campbell and Fiske (1959) recommend that the correlation value must be less than 85% (Sorensen and Slater, 2008). The results of this study present no such similarity between latent variables but confirm discriminant validity between constructs. 4.1.3. Data analysis: measurement and structural model Structural Equation Modelling (SEM) was employed to analyse the research data. The main statistics (mean, standard deviation, and confirmatory factor loadings) are detailed in Table 1. Items with factor loadings of 0.5 or below are deleted following confirmatory factor analysis (CFA), denoted in Table 1 with an asterisk. The measurement model fit is satisfactory and there are no issues with residuals (χ2/df = 2.75, NFI = .91, IFI = .92, CFI = .92, RMSEA = .064). Hypothesis 1 is tested by means of the structural model, which also exhibits good fit (χ 2 /df = 2.22, NFI = .84, IFI = .91, CFI = .91, RMSEA = .054). However, the model provides no support whatsoever for Hypothesis 1; the path from e-lifestyle to Internet advertising avoidance is not significant. In light of the (unexpected) negative result for Hypothesis 1, there is little purpose served in testing further hypotheses. Hence, a means to modify and re-analyse the data is investigated, and is reported in Study 2. 4.2. Study two: data modification The length of reported Internet usage between survey respondents varied greatly. This varied pattern of Internet usage is indicative of a diverse cohort of student respondents and their respective Internet usage. Some participants, for example, use the Internet two hours a day whilst some report Internet usage of eight hours, or even more, a day. This wide difference suggests the possibility that actual usage pattern, rather than declared, or estimated, e-lifestyle, might be a confounding variable. Consequently, in Study 2 the average hours of Internet use is collected from survey respondents by inviting

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Table 1 Main statistics, unmodified data. Latent variable

Items

N

Mean

CV

Loading factors

Alpha

AVE

CR

Need-driven e-lifestyle

I frequently perform my job via Internet services/products. Internet services/products greatly enhance the convenience of my life. Internet services/products greatly improve my job efficiency. I frequently use Internet services/products to read news or get data. I frequently shop or make purchase via Internet services/products. I frequently do my banking or finances via Internet services/products. The living environment has been influenced by Internet, and I have benefited from the impact. The working environment has been influenced by Internet, and I have benefited from the impact The more time with Internet services/products I spend, the more advantages I take. I frequently spend a lot of time involved with Internet services/products. I stay updated as to the latest development in Internet services/products. I am very interested in discovering how to use Internet services/products. I am very excited to know new Internet services/products. I like gaining knowledge regarding Internet services/products. Keeping alerts to the latest trends of Internet services/products is very important. I like involving Internet services/products in my entertainment I frequently play games via Internet services/products I frequently listen to music via Internet services/products Using Internet services/products really give me a lot of fun I frequently watch movies or sports via Internet services/products The leisure environment has been influenced by the Internet, and I have enjoyed from the impact I frequently chat via Internet services/products Internet services/products greatly enhance interaction among people Internet services/products greatly expand my friends circle I frequently share my opinions via Internet services/products I frequently participate in social events via Internet services/products Continued development of Internet services/products is positive for our economy. Continued development of Internet services/products is positive for our society. Continued development of Internet services/products is positive for our education. The more new knowledge regarding Internet services/products I gain, the more advantages I take. Being able to use the newest Internet services/products gives me a sense of achievement. The more the development on Internet services/products, the more the pressures on human lives. Continued development of Internet services/products has negative effect for our education. I don’t like my life to involve with too many Internet services/products. Continued development of Internet services/products has negative effect for our society. Internet services/products markedly decrease face-to-face emotional interaction among people. I like to share with people about new knowledge of Internet services/products Being able to use the newest Internet services/products makes me happy I like the challenge brought by Internet services/products Keeping inaugurating new Internet services/products is very important I intentionally ignore any ads on the web. I intentionally don’t put my eyes on banner ads. I intentionally don’t put my eyes on pop-up ads. I intentionally don’t put my eyes on any ads on the web. I intentionally don’t pay attention to banner ads. I intentionally don’t pay attention to pop-up ads. I intentionally don’t pay attention to any ads on the web. I intentionally don’t click on any ads on the web, even if the ads draw my attention. I hate banner ads. I hate pop-up ads. I hate any ads on the web. It would be better if there were no banner ads on the web. It would be better if there were no pop-up ads on the web. It would be better if there were no ads on the web. I scroll down web pages to avoid banner ads. I close windows to avoid pop-up ads. I do any action to avoid ads on the web. I click away from the page if it displays ads without other contents.

