Mobile marketing can be seen as the evolution of Internet-based marketing (e-commerce scenario) to the mobile channel to respond to the emerging trend of consumers losing interest in traditional marketing channels ( Hinz et al., 2011 ). In the last few years, there has been an exponential rise in the use of mobile devices for internet access. Now, it is much easier to do electronic transactions using mobile devices, Personal Digital Assistant (PDA), and tablet computers, allowing wireless transactions from any location with internet connectivity. As a result, mobile commerce has grown as a separate channel from $70.7 billion in 2007 to $187.9 billion in 2012 ( Uglow, 2007 ). This growth is easily reflected in millions of consumers glued to mobile screens at coffee shops, train stations, and airport gates. The significant uncertainty is regarding the size and characteristics of the target mobile consumer market. Various statistics support the increasing potential of mobile commerce. For instance, according to The Nielsen Company (2010) , among smartphone users, 29% report using their devices for shopping or retail purposes. Nielsen also identified popular transaction-related activities such as locating nearby stores (50%), comparing prices (38%), accessing “Deal of the Day” apps (29%), and sharing product recommendations via mobile apps (20%). Predictive models, such as those employing random forest algorithms, have demonstrated considerable accuracy in forecasting online consumer behavior across different cultural settings ( Joshi, Gupte, & Saravanan, 2018 ). These findings indicate that understanding and forecasting consumer behavior patterns can significantly boost both consumer engagement and revenues.

These statistics indicate that wireless connections can boost both consumer usage and revenues. Some studies ( Sterling, 2001 ; Napoli & Obar, 2014 ) suggest users may still prefer desktop or laptop computers connected to stable internet networks for online shopping, particularly at home and during weekends.

Mobile-only Internet Users (MOIU) are individuals who exclusively use smartphones or feature phones for internet access, having neither owned nor used other connected devices (see Figure 1 below).

This paper aims to study MOIU’s online buying behavior patterns across cultures, using two of Hofstede’s cultural dimensions and income per capita (IPC) as independent variables. Until now, research on Internet shopping implied accessing the Internet via desktop computers.

In the desktop-only era, it was reasonable to believe that how people shopped online or reacted to advertisements could be attributed to their demographics, goals, and attitudes. In the early days of desktop shopping, for instance, navigational difficulties increased consumer reluctance to engage in online shopping ( Jarvenpaa & Todd, 1997 ) even though consumers recognized internet shopping to be timesaving ( Alreck & Settle, 2002 ). Interest in online shopping was higher among men than women ( Meuter et al., 2003 ), perhaps because women found online shopping riskier than men ( Garbarino & Strahilevitz, 2004 ). As online shopping became more popular, women expressed a “shopping as fun” orientation, while men were more likely to be “quick” shoppers ( Hansen & Jensen, 2009 ).

With the emergence of mobile Internet, access to the desktop computer becomes less relevant, and the physical context becomes more consequential. According to survey reports by InMobi ( Table 1 ), Asian and African nations demonstrate more situated mobile web usage, where mobile web is mainly used in bed, while watching TV, or while waiting for something. However, UK and USA users demonstrate ubiquitous use, where frequent usage occurs while spending time with family, commuting, shopping, and in social gatherings. The USA and UK also demonstrate a higher percentage of shopping-related usage and a higher number of minutes used on mobile devices. Understanding how the physical context affects consumer engagement with the electronic market space becomes essential once ownership and access are ascertained.

Data Source: https://www.statista.com/topics/779/mobile-internet/#topicOverview (year 2021).

Physical-social Context Cues and their Influences:

Increasing mobile device ownership has led to many location-based mobile applications such as Foursquare, Gypsii, Brightkite, Whrrl, and Gowalla. Conventional marketers such as HSN.com, for instance, have a full series of mobile applications for the iPhone^TM and iPod touch®, allowing the consumer to shop. These technologies change how consumers access and consume information and how firms and organizations reach clients and deliver their services ( Demirkan & Spohrer, 2014 ; Gao et al., 2013 ).

Effect of UAI, IDV on Online Purchase through Mobile Devices

According to some studies, public Wi-Fi and 4G internet connections are less secure than VPNs (virtual private networks). Regarding public Wi-Fi, attackers have more opportunities to exploit vulnerabilities via a connection over Wi-Fi than over 4G. As far as the security of these connections, here is how they rank from most secure to least secure:

1) Using a VPN over a cellular network or using a VPN over Wi-Fi

2) Cellular only

3) Wi-Fi only

Therefore, based on the insights from the literature review, regarding online purchase behavior using various Hofstede’s cultural dimensions, the following hypotheses are proposed, and tests are conducted:

Hypothesis 1 (H1): Countries with higher UAI and IDV scores will have lower MOIU percentages.

Hypothesis 2 (H2): Young users (15 - 29 years of age) across countries are more likely to be MOIU but more so in countries with high IDV scores.

