Investigating the Acceptance of Ad Blockers in Egypt

Investigating the Acceptance of Ad Blockers in Egypt: An Empirical Study By By Ibrahim Al Sahouly, Assistant Professor at the American University in Cairo Noha Bendary, Assistant Professor at The British University in Cairo

Major Points of the Presentation Primary Purpose of the Research Overview on Ad blockers Technology Adoption Models Variables and Hypotheses Research Methodology Data Analysis Research Model Discussion and Future Implications for Marketers

The primary purpose of this empirical research is to predict factors determining usage behaviour of Egyptian consumers towards ad blockers using UTAUT 2 Model.

Overview Advertisements blockers threaten the revenue of many websites and critically raises concerns about the reach of digital advertising in general It remains largely unknown which factors drive ad blocking or help unblocking or “whitelisting” of selected websites Ben Miroglio, David Zeber, Jofish Kaye, and Rebecca. Weiss. 2018).

Ad blocking as a Mobile Technology Adoption Application Before discussing the UTAUT 2 model which will be adopted on this study, It is worth mentioning that UTAUT 2 model has been used as a base model in several studies on technological field.

Technology Adoption Models There are different theories and models of behaviour that can help us understand predict the behaviour of an individual, whether to accept or reject adoption of new ICT including the eight acceptance and adoption models/theories.

Adopted from (Venkatesh et al. , 2003) Key Terms Behaviour Attitude Control Beliefs Behavioural Beliefs Intension Norms Meaning An action that is carried out described in terms of the action itself, its target the context and time. A psychological tendency that is expressed by evaluating a particular behaviour, with some degree of favour or disfavour. Beliefs about the likelihood that one possesses the resources and opportunities thought necessary to execute the behaviour. Perceived consequences of an action. A person’s conscious motivation to exert the effort to carry out such behaviour. Descriptive perceptions – what is important to what people actually do; Injunctive perceptions – importance of what people think a person should do; Subjective perceptions – social pressure to perform behaviour. Normative Beliefs Perceptions of significant others’ preferences about whether one should perform a behaviour. Outcome Evaluation of the perceived consequences of an action. Perceived Behavioural Self-efficacy Perceptions about how easy or difficult it is to perform the suggested behaviour. The conviction that one can successfully execute a given behavior.

This research concludes that the extended Unified Theory of Acceptance and Use Technology (UTAUT 2) is theoretical framework of this study. The following section will reflect the adopted variables of UTAUT 2 to this research: Performance expectancy is similar to the perceived usefulness and the relative advantage (Venkatesh et al, 2003) Effort expectancy is extracted from perceived ease of use and the complexity of interactive digital technology. (Venkatesh et al, 2003) H 4: Performance Expectancy of Ad blockers has a significant positive influence on usage behaviour of Ad Blockers H 1: Effort Expectancy of Ad blockers has a significant positive influence on Performance Expectancy of Ad blocker.

Hedonic motivation is conceptualized as perceived enjoyment in literature, and it is a significant antecedent of consumer acceptance of technology((Venkatesh et al. , 2012) H 3: Hedonic motivation of Ad blockers technology has a significant positive influence on Performance Expectancy of Ad blockers. H 6: Hedonic motivation of Ad blockers technology has a significant positive influence on Usage behaviour of Ad blockers. H 7: Effort Expectancy of Ad blockers technology has a significant positive influence on Hedonic motivation of Ad blockers.

Social influence resembles subjective norms of theory of reason action (TRA) (Venkatesh et al. , 2003), which reflects the effect of user’s close connections as friends and relatives on his/her behaviour. H 2: Social influence of Ad blockers technology has a significant positive influence on Hedonic motivation of Ad blockers Facilitating conditions Are factors in the surrounding environment of the technology that affect people’s perception of how easy or difficult it is to use or get a task done H 5: Facilitating conditions of Ad Blockers technology has a significant positive influence on Usage Behaviour of Ad blockers. H 8: Facilitating conditions of Ad blockers environment has a significant positive influence on Effort Expectancy of Ad blockers.

Research Methodology Based on the survey of 186 Egyptian respondents, eight hypotheses were tested using structural equation modelling (SEM). The respondents were selected through non probability sampling with quota, which considers the distribution of gender and age as demographic characteristics. The survey was distributed in early 2019 through kwiksurvey website and face to face interviewing, mainly in Cairo and Giza governates in Egypt The constructs used in this research is 5 points scale , where 1 indicating strongly disagree and 5 strongly agree. There are 6 constructs with measurements extracted from the UTUAT 2 model developed by Venkatesh et al, 2012. and the questionnaire was developed included the scales along with demographics and opening questions.

