Wich E-lifestyle Avoid Internet Advertising

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Mostafa Nejati, Universiti Sains Malaysia, Malaysia Akinkugbe Oluyele, U. of Botswana, Botswana Catalin Popescu, Oil and Gas U., Romania Maria Angeles Davia Rodriguez, Facultad Ciencias

Económicas y Empresariales, Spain Azadeh Sahafei, Universiti Sains Malaysia, Malaysia Taras Sakalosh, The Polytechnic Institute, Ukraine Mika Saloheimo, U. of Lapland, Finland Larry Stillman, Monash U., Australia Christina Suciu, The Academy of Economic Studies,

Romania Alexandru Tanadis, The Academy of Economic Studies,

Romania Maria Angeles Tobarra, Facultad Ciencias Económicas

y Empresariales, Spain Zulnaidi Yaacob, Universiti Sains Malaysia, Malaysia

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Guest Editorial Prefaceiv Special Issue on Online Advertising

Mehdi Behboudi, Islamic Azad University, Iran

Research Articles1 Advertisements on the Internet: Ethics, Involvement and Product Type

Mehdi Behboudi, Department of Business Management, School of Management and Accountancy, Islamic Azad University, Qazvin, IranHamideh Mokhtari Hasanabad, Management and Productivity Research Center, Islamic Azad University, Qazvin, Iran

22 Which E-Lifestyle Avoids Internet Advertising More?Amir Abedini Koshksaray, Department of Business Management, School of Management, Islamic Azad University, Tehran, IranKambiz Heidarzadeh Hanzaee, Department of Business Management, School of Management and Economics, Islamic Azad

University, Tehran, Iran

37 Internet Usage, Motives and Advertisements: Empirical Evidences from IranManoocher Niknam, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, IranKobra Najafi, Department of Business Management, Islamic Azad University, Qazvin, Iran.Azamosadat Hoseini, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, IranSima Amirpoor, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, IranParisa Bahmandar, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, IranElham Rahmani, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, IranMaryam Faraji, Department of Business Management, Ghazali’s Higher Education Institute, Qazvin, Iran

50 Digital Marketing Analytics: The Web Dynamics of Inside Blackberry BlogShirin Alavi, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, IndiaVandana Ahuja, Jaypee Business School, Noida, Uttar Pradesh, India

66 The Effectiveness of Website Quality and SEMs’ Successful in IranHamideh Mokhtari Hasanabad, Department of Business Management, Islamic Azad University, Qazvin, IranLeila Behboudi, Department of Business Management, Islamic Azad University, Qazvin, IranMasumeh Sadat Abtahi, Department of Business Management, Islamic Azad University, Qazvin, Iran

Table of ContentsOctober-December 2014, Vol. 5, No. 4

International Journal of Innovation in the Digital

Economy

22 International Journal of Innovation in the Digital Economy, 5(4), 22-36, October-December 2014

Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

ABSTRACTThis study aimed at finding out which e-lifestyles avoid internet advertising more. To this aim, a survey was conducted on 412 students working with internet. Structural Equation Modeling approach was used for estimat-ing the validity of research constructs and multiple regression was utilized for hypothesis testing. According to the findings, individuals with interest-driven e-lifestyle avoid from internet advertising more than others. Novelty-driven, importance-driven, sociability-driven, need-driven, entertainment-driven, and uninterested or concern-driven e-lifestyles avoid from internet advertising, respectively. This study has considered e-lifestyle’s avoidance from internet advertising for the first time. It is the first attempt to investigate which e-lifestyle avoids internet advertising more. Also, it is the first study modifying research data according to the significant effect of “the average hours of using internet” and controlling and analyzing the effect of this variable.

Which E-Lifestyle Avoids Internet Advertising More?

Amir Abedini Koshksaray, Department of Business Management, School of Management, Islamic Azad University, Tehran, Iran

Kambiz Heidarzadeh Hanzaee, Department of Business Management, School of Management and Economics, Islamic Azad University, Tehran, Iran

Keywords: Avoidance, E-Lifestyle, Entertainment-Driven E-Lifestyle, Interest-Driven E-Lifestyle, Internet Advertising

INTRODUCTION

Understanding individual differences with respect to their reactions and behaviors affects the decisions related to developing market-ing and advertising strategies. By identifying psychological factors of their target consum-ers, marketers try to promote their business activities and please their customers. However, consumers’ lifestyles are important factors in understanding and predicting behaviors of a group of individuals. Individual difference variables explain the difference of individuals

from each other in distinct behavioral pat-terns. These variables have three managerial applications; first, a group of individuals with similar personality, self-concept, and psy-chographic characteristics, might be so large in a section to be the target of the company. Second, by expanding the understanding from personality, self-concept, and psychographic characteristics of target market, companies can create advertising messages which optimally benefit from the needs and wants of the target group. Third, the mental position of brands can be determined based on one of the common features of target market individual differ-ences (Moven and Minor, 1998). According to Michman et al. (2003, p. 67), “behaviors DOI: 10.4018/ijide.2014100102