412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412

3.34 3.65 3.61 4.02 2.91 3.69 3.49 3.40 3.19 3.49 3.43 4.10 4.12 4.18 3.86 3.83 2.34 3.05 3.76 2.88 3.25 2.88 3.64 3.42 3.18 3.10 3.94 3.92 3.99 4.06 4.02 2.53 2.13 2.84 3.43 2.30 3.88 4.35 3.58 4.46 3.18 3.10 3.31 2.89 2.98 3.17 2.85 3.0 3.48 3.50 2.96 3.10 3.30 2.92 3.34 3.32 3.25 3.91

.932 .889 .988 .986 .995 .949 .875 .914 .943 .812 .833 .781 .833 .792 .908 .912 1.160 1.222 .901 1.250 .999 1.153 .828 1.063 1.064 .995 .735 .782 .825 .843 .810 1.136 .910 1.085 1.026 .975 .855 .628 .991 .748 1.124 1.013 1.035 1.055 1.020 1.052 1.069 1.085 1.023 .980 1.109 1.145 1.065 1.100 1.156 .943 1.078 1.026

.49 .56 .74 .43 .29 .36 .79 .74 .64 .29 .41 .62 .78 .73 .61 .44 .32 .46 .81 .54 .65 .36 .56 .68 .65 .59 .57 .89 .79 .57 .63 .46 .55 .46 .30 .83 .50 .53 .67 .32 .69 .76 .73 .87 .88 .81 .82 .81 .62 .70 .87 .91 .81 .82 .52 .57 .85 .34

.77

.51

.82

.74

.50

.78

.74

.39

.71

.75

.38

.71

.76

.49

.82

.78

.50

.63

.70

.53

.59

.76

.62

.93

.73

.63

.91

.74

.54

.69

Interestdriven e-lifestyle

Entertainmentdriven e-lifestyle

Sociabilitydriven e-lifestyle

Importancedriven e-lifestyle

Uninterested or concerndriven e-lifestyle Novelty-driven e-lifestyle

Cognitive ad avoidance

Affective ad avoidance

Behavioural ad avoidance

Note: The factor loading of deleted items reported from measurement model but others reported from structural model result.

respondents to answer an open-ended question: “on average, how many hours a day do you spend using the Internet?” The Multi Criteria Decision Making (MCDM) methodology utilises an algorithm to structure data into a common norm. Normalisation has two advantages in MCDM; firstly, it homogenises different data units (such as metre, kilogram and weight) so as to be comparable and, secondly, it provides for comparison of two sets of decimal and integer numbers. In this study, normalisation of the hours of Internet use between participants serves to establish the relative percentage, or weighting, of every participant’s Internet use. Each

participant response was multiplied by this weighting to get more accurate results. In this study, a linear normalisation method was employed; that is, as the maximum number of hours of Internet use was determined to be 10 hours, all values were divided by 10 and recorded for analysis. For example, three hours divided by 10 equals 0.3, which is considered as the weight of respondent Internet use. All variables within the study were multiplied by this value before re-examining the modified data using structural equation analyses. These results proved very interesting and are detailed in the following section.

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4.2.1. Measurement model for modified data The measurement models for both conditions were rerun and analysed using the new, modified dataset. Based on the results obtained from measurement models, the factor loadings of all items are over 0.7 and their significance values are above 2 (P-value < .001) and feature a less than 0.05 error level, so were not removed from the model in this process. Table 2 contains the data for the updated structural model. The measurement model fits the data satisfactorily (χ2/DF = 3.92, NFI = .927, IFI = .945, CFI = .944, RMSEA = .084). To assess common method bias, an un-rotated exploratory factor analysis was applied following Harman’s single-factor model (Brock and Sanchez, 1996).

