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Soc. Sci. 2018, 7(9), 163; doi:10.3390/socsci7090163

Article
Introduction of a New Mobile Player App Store in Selected Countries of Southeast Asia
1
Faculty of Management, Comenius University in Bratislava, Odbojárov 10, P.O.BOX 95, 82005 Bratislava, Slovakia
2
Faculty of Economics, Matej Bel University in Banská Bystrica, Tajovského 10, Banská Bystrica 97590, Slovakia
3
Faculty of Wood Science and Technology, Technical University in Zvolen, T. G. Masaryka 24, 96053 Zvolen, Slovakia
4
Faculty of Multimedia Communications, Tomas Bata University in Zlín, 2431 Stefanikova Str., 76001 Zlín, Czech Republic
*
Author to whom correspondence should be addressed.
Received: 12 July 2018 / Accepted: 13 September 2018 / Published: 15 September 2018

Abstract

:
Trends in modern society have a significant impact on the way organizations operate. The use of mobile phones makes it possible to create completely new high-availability communication and business channels. Mobile phones are used in mobile marketing, which has come to the fore via SMS marketing. In this article, the focus is on the use of mobile phones in e-business. The introduction of a new mobile player app store was analyzed through research conducted in 2017. The aim of the research was to find out whether it is possible—in terms of the sustainability of the consumption of a marketing product—to introduce a single campaign with the same content but in different language mutations in selected markets, or whether it is necessary to use a completely different campaign and means of communication for each market. Overall, 287 respondents from the Philippines, Thailand, and India were examined. The dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and site engagement was tested, and user experience was tested, too. The results of the research revealed that there was no dependency between belonging to the selected countries and site engagement. Furthermore, there was also no dependency between gender and site engagement. On the other hand, there was a statistically significant dependency between belonging to the country and the design of the website.
Keywords:
e-business; mobile marketing; Chi-square test; app store; Philippines; Thailand; India