International Journal of Innovation in the Digital Economy, 5(4), 22-36, October-December 2014 23

related to changing the good or brand by the customer not only result from the fact that they are unsatisfied with that brand, but are caused by the change in consumer lifestyle, as well”. Additionally, Kucukemiroglu (1999) suggest that lifestyles describe individual’s behavior and interactive groups of people. According to Plummer (1974), the more you know and understand your customers, the more effective communications and transactions you can have with them. Hence, studying individual values and lifestyles is considered as a standard tool for both social sciences and world marketers. Bellman, et al. (1999) emphasize that the basic information for predicting shopping behaviors (whether online or offline) is the lifestyle of consumers rather than demographical factors. In other words, to effectively manage a shop-ping website, online retailers must be familiar with characteristics and lifestyles of consumers (Chu and Lee, 2007). Thus, due to the impor-tance of individuals’ lifestyles, the necessity of investigating it from different aspects of marketing science is clearly understandable. In addition, the increasing growth internet has created a new space of transactions for both consumers and sellers. Based on the studies conducted, iab.net (2011) announced that the income of internet advertising has reached 2.6 billion dollars in the second quarter of 2010 showing 4.1 percent increase in comparison to the first, and 13.9 percent increase compared to the second quarter of 2009. Despite the ap-parent benefits of internet advertising, statistics reveal continuous decrease in the click rate by the users. According to the institute of Nielsen (2000), click rate in 1995 was about 2% which decreased to 0.3 in 2008 (MediaPost, 2008), and this descending trend continues. Getting used to the novelty of this medium and excessive publication of ads in web pages have created a kind of avoidance from internet advertising (Cho and Cheon, 2004). Avoidance refers to a state where the consumers consciously and deliber-ately try to avoid from a stimulus (Tellis, 1997). Advertisement avoidance refers to all activities of internet users which distinctly prevent them from being exposed to advertisement (Speck

and Elliott, 1997). Advertisement avoidance involves all activities of internet users and prevents their exposure to ads in different ways (Speck and Elliott, 1997). Advertisers need to completely understand the reasons underlying advertisement avoidance to be able to develop strategies for conveying their messages to the target market effectively and efficiently. Ac-cording to Cho and Cheon (2004), advertisement avoidance can be investigated in three types of cognitive, affective, and behavioral avoidance. One factor which appears to affect the formation of advertisement avoidance is users’ e-lifestyle. Many studies have revealed that lifestyle is an important variable which affects users’ use of internet in different activities (Schiffman et al., 2003; Kim et al., 2001). The features of lifestyle provide marketers and advertisers with accurate and practical information about con-sumers. This enables them to meet the needs of consumers in complex and competitive markets (Kamakura and Wedel, 1995). This issue gains more importance since internet is increasingly penetrating different layers of society and is facing a variety of lifestyles (Schiffman et al., 2003; Weiss, 2001). Lifestyle segmentation identifies the important and useful segments so that the advertisers can target appropriate consumers and provide more efficient internet ads. Yu (2011) introduced and tested seven types of e-lifestyles. These seven e-lifestyles are need-driven, interest-driven, entertainment-driven, sociability-driven, importance-driven, uninterested or concern-driven, and novelty-driven. Every individual behaves in internet with specific lifestyle. Whether individuals of an e-lifestyle pay attention to an internet advertisement or click on a certain ad depends upon the features of that lifestyle. Therefore, this study seeks to find out which e-lifestyle avoids more than other from internet advertisement.

Review of the Literature

Numerous studies have been conducted in these two areas. Since these two concepts have been addressed in no local and international studies, separate studies on them are reported here. El-



liot and Speck (1998) have addressed the role of demographic variables, variables related to media, and communication problems of ad content. Their findings indicated that perceived advertising clutter, hindered search, and disrup-tion lead to less favorable attitude and high level of avoidance from advertising. These effects vary in different media. Demographic variables were also identified with minor effect on the variable of perceived ad clutter.

Li and Edwards (2002) introduced per-ceived goal impediment, i.e. impeding purpose-ful activities of the user, as the main factor of avoidance from advertising. In this study, they presented ways of avoidance from Internet ad-vertising as behavioral, cognitive (inattention), and emotional (negative attitude).

Kelly et al. (2010) conducted an explor-atory study employing qualitative approaches on avoidance from Internet advertising in online social networking environment. They collected data using focus groups and in-depth interviews and proposed a model for avoidance from In-ternet advertising in the websites of online net-works. The results indicated that the advertise-ments in online social networking environments will be avoided more if the consumers expect a negative experience, the ad is not related to the consumers, and the consumers are skeptical about the advertising message or media. Kelly et al. (2010) proposed four factors ad affecting avoidance from advertising in Internet social networks, including: 1) Expectation of negative experience, 2) Relevance of advertising mes-sage, 3) Skepticism of Advertising Message, and 4) Skepticism of Online social networking as an advertising medium. They believe that the websites of online networks are invalid, and there is paucity of policies about advertis-ing claims in these media (Kelly et al., 2010).

Cho and Cheon (2004) identified and tested the reasons of individuals’ avoidance from Inter-net advertising in three factors of goal impedi-ment, perceived ad clutter, and prior negative experience. Their findings revealed that these three factors explain cognitive, affective, and behavioral avoidance from advertising mes-sages in the Internet, and goal impediment has

the highest effect on avoidance from Internet advertising.

In this respect, it seems that there are other important reasons for individual’s avoidance from Internet advertising. According to the literature of e-lifestyle, this factor can help us gain insights into the area of avoidance from Internet advertising. Hence, the present study intends to investigate the effect of differences of e-lifestyles on avoidance from Internet advertis-ing and three kinds of avoidance, i.e. cognitive, affective, and behavioral, introduced by Cho and Cheon (2004). Few studies have been conducted on e-lifestyles which are reviewed below.