7

The results show that when all of the measurement scale items are tested in the un-rotated factor analysis, the first factor does not account for the majority of the variance (43.4%). This suggests that any potential for common method bias is small. 4.2.2. Structural model for modified data The model fit statistics reveal a potential problem (χ2/DF = 4.48, NFI = .88, IFI = .91, CFI = .91, RMSEA = .09). An χ2/DF statistic of 4.48 is not ideal, but Ghasemi (2012) claims it is acceptable if other fit statistics are favourable. In this instance it is evident that the other indices are acceptable, hence the analysis proceeds. In this model there is clear support for Hypothesis 1, as the standard coefficient

Table 2 Main statistics, modified data. Latent variable

Items

N

Mean

CV

Loading Factors

Alpha

AVE

CR

Need-driven e-lifestyle

I frequently perform my job via Internet services/products. Internet services/products greatly enhance the convenience of my life. Internet services/products greatly improve my job efficiency. I frequently use Internet services/products to read news or get data. I frequently shop or make purchase via Internet services/products. I frequently do my banking or finances via Internet services/products. The living environment has been influenced by Internet, and I have benefited from the impact. The working environment has been influenced by Internet, and I have benefited from the impact The more time with Internet services/products I spend, the more advantages I take. I frequently spend a lot of time involved with Internet services/products. I stay updated as to the latest development in Internet services/products. I am very interested in discovering how to use Internet services/ products. I am very excited to know new Internet services/products. I like gaining knowledge regarding Internet services/products. Keeping alerts to the latest trends of Internet services/products is very important. I like involving Internet services/products in my entertainment I frequently play games via Internet services/products I frequently listen to music via Internet services/products Using Internet services/products really give me a lot of fun I frequently watch movies or sports via Internet services/products The leisure environment has been influenced by Internet, and I have enjoyed from the impact I frequently chat via Internet services/products Internet services/products greatly enhance interaction among people Internet services/products greatly expand my friends circle I frequently share my opinions via Internet services/products I frequently participate in social events via Internet services/products Continued development of Internet services/products is positive for our economy. Continued development of Internet services/products is positive for our society. Continued development of Internet services/products is positive for our education. The more new knowledge regarding Internet services/products I gain, the more advantages I take. Being able to use the newest Internet services/products gives me a sense of achievement. The more the development on Internet services/products, the more the pressures on human lives. Continued development of Internet services/products has negative effect for our education. I don’t like my life to involve with too many Internet services/products. Continued development of Internet services/products has negative effect for our society. Internet services/products markedly decrease face-to-face emotional interaction among people. I like to share with people about new knowledge of Internet services/products Being able to use the newest Internet services/products makes me happy I like the challenge brought by Internet services/products Keeping inaugurating new Internet services/products is very important I intentionally ignore any ads on the web. I intentionally don’t put my eyes on banner ads. I intentionally don’t put my eyes on pop-up ads. I intentionally don’t put my eyes on any ads on the web. I intentionally don’t pay attention to banner ads. I intentionally don’t pay attention to pop-up ads. I intentionally don’t pay attention to any ads on the web. I intentionally don’t click on any ads on the web, even if the ads draw my attention. I hate banner ads. I hate pop-up ads. I hate any ads on the web. It would be better if there were no banner ads on the web. It would be better if there were no pop-up ads on the web. It would be better if there were no ads on the web. I scroll down web pages to avoid banner ads. I close windows to avoid pop-up ads. I do any action to avoid ads on the web. I click away from the page if it displays ads without other contents.

412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412 412

1.2 1.33 1.29 1.44 1.05 1.32 1.26 1.22 1.72 1.32 1.25 1.47 1.46 1.47 1.37 1.40 .87 1.12 1.36 1.06 1.22 1.06 1.31 1.24 1.17 1.12 1.40 1.39 1.42 1.45 1.44 .89 .72 .96 1.19 .77 1.37 1.54 1.27 1.56 1.12 1.06 1.14 .99 1.03 1.10 .99 1.05 1.20 1.21 1.01 1.06 1.13 1.02 1.39 1.58 1.12 1.38

.79 .88 .83 .91 .78 .82 .81 .81 .84 .95 .83 .90 .88 .87 .84 .95 .76 .88 .89 .82 .92 .86 .81 .85 .83 .80 .83 .84 .87 .88 .89 .67 .44 .58 .75 .48 .80 .86 .81 .87 .76 .66 .72 .69 .71 .72 .70 .73 .72 .76 .67 .74 .75 .72 .73 .75 .73 .84

.92 .96 .92 .91 .89 .92 .95 .93 .93 .97 .94 .97 .95 .96 .94 .96 .80 .89 .95 .85 .94 .87 .97 .93 .93 .91 .95 .96 .96 .95 .95 .79 .73 .74 .88 .73 .92 .95 .86 .93 .91 .93 .93 .95 .96 .95 .95 .94 .87 .93 .92 .93 .94 .94 .74 .88 .88 .86