The MOIU numbers in Table 2 (as a percentage of total online buyers) and Hofstede’s IDV and UAI scores for 34 countries are used to test these hypotheses. The MOIU data for years 2021-2022 are collected from https://www.globalwebindex.net/ .

H1: Countries with higher UAI and IDV scores will have lower MOIU percentages.

Using MOIU percentage as the dependent variable and UAI, IDV scores, and GDP per capita as independent variables, linear regression is conducted to test H1.

Table 3 indicates a significant negative correlation between MOIU percentages and Individualism (IDV) scores (β = −0.657, p < 0.0001), implying that individualistic societies have fewer mobile-only users, possibly due to higher concerns about data privacy. Conversely, Uncertainty Avoidance (UAI) and GDP per capita showed no significant effects. This can be attributed to the trend that users of individualistic societies are more concerned about their data privacy than those of collectivist societies. Another explanation would be that the more individualistic nations have better infrastructure than the more collectivist nations; the users have more opportunities to use other devices like iPads and laptops to access the Internet. So, they are less likely to be mobile only.

H2: Young users (15 - 29 years of age) across countries are more likely to be MOIUs.

We first regress the percentage of MOIU against yp (the percentage of the young population aged between 15 - 29 years) and GDP_pcap (GDP per capita) The results are below:

Table 4 highlights that young users (15 - 29 years) significantly correlate with higher MOIU percentages across countries (β = 0.685, p = 0.0001). We find that Generation-Z users are more likely to be Mobile-Only-Internet-Users across countries, and GDP per capita has no effect on the same (p = 0.6614).

Next, we regress MOIU against YP, IDV, and GDP_pcap to check the moderating effect of IDV on MOIU in combination with YP ( Table 5 ):

As expected, young users aged between 15 - 25 years are more likely to be MOIU in collective societies. Higher IDV is still having a negative correlation with MOIU numbers (β = −0.558, p < 0.0001).

This research study explores the effects of Hofstede’s cultural dimensions and demographic variables on the online buying behavior of MOIU consumers from different countries. Cultures high on individualism are harder to penetrate. The findings of this study are in accordance with expectations. The primary factors affecting internet penetration in different countries are also investigated. This gives an important perspective of the critical foundation of the e-commerce industry, i.e., access to the Internet to the populace—which forms the superset of potential buyers. Internet penetration positively correlates with high education levels and per-capita incomes in countries. The level of landline phone penetration does not affect the internet penetration.

Future studies could explore how search time is related to uncertainty avoidance and other contextual factors. We find that users from countries low on IDV are more likely to write post-purchase online reviews. Therefore, examining the cultural dimensions and customers’ attitudes toward online shopping for e-commerce players is essential.

It could also be explored how two culturally dissimilar countries differ in consumers’ buying intention and search time on e-commerce websites by doing moderated regression and simple regression analyses with income, education level, social influence, attitude, and behavioral control as independent variables. Consumers do not have complete control over their online transactions. Therefore, perceived behavioral control described in the theory of planned behavior (TPB) ( Ajzen, 1985, 1991 ) and trust have become critical factors in e-commerce ( Pavlou & Chai, 2002 ). According to TPB, attitude and subjective norms could also be incorporated as key predictors of online behavior ( Pavlou & Chai, 2002 ).

Future research should try to enhance the predictive power of the proposed model as to how consumers from collectivist countries maneuver from social networking websites to shopping websites by considering the interaction effects of additional cultural dimensions. Additionally, the issue of small sample size could be alleviated in future studies to avoid adverse effects on the study about the same.

We find that per capita income on a PPP (Purchasing Power Parity) basis does not affect the percentage of MOIU globally. This can be attributed to the fact that broadband and Wi-Fi infrastructure are still unavailable for most users, particularly in small towns—which in turn means they cannot access the Internet on desktops, and MOIU becomes a viable option.

The roles of uncertainty avoidance and the demographic variables are identified in the online purchase behavior of mobile-only-internet-users (MOIU) vs. desktop-internet users. It is found that the countries with high IDV scores have lower percentages of MOIU, and UAI has no effect, irrespective of the fact that internet access with mobile devices offers less data privacy. The more individualistic nations have better infrastructure and more opportunities to use other mobile devices like iPads and laptops to access the Internet. So, they are less likely to be mobile only.

In addition, it was found that the effect of IDV scores and the age of users—on MOIU number—is significant, too. Also, young users between 25 and 35 years old are much more likely to fall into the MOIU category, irrespective of which country they come from.

The continuous development in technology is expected to keep changing the current mobile landscape, as functions included in mobile apps provide better experiences for users across cultures. Some research has been done about ubiquitous vs. situated browsing patterns of MOIU users segmented into various clusters based on demographic variables ( Banerjee & Dholakia, 2013 ). However, the cross-cultural patterns about the same are still unexplored, and future research will involve finding the patterns of situated vs. ubiquitous online buying behavior across cultures.