Kim et al. (2001) investigated the lifestyles of Internet users and found out that there are 6 main styles including: fashion leader/innovator, imitator/flatter, 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 toward Internet advertising. For example, fashion leader/in-novator considers that Internet involves useful and special information (Kim et al., 2001).

Lee et al. (2009) conducted a study for analyzing the relationship between lifestyle and selection of high-technology products. They introduced four interesting lifestyles in this area including fashion consciousness, leisure orientation, Internet involvement, and e-shopping preferences (Lee et al., 2009). The results indicated that these four types of lifestyles are direct or indirect antecedents of tendency to adopt high-tech products. The findings of this study provided marketers with insights into how knowledge about the factors of lifestyle are combined with marketing and advertising strategies.

However, the most important and interest-ing lifestyle has been proposed by Yu (2011). Through an exploratory study and using explor-atory factor analysis, he identified e-lifestyles in seven groups and proposed and evaluated a scale for measuring these styles. These seven e-lifestyles include need-driven e-lifestyle, interest-driven e-lifestyle, entertainment-driven e-lifestyle, sociability-driven e-lifestyle,



perceived importance-driven e-lifestyle, unin-terested or concern-driven e-lifestyle, novelty-driven e-lifestyle. The empirical findings of this study revealed that 39 items of e-lifestyle scale grouped in 7 factors form personal Internet and e-lifestyle of individuals. Also, this study implied that by developing marketing and adver-tising strategies according to e-lifestyles of the individuals, the effectiveness of the strategies are enhanced. Therefore, attention to lifestyle is a factor which can increase the efficiency of internet advertisement. The more a designed ad is in line with the lifestyle of target user. The more it is expected to increase the click rate and lead to decreasing users’ advertisement avoidance. The present addresses seven types of e-lifestyles and seeks to find answer to the following question:

Research Question: Which type of e-lifestyle avoids more from internet advertisement? And how is the prioritization of avoidance from internet advertisement by the seven e-lifestyles?

Theoretical Framework

According to American Marketing Associa-tion (AMA), lifestyle in the area of consumer behavior refers to set of behaviors individuals show in the physical and mental environment of their daily lives. Specifically, some scholars of theoretical sciences use the word “lifestyle” as a term describing the values, attitudes, opinions, and behavioral patterns of consumers. Also, awareness from lifestyle enables advertisers to perceive the differences of users’ attitudes (Yang, 2004). Therefore, advertisers must be aware of different groups of internet users. Also, they must gain information about the different attitudes toward internet advertising based on differences of lifestyles to be able to present more purposeful ads (Yang, 2004). Segmenta-tion is a useful tool for planning advertising (Kaynak and Kara, 1996). Many studies have revealed that lifestyle is an important variable affecting the way consumers use the internet for various activities (Schiffman et al., 2003; Kim

et al., 2001). The features of lifestyle provide the advertisers with practical and accurate in-formation about the consumers enabling them to meet the present needs in the competitive and complex markets (Kamakura and Wedel, 1995). Individuals in different groups can show different behaviors. Some have positive, and some others have negative feelings about advertisements; some click on internet ads and some avoid from them. According to Speck and Elliot (1997), internet advertisement avoidance refers to all activities done by internet users which distinctly prevent them from being ex-posed to ads. Based on the definition lifestyle, people with different e-lifestyles are expected to show different reactions to ads. The lifestyles introduced by Yu (2011) which are also used in the present study are as following:

1. Need-Driven E-Lifestyle: The content of need-driven e-lifestyle reflects the personal lifestyle which is considerably developed through meeting the job and life needs by internet services which depend upon internet services (Yu, 2011). This lifestyle reflects the way individuals live using internet and e-services because they can bring comfort, efficiency and usefulness to daily and occupational lives of individuals. Thus, in order to better interpret and analyze the content of this group’s lifestyle, the need-driven e-lifestyle has been selected for them (Yu, 2011).

2. Interest-Driven E-Lifestyle: Individuals with this e-lifestyle often spend more time with internet services, follow the latest advances of these services, and are more interested in learning them. Learning such new services is exciting to them and they seek new information about these services. Hence, internet services play an important role in their lives (Yu, 2011).

3. Entertainment-Driven E-Lifestyle: These individuals use internet and electron-ic services more for fun and entertainment. They feel well using these services and love them. Listening to music, watching movies and sports, playing computer games, etc.



are some activities these people do on the net. They seek to enjoy from using the internet (Yu, 2011).

4. Sociability-Driven E-Lifestyle: Sociabili-ty-driven e-lifestyle belongs to individuals who use internet and electronic services for creating an online conversation environ-ment. Interpersonal interaction is provided for these people via internet and their inter-action circles are expanded. These people exchange their information, beliefs, and ideas through this medium and participate in social events (Yu, 2011).

5. Perceived Importance-Driven E-Lifestyle: Individuals with perceived importance-driven e-lifestyle believe in the positive influence of internet and electronic services upon the economy, society, and education of the country and put emphasis on its importance for expanding the cycle of economic, social, and educational ac-tivities. These people use such services and feel successful knowing that gaining new information about electronic services provides them with great advantage (Yu, 2011). Individuals with this lifestyle con-sider internet and other ICT products as the inevitable part of a country’s progress and have positive attitude toward them. These people always seek advantage and think about their success (Yu, 2011).