.95

.86

.98

.96

.91

.98

.94

.81

.96

.93

.85

.97

.96

.91

.98

.78

.60

.88

.94

.84

.95

.86

.88

.98

.85

.85

.97

.92

.71

.91

Interestdriven e-lifestyle

Entertainmentdriven e-lifestyle

Sociabilitydriven e-lifestyle

Importancedriven e-lifestyle

Uninterested or concerndriven e-lifestyle Novelty-driven e-lifestyle

Cognitive ad avoidance

Affective ad avoidance

Behavioural ad avoidance

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8

Table 3 Results of path analysis, modified data. Hypothesised paths

Avoidance Type

Standardised coefficient

Supported or unsupported

H2

Need Driven e-Lifestyle → Internet Advertising Avoidance Interest Driven e-Lifestyle → Internet Advertising Avoidance

H4

Entertainment Driven e-Lifestyle → Internet Advertising Avoidance

H5

Sociability Driven e-Lifestyle → Internet Advertising Avoidance

H6

Importance Driven e-Lifestyle → Internet Advertising Avoidance

H7

Uninterested Driven e-Lifestyle → Internet Advertising Avoidance

H8

Novelty Driven e-Lifestyle → Internet Advertising Avoidance

.447 −.371 .817 .377 .215 .745 .311 .189 .762 .397 .245 .739 .762 .259 .396 ns .368 .432 .237 .284 .906

Supported

H3

Cognitive Affective Behavioural Cognitive Affective Behavioural Cognitive Affective Behavioural Cognitive Affective Behavioural Cognitive Affective Behavioural Cognitive Affective Behavioural Cognitive Affective Behavioural

for the path from e-Lifestyle to Internet advertising avoidance is positive (.83) and significant (p < .001). 4.2.3. Testing hypotheses 2 through hypothesis 8 with modified data The tests of Hypotheses 2 through 8 require a different technique, as the structural model treats Internet advertising avoidance as a single variable, and to address these hypotheses calls for the variable to be broken up to it’s three components; cognitive, affective and behavioural (Cho and Cheon, 2004). These tests use multiple regression analysis and the results are contained in Table 3. 5. Conclusion and discussion The present study was designed to investigate the effect of e-lifestyle on Internet advertising avoidance within an Iranian context. This study also sought to establish the type of Internet advertising avoidance (cognitive, affective and behavioural) within each respective e-lifestyle. The contributions of this study are threefold. Firstly, by reviewing two previous studies within the domain of Internet advertising avoidance by Cho and Cheon (2004) and Kelly et al. (2010), this study investigates, for the first time, different factors affecting Internet advertising avoidance whilst testing and developing the relationship with e-lifestyle. Secondly, this study is the first attempt to investigate the type of Internet advertising avoidance, relative to each e-lifestyle, within both a specific Iranian and larger, global, context. Thirdly, it is the first study that, due to the significant effect of the range of self-reported Internet use among study participants, sought to normalise the hours of Internet use for analysis, thereby controlling the confounding effect of this variable. Hypothesis 1 proposes that e-lifestyle has a direct effect on Internet advertising avoidance. Data analysis provided insufficient evidence to confirm the effect of e-lifestyle on Internet advertising avoidance. However, when the data were adjusted by the normalised average hours of Internet use, this hypothesis was confirmed with a significant effect. Therefore, the type of e-lifestyle is likely to affect Internet advertising avoidance, suggesting that Internet users with different e-lifestyles can avoid, or avail themselves of, Internet advertising in different ways. Considering these results, it is clear that an understanding of Internet user e-lifestyle is critical to developing sound Internet advertising strategy. These e-lifestyle considerations will serve to increase the effectiveness of

