Assessing the Adoption of Mobile Technology for Commerce by Generation Z

Abstract

E-commerce has gained momentum with the rapid development of technology, and nowadays, we are permanently connected, with constant access to information and a wide range of products. Not only does a desktop computer offer us this possibility, but the latest-generation tablets and mobile phones create a broad framework. This paper investigates Romanian consumers’ attitudes towards adopting mobile technology for commerce (m-commerce), taking into account its development in the last few years, especially among younger generations. The main objectives of the research are to identify the preference for m-commerce use among Generation Z, establish the ways and the devices used by Gen Z individuals to inform about the products and services and order them, and analyze the factors influencing the use of m-commerce applications. The research methodology consists of conducting an empirical analysis using a distributed survey among youngsters from Generation Z in Romania. We used descriptive statistics, such as the analysis of frequency and the mean of variables, artificial neural network analysis (ANN), and multivariate analysis of variance (MANOVA), to validate the hypotheses. The research results indicate a solid inclination for m-commerce among Generation Z. The results are helpful for companies that can shape their marketing strategies to boost their sales using m-commerce channels among the younger population.

1. Introduction

The world is dominated by consumption, and society has turned this into a unit of measurement for assessing people’s progress and well-being and their ability to pay higher bills. Consumption has become practically “one of the codes that place you in the social hierarchy” [1]. People’s shopping habits have undergone multiple changes over the years, and everything is accessible without requiring the effort to go to a physical store. Virtual reality, in which any product can be accessed and ordered from thousands of miles away, has become an integral part of any individual’s life. Science predicts even more significant leaps in the future, even if there is another perspective represented by anti-consumerism [2].

The term “consumption” is directly associated with “need”. However, the ideas enunciated in specialized works have both positive and negative values, going beyond the scope of explanations, according to which “consumption means satisfying needs” [3], and questioning the relevance and reality of the existence of these needs, in the context of psychological, social, and cultural influences.

There is a classification of consumption styles among the population in this evolution [4]. One has identified several typologies, such as visible consumption, symbolic consumption, dependent consumption, impulsive consumption, and obsessive consumption, depending on the cultural influences and age of subjects. Beyond all of this, a distinct typology of the online consumer can be outlined.

In this context, especially against the background of emphasizing the role of the internet in everyday life and as business support, we can talk about the need to increase the adaptability of trading companies in correlation with consumers’ dynamics.

Alvin Toffler considers technological change the “great engine of evolution” [5]. As stated by Rusu, today, we can speak of e-commerce networks worldwide [6] with notoriety and tradition, which shows that the transition to this environment has been relatively easy. However, the required conditions are numerous and assume the existence of a solid information infrastructure and specific skills in the field. Moreover, the benefits of the internet in business are undeniable. Popescul [7] mentioned the reduction of fixed and variable expenses of companies, the increase in incomes, the enhancement of firms’ productivity, and the identification of new development opportunities for small firms.

Two notions are prevalent today concerning commerce through internet technology: e-commerce and mobile commerce (m-commerce). The relevant literature stipulates both differences and similarities between them. However, the context in which they occur is the same, facilitating the purchasing process using the internet, while mobile applications and electronic devices nowadays represent an essential part of our lives [8]. The two concepts are firmly related; still, whereas e-commerce is considered a broader and older concept, m-commerce is a newer concept, used concerning purchases carried out with mobile devices, representing, in fact, a secondary category of e-commerce that uses mobile devices in order to carry out the act of purchase. An advantage in the case of m-commerce is that these devices need not be connected to the internet every time.

It is essential to briefly explain the main differences between the three concepts: traditional trade, e-commerce, and m-commerce. Traditional trade involves physical presence in the store without requiring the internet. However, in the current reality, this form of trade is combined in some cases with a cell phone by scanning QR codes on product labels through mobile applications. On the other hand, e-commerce involves transactions made using internet services. However, it is no longer a new commerce method, although the level of popularity is still limited, especially among certain age groups. Andam [9] referred to e-commerce as “any form of business transaction in which the parties interact electronically rather than by physical exchanges or direct physical contact.” However, users choose this form of commerce for various reasons, including the time saved, the possibility to compare products online, the possibility to access the site [10] without a time limit (as in the case of a physical store), the vast possibilities of choice, the detailed information, and the opportunity to interact with other customers through online forums [11]. Hence, there is excellent potential for development. As for the disadvantages of e-commerce, we can mention the time required to deliver the products (which can often vary from a few hours to a few weeks, depending on the type of product), the dependence on the internet compared to traditional trade, and the inability to test product quality before purchase [12].