6. Uninterested of Concern-Driven E-Life-style: According to Yu (2011), individuals with this e-lifestyle do not have a positive attitude toward internet and believe that expansion of internet services place more pressure on the lives of people. They think such services have negative impact on the education and society, thus they do not want them to get involved with people’s lives. Since communications and interactions among people are done without transfer-ring body movements and emotions in the internet, they consider this space as a barrier of communications among people in the society. Hence, these individuals use these services only in emergencies (Yu, 2011).

7. Novelty-Driven E-Lifestyle: Individuals with this e-lifestyle are willing to share in-formation about new knowledge in the area of internet and IT services with others. The necessity of modifications and continuous changes of services and their progress are important to them. They always anticipate novel technologies. Sometimes, novel tech-nologies challenge the lives of people, and individuals with novelty-driven e-lifestyle are interested in such challenges. They feel happy when they achieve the ability to use these new services (Yu, 2011). Based on the explanations offered for each e-lifestyle, the following hypothesis has been developed:Research Hypothesis: Individuals of

interest-driven e-lifestyle avoid more than others from internet advertise-ment (Figure 1).

METHODOLOGY

Study1: Confirmatory Factor Analysis

Instrument

Measurement indicators related to the variables of Internet advertising avoidance were extracted from Cho and Cheon (2004). The items related to e-lifestyles were extracted from Yu (2011). We use back translation method and modified some items to matching with our people`s perception. 5-scale Likert format used in questionnaire (SD=1 & SA=5).

In order to investigate the validity of in-strument four types of validity were estimated; i.e. content validity, face validity, convergent validity, and discriminant validity respectively. For estimating content validity we use Lawshe method. First, 12 questionnaires were admin-istrated among the experts of marketing and e-commerce. The achieved coefficients were compared with Lawshe content validity table indicating the acceptability of instrument con-tent validity. In this regard, Lawshe coefficient for 12 experts was equal to 0.56 and coefficients



of all items were over this value (Hanafizadeh et al., 2012). To estimate the face validity, 30 questionnaires were administered among the sample and the views of respondents about the quality of items were gathered. After neces-sary adjustments such as giving examples to explore some items, the final instrument was developed to be distributed among the whole population. in order to verify the reliability of the instrument, its internal consistency was measured through Cronbach Alpha. The alpha was 87 confirming the reliability of the instru-ment. The alpha coefficients of each variables shows the appropriate reliability of the instru-ment. The construct validity explained below (after Table 2).

Sample

The participants of this study were selected from among the students of computer and IT faculty and management and accounting faculty of Qazvin Islamic Azad University in conditions that this students have daily aver-age 2-3 hour use from Internet. Due to easy access to this group of people, the sample has

been taken from these students. According to Davis (1999), students are the biggest group of users of new technologies in all around the world. Thus, it can be expected that a higher percentage of students are concerned in ICT-related new product or services and use them. Using stratified random sampling, these two faculties were chose and the students of every faculty were chose through systematic random sampling relative to faculties classes. In this case, we have been chose 2-3 class in each faculty for gathering data. Totally, 412 students participated in this study.

Data Analysis: Measurement Model

Data modification: The participants of the present study were students whose hours of using the Internet differed from each other. This resulted in the difference between the responses of a participant who, for example, used the Internet 2 hours a day from those of a person who used it 8 hours a day. Therefore, this is considered as a participant variable which strongly influences the results and misleads the researcher. Thus, in this study, a method was

Figure 1. Research conceptual model



sought to control this variable. To this aim, it appeared that introducing the hours of using In-ternet to the data obtained from the participants would control this variable and lead to more accurate results. Hence, the first question in the questionnaire was “on the average, how many hours do you spend a day for using Internet?” to which the respondents answered by writing the number of hours. This value was entered into the software together with other responses to the questionnaire.

To introduce these values, first the maxi-mum hours of using Internet were agreed to be 10 hours. The reason was that in Iran, the hours of daily work for office personnel has been considered as 8 hours by the Ministry of Work and Social Welfare, and at most 2 hours are considered for extra work for each employee. Some participants had declared more than 10 hours a day for using Internet, whose question-naires were excluded from coding and analysis as outliers. In Multi Criteria Decision Making (MCDM) method, an algorithm is required for transferring data to put them in a common norm. This is called normalization of the data.

Normalization has two advantages in MCDM; first, it homogenizes different data units (such as meter, kilogram, weight, etc) to be comparable, and second, it makes it possible to compare two columns of decimal and integer numbers. In this study, normalization of the hours of using Internet was meant to obtain the percent or weight of every participant in relation to using Internet. Then, the responses of each participant were multiplied by this weight to get proper results of data analysis.

In this study, linear normalization method was employed; that is, since the number of hours of using Internet which were determined as 10, all values were divided by 10 and placed in another column. For example, 3 hours divided by 10 equals 0.3 which is considered as the weight of respondent in the responding to the questionnaire. In the next stage, this column was multiplied by all items of the study to affect the data. In the way, data was modified and used for structural equation analyses.

Structural Equation Modeling (SEM) was used to investigate construct validities. The second stage measurement models are showed in Figure 2 & 3 and fitness indexes in Table 1 & 2 for each one of research concepts. The main statistics (means, standard deviation, and confirmatory factor loadings) are showed in Table 3 for all variables. All items have higher than 0.50 factor loading.