Data Analysis The following tables (2, 4, 5, 6, 7) indicate a summary of the descriptive, reliability and validity, CFM goodness of Fit and the significance of the resulted model. The reliability and validity tests used are the factor loadings, the AVE and table of correlations, to test their discriminant, and divergent validity and CR to test their reliability. The confirmatory factor analysis indicators was used to indicate their goodness of Fit. Finally the Structural equation modeling to test the significance of the model. The analysis was conducted using the SPSS and AMOS

Data analysis: Descriptive analysis Table (2) Descriptive Analysis Characteristics Frequency Percentage Gender 1 - Male 2 - Female Age 117 62. 90% 69 37. 10% 1 - less than 20 3 1. 60% 2 - From 20 till less than 25 72 38. 70% 3 - From 25 till less than 30 51 27. 40% 4 - From 30 till less than 40 50 26. 90% 5 - From 40 till less than 50 7 3. 80% 6 - from 50 and above 3 1. 60% Education 1 - High school graduate 7 3. 80% 2 - Undergraduate 44 23. 70% 3 - Bachelor Graduate 84 45. 20% 4 - Masters Graduate 42 22. 60% 5 - Ph. D and Above 9 4. 80% Occupation 1 - unemployed 2 - Employed in Public sector 3 - Employed in private sector 4 - Business Owner 5 -Retired 51 27. 40% 9 4. 80% 108 58. 10% 17 9. 10% 1 0. 50%

Validity and reliability tests Table (4) Validity and reliability tests Construct PE EE SI FC HM Standardized Item loading AVE PE 1 0. 824 PE 2 0. 784 PE 3 0. 746 PE 4 0. 841 PE 5 0. 867 PE 6 0. 819 0. 613 EE 1 0. 871 EE 2 0. 894 EE 3 0. 836 SI 1 SI 2 SI 3 SI 4 SI 5 FC 1 FC 2 FC 3 0. 906 0. 921 0. 891 0. 673 0. 675 0. 676 0. 648 0. 795 FC 4 0. 753 HM 1 HM 2 HM 3 0. 91 0. 924 0. 911 HM 4 0. 925 UB 1 UB 2 0. 732 0. 822 UB 3 0. 655 0. 725 0. 599 CR 0. 905 0. 888 0. 877 Alpha 0. 907 0. 886 0. 872 0. 507 0. 797 0. 801 0. 813 0. 945 Table (5) The square root of AVEs (shown in bold at diagonal) and factor correlation coefficients FC PE HM SI EE UB FC 0. 712 PE 0. 412 0. 783 HM 0. 375 0. 408 0. 901 SI 0. 174 0. 28 0. 371 0. 774 EE 0. 71 0. 284 0. 281 0. 105 0. 851 UB 0. 545 0. 427 0. 527 0. 244 0. 45 0. 74

Table (6) Model fit for initial model and result model Model goodness- fit index recommended value Initial model result model Chi-square/df ≤ 5. 0 1. 864 1. 762 Goodness of Fit Index(GFI) ≥ 0. 84 0. 851 Adjusted goodness of Fit index (AGFI) ≥ 0. 805 Root mean square error of Approximation (RMSEA) ≤ 0. 068 0. 064 Normalised fit index (NFI) ≥ 0. 9 0. 876 Non-normalised fit index (TLI) ≥ 0. 929 Comparative fit index(CFI) ≥ 0. 934 0. 942 Incremental fit index(IFI) ≥ 0. 935 0. 943 Confirmatory factor Analysis

The results of the hypothesis are explored on the following Table, and all relationships are statistically significant. Table (7) Results of Hypothesis tests Hypothesis Path H 1 EE→PE H 2 SI →HM H 3 HM →PE H 4 PE→UB H 5 FC→UB H 6 HM→UB H 7 EE→HM H 8 FC→EE coefficient C. R. P Value 0. 183 2. 58 0. 01 0. 379 4. 775 *** 0. 216 4. 423 *** 0. 204 2. 385 0. 017 0. 386 4. 514 *** 0. 229 4. 362 *** 0. 39 3. 616 *** 0. 811 8. 45 ***

Research Model

Discussion The highest effect on usage was from facilitating conditions and this confirm with literature and UTAUT model developed by Venkatesh, (2003). In this model, facilitating conditions affect usage behaviour also indirectly through hedonic motivation, effort expectancy and performance expectancy, this, also, confirm with Zhou et al, (2010) who found an indirect relationship between effort expectancy and user adoption of mobile banking through performance expectancy. Also, usage of ad blockers are affected indirectly by social influence through hedonic motivation confirming with literature that shows a relationship between social influence and perceived enjoyment or subjective norms and perceived enjoyment (Leung, L. and R. Wei , 1999; Ling, 2001; Hung et a, 2003; Kleinjenen et al, 2004). Also, direct relationship between hedonic motivation and usage was apparent in literature.

Discussion Effort expectancy has a direct effect on hedonic motivation and Performance expectancy and this confirm with literature. Finally, usage of Ad blockers is highly affected by the fun aspects related to the application, the user’s resources and capabilities and finally by the usefulness induced from the application. However, the user’s capabilities and his resources are a determining factor in the usage of such technology.

Conclusion and Implications for Marketers Providing the factors that determine the usage behaviour of Egyptian consumers towards ad blockers Results of the research showed that all relationships are statistically significant; accordingly, marketers should discuss these effects in strategy formation. marketers can focus as well on targeting the social cycle of the younger Egyptian segments as well. Egyptian marketers should collaborate with advertising agencies to tailor and develop campaigns that best encourage the exposure of online ads; thus, help support online purchase and minimize the blocking of ads that might threaten the growth of an online purchasing community.

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