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Supported

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Internet advertising by dictating what design and delivery elements are best suited to each respective e-lifestyle. The more effective the Internet advertising appeals, the more likely the click-through rate of the advertisement will be increased. The proposition that Internet advertising avoidance is mostly behavioural among individuals with a need-driven e-lifestyle was presented in Hypothesis 2. Based on these findings, individuals with this e-lifestyle exhibit two types of avoidance, affective and behavioural, and mostly avoid Internet advertising in a behavioural manner. As these individuals engage in the use of the Internet as a tool towards progression of personal and professional goals, they actively exhibit behavioural avoidance of Internet advertising. In this respect, these users do not pay attention to banner and pop-up advertisements, even at a cursory level. Additionally, as these users also exhibit some affective avoidance, it suggests a disdain for Internet advertising and the desire for an Internet free of advertising. A strong, contributing factor towards Internet advertising avoidance within the need-driven e-lifestyle is perceived goal impediment (Cho and Cheon, 2004). For example, organisations whose target users have a need-driven e-lifestyle and employ the Internet as a means to facilitate their personal and professional ambitions must be mindful of webpage design that features overt, distracting advertising. This may, in fact, only serve to annoy or frustrate their users. With this in mind, organisations must purposefully expose their target customers to advertisements that are relevant to their needs, desires and goals. These website design and delivery rules, if adopted, will serve to enhance the effectiveness of the advertising and the brand image will remain safe and sound in the minds of these customers (Hanafizadeh and Behboudi, 2012). The notion that avoidance from Internet advertising is mostly affective among individuals with an interest-driven e-lifestyle was raised in Hypothesis 3. Our results, whilst rejecting Hypothesis 3, establish that individuals with this e-lifestyle exhibit only behavioural avoidance from Internet advertising. Although these individuals spend a great deal of time using the Internet and this exercise tends to excite or stimulate them, they are prone to rejecting or discounting any Internet advertising as uninteresting. Thus, it is suggested that organisations whose target users exhibit an interest-driven e-lifestyle adopt creative Internet advertising strategies and techniques so as to provide distinction within the online advertising landscape. Individuals with an interest-driven e-lifestyle are continually seeking exciting and emerging products and services in the

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ICT domain, so any advertisements should reflect this creative predisposition. Interest-driven e-lifestyle individuals may also serve as a key market segment when launching any new ICT products or services, as they are typically early adopters of new technologies. Any new launch material, such as Internet advertising, should reflect sensitivity to these characteristics. Hypothesis 4 suggests that Internet advertising avoidance is mostly affective among individuals with an entertainment-driven e-lifestyle. Our findings contradict the hypothesised effect, in that these individuals show cognitive and behavioural as well as weaker affective avoidance of Internet advertising. These individuals employ the Internet as an entertainment medium and enjoy using the Internet as a central part of their leisure activity. Entertainmentdriven e-lifestyle individuals use the Internet to listen to music, watch video content and generally enjoy browsing the Internet as a means of entertainment. These individuals appear to explicitly avoid Internet advertising. Whilst these individuals exhibit less Internet advertising avoidance than some other e-lifestyle individuals, this negative attitude has almost certainly served to decrease their click-through rate within Internet advertising. Thus, it is recommended that organisations display a measure of transparency in their Internet advertising to entertainment-driven e-lifestyle individuals to gain cognitive acceptance of relevant advertisements in an effort to curb any feeling of distrust. In addition, any advertising should have entertainment value within the advertising. Fundamentally, individuals within the entertainment-driven e-lifestyle are engaging with the Internet as a means of entertainment. According to Yu (2011), this e-lifestyle can be the most attractive Internet user segment when designing and delivering Internet advertising content as the graduation from message delivery to click-through and, ultimately, to purchase is more easily realised. The view that individuals with a sociability-driven e-lifestyle avoid Internet advertising cognitively is proposed in Hypothesis 5. There is limited support for this hypothesis, as cognitive avoidance is shown, but it is not as strong as behavioural. These individuals typically seek human–human interaction, according to Ko et al. (2005), and like to share their interests and beliefs on the Internet, widely adopting social networks to initiate online dialogues with other users. As the results indicate, these individuals are dubious of Internet advertisements and are more likely to gather necessary pre-purchase information from consumers of products and services, directly. As such, website advertising banners, or other advertising methods, do not appeal to these individuals. A more critical consideration within this e-lifestyle subset is that of electronic word-of-mouth within social networks and the initiation of human–human interaction via said networks. It is suggested that by promoting the quality of products and services, online, whilst espousing a breadth of social acceptability, if present, the attention of these individuals will be best captured. Identifying, and engaging-in, key opinion leadership strategies within the most applicable online social networks will also serve to provide a platform to foster positive word-ofmouth. Traditional Internet advertising tactics, such a banner advertising or pop-up advertising, are not appropriate within sociability-driven e-lifestyle individuals. Hypothesis 6 proposes that Internet advertising avoidance among individuals with a perceived importance-driven e-lifestyle is mostly behavioural. The results of analysing the modified data indicate that these individuals exhibit all three kinds of avoidance, most of which is behavioural avoidance. These individuals believe that the Internet and other electronic services have a positive impact on the economy, society, education-sector and individuals’ lives, in general. Consequently, a strategy advertisers can implement to enhance the effectiveness of Internet advertising for these users is to ensure a consistent quality of advertisement whilst adopting new and novel technologies to remain relevant in the estimations of these group members.