Validity & Reliability (Measuring Variables)

The composite reliability (CR) and the Average Variance Extracted (AVE) of every construct shows in Table 1. These techniques measure the convergent validity of the instrument veri-fying with their high value. AVE estimate the variance extracted by the items in relation to measurement errors and must be more that 0.50 to justify using a construct (Barclay et al., 1995; Hanafizadeh et al., 2012). The values of CR and AVE, respectively more that 0.60 and 0.50 refer to the appropriate construct reliability and convergent validity (Fornell and Larcker, 1981). AVE and CR are showed in Table 3 indicating the acceptable values of research constructs.

The Discriminant validity was also verified by looking into the correlation of the indicators of different variables in the covariance matrix. Previously, it was believed that Discriminant validity is verified when the correlation among two construct is not high. There is no standard value for it, but Campbell and Fiske (1959) recommended that the value of correlation must be less than 85% (Sorensen and Slater, 2008). Therefore, in order to appraisal Discriminant validity, the correlation between the constructs must be less than 0.85. The correlation coef-ficient of more than this value indicates that the constructs measure the same concept. Based on the results, there is no similar concept in latent variables. Thus, all constructs have Discrimi-nant validity.



Study 1: Testing research hypothesis

After the investigation of construct validities, we used multiple-regression for testing research hypothesis. In this case, all items participated for measuring their variables and we don’t have deleted items in CFA process. The result of regression shows in Table 4.

RESULTS

Based on the results of multiple regression analysis, presented in Table 4, individuals with interest-driven e-lifestyle (Sig<0.05, Beta= 0.86) avoid more than lifestyles from internet advertisement. Novelty-driven e-lifestyle (Sig<0.05, Beta= 0.79), importance-driven e-lifestyle (Sig<0.05, Beta= 0.44), sociability-driven e-lifestyle (Sig<0.05, Beta= 0.26),

Figure 2. Second stage measurement model for e-lifestyle



Figure 3. Second stage measurement model for Ad Avoidance

Table 1. Fitness indexes

Amount Index

2.215 χ2/df

0.84 NFI

0.907 IFI

0.906 CFI

0.054 RMSEA



need-driven e-lifestyle (Sig<0.05, Beta= 0.25), entertainment-driven e-lifestyle (Sig<0.05, Beta= 0.20), and uninterested or concern-driven e-lifestyle (Sig<0.05, Beta= 0.19) respectively avoid internet advertisement. The value of (Sig<0.05) in ANOVA table indicates that the test is significant and acceptable. Therefore, the research hypothesis stating that individuals with interest-driven avoid more than others from internet advertisement was confirmed.

CONCLUSION AND DISCUSSION

The main purpose of the study was to find which e-lifestyle avoids more from internet advertise-ment. The question was answered by collecting data from 412 participants and analyzing the results. The possible scientific contribution of the present study involves three main aspects. First, this study, by reviewing two previous studies, i.e. Cho and Cheon (2004) and Kelly, et al. (2010), in the area of internet advertis-ing avoidance, has investigated the factors affecting internet advertising and addressed internet advertisement avoidance by different e-lifestyles for the first time. Second, this study is the first attempt for investigating which type of e-lifestyle avoids more from internet adver-tisement. Third, this is the first study that, due to the significant effect of “the average hours of using the internet”, has normalized the hours of using internet and multiplied it by the responses of the participants, thereby controlling and analyzing the effect of this variable.

Based on the findings of the study, individu-als with interest-driven e-lifestyle avoid more than other lifestyles from internet advertisement. Hence, companies whose target users have mostly interest-driven e-lifestyle must be aware that the click rate on internet ads by these users is very low. They must work on developing strategies for attracting such users to internet advertisements, or try to communicate their advertising messages in another way. According to Yu (2011), these individuals spend most of their time with internet, love working with it, reject any kind of internet ads, and do anything to avoid from them. Hence, the businesses whose target users have mostly interest-driven e-lifestyle are suggested to use creative ads in internet so that the distinction between the ad and other advertisements in the websites attract the attention of these people effectively convey-ing the message of producer or advertiser to target customers. These individuals mostly seek new things in the area of ICT such as internet, thus advertisement highlighting new creative products or new creative ads can catch their at-tention. It is suggested that businesses offering new and creative products target individuals with this e-lifestyle as their target market and attract their attention by increasing the attrac-tions of internet advertisement.

On the other hand, individuals with unin-terested or concern and entertainment-driven e-lifestyles avoid less than other lifestyles from internet advertisement. Thus, this information provides the insights necessary for publishing ads for a target market with these e-lifestyles. It seems that the decision-making process of these individuals is affected by internet adver-

Table 2. Fitness indexes

Amount Index

2.745 χ2/df

0.91 NFI

0.924 IFI

0.915 CFI

0.064 RMSEA



tisement. Individuals with entertainment-driven e-lifestyle enjoy from working with internet and use it for fun and entertainment. They listen to music and watch clips via it, do not avoid much from internet advertisement, and since seek

to surf the net, click on their desired internet advertisement. Nevertheless, due to increase in distrust in internet advertisement, they have a negative attitude toward ads which decreases their attention and click on the ads. However,

Table 3. Main statistic

Latent variable

Items N Mean CV Loading Factors

Cronbachs Alpha

AVE CR

Need-driven e-lifestyle

I frequently perform my job via Internet services/products.