E-commerce is not a novelty; the first commercial acts took place in the 1970s, whereas m-commerce has only been developed since the 1990s. Some sources identify a reasonably strong link between the two, considering m-commerce a branch of e-commerce [13]. It is now well known that both play an essential part in society, gaining more and more popularity and playing a “dynamic role” [14], as their emergence also determined a paradigm shift into global markets [15]. One of the most critical aspects of e-commerce is that e-comm created a very important opportunity for businesses, namely, companies using the internet nowadays to determine a larger number of consumers to benefit from their products [16].

Statistical data used to analyze the scope of e-commerce expansion show an increase in online sales revenues in Romania, from EUR 3.6 billion in 2018 to EUR 4.3 billion in 2019 [17], whereas the distribution sales indicate that a percentage of 63.6% used mobile devices and 36.4% used desktop devices. This phenomenon happens in the context of an increase in e-commerce users from 6.02 million in 2018 to 7.54 million in 2019, reaching 8.34 million in 2020. Furthermore, the statistics show the following distribution by age group of e-commerce users in Romania: 14.14%, 18–24 years; 25.35%, 25–34 years; 25.56%, 35–44 years; 22.42%, 45–54 years; and 12.53%, 55–64 years.

An important question arises: How prepared is the Romanian market for significant progress in e-commerce and, at the same time, how ready are Romanian organizations for this, taking into account the fact that the country is an emerging economy? The benefits of e-commerce for companies and individuals have been mentioned by many authors who emphasize the role played in economic development [18,19,20]. Still, despite the numerous advantages, there are also critical challenges that can represent a significant barrier to e-commerce development, such as technology issues, network problems, obsolete mentalities, lack of financial resources among companies—especially in emerging economies, and security threats [21,22]. These challenges are also encountered by companies in Romania, an emerging economy, but recent events in the world (mainly the COVID-19 pandemic) have led to a positive development [23], with companies investing in their online presence and e-commerce activities. Another challenge is posed by almost half of the population in Romania living in rural areas [24].

Globally, companies have shown flexibility and a high degree of adaptability since the early 2000s, going through a series of processes of adaptation to the online environment, which gradually involved creating their websites, implementing order buttons that allow customers to shop online, creating pages on social networks, and last but not least, developing applications adapted to the needs of new generations and also to new types of devices. As a result, a substantial percentage of today’s companies use the internet in their commercial activity, offering customers the possibility to use the web interface for easy order placement; however, the segment that has developed mobile applications is much smaller.

The present study analyzes the attitude of Gen Zers towards m-commerce. Considering that most studies mention this generation’s preference for online platforms, we expect a higher prevalence of m-commerce than e-commerce, which we test in our empirical research.

The paper includes the following sections: a literature review, which comprises the most relevant studies on m-commerce among Generation Z; the methodology, wherein we present the method used and the hypotheses; the results, which focus on the validation of the hypotheses established; and the conclusions, which include the aspects that should be taken into account by retailers to address the needs of this generation better and thus raise their market share and sales.

2. Literature Review

Although often confused, there is a strong relationship between e-commerce and m-commerce. The first category developed earlier, following the development of the internet and electronic systems, using mainly desktop computers or laptops and requiring a fixed place to complete the act by accessing the store’s website. According to Taranenko et al. [25], electronic commerce contributes to replacing “traditional trading operations”. On the other hand, although m-commerce is a younger concept that does not require a fixed device [26], the concept represents a form of commerce that is continuously evolving while relying significantly on the use of the internet or cellular data. Various sources mention m-commerce as having an “increasing intensity”, and trends in its evolution include the following: mobile services, payment, banking, advertising, applications, the internet, and shopping [27]. Transactions are made in any location that offers an internet connection or through mobile data [9] available on smartphone or tablet, with a widely used tool being mobile applications [28], which have proven their usefulness in increasing the quality of services over time [29].

E-commerce, therefore, implies a fixed element of transaction mediation [30], whereas m-commerce is independent of these elements. However, both of these are connected to the online environment and assume online transactions. The term “m-commerce” “refers to the growing practice of conducting financial and promotional activities using a wireless handheld device” [31]. In this sense, a significant contribution is that the number of smartphones used is very high and dependent on internet service. However, m-commerce adoption is different depending on the country [32]. Concerning its degree of development, specific factors characterize the adoption of m-commerce. Its effect is also encouraged by the expansion of the electronic payment system increasingly preferred by users, and the lower level of restrictions [33] compared to e-commerce, which involves additional actions to access search browsers to perform commercial acts. Furthermore, M-commerce offers a new perspective both by increasing the level of accessibility and by eliminating time and location barriers [34], being characterized as “convenient and ubiquitous” [35].

Other sources [7] address the differences between e-commerce and mobile commerce from multiple perspectives, such as the technology used, consumer behavior, and the general framework of business development. The author also referred to the costs of the devices needed to practice a specific type of trade and accessibility level. The business environment has primarily adapted to the conditions of the mobile version, attracting potential customers by offering additional promotions or benefits such as vouchers, used only in a smartphone application, adjusted according to the users’ age [36].