9

Internet advertising avoidance among individuals with an uninterested or concern-driven e-lifestyle was explored in Hypothesis 7. Our results give partial support by establishing that individuals with this e-lifestyle exhibit two types of Internet advertising avoidance – affective and behavioural avoidance. By definition, these individuals want no involvement with the Internet, beyond absolute necessity, and tend to evaluate the effects of the Internet, and the continuous progress of the online domain, as being to the detriment of society and the economy. These individuals demonstrate an emotional aversion to the Internet and all associated content, and thus exhibit cognitive and affective Internet advertising avoidance. Businesses seeking to engage with individuals within this e-lifestyle must employ purely informative advertising strategies to provide information about advantages of using the Internet and the associated benefits to the economy, education and society. The notion that Internet advertising avoidance is mostly behavioural among individuals with a novelty-driven e-lifestyle was proposed in Hypothesis 8, and the results offer strong support for this hypothesis. Individuals with a novelty-driven e-lifestyle are highly motivated towards accessing and acquiring information about the latest online and ICT trends. The acquisition, and subsequent sharing, of this knowledge within their online network of similarly minded novelty-driven e-lifestyle individuals are of paramount importance. However, these individuals exhibit behavioural Internet advertising avoidance if the advertising is not consistent with the most recent online trends or developments. To be most effective, advertising targeted towards these e-lifestyle individuals must reflect the most up-to-date information and mechanisms. 6. Managerial implications Knowledge about specific e-consumer lifestyle, attitudes and patterns of consumption enables marketers to explain, in most situations, the motivations to purchase products or services online (Ahmad et al., 2010). An understanding of the e-consumer and their respective characteristics, such as e-lifestyle, will serve to enable marketers to develop more effective marketing and advertising strategies. According to Plummer (1974), the more you know about your consumers, the more effective your communications and transactions become. Bellman et al. (1999) emphasise that the basic information for predicting shopping behaviour (online or offline) is lifestyle of the consumers, rather than demographic factors. When marketers combine personal variables with knowledge about lifestyle preference, they gain customer insight allowing for a more powerful focus on each respective consumer segment. In this regard, to effectively design and deliver advertising within a shopping website, online retailers must be familiar with the features and lifestyles of consumers (Chu and Lee, 2007) to be able to effectively convey the right message at the right time to the right audience, or user. At a practical level, online vendors must have the ability to glean this categorical and lifestyle data from their current or prospective users. The literature suggests that one such method of identifying target users on the Internet is lead generation (Hanafizadeh and Behboudi, 2012). This method tasks advertising agencies, or lead generators, to collect data from Internet users based on their characteristics and interests. This information includes a profile for each user detailing links they follow, keywords they search, advertisements they look at, ideas they exchange through email or social networks and prior shopping and consumption behaviour. Lead generators, through a process of combining various data streams and complex behavioural classifications, consolidate ideals and characteristics of consumers within the vast domain of Internet networks. In other words, lead generation is a process through which an advertiser can become aware of the outlook, or attitude, of an online consumer (Hanafizadeh and Behboudi, 2012). These leads serve to