412 1.2 .79 .92 .95 .86 .98

Internet services/products greatly enhance the convenience of my life.

412 1.33 .88 .96

Internet services/products greatly improve my job efficiency.

412 1.29 .83 .92

I frequently use Internet services/products to read news or get data .

412 1.44 .91 .91

I frequently shop or make purchase via Internet services/products .

412 1.05 .78 .89

I frequently do my banking or finances via Internet services/products .

412 1.32 .82 .92

The living environment has been influenced by Internet, and I have benefited from the impact.

412 1.26 .81 .95

The working environment has been influenced by Internet, and I have benefited from the impact

412 1.22 .81 .93

The more time with Internet services/products I spend, the more advantages I take.

412 1.72 .84 .93

Interest-driven e-lifestyle

I frequently spend a lot of time involved with Internet services/products.

412 1.32 .95 .97 .96 .91 .98

I stay updated as to the latest development in Internet services/products.

412 1.25 .83 .94

I am very interested in discovering how to use Internet services/ products.

412 1.47 .90 .97

I am very excited to know new Internet services/products.

412 1.46 .88 .95

I like gaining knowledge regarding Internet services/products.

412 1.47 .87 .96

Keeping alerts to the latest trends of Internet services/products is very important.

412 1.37 .84 .94

Entertainment-driven

e-lifestyle

I like Internet services/products involving in my entertainment

412 1.40 .95 .96 .94 .81 .96

I frequently play games via Internet services/products

412 .87 .76 .80

I frequently listen to music via Internet services/products

412 1.12 .88 .89

Using Internet services/products really give me a lot of fun

412 1.36 .89 .95

I frequently watch movies or sports via Internet services/products

412 1.06 .82 .85

The leisure environment has been influenced by Internet, and I have enjoyed from the impact

412 1.22 .92 .94



Latent variable


Cronbachs Alpha

AVE CR

Sociability-driven

e-lifestyle

I frequently chat via Internet services/products 412 1.06 .86 .87 .93 .85 .97

Internet services/products greatly enhance interaction among people

412 1.31 .81 .97

Internet services/products greatly expand my friends circle

412 1.24 .85 .93

I frequently share my opinions via Internet services/products

412 1.17 .83 .93

I frequently participate in social events via Internet services/products

412 1.12 .80 .91

Importance-driven

e-lifestyle

Continued development of Internet services/ products is positive for our economy.

412 1.40 .83 .95 .96 .91 .98

Continued development of Internet services/ products is positive for our society.

412 1.39 .84 .96

Continued development of Internet services/ products is positive for our education.

412 1.42 .87 .96

The more new knowledge regarding Internet services/products I gain, the more. advantages I take.

412 1.45 .88 .95

Being able to use the newest Internet services/ products gives me a sense of achievement.

412 1.44 .89 .95

Uninterested or concern-

driven e-lifestyle

The more the development on Internet services/products, the more the pressures on human lives.

412 .89 .67 .79 .78 .60 .88

Continued development of Internet services/ products has negative effect for our education.

412 .72 .44 .73

I don’t like my life to involve with too many Internet services/products.

412 .96 .58 .74

Continued development of Internet services/ products has negative effect for our society.

412 1.19 .75 .88

Internet services/products markedly decrease face-to-face emotional interaction among people.

412 .77 .48 .73

Novelty-driven e-lifestyle

I like to share with people about new knowledge of Internet services/products

412 1.37 .80 .92 .94 .84 .95

Being able to use the newest Internet services/ products makes me happy

412 1.54 .86 .95

I like the challenge brought by Internet services/products

412 1.27 .81 .86

Keeping inaugurating new Internet services/products is very important

412 1.56 .87 .93

Cognitive ad avoidance

I intentionally ignore any ads on the web. 412 1.12 .76 .91 .86 .88 .98

I intentionally don’t put my eyes on banner ads. 412 1.06 .66 .93

I intentionally don’t put my eyes on pop-up ads. 412 1.14 .72 .93

I intentionally don’t put my eyes on any ads on the web.

412 .99 .69 .95

I intentionally don’t pay attention to banner ads. 412 1.03 .71 .96

I intentionally don’t pay attention to pop-up ads. 412 1.10 .72 .95

I intentionally don’t pay attention to any ads on the web.

412 .99 .70 .95

I intentionally don’t click on any ads on the web, even if the ads draw my attention.

412 1.05 .73 .94



compared to other e-lifestyles, individuals with entertainment-driven e-lifestyle avoid less from internet advertisement. The trend of decreasing deceptive and fake ads in the internet would reduce tendency to avoid from internet advertisement and would change the negative attitude toward it. Thus, it is suggested that a system is launched for tracing deceptive and fake ads to increase the willingness to trust in internet ads among internet users. This e-lifestyle can be the best market segment in market segmentation and selection, because the producer or advertiser can easily convey their message to their potential audiences and affect

their decision-making process. Individuals with uninterested or concern-driven e-lifestyle do not want their lives to be involved much with internet, and evaluate the effect of continuous progress of internet services on society and economy negatively. This e-lifestyle is the least rank in avoidance from internet advertisement, in this study.