The literature analyzes the complex factors that influence the degree of m-commerce adoption. Some authors [37] highlighted the increased role of electronic devices in everyday life and the buying process. All contribute to shaping a “digital economy”, a term often associated with phrases such as “internet economy”, “new economy” [38], or even “web economy” [39], the potential for theoretical and practical development supporting the idea of the need to amplify the contribution of artificial intelligence in the m-commerce sector to maximize the value of a business [35]. Other factors contributing to m-commerce development include expanding the Internet of Things and artificial intelligence, especially among younger generations. Both innovations can be incorporated to enhance users’ experience [40,41,42] and build their loyalty towards brands.

The reports published by Criteo [43] mention the “consumer of the future”, who, compared to the traditional one, distances himself from the latter through the level of formulated expectations, but also through the advanced digital knowledge he has, a higher level of sophistication, and the desire to have memorable shopping experiences [44].

In this context, in the future, electronic payments will also take a substantial boost as part of the m-commerce revolution. Moreover, the current context, in which COVID-19 has generated severe changes in individuals’ buying and consuming habits [26,36], is the beginning of a new digital revolution.

The trend of hyperconnectivity [43] will generate “a new dynamic in retail” as long as the internet is present in every moment of our lives, which will also contribute to changes in the storage of products, the design of promotional actions, and the adaptation of logistics and distribution channels. The data on the share of web traffic in Romania in December 2020, depending on the device used, indicate 54.5% was through mobile phones, 44.1% was achieved through laptops and desktops, and only 1.42% of web traffic was from tablets or other means of accessing the internet [17].

The issue of the security of m-commerce transactions arises [11], given that a large part of payments for products ordered online also take place through mobile applications. Nowadays, the online environment is vulnerable to hacking and other threats [45]. Chun [46] mentioned the problems that may arise in this regard concerning the risks posed by wireless networks, as well as the types of possible threats: “botnets, spyware, malicious applications, phishing, and social networking” [47] and the security level of a device and therefore of a transaction, depending on numerous factors, among which we can mention both the performance of a machine and the vigilance of the user. Nguyen and Khoa [48] argued that “businesses must publish their privacy policies on their e-commerce sites to ensure customer privacy.” The problem of the risk perceived by the users is significant. Consumers choose to buy only, considering various factors: “brand reputation or awareness, product quality, security, user-friendliness, price competitiveness, product diversity, and others” [49].

Various sources defined Generation Z (Gen Z) and provided different years for when the youngsters were born. Deloitte [50] mentioned the interval of 1995–2012, McKinsey et al. [51] 1995–2010, and the European Parliamentary Research Service [52] only mentioned the people born after 1995/1996 without an end year. Ernst and Young [53] referred to Gen Zers as those born between 1997 and 2003, whose loyalty towards retailers is not gained quickly. Criteo [54] mentioned the interval of 1994–2002 for Gen Zers.

Even if the gap for the years defining this generation varies slightly, there is a consensus on this generation’s characteristics. Gen Z (Gen Zers) youngsters appreciate diversity, are more individualistic, and analyze a job from the perspective offered to them financially and from the values associated with the company; they also prefer a more personalized style both in education and at the workplace [50]. Also named “identity nomads” or “digital natives” [51], Gen Z is characterized by individuality, being more present on social media. In addition, those from Gen Z value ethics, inclusiveness, dialogue, communication, curiosity, and consumption behavior and are more focused on ways to express their uniqueness.

Levickaitė [55] mentioned that this generation does not know any other way of living beyond the digital age. They are always connected online, shaping how they work, learn, communicate, and consume. Gaidani et al. [56] added features such as optimism towards the future and an inclination for “do-it-yourself” projects, this second feature being similar to the characteristic of individuality mentioned by other papers [50,51].

Priporas et al. [57] stated that retailers nowadays are challenged by Gen Zers, with different features and behaviors compared to other generations. The authors showed that Gen Z is more attracted to technology, innovation, and “fast transactions” [57]. This rapidity is specific to m-commerce more than to e-commerce.

Regarding the way they do shopping, McKinsey et al. [58] presented six types of Gen Zers: “brand-conscious followers”, “premium shopaholics”, “ethical confidants”, “value researchers”, “quality-conscious independents”, and “disengaged conformists”. These types influence how they buy the products and services they need to gather information before purchasing anything. They focus on ethical values, quality, the band value, and a good ratio between quality and price.

Lissita and Kol [59] analyzed the connection between m-commerce and each generation’s characteristics for buying “hedonic products”. The authors concluded that Gen Zers are more inclined to use their phones to purchase products, mentioning that companies should use the image of people considered nonconformists in their marketing campaigns because this generation will resonate more with them.

Gupta and Gulati [60] presented the psychological dimension behind the use of mobile apps by Gen Zers and showed how these psychological traits influence the type of mobile app. Thus, their research revealed that shyness behind the apps is related to mobile banking and payments, which are also part of m-commerce.