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remove the hidden nature of Internet users within different Internet domains and can prove instrumental to developing more effective online advertising strategies (Heidarzadeh et al., 2011). When a more clearly defined Internet user profile emerges from that of the mire of Internet traffic, an opportunity is presented to personalise, or customise, advertising to each respective type of user. As Krishnan and Murugan (2007) affirm, a consumer’s, or Internet users’, tendency towards particular products, information sources, influencers, shopping patterns, and brands are all affected by their lifestyle. Therefore, referring to the findings of the present study, it can be maintained that attention to Internet advertisements and click-through rates are affected by an Internet user’s e-lifestyle. The selection and consumption of different brands are an expression of an individual’s lifestyle. Similarly, the selection and consumption of different website content and exhibiting different online behaviour is an expression of an individual’s e-lifestyle. With this in mind, the more effectively advertisers and organisations can identify their target consumers’, or Internet user’s, e-lifestyle the more effective their Internet advertising strategies. Knowledge of the online behaviours a target group exhibits, the types of websites they visit, their online ambitions, desires and dislikes all contribute to a level of consumer intimacy that serves to determine how best to advertise a product or service online to that group. Many websites, even those of major vendors, appear to publish Internet advertising without consideration to the characteristics, needs and wants of their users. The results of this study establish that, at a very practical level, Internet advertisers or marketers must remain critically mindful of target Internet user e-lifestyles when developing Internet advertising copy strategy. At a countryspecific level, this study establishes that Iranian Internet users mostly exhibit an entertainment-driven e-lifestyle. This may be indicative of the level or Internet awareness, or use, within a particular context or environment. Iran is typical of a high-growth and highly penetrated Internet market and the graduation within its user base to that of mostly entertainment-driven usage suggests a movement from strictly utilitarian usage of the Internet. This is characteristic of many developed countries with large investment in telecommunications infrastructure as a means to support more widespread adoption of the Internet. The literature suggests that individuals with an entertainment-driven e-lifestyle enjoy using the Internet for both work and pleasure, and engage in entertainment mediums such as music, video and social interaction. Subsequently, these individuals do not explicitly avoid Internet advertising and click-through on Internet advertising banners and the like, as they genuinely enjoy spending time on the Internet (Yu, 2011). Due to their affinity to the Internet, entertainment-driven e-lifestyle individuals seek new and creative products, online, and are more predisposed to purchase. This e-lifestyle has been identified as the most effective segment to communicate to via Internet advertising as the advertiser or organisation is contending with less avoidance behaviour than other e-lifestyle types. These users typically remain longer on websites and advertisers can influence their decision-making processes more easily by targeting and tailoring messaging specific to their e-lifestyle. As the lifestyle of target offline consumers dictates specific strategies relative to the design and production of products, promotional activities and other business activities, so does the e-lifestyle of target online consumers. Ignoring these e-lifestyle characteristics when designing Internet advertising may lead to incongruence of the advertising message and value offered to that of the needs and wants of the target market. This may result in little, or no, engagement with the advertisement and a decline in click-through rate. This study presents an in-depth analysis of each Internet e-lifestyle and their respective types of Internet advertising avoidance. This insight serves to benefit advertisers and organisations

when designing Internet advertising so as to ensure the right message, and medium, characteristics are considered. These insights also serve to present a more rich understanding of Internet users advertising consumption, or avoidance, than that of merely demographic factors. More effective, and engaging, advertisements can be designed and published by taking into consideration the e-lifestyles presented in this study.