In this respect, knowledge about lifestyle, attitudes, and patterns of consumption enables marketers to explain in most situations the rea-sons of buying or not buying a specific product online by specific consumers (Ahmad et al., 2010), and they can develop their marketing

Table 4. Result of regression

Avoidance Rank

ANOVA Sig.

ANOVA F-value

t-value Sig SD Beta E-lifestyle

5 0.000 215.589 2.126 0.034 0.201 0.253 Need-driven

1 7.449 0.000 0.269 0.861 Interest-driven

6 2.785 0.006 0.176 0.200 Entertainment-driven

4 3.888 0.000 0.210 0.268 Sociability-driven

3 4.120 0.000 0.308 0.447 Important-driven

7 4.959 0.000 0.185 0.191 Uninterest-driven

2 8.201 0.000 0.365 0.798 Novelty-driven

Latent variable


Cronbachs Alpha

AVE CR

Affective ad avoidance

I hate banner ads. 412 1.20 .72 .87 .85 .85 .97

I hate pop-up ads. 412 1.21 .76 .93

I hate any ads on the web. 412 1.01 .67 .92

It would be better if there were no banner ads on the web.

412 1.06 .74 .93

It would be better if there were no pop-up ads on the web.

412 1.13 .75 .94

It would be better if there were no ads on the web. 412 1.02 .72 .94

Behavioral ad avoidance

I scroll down web pages to avoid banner ads. 412 1.39 .73 .74 .92 .71 .91

I close windows to avoid pop-up ads. 412 1.58 .75 .88

I do any action to avoid ads on the web. 412 1.12 .73 .88

I click away from the page if it displays ads without other contents.

412 1.38 .84 .86



strategies based on the above-mentioned rea-sons. According to Plummer (1974) the more you know about consumers, the more effective communication and transactions you can have with them. Bellman et al. (1999) emphasize that the basic information for predicting shopping behaviors (online or offline) is lifestyle of the consumers, not their demographical factors. When marketers combine personal variables with the knowledge about lifestyle preferences, they gain power to focus on consumer segments. Hence, it is suggested that by gaining complete understanding of target market, different strate-gies for accessing the users through internet ads and message transfer are developed in relation to different groups and lifestyles of end consumers.

REFERENCES

Ahmad, N., Omar, A., Ramayah, T. (2010). Con-sumer lifestyles and online shopping continuance intention. Business strategy series, 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 an Ilustration. Technology Studies, 2(2), 285–309.

Bellman, S., Lohse, G. L., & Johnson, E. J. (1999). Predictors of online buying behavior. Communications of the ACM, 42(12), 32–38. doi:10.1145/322796.322805

Campell, D. T., & Fiske, D. W. (1959). Convergent and Discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. doi:10.1037/h0046016 PMID:13634291

Cho, C., & Cheon, H. (2004). Why do people avoid advertising on the internet. Journal of Advertising, 33(4), 89–97. doi:10.1080/00913367.2004.10639175

Chu, Y., & Lee, J. J. (2007). Lecture Notes in Com-puter Science: Vol. 4553. The experiential preferences of the online consumers in different Internet shop-ping lifestyles towards online shopping web sites’’, Human-Computer Interaction. HCI Applications and Services (pp. 3–11). Berlin: Springer.

Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social Cognitive Theory and Individual Reactions to Computing Technology – A Longitudinal-Study. Management Information Systems Quarterly, 23(2), 145–158. doi:10.2307/249749

Davis, F. D. (1999). Perceived usefulness, perceived ease of use, and user acceptance of information tech-nology. Management Information Systems Quarterly, 13(3), 318–339.

Fornell, C., & Larcker, D. F. (1981). Evaluating Struc-tural Equation Models with Unobservable Variables and Measurement Error. JMR, Journal of Marketing Research, 48(1), 39–50. doi:10.2307/3151312

Hanafizadeh, P., Behboudi, M., Abedini, K. A., & Jalilvand, S. H. M. (2012). Mobile-banking adop-tion by Iranian bank clients. Telemat. Informat; doi:10.1016/j.tele.2012.11.001

Janz, B. D., & Prasarnphanich, P. (2003). Under-standing the Antecedents of Effective Knowledge Management: The Importance of a Knowledge-Centered Culture. Decision Sciences, 34(2), 351–384. doi:10.1111/1540-5915.02328

Kamakura, W. A., & Wedel, M. (1995). Life-style segmentation with tailored interviewing. JMR, Journal of Marketing Research, 32(3), 308–317. doi:10.2307/3151983

Kaynak, E., & Kara, A. (1996). Consumer life-style and ethnocentrism: a comparative study in Kyrgyz-stan and Azarbaijan. In U. Schoeneberg (ed.), 49th Esomar Congress Proceedings. Istanbul/Amsterdam: ESOMAR, pp. 577–96.

Kelly, L. Kerr, L., Drennan, J (2010). Avoidance of advertising in social networking sites: The teenage perspective. Journal of Interactive Advertising, 10 (2) spring 2010., Retrieved April 2011 from: http://jiad.org/article129

Kim, K. H., Park, J. Y., Ki, D. Y., & Moon, H. I. (2001). Internet user lifestyle: its impact on ef-fectiveness and attitude toward Internet advertising in Korea. In RayC. (ed.), Proceedings of the 2001 Annual Conference of the American Academy of Advertising. Salt Lake City: American Academy of Advertising, pp. 19–23.