Ernst and Young [53] mentioned a few solutions that retailers should consider, because this generation expects different things than the previous ones and has a different mindset. For example, they usually buy products online and want them to be shipped to their homes without going to the store to see them. We can explain this by their constant presence online, especially on their smartphones. Moreover, mobile applications make it easier for them to buy and pay for the goods they want. Thus, retailers should pay attention to the needs of Gen Z and “make them part of the solution”, showing them “respect and loyalty before asking for it” [53].

Wood [61] mentioned that the way this generation buys things is strongly influenced by the convenience to which they have become accustomed. Thus, they expect to get the products they want faster, which they can do through m-commerce using their mobile devices. The author also repeated many times the words “ease” and “easy” related to Gen Zers and highlighted that even marketing campaigns targeting these youngsters use expressions like “just in time” or point to the easiness provided by the goods they advertise or the way they deliver them to Gen Z [61].

Criteo [54] analyzed Generation Z and their behavior related to commerce as being influenced by their main traits: connected and “empowered”, tech-savvy, “influential”, and “open”. In addition, Gen Z is “at the forefront of the m-commerce revolution” [54].

OC&C Strategy Consultants [62] highlighted that there are not many differences between the Gen Zers in various countries regarding how they behave and shop. It is essential to understand how shopping might be slightly influenced by a country’s culture or economic and technological development, but the behavioral patterns are mostly similar.

3. Research Methodology

3.1. Sample Selection and Variables

To research and evaluate Gen Z representatives’ preferences for m-commerce, we conducted research based on a questionnaire in February and March 2021 using Google Forms. The survey was sent by email and social media channels to 1500 youngsters between 16 and 25 years old from Romania’s West, North West, and Centre. Of them, 771 returned their answers.

The sample was established using the non-probabilistic method, more precisely the snowball method, without pre-establishing numerical limitations on the level of representativeness of specific demographic characteristics such as gender, income, or place of residence. However, the applied questionnaire was addressed to a single category of respondents, namely, Generation Z representatives, which leads us to mention the sampling’s targeted nature.

The variables taken into account in our research were the following: gathering information on products and services (using smartphones, tablets, laptops, desktop computers), placing orders (using smartphones, tablets, laptops, desktop computers), the payment method for online purchases, the factors strongly influencing the use of mobile applications, and the perception regarding the purchase of products and services in the future. The variables “payment method” and “perception regarding future acquisitions” had predefined answer options. Using a Likert scale, we built the other variables with 1 (minimum value) to 5 (maximum value).

We added the demo-socio-economic variables that characterized the research sample (gender, occupation, studies, income, environment) to these variables. These variables were used to structure the sample and analyze their influences on some perceptions or inclinations to consumption.

Descriptive statistics for the variables used in the research, collected through the survey applied to Gen Z representatives, are presented in

Table 1. Descriptive statistics.

Variable	N	Minimum	Maximum	Mean	Std. Deviation	Skewness	Kurtosis
Gender	771	1	2	1.74	0.438	−1.108	−0.775
Occupation	771	1	6	3.53	0.776	−1.036	0.690
Studies	771	1	4	2.25	1.069	−0.127	−1.567
Income	771	1	5	2.08	1.369	1.038	−0.260
Environment	771	1	2	1.36	0.479	0.605	−1.638
Smartphone_information	771	1	5	4.52	0.845	−1.970	3.869
Tablet_information	771	1	5	1.56	1.095	1.953	2.727
Laptop_information	771	1	5	3.70	1.297	−0.705	−0.568
Computer_information	771	1	5	1.99	1.432	1.124	−0.283
Smartphone_order	771	1	5	4.33	1.091	−1.704	2.128
Tablet_order	771	1	5	1.45	1.044	2.339	4.320
Laptop_order	771	1	5	3.32	1.575	−0.340	−1.418
Computer_order	771	1	5	1.88	1.421	1.307	0.125
Payment_orders	771	1	5	1.80	1.402	1.349	0.060
Factor_1	771	1	5	4.18	1.238	−1.431	0.923
Factor_2	771	1	5	3.74	1.257	−0.707	−0.500
Factor_3	771	1	5	4.15	1.168	−1.302	0.756
Factor_4	771	1	5	3.99	1.209	−1.063	0.149
Factor_5	771	1	5	3.96	1.250	−1.001	−0.062
Factor_6	771	1	5	3.87	1.272	−0.892	−0.282
Factor_7	771	1	5	3.89	1.338	−0.950	−0.342
Future_acquisitions	771	1	3	2.43	0.637	−0.660	−0.553
Valid N (listwise)	771

Source: developed by authors using SPSS.

.