References Ahmad, N., Omar, A., Ramayah, T., 2010. Consumer lifestyles and online shopping continuance intention. Bus. Strategy Ser. 11 (4), 227–243. doi:10.1108/ 17515631011063767. Barclay, D.W., Thompson, R., Higgins, C., 1995. The Partial Least Squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technol. Stud. 2 (2), 285–309. Bellman, S., Lohse, G.L., Johnson, E.J., 1999. Predictors of online buying behaviour. Commun. ACM 42 (12), 32–38. Brock, P., Sanchez, J.L., 1996. Outcomes of perceived discrimination among Hispanic employees: is diversity management a luxury or a necessity? Acad. Manage. J. 39 (3), 704–719. Campbell, D.T., Fiske, D.W., 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105. Cho, C., Cheon, H., 2004. Why do people avoid advertising on the Internet? J. Advert. 33 (4), 89–97. Chu, Y., Lee, J.J., 2007. The Experiential Preferences of the Online Consumers in Different Internet Shopping Lifestyles Towards Online Shopping Web Sites, Human-Computer Interaction. HCI Applications and Services, Lecture Notes in Computer Science, vol. 4553. Springer, Berlin, pp. 3–11. Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340. Elliott, M., Speck, P.S., 1998. Consumer perceptions of advertising clutter and its impact across various media. J. Advert. Res. 38 (January/February), 29–41. Fornell, C., Larcker, D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 48, 39–50. Ghasemi, K., 2012. Structural Equation Modeling, first ed. Jameeshenasan Publication, Tehran, Iran. Hanafizadeh, P., Behboudi, M., 2012. Online Advertising and Promotion: Modern Technologies for Marketing. IGI Global, Hershey, PA, pp. 1–430. http://dx.doi.org/ 10.4018/978-1-46660-885-6. . Hanafizadeh, P., Behboudi, M., Abedini, A.K., Jalilvand, M.S.H., 2014. Mobile-banking adoption by Iranian bank clients. Telematics Inform. 31, 62–78. Heidarzadeh, K., Behboudi, M., Sadr, F., 2011. Emerging new concept of electronic police and its impact on the websites sales. Interdiscipl. J. Res. Bus. 1 (3), 8–14. Janz, B.D., Prasarnphanich, P., 2003. Understanding the antecedents of effective knowledge management: the importance of a knowledge-centered culture. Decis. Sci. 34 (2). Kamakura, W.A., Wedel, M., 1995. Life-style segmentation with tailored interviewing. J. Mark. Res. 32 (3), 308–317. Kaye, B.K., Johnson, T.J., 2001. A Web for All Reasons: Uses and Gratifications of Internet Resources for Political Information. Paper presented at the Association for Education in Journalism and Mass Communication Conference, Washington, DC, August. Kelly, L., Kerr, L., Drennan, J., 2010. Avoidance of advertising in social networking sites: the teenage perspective. J. Interactive Advert. 10 (2), (accessed 05.12). Kim, K.H., Park, J.Y., Ki, D.Y., Moon, H.I., 2001. Internet user lifestyle: its impact on effectiveness and attitude toward Internet advertising in Korea. In: Ray, C. (Ed.), Proceedings of the 2001 Annual Conference of the American Academy of Advertising. American Academy of Advertising, Salt Lake City, pp. 19–23. Ko, H., Cho, C., Roberts, M., 2005. Internet uses and gratifications structural equation model of interactive advertising. J. Advert. 34 (2), 57–70. Krishnan, J., Murugan, M.S., 2007. Lifestyle – a tool for understanding buyer behaviour. AIMA J. Manage. Res. 1 (1–4), 1–25. Lee, H.-J., Lim, H., Jolly, L.D., Lee, J., 2009. Consumer lifestyles and adoption of high technology products: a case of South Korea. J. Int. Consum. Mark. 21 (21), 153–167. Lee, Y., Kozar, K.A., Larse, K.R.T., 2003. The technology acceptance model: past, present and future. Commun. Assoc. Inf. Syst. 12 (50), 752–780. Li, H., Edwards, S.M., Lee, J.-H., 2002. Measuring the inclusiveness of advertisements: scale development and validnrion. J. Advert. 31 (2), 38–47. MediaPost, 2008. Beyond the Click through. 01/14/2008 by Jeff Hirsch, Monday, January 14, 2008. . Nielsen, J., 2000. Methodology Weaknesses in Poynter Eyetrack Study. (May 14). . Papacharissi, Z., Rubin, A.M., 2000. Predictors of Internet use. J. Broadcast. Electron. Media 44 (2), 175–196. Plummer, J.T., 1974. The concept and application of life style segmentation. J. Mark. 38 (1), 33–37. Rodgers, S., Thorson, E., 2000. The interactive advertising model: how users perceive and process online ads. J. Interactive Advert. 1 (1), .

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Schiffman, L.G., Sherman, E., Long, M.M., 2003. Toward a better understanding of the interplay of personal values and the Internet. Psychol. Mark. 20 (2), 169–186. Shamdasani, P.N., Stanland, A.J.S., Tan, J., 2001. Location, location, location: insights for advertising placement on the Web. J. Advert. Res. 41 (7), 7–21. Sorensen, H.E., Slater, S.F., 2008. Development and empirical validation of symmetric component measures of multidimensional constructs: customer and competitor orientation. Psychol. Rep. 103, 199–213.

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Speck, P.S., Elliot, M.T., 1997. Predictors of advertising avoidance in print and broadcast media. J. Advert. 26 (3), 61–76. Tellis, G.J., 1997. Advertising and Sales Promotion Strategy. Addison-Wesley, USA. Weiss, M.J., 2001. Online America. Am. Demogr. 23 (3), 53–60. Yang, K.C.C., 2004. A comparison of attitudes towards Internet advertising among lifestyle segments in Taiwan. J. Mark. Commun. 10 (3), 195–212. Yu, C.S., 2011. Construction and validation of an e-lifestyle instrument. Int. Res. 21 (3), 214–235.

Please cite this article in press as: Amir Abedini Koshksaray, Drew Franklin, Kambiz Heidarzadeh Hanzaee, The relationship between e-lifestyle and Internet advertising avoidance, Australasian Marketing Journal (2015), doi: 10.1016/j.ausmj.2015.01.002