Kucukemiroglu, O. (1999). Market segmenta-tion by using consumer lifestyle dimensions and ethnocentrism: An empirical study. Euro-pean Journal of Marketing, 33(5/6), 470–487. doi:10.1108/03090569910262053

http://dx.doi.org/10.1145/322796.322805

http://dx.doi.org/10.1037/h0046016

http://www.ncbi.nlm.nih.gov/pubmed/13634291

http://dx.doi.org/10.1080/00913367.2004.10639175

http://dx.doi.org/10.1080/00913367.2004.10639175

http://dx.doi.org/10.2307/249749

http://dx.doi.org/10.2307/3151312

http://dx.doi.org/10.1016/j.tele.2012.11.001

http://dx.doi.org/10.1111/1540-5915.02328

http://dx.doi.org/10.2307/3151983

http://dx.doi.org/10.1108/03090569910262053



Lee, H.-J., Lim, H., Jolly, L. D., & Lee, J. (2009). Consumer lifestyles and adoption of high-technology products: A case of South Korea. Journal of Inter-national Consumer Marketing, 21(21), 153–167. doi:10.1080/08961530802153854

Lee, Y., Kozar, K. A., & Larse, K. R. T. (2003). The technology acceptance model: Past, present and future. Communications of the Association for Information Systems, 12(50), 752–780.

Li, H., Edwards, S. M., & Lee, J. H. (2002). Measuring the Intrusiveness of Advertisements: Scale Develop-ment and Validation. Journal of Advertising, 31(2), 37–47. doi:10.1080/00913367.2002.10673665

Martyn, Sh. (2009). Convergent Validity and Dis-criminant Validity. Retrieved [Date of Retrieval] from Experiment Resources: http://www.experimen-tresporces.com/convergent-validity.html

MediaPost. (2008). Beyond the Click through. 01/14/2008 by Jeff Hirsch, Monday, January 14, 2008, available at: www.mediapost.com/publications/?fa=Articles...art_aid

Michman, R. D., Mazze, E. M., & Greco, A. J. (2003). Lifestyle Marketing: Reaching the new American Consumer. Westport, CT: Praeger.

Moven, J. C., & Minor, M. (1998). Consumer behav-ior (5th ed.). Upper Saddle River, NJ: Prentice-Hall.

Nielsen, J. (2000). Methodology Weaknesses in Poynter Eyetrack Study.(May 14), available at: www.useit.com/alertbox/20000514_weaknesses.html

Nunnally, J. C., & Bernstein, I. H. (1994). Psycho-metric Theory. New York: McGraw-Hill, Inc.

Plummer, J. T. (1974). The concept and application of life style segmentation. Journal of Marketing, 38(1), 33–37. doi:10.2307/1250164

Schiffman, L. G., Sherman, E., & Long, M. M. (2003). Toward a better understanding of the interplay of personal values and the Internet. Psychology and Marketing, 20(2), 169–186. doi:10.1002/mar.10066

Sorensen, H. E., & Slater, S. F. (2008). Development and emprical validation of symmetric component measures of multidimensional constructs: Customer and competitor orientation. Psychological Reports, 103, 199–213. PMID:18982953

Speck, P. S., & Elliot, M. T. (1997). Predictors of advertising avoidance in print and broadcast media. Journal of Advertising, 26(3), 61–76. doi:10.1080/00913367.1997.10673529

Tellis, G. J. (1997). Advertising and Sales Promotion Strategy. USA: Addison-Wesley.

Weiss, M. J. (2001). Online America. American Demographics, 23(3), 53–60.

Yang, K. C. C. (2004). A comparison of attitudes to-wards Internet advertising among lifestyle segments in Taiwan. Journal of Marketing Communications, 10(3), 195–212. doi:10.1080/1352726042000181657

Yu, C. S. (2011). Construction and validation of an e-lifestyle instrument. Internet Research, 21(3), 214–235. doi:10.1108/10662241111139282

Amir Abedini Koshksaray, 1987, is an PHD candidate in Marketing Management, Department of Business Management, School of Management, Islamic Azad University - Tehran Central Branch. His publications have appeared in international journals, including The International Journal of Marketing Studies, Telemat-ics and Informatics. His research interests include Internet Advertising, Technology Adoption Behavior, Marketing Research, Mixed Method Research approaches.

Kambiz Heidarzadeh Hanzaee is an Associate Professor of Marketing, Department of Business Manage-ment, School of Management and Economics, Islamic Azad University - Tehran Science and Research Branch (email: [email protected]). His publications have appeared in leading international journals, including The Journal of American Academy of Business, The Business Review, Contemporary Manage-ment Research Journal, World Applied Sciences Journal and Journal of Islamic Marketing. His research interests include consumer behavior, marketing research, branding and integrated marketing communication.

http://dx.doi.org/10.1080/08961530802153854

http://dx.doi.org/10.1080/00913367.2002.10673665

http://dx.doi.org/10.2307/1250164

http://dx.doi.org/10.1002/mar.10066

http://www.ncbi.nlm.nih.gov/pubmed/18982953

http://dx.doi.org/10.1080/00913367.1997.10673529

http://dx.doi.org/10.1080/00913367.1997.10673529

http://dx.doi.org/10.1080/1352726042000181657

http://dx.doi.org/10.1108/10662241111139282

Wich E-lifestyle Avoid Internet Advertising

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