3.2. Hypotheses and Methods

To analyze the preferences of youngsters in Gen Z for m-commerce, we formulated, starting from the literature review and using inductive character methods, four hypotheses tested in the research (

Table 2. Factors influencing the decision to use mobile applications for commerce.

Code	Rank 1 Hypotheses	Rank 2 Hypotheses
H.1.	Generation Z uses smartphones to gather information on products and services and order them.
H.2.	The ease of using applications, getting information or relevant presentations regarding the products, and the customization possibility are the factors that strongly influence the use of m-commerce applications.
H.3.	The demo-socio-economic variables strongly influence the inclination of Gen Z individuals towards using mobile applications.
H.3.1.		The variables “gender”, “studies”, and “environment” strongly influence Gen Z’s inclination towards using mobile applications for gathering information on products and services.
H.3.2		The variables “gender” and “environment” strongly influence Gen Z individuals’ inclination towards using mobile applications for placing orders to buy products and services.
H.3.3		The variables “studies”, “environment”, and “income” strongly influence the inclination towards online commerce and very strongly influence the use of mobile applications for purchases regarding the payment for products and services.
H.4.	“Occupation” and “studies” influence the higher optimism regarding mobile applications in the future.

).

We used descriptive and inferential statistics to validate the hypotheses, such as the ANN analysis used in other economic papers [63]. The validation process of the research hypotheses can be helpful to companies in the retail sector.

We used descriptive statistics to research the H.1. hypothesis, such as analyzing the frequency and mean variables. We used ANN analysis to investigate hypotheses H.2. and H.4. This analysis involves getting output variables from input variables by creating a linear combination depending on the input variables’ values, whose results are then processed using a nonlinear activation function. A hidden layer between the two formed layers (input and output) influences input and output variables through some linear functions. The mathematical formula of the most known type of ANN analysis (multilayer perceptron—MLP) is the following:

$$y=({{\displaystyle \sum }}_{i=1}^n{w}_i{x}_i+b)=\phi (W^{T}X+b)$$

(1)

where

• w—vector of weights;
• x—vector of inputs;
• b—bias;
• φ—activation function.

For MLP activation functions, we chose the sigmoid function (Formula (2)). Therefore, the formula that defines the sigmoid function formula is:

$$f\left(n\right)=\frac{1}{1+e^{-n}}=\frac{e^n}{e^n+1}$$

(2)

where

• n—input variable;
• e—Euler’s number;
• f(n)—output variable.

To research the H.3 hypothesis, we used multivariate analysis of variance (MANOVA), which identifies the intensity and direction of independent variables’ influences on the dependent variables.

The research flowchart is summarized in Figure 1.

4. Results

The first hypothesis targeted e-commerce in general, identifying the ways and the devices used by Gen Z individuals to inform themselves about products and services and order them. To validate the hypothesis, we considered the mean of the variables: gathering information on the products and services (using smartphones, tablets, laptops, desktop computers) and placing orders (using smartphones, tablets, laptops, desktop computers). As shown in Table 1, the mean of the variables “gathering information on the products and services” and “placing orders” was highest for smartphones (4.52 for information and 4.33 for ordering). From the other devices used for information and placing orders, laptops were in the second position, followed at a great distance by desktop computers and tablets. The frequencies for the two variables are illustrated as a percentage in Figure 2.

Analyzing the mean (Table 1) and the frequencies illustrated as a percentage (Figure 1), we noticed that Generation Z individuals mostly use smartphones to gather information on products and services and order them, validating the H.1 hypothesis.

Starting from the youngsters’ preference for using smartphones and m-commerce applications, we formulated a second hypothesis that targets the factors strongly influencing mobile applications used by Generation Z individuals.

Table 3. Factors influencing the decision to use mobile applications for commerce.

Variable		Minimum	Maximum	Mean
Factor_1	The ease of using applications	1	5	4.18
Factor_2	The user-friendly nature of the interface used	1	5	3.74
Factor_3	Offering images/relevant presentations regarding the products	1	5	4.15
Factor_4	The possibility to save individual preferences	1	5	3.99
Factor_5	The possibility to close the application and resume the transaction later	1	5	3.96
Factor_6	Online support/prompt feedback	1	5	3.87
Factor_7	I have all functions in one place (the application of the store and the bank on the same device)	1	5	3.89

Note: (1—Not at all important, 5—Very important). Source: developed by authors using SPSS v.20.

shows the factors taken into consideration resulting from the literature review.

We conducted an ANN analysis to determine the factors’ influence on the variables regarding “information” and “placing orders” using mobile applications on smartphones. It allowed for the identification of a hidden layer (the inclination to consumption of the individuals in the sample), influenced by input variables (the factors) and influencing output variables (“information” and “placing orders” using mobile applications on the smartphone). The activation function of the hidden and exit layers was sigmoid. The general error resulting after testing the model was 0.899. Figure 3 illustrates the synaptic relations established between layers, taking into account external influences through biases.