1. Introduction

The third millennium is linked to globalization, the rapid development of information, and communication technologies. In addition to these trends, the modern economy is characterized by growth in both consumption and information. The role of national governance is to ensure the social responsibility and sustainable development of all actors in the economic sphere. In the corporate sector, these trends are reflected in the introduction of innovations which make it possible to implement a new way of business.
The rapid development of new technologies also penetrates marketing (Aretoulis 2018; Čierna et al. 2017; Waheed and Yang 2017; Zaborova et al. 2017; Hiadlovský et al. 2016; Malichova et al. 2016; Hockicko et al. 2015; Tokarčíková and Kucharčíková 2015; Jaroslav et al. 2013; Li and Zhang 2013; Kuzman et al. 2012; Chu et al. 2011; Deng et al. 2009; Metke 2006). A few years ago, people watched the same media, and so advertising could act simultaneously for several million consumers. Currently, consumers have unlimited opportunities, so the classical media that used to promote advertising messages have lost their position (Tsai et al. 2017; Parobek et al. 2016; Liu et al. 2018; Marsden and Chaney 2013; Vysekalová and Mikeš 2007). In this context, the mobile phone is becoming increasingly relied upon to create completely new communications and business channels with high availability. Mobile phones are used in e-business, which can be defined as the use of the internet to network and empower business processes, electronic commerce, organizational communication, and collaboration within a company and with its customers, suppliers, and other stakeholders (Combe 2006). The advantage is that customers, suppliers, and other stakeholders can be placed anywhere in the world, in other time zones, possibly speaking different languages and paying in different currencies. Currently, in this respect, mobile phones know no boundaries (Brzozowska and Bubel 2015).
Mobile phones are considered to be one of the latest technological inventions that affect a company’s marketing activities. Mobile phones—in particular smartphones—are used in the promotion of goods and services (Bakopoulos et al. 2017; Wu and Stilwell 2017; Berman 2016; Dovaliene et al. 2015; Lim et al. 2015; Öztaş 2015; Mirchev and Dicke 2011). Marketing activities are provided through a ubiquitous network, to which consumers are constantly connected by using a personal mobile device (Kaplan 2012). It can be multilateral or bilateral communication and promotion between the company and its customers (Shankar and Balasubramanian 2009). Marketing activities take place through wireless mobile technologies using services such as SMS (Short Message Service), MMS (Multimedia Messaging Service), WAP (Wireless Application Protocol), third-generation network, or other technology (Ližbetinová 2017; Persaud and Azhar 2012; Smutkupt et al. 2010; Leppäniemi et al. 2006; Částek and Škapa 2005; Dickinger et al. 2004).
The high penetration of mobile phones shows that they have become a widespread mass media. Thanks to the fact that almost all people now own a mobile phone, communication has become simpler and faster. This enables mobile marketing to reach a much wider and more diverse audience. Promotional campaigns via mobile devices are faster, easier, and cheaper to create and launch. Users can save information to their device, which they can retrieve and use whenever necessary at anytime and anywhere. Customer communication is personal, direct, targeted, and it has an immediate impact on customers (Musová 2015). Because users have their mobile phones on their person almost at all times, they receive messages right after they are sent. Therefore, user responses can be tracked in real-time. In addition, after getting feedback, marketers can better understand and analyze user behavior and thereby improve their products and services (Wilhite 2012; Michael and Salter 2006).
In order to maximize the efficiency of promotional campaigns, companies need to consider different factors affecting consumer behavior, such as cultural (the society, culture, sub-culture, and social class system in which a person grows and lives), social (the family, family life cycle, and social class to which an individual belongs), personal (life cycle stage, occupation, financial and economic conditions, lifestyle, personal situation, self-concept, and personality), psychological (internal and external motivation, perceptions, beliefs, and attitudes), and individual (age, gender, education level, income), as presented in Figure 1 (Abdolmaleki et al. 2018; SivaKumar and Gunasekaran 2017; Anisimova 2016; He et al. 2016; Olšiaková 2003).
The key to a successful marketing strategy is to take these factors into account because it is demanding to address and engage customers correctly, especially if the target customer group comes from completely different social classes, and from other geographic locations with diverse beliefs. It is confirmed by international literature (i.e., Kim and Baek 2018; Benda-Prokeinová et al. 2017; Lin et al. 2017; Ližbetinová 2017; Musa et al. 2017; Xu et al. 2017; Grencikova et al. 2016; Knapcova and Kucharcikova 2015; Venkatesh et al. 2012). The research of Kim et al. (2015) demonstrates that socio-demographic characteristics (e.g., gender, age, education, and income) are the main predictors for mobile applications. Studies by Seneviratne et al. (2014a, 2014b) investigated the relationship between mobile apps and user attributes such as gender, religion, country, and language. The results showed that user gender can be predicted with an accuracy of 70%, and most of their personality traits with an accuracy of over 90%. Gender differences were also confirmed by the research of Unal et al. (2017). Liu et al. (2018) highlight gender differences in the average amount of money spent on online shopping. Kim et al. (2015) further found that women tend to make greater use of e-commerce applications. According to Kraft and Weber (2012), women meet long-term needs while men look to meet immediate, short-term needs. The findings of Bazhan et al. (2018) show that women have quite diverse preferences.
Understanding the individual socio-demographic characteristics of consumers allows programmers to understand preferences, and therefore to better customize mobile applications to their users (Unal et al. 2017). This is a key process for properly designing and targeting advertisements (Hsu and Chen 2018; Tarute et al. 2017; Li and Zhang 2013; Venkatesh et al. 2012).