The method for data rescaling for dependent and independent variables was normalization.

Table 4. The predictors of the MLP model.

Predictor		Predicted
		Hidden Layer 1	Output Layer
		H(1:1)	Smartphone_Information	Smartphone_Order	Importance	Normalized Importance
Input Layer	(Bias)	−2.351
	Factor_1	0.939			0.217	75.2%
	Factor_2	−0.080			0.015	5.2%
	Factor_3	0.939			0.216	74.7%
	Factor_4	1.216			0.289	100.0%
	Factor_5	0.440			0.090	31.2%
	Factor_6	0.328			0.065	22.6%
	Factor_7	0.524			0.108	37.5%
Hidden Layer 1	(Bias)		0.517	−0.303
	H(1:1)		2.221	2.809

Source: developed by authors using SPSS v.20.

offers information regarding the influences’ intensity and direction.

The influences show that the main new factors of influence on the decision to use m-commerce applications were the possibility to save individual preferences, the ease of using the application, and offering images/relevant presentations regarding the products. The other factors influenced the decision to use m-commerce applications only a little. A minor influencing factor was the interface’s user-friendly nature, considering that most respondents had a smartphone and were used to accessing mobile applications for various activities.

The research revealed that the ease of using the applications, getting images or relevant presentations regarding the products, and the customization possibility were the factors that strongly influenced the use of mobile applications, which validates the H.2 hypothesis.

The third research hypothesis analyzed demo-socio-economic variables’ influences on Generation Z individuals’ inclination towards m-commerce applications. To test the relations between the demo-socio-economic variables on Generation Z individuals’ tendency towards m-commerce applications, we used MANOVA analysis (multivariate analysis of variance). The model defines the information on products and services using smartphones, placing orders using smartphones, and the payment method for online purchases as dependent variables, and the demo-socio-economic variables are independent.

Table 5. MANOVA tests for determining the influences of demo-socio-economic variables on using mobile applications on smartphones.

Effect		Value	F	Significance	Partial Eta Squared
Intercept	Pillai’s trace	0.305	111.418	0.000	0.305
	Wilks’ lambda	0.695	111.418	0.000	0.305
	Hotelling’s trace	0.438	111.418	0.000	0.305
	Roy’s largest root	0.438	111.418	0.000	0.305
Gender	Pillai’s trace	0.050	13.344	0.000	0.050
	Wilks’ lambda	0.950	13.344	0.000	0.050
	Hotelling’s trace	0.052	13.344	0.000	0.050
	Roy’s largest root	0.052	13.344	0.000	0.050
Occupation	Pillai’s trace	0.006	1.513	0.210	0.006
	Wilks’ lambda	0.994	1.513	0.210	0.006
	Hotelling’s trace	0.006	1.513	0.210	0.006
	Roy’s largest root	0.006	1.513	0.210	0.006
Studies	Pillai’s trace	0.006	1.547	0.201	0.006
	Wilks’ lambda	0.994	1.547	0.201	0.006
	Hotelling’s trace	0.006	1.547	0.201	0.006
	Roy’s largest root	0.006	1.547	0.201	0.006
Income	Pillai’s trace	0.044	11.604	0.000	0.044
	Wilks’ lambda	0.956	11.604	0.000	0.044
	Hotelling’s trace	0.046	11.604	0.000	0.044
	Roy’s largest root	0.046	11.604	0.000	0.044
Environment	Pillai’s trace	0.010	2.510	0.058	0.010
	Wilks’ lambda	0.990	2.510	0.058	0.010
	Hotelling’s trace	0.010	2.510	0.058	0.010
	Roy’s largest root	0.010	2.510	0.058	0.010

Source: developed by authors using SPSS v.20.

presents the results of multivariate tests specific to MANOVA.

Table 5 shows a significant influence of exogenous factors for the model (represented by intercept). The influences of endogenous factors in the model (demo-socio-economic variables) were more reduced than those of the exogenous factors but significant in two cases (gender and income). Therefore, we can state that the H3 hypothesis is partially validated based on these test results.

Table 6. The parameters of the MANOVA model to determine the demo-socio-economic variables’ influences on using mobile applications on smartphones.

Dependent Variable	Parameter	B	Standard Error	t	Significance	Partial Eta Squared
Smartphone information	Intercept	3.683	0.205	17.950	0.000	0.296
	Gender	0.371	0.069	5.377	0.000	0.036
	Occupation	0.050	0.042	1.199	0.231	0.002
	Studies	0.033	0.030	1.107	0.269	0.002
	Income	−0.018	0.023	−0.766	0.444	0.001
	Environment	−0.014	0.064	−0.224	0.823	0.000
Smartphone order	Intercept	3.325	0.265	12.528	0.000	0.170
	Gender	0.474	0.089	5.313	0.000	0.036
	Occupation	0.053	0.054	0.979	0.328	0.001
	Studies	−0.024	0.039	−0.619	0.536	0.001
	Income	−0.001	0.030	−0.029	0.977	0.000
	Environment	0.035	0.083	0.418	0.676	0.000
Payment orders	Intercept	1.595	0.336	4.750	0.000	0.029
	Gender	−0.230	0.113	−2.039	0.042	0.005
	Occupation	0.123	0.068	1.805	0.071	0.004
	Studies	0.047	0.049	0.960	0.337	0.001
	Income	0.215	0.038	5.640	0.000	0.040
	Environment	−0.285	0.105	−2.711	0.007	0.010

Source: developed by authors using SPSS v.20.

presents the MANOVA model’s parameters, showing the effects of independent variables (demo-socio-economic) on the three dependent variables.

Analyzing the parameters, we noticed that for gathering information on products and services and ordering smartphones, the variable “gender” influenced these output variables significantly. The positive influence showed that more men use their smartphones to gather information on products and services and order them. Regarding the payment method chosen for online purchases, both gender and income were the factors that influenced this variable. Most pay cash on delivery due to the lack of trust in the payment services offered by online retailers. On the other hand, most of those who used applications or banking cards as payment methods were women with a higher income. After analyzing the MANOVA model’s parameters, we can conclude that the secondary hypotheses of H.3.1, H.3.2, and H.3.3 are partially validated.

To analyze Generation Z individuals’ perspectives regarding the future of online purchases, we formulated a fourth hypothesis that involved researching the influences of demo-socio-economic variables on their perception. Thus, we conducted an ANN analysis. It allowed for the identification of a hidden layer (the optimism of the individuals in the sample regarding online commerce), influenced by the input variables (demo-socio-economic variables) and also influencing the output variables (the future of online purchases). The activation function of hidden and exit layers was sigmoid. The general error resulting after testing the model was 0.988. Figure 4 illustrates the synaptic relations between layers, considering the external influences through biases.

The method for data rescaling for dependent and independent variables was normalization.

Table 7. The predictors of the multilayer perceptron model (MLP).

		Hidden Layer 1	Output Layer	Importance	Normalized Importance
		H (1:1)	Future Acquisitions
Input Layer	(Bias)	0.469
	Gender	−0.438		0.212	35.2%
	Occupation	0.089		0.044	7.3%
	Studies	0.073		0.036	6.0%
	Income	1.350		0.601	100.0%
	Environment	−0.212		0.107	17.8%
Hidden Layer 1	(Bias)		0.151
	H (1:1)		1.131

Source: own construction using SPSS v.20.

offers information regarding the intensity and direction of the influences.

After analyzing the influences, it can be stated that gender and especially income were the demo-socio-economic variables that influenced the perception of Generation Z individuals regarding the way online commerce will be made in the future. The other factors influenced this perception to a lesser extent. The men in the sample and those with a higher income were more optimistic about the online commerce expansion. Therefore, the H.4 hypothesis is partially validated based on the ANN analysis results.

5. Discussion

To assess the adoption of mobile technology for commerce by Generation Z, we used a methodology that uses a mix of descriptive and inferential statistics (including ANN and MANOVA). In the literature, other researchers used various methods to investigate the adoption of m-commerce, but one can see a predilection for structural equation modeling.

Table 8. Statistical methods used for assessing the adoption of mobile technology and their advantages.

	Structural Equation Modeling (SEM)	MANOVA	Artificial Neural Network (ANN)	Descriptive Statistics
Advantages	Assessment of measurement error	Analyzes the relationship between several response variables	Can be applied to complex nonlinear problems	High degree of objectivity and neutrality
	Estimation of latent variables	Increases chance of finding an effect	Can be used for problems with the target function	Broader picture of an event or phenomenon
	A structure can be imposed and assessed	Analyze differences across two or more variables	Accuracy ratio with a smaller dataset	Flexibility to use both quantitative and qualitative data

Source: developed by authors based on [64].

summarizes the methods used and their advantages.

Although the structural equation modeling method provides information on the relationships between the variables studied, it does not consider the associations that can be made between sociodemographic variables and variables that describe the adoption of m-commerce. The mix of descriptive and inferential statistics (including ANN and MANOVA) allows one to investigate these associations in more detail. In addition, ANN has the advantage of being applicable to complex nonlinear problems.

In our empirical study, a first hypothesis aimed at using smartphones to gather information on products and services and order them. Following the research of this hypothesis, we noticed that Generation Z individuals mostly use smartphones to gather information on products and services and collect them. The studies developed at the European Union level by Eurostat [65] and PwC [66] supported and confirmed this hypothesis. In 2019, “94% of young people in the EU-27 made daily use of the internet” [65], with smartphones used at a very high level, and 92% of young people using “mobile phones to access the internet away from home or work, compared to 52% who used a portable computer in this way” [65].