2. Methodological Approach

In the context of e-business, the introduction of a new mobile player app store was investigated. Different studies address this issue (Li et al. 2017; Bastinos et al. 2014; Marmol et al. 2014; Roman et al. 2014; Ok et al. 2013; Pagano and Maalej 2013). Our research is a joint venture project between a Slovak company and an Asian mobile game reseller. A blockchain was created between 2016 and 2017 to sell any digital content (video, audio, e-books, etc.). Compared to traditional sales, the advantage of this solution lies in the fact that intermediaries (i.e., third parties) are excluded from the buying and selling process, which leads to a reduction of commission costs that have disproportionately increased the final cost of digital content sold to end users.
The research was conducted through the purchase of a research panel in 2017. When designing a campaign for the Philippines, Thailand, and India, ethnic, religious, cultural, and linguistic differences were taken into account, as these are markets where consumers—in comparison to European ones—have a completely different mentality, religion, and perception of reality, as well as different habits. The aim of this research was to determine if it is possible—in terms of the sustainability of the consumption of a marketing product—to launch a single campaign aimed at all markets using the same content but different languages, or whether it is necessary to use a completely different campaign and means of communication for each market.
Altogether, 317 respondents from the Philippines, Thailand, and India were addressed. In total, 287 questionnaires were used for statistical evaluation. The return rate was 90.54%. Table 1 shows the sample size.
The model presented in Figure 1 shows that there is a wide spectrum of different factors affecting consumer behavior. The research was focused on the individual factors—mainly socio-demographic characteristics.
At the level of significance α = 5%, in the context of inductive statistics through the Chi-square goodness of fit test, the dependency between the two categorical variables was verified. Two scientific hypotheses were tested:
Hypothesis 1 (H1).
Is there a dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and site engagement of visitors?
Hypothesis 2 (H2).
Is there a dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and the user experience?
If the calculated p-value was less than the chosen significance level (α = 5%), we rejected the zero hypothesis H0 of the Chi-square test (Pacáková 2009; Rimarčík 2007) and support H1 (i.e., the statistically significant dependence between socio-demographic characteristics and the site engagement, as well as user experience). The categorical data, at which statistically significant dependency was confirmed, are presented in the table of residual abilities (observed/theoretical). Looking at the table, it can be seen where dependency was most visible.
This project, aiming to introduce a new mobile app store built on blockchain technology, can serve as a test for modern communication media, such as: advertising space directly in the app store; web/banner campaigns; video campaigns; campaigns through affiliate programs; social networking campaigns; sales promotion campaigns using influencers such as bloggers, YouTubers, and chatters in new discussion forums; push notification campaigns; and SMS campaigns. The aim of the article is to explore whether a single e-business marketing campaign can be launched in the selected Southeast Asian countries.

3. Results

In the first step, the H1 hypothesis (Is there a dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and visitor site engagement?) was tested.
Based on the contingency coefficient presented in Table 2, it can be concluded that there was a slight dependency between the country and site engagements. Following the results in Table 2, the Chi-square test presented no statistically significant dependency on the country’s relevance and site engagement.
In all countries analyzed, the site engagement was above 80% (Table 3).
Table 4 presents the dependence between the country and site engagement. The contingency coefficient (Table 5) presented a slight dependence between the gender and site engagements. Based on the Chi-square test results (Table 5), there was no statistically significant dependence between gender and the site engagement.
From the point of view of gender, there was high site engagement, at the level of 80% (Table 6). Table 7 presents the dependence between gender and site engagement. Table 8 presents the contingency coefficient. It can be concluded that there was a slight dependence between age and site engagement. Based on the Chi-square results (Table 8), there was no statistically significant dependence between age and site engagement. Against the age group of respondents, there was a high interest in the site engagement of over 79% (Table 9). Table 10 presents the dependence between age and site engagement. As age increased, the level of interest grew.
In the following step, the H2 hypothesis was tested. The dependence between the socio-demographic characteristics of the respondents (country, gender, and age) and the user experience was tested.
Based on the contingency coefficient presented in Table 11, it can be concluded that there was a stronger dependence between the country and user experience. Based on the Chi-square results presented in Table 11, there was a statistically significant dependence between the country and the user experience. Table 12 presents the user experience in the countries analyzed. Dependence between the country and the user experience is presented in Table 13.
A slight dependence between age and the user experience based on the results of the contingency coefficient is presented in Table 8. Based on the results presented in Table 14 and the Chi-square results, it can be stated that there was no statistically significant dependency between age and the user experience.
The user experience according to the age group analyzed is presented in Table 15. It can be stated that for most respondents, the user experience was characterized as calm/peaceful. Table 16 presents the dependence between age and the user experience.
The results of the contingency coefficient presented in Table 17 indicate a slight dependence between gender and the user experience. No statistically significant dependence between gender and the user experience was confirmed by the Chi-square results (Table 17).
Table 18 presents the user experience according to gender. For most respondents, the user experience was characterized as calm/peaceful. Gender dependency and the user experience is presented in Table 19.