Thus, Generation Z becomes an important decision-maker on how trade will progress, with individuals being users of technology, focusing on online shopping and high accessibility, and smartphones being “the most used device” [67]. Other studies also confirm this hypothesis [54,68,69,70].

However, we can see that this is a relatively recent trend in Romania, as previous works [71] identified a lower level of smartphones for mobile commerce than social media services [72], which demonstrates a reorientation of consumers regarding the usefulness of this tool.

The second hypothesis studied the factors strongly influencing mobile applications used by Generation Z individuals. The research revealed that the ease of using the applications, getting images or relevant presentations regarding the products, and the customization possibility are the factors that strongly influence the use of mobile applications. Sources from the literature partially support this hypothesis. However, in addition to the factors identified by us, we also propose the convenience of navigation [73], enjoyment [74], and, respectively, interactivity [75]. Furthermore, developments in mobile applications greatly influence consumer behavior, considering their purpose and usefulness, an essential contribution being represented by the users’ attitude towards m-commerce and its perceived benefits [76]. Finally, Nkulenu [77] emphasized the importance of the security criterion in the context in which mobile applications are based on wireless connections or mobile data. A paradox encountered in this regard is that online vulnerability is not perceived by the users or is ignored to obtain the desired product.

The third hypothesis investigated the influence of the demo-socio-economic variables on the inclination of Gen Z individuals towards using mobile applications; the hypothesis is partially validated. The role played by exogenous and endogenous variables in determining the attitude of Generation Z representatives towards mobile commerce has been studied by other authors. Following the research, the authors confirmed the hypothesis that the gender of users and income levels are substantial influencing factors for the use of mobile applications. They also mention the residential, urban, or rural environment as additional factors [78]. The gender criterion was also addressed by Wei et al. [79], who mentioned the differences between women’s and men’s behavior regarding receptivity to mobile applications for information search, order placement, and payment. This aspect was developed by other authors [80,81,82,83,84], who indicated some divergences, depending on the samples of respondents analyzed.

Optimism regarding mobile applications in the future and the influence of occupation and studies was the subject of the fourth hypothesis, also partially validated. The income criterion was one of the predominant factors in the analyses carried out by some researchers. Thus, Yildiz [85] indicated differences between consumers, even regarding income groups. Similar differences were identified depending on the level of education. Income was considered one of the most significant factors involved in the purchasing process, which, in turn, contributes to shaping a pattern of consumers [86].

Dependence on mobile devices was studied by other sources [87,88], and there was a high level of optimism about the development and usefulness of mobile applications in the future. However, the way education and respondents’ occupation influence the degree of optimism regarding this aspect is less present in the relevant literature and can serve as a starting point for future studies.

6. Conclusions

The heavy presence on mobile devices and the fact that Generation Z values their friends and known influencers when they decide to buy something is of utmost importance for retailers in the digital era. Therefore, retailers should provide a great experience to the buyers in this generation, create apps for their online platforms, allow Gen Zers to express themselves when buying, and personalize the shopping experience.

6.1. Theoretical Implication

Through the empirical research conducted by applying a survey on a sample of 771 individuals from Generation Z, a series of conclusions can help online retailers establish segmentation and marketing strategies in the future. Youngsters in Generation Z prefer online commerce, using their smartphones to gather information on products and services and order them, and they are optimistic about this business model.

6.2. Practical Implication

The main factors influencing m-commerce application use are the possibility of saving individual preferences and the ease of using the application, offering images or relevant presentations regarding the products and services. Concerning the influences of demo-socio-economic variables on Generation Z individuals’ inclination to use m-commerce applications, the models’ endogenous factors (the demo-socio-economic variables) were more reduced than those of the exogenous factors but significant in two cases (gender and income).

For information on the products and services, and for placing orders using smartphones, gender is the variable that most significantly influenced these output variables. The positive influence shows that more men use smartphones to gather information on products and services and order them, choosing this payment method for online purchases; both gender and income influenced this variable. On the other hand, most of those who use mobile applications or banking cards as payment methods for online purchases were women with a higher income. Analyzing the optimism regarding the expansion of m-commerce among Generation Z individuals, we noticed that gender and especially income were the demo-socio-economic variables most influencing Generation Z’s perception of how online commerce will be conducted. Most respondents believed that online commerce will prevail in the end (50.7%), and among them, men with a higher income were more optimistic.

6.3. Limitations and Further Research

The research on Generation Z is transversal, offering a static image when the research on the phenomenon is analyzed. This limitation of the study can be surpassed in future studies by analyzing Generation Z individuals’ perceptions by applying the same survey in the following years. Another limit resides in the fact that we conducted qualitative research, and the sample is not representative of a particular region or the entire country. Nevertheless, the research results are still valuable because they reflect Generation Z individuals’ perception of m-commerce, which is thus beneficial for online retailers.