4. Discussion and Conclusions

Increasing customer diversity is exerting pressure on businesses to keep track of current trends, adjust to them, and reach a strong market position with comprehensive marketing activities (Van Kerrebroeck et al. 2017; Krasnova et al. 2017; Gejdoš and Danihelová 2015; Ďurišová et al. 2015; Gubiniová and Bartáková 2014; Pyatnitskaya 2013; Cambal et al. 2012; Strišš 2008; Vaštíková 2008). Old methods of marketing communications are not bad, but if a company wants to be a step ahead of its competition, and if it wants to be more effective in addressing increasingly demanding customers, it must adapt to these trends and include them in its processes. The same view is shared by the research of Chernova et al. (2018). The authors argue that in order to increase the effectiveness of commercial activities, PR and marketing specialists have to use new technologies, marketing tools, and non-standard approaches to mass communications.
Due to the opportunities brought about by the digital revolution, customer behavior has changed significantly. Customers are less tolerant, less loyal, more informed, and they are becoming multichannel users (Urbancova et al. 2017; Ližbetín et al. 2016; Poliacikova and Vaclavikova 2016; Fu et al. 2015; Buehlmann et al. 2013). Therefore, in an effort to secure and maintain a strong competitive position, companies are looking for new ways to impress the customer and raise awareness of the brand and its products (Javorčíková 2017; Korauš et al. 2018; Mayett-Moreno et al. 2018; Ďuračík et al. 2017; Gamache et al. 2017; Lee 2017; Stacho et al. 2015; Makhnush and Oliynyk 2011).
In our research, we explored the introduction of a new mobile player app store. The research was conducted in 2017 by purchasing a research panel. With a sample of 287 respondents, we analyzed whether a single marketing campaign could be conducted for selected countries in Southeast Asia. Based on the results of Chi-square tests, we could define statistically significant dependencies between the socio-demographic characteristics of respondents (country, gender, and age) and the selected analyzed factors.
In the H1 research hypothesis as to whether there is a dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and the site engagement, it can be concluded that—based on the results of the Chi-square test—there was no statistically significant dependency between the country and the site engagement, there was no statistically significant dependency between age and the site engagement, and there was no statistically significant dependency between gender and the site engagement. This analysis did not confirm H1.
In the H1 hypothesis, as to whether there is a dependency between the socio-demographic characteristics of the respondents (country, gender, and age) and the user experience, it can be concluded that—based on the results of the Chi-square tests—there was a statistically significant dependency between the country and the user experience, there was no statistically significant dependency between age and the user experience, and there was no statistically significant relationship between gender and the user experience. In our analysis, we confirmed H2 in only one of three demographic characteristics, and in two the input assumption was not confirmed. Thus, H2 was rejected.
Looking at the analyses and practical benefits of the research, it is clear that in the field of e-business in the analyzed countries it is possible to carry out marketing campaigns uniformly, because there was no statistically significant dependency between the analyzed characteristics and the selected results of the marketing survey. In the field of e-business, mobile marketing has great potential. If a company uses the right combination of marketing communication tools, the final effect of advertising can be multiplied, leading to an increased demand for products and the sustainability of the product consumption (Wilhite 2012; Michael and Salter 2006).
Future research can be provided in comparing European and Asian markets, as well as other markets around the world. Due to the globalization of the world market, it is possible to analyze the similarities between marketing campaigns in different parts of the world. Research is a rather limited by necessary method by which to obtain additional data from markets in various other countries.

Author Contributions

Conceptualization, P.S., M.V., J.M., S.L., M.H. and D.W.; Methodology, J.M., M.H., S.L.; Software, J.M. and M.H.; Validation, P.S. and M.H.; Formal Analysis, J.M., M.H. and S.L.; Investigation, J.M. and M.H.; Resources, P.S., M.V., J.M., S.L., M.H. and D.W.; Data Curation, J.M. and M.H.; Writing-Original Draft Preparation, P.S., M.V., J.M., S.L., M.H. and D.W.; Writing-Review & Editing, P.S., M.V., J.M., S.L., M.H. and D.W.; Visualization, M.H. and S.L.; Supervision, P.S., M.V., M.H.; Project Administration, M.H. and S.L.; Funding Acquisition, M.H. and S.L.

Funding

This research was funded by VEGA grant number 1/0024/17, VEGA grant number 1/0116/18 and APVV grant number 16-0297.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Factors affecting consumer behavior. Sources: (Abdolmaleki et al. 2018; SivaKumar and Gunasekaran 2017; Anisimova 2016; He et al. 2016; Olšiaková 2003).
Figure 1. Factors affecting consumer behavior. Sources: (Abdolmaleki et al. 2018; SivaKumar and Gunasekaran 2017; Anisimova 2016; He et al. 2016; Olšiaková 2003).
Socsci 07 00163 g001
Table 1. Sample size.
Table 1. Sample size.
Absolute FrequencyRelative Frequency
CountryPhilippines11138.68
Thailand7626.48
India10034.84
GenderMen16557.49
Women12242.51
Age<1800.00
18−2915353.31
30−4411640.42
45−60186.27
>6000.00
Table 2. Dependency between the country and site engagement.
Table 2. Dependency between the country and site engagement.
Chi-Squaredfp
Pearson’s Chi-square0.7918211df = 2p = 0.67307
Chi-square0.8063621df = 2p = 0.66819
Phi0.0525258
Contingency coefficient0.0524535
Cramér’s V0.0525258
Table 3. The number of responses based on the country and site engagement.
Table 3. The number of responses based on the country and site engagement.
CountryDid This Website Catch Your Attention?
YesNoRow Sums
Philippines9120111
Column sum37.76%43.48%
Row sum81.98%18.02%
Total31.71%6.97%38.68%
Thailand661076
Column sum27.39%21.74%
Row sum86.84%13.16%
Total23.00%3.48%26.48%
India8416100
Column sum34.85%34.78%
Row sum84.00%16.00%
Total29.27%5.57%34.84%
Total absolute frequency24146287
Total relative frequency83.97%16.03%100.00%
Table 4. Contingency table of dependence between the country and site engagement.
Table 4. Contingency table of dependence between the country and site engagement.
CountryDid This Website Catch Your Attention?
YesNoRow Sums
Philippines−2.209062.209060.00
Thailand2.18118−2.181180.00
India0.02787−0.027870.00
Total0.000000.000000.00
Table 5. Dependence between gender and site engagement.
Table 5. Dependence between gender and site engagement.
Chi-Squaredfp
Pearson’s Chi-square1.604175df = 1p = 0.20531
Chi-square1.588636df = 1p = 0.20752
Phi−0.075423
Contingency coefficient−0.141547
Cramér’s V0.0752089
Table 6. The number of responses based on gender and site engagement.
Table 6. The number of responses based on gender and site engagement.
GenderDid This Website Catch Your Attention?
YesNoRow Sums
Female9723120
Column sum40.93%51.11%
Row sum80.83%19.17%
Total34.40%8.16%42.55%
Male14022162
Column sum59.07%48.89%
Row sum86.42%13.58%
Total49.65%7.80%57.45%
Total absolute frequency23745282
Total relative frequency84.04%15.96%100.00%
Table 7. Contingency table of dependence between gender and site engagement.
Table 7. Contingency table of dependence between gender and site engagement.
GenderDid This Website Catch Your Attention?
YesNoRow Sums
Female−3.851063.851060.00
Male3.85106−3.851060.00
Total0.000000.000000.00
Table 8. Dependence between age and site engagement.
Table 8. Dependence between age and site engagement.
Chi-Squaredfp
Pearson’s Chi-square5.084163df = 2p = 0.07870
Chi-square5.225980df = 2p = 0.07332
Phi0.1342719
Contingency coefficient0.1330776
Cramér’s V0.1342719
Table 9. The number of responses based on age and site engagement.
Table 9. The number of responses based on age and site engagement.
AgeDid This Website Catch Your Attention?
YesNoRow Sums
18–2912031151
Column sum50.63%68.89%
Row sum79.47%20.53%
Total42.55%10.99%53.55%
30–4410212114
Column sum43.04%26.67%
Row sum89.47%10.53%
Total36.17%4.26%40.43%
45–6015217
Column sum6.33%4.44%
Row sum88.24%11.76%
Total5.32%0.71%6.03%
Total absolute frequency23745282
Total relative frequency84.04%15.96%100.00%
Table 10. Contingency table of dependence between age and site engagement.
Table 10. Contingency table of dependence between age and site engagement.
AgeDid This Website Catch Your Attention?
YesNoRow Sums
18–29−6.904266.904260.00
30–446.19149−6.191490.00
45–600.71277−0.712770.00
Total0.000000.000000.00
Table 11. Dependency between the country and the user experience.
Table 11. Dependency between the country and the user experience.
Chi-Squaredfp
Pearson’s Chi-square42.73984df = 10p = 0.00001
Chi-square44.25333df = 10p = 0.00000
Phi0.3859006
Contingency coefficient0.3600234
Cramér’s V0.2728730
Table 12. The number of respondents’ responses based on the country and the user experience.
Table 12. The number of respondents’ responses based on the country and the user experience.
CountryHow Does This Web Design Make You Feel?
Calm/PeacefulIntriguedOtherHappyConfused/UncertainExcited/EnergizedRow Sums
Philippines5118810159111
Column sum45.95%66.67%42.11%14.93%53.57%25.71%
Row sum45.95%16.22%7.21%9.01%13.51%8.11%
Total17.77%6.27%2.79%3.48%5.23%3.14%38.68%
Thailand2264324876
Column sum19.82%22.22%21.05%47.76%14.29%22.86%
Row sum28.95%7.89%5.26%42.11%5.26%10.53%
Total7.67%2.09%1.39%11.15%1.39%2.79%26.48%
India383725918100
Column sum34.23%11.11%36.84%37.31%32.14%51.43%
Row sum38.00%3.00%7.00%25.00%9.00%18.00%
Total13.24%1.05%2.44%8.71%3.14%6.27%34.84%
Total absolute frequency1112719672835287
Total relative frequency38.68%9.41%6.62%23.34%9.76%12.20%100.00%
Table 13. Contingency table of dependence between the country and the user experience.
Table 13. Contingency table of dependence between the country and the user experience.
CountryHow Does This Web Design Make You Feel?
Calm/PeacefulIntriguedOtherHappyConfused/
Uncertain
Excited/
Energized
Row Sums
Philippines8.069697.557490.65157−15.91294.17073−4.536590.00
Thailand−7.39373−1.14983−1.0313614.2578−3.41463−1.268290.00
India−0.67596−6.407670.379791.6551−0.756105.804880.00
Total0.00000.00000.00000.00000.00000.00000.00
Table 14. Dependency between the age and the user experience.
Table 14. Dependency between the age and the user experience.
Chi-Squaredfp
Pearson’s Chi-square13.34995df = 10p = 0.20476
Chi-square13.21551df = 10p = 0.21187
Phi0.2175781
Contingency coefficient0.2126040
Cramér’s V0.1538510
Table 15. The number of responses based on age and the user experience.
Table 15. The number of responses based on age and the user experience.
AgeHow Does This Web Design Make You Feel?
Calm/PeacefulIntriguedOther
18–29671310
Column sum62.62%48.15%52.63%
Row sum44.37%8.61%6.62%
Total23.76%4.61%3.55%
30–4433137
Column sum30.84%48.15%36.84%
Row sum28.95%11.40%6.14%
Total11.70%4.61%2.48%
45–60712
Column sum6.54%3.70%10.53%
Row sum41.18%5.88%11.76%
Total2.48%0.35%0.71%
Total absolute frequency1072719
Total relative frequency37.94%9.57%6.74%
Table 16. Contingency table of dependence between age and the user experience.
Table 16. Contingency table of dependence between age and the user experience.
AgeHow Does This Web Design Make You Feel?
Calm/
Peaceful
IntriguedOtherHappyConfused/
Uncertain
Excited/
Energized
Row Sums
30–44−10.25532.08511−0.6808519.319150.68085−1.148940.00
45–600.5496−0.627660.854610−0.978721.31206−1.109930.00
Total0.00000.00000.00000.00000.00000.00000.00
Table 17. Dependence between gender and the user experience.
Table 17. Dependence between gender and the user experience.
Chi-Squaredfp
Pearson’s Chi-square4.359730df = 5p = 0.49887
Chi-square4.383676df = 5p = 0.49560
Phi0.1243384
Contingency coefficient0.1233883
Cramér’s V0.1243384
Table 18. The number of responses based on gender and site engagement.
Table 18. The number of responses based on gender and site engagement.
GenderHow Does This Web Design Make You Feel?
Calm/
Peaceful
IntriguedOtherHappyConfused/
Uncertain
Excited/
Energized
Row Sums
Female45158271411120
Column sum42.06%55.56%42.11%40.91%50.00%31.43%
Row sum37.50%12.50%6.67%22.50%11,67%9.17%
Total15.96%5.32%2.84%9.57%4.96%3.90%42.55%
Male621211391424162
Column sum57.94%44.44%57.89%59.09%50.00%68.57%
Row sum38.27%7.41%6.79%24.07%8.64%14.81%
Total21.99%4.26%3.90%13.83%4.96%8.51%57.45%
Total absolute frequency1072719662835282
Total relative frequency37.94%9.57%6.74%23.40%9.93%12.41%100.00%
Table 19. Contingency table of gender dependency and the user experience.
Table 19. Contingency table of gender dependency and the user experience.
Gender How Does This Web Design Make You Feel?
Calm/
Peaceful
IntriguedOtherHappyConfused/
Uncertain
Excited/
Energized
Row Sums
Female−0.5319153.51064−0.085106−1.085112.08511−3.893620.00
Male0.531915−3.510640.0851061.08511−2.085113.893620.00
Total0.0000000.000000.0000000.000000.000000.000000.00

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