Influencing Factors of Behavioral Intention to Use Cloud Technologies in Small–Medium Enterprises

Abstract

As small–medium-sized enterprises (SMEs) increasingly adopt cloud technologies, understanding the factors influencing this shift is crucial as it helps to optimize cloud integration strategies, enabling SMEs to thrive in today’s digital economy. A cross-sectional, quantitative survey was conducted in February 2022 on 626 employees of SMEs in the USA, based on the TAM-2, TAM-3, and UTAUT-2 models. The questionnaire presented satisfactory reliability, as well as factorial and convergent validity. Employees presented positive behavioral intentions to use cloud technologies, particularly during the COVID-19 period. SMEs were satisfied with the use of Software as a Service (SaaS), Infrastructure as a Service (IaaS), and the public cloud development model in the wake of the COVID-19 period. Behavioral intention to use cloud technologies was linked with higher performance and effort expectancy, price, perceived enjoyment, computer self-efficacy, and social influence. A higher behavioral intention was observed in employees (a) with a mid–top-level role; (b) who worked in finance and insurance, information services data, construction, or software and in an SME with 26–500 employees; (c) who had a master’s degree; (d) were 35–44 years old; and (e) had family obligations. Higher experience with the use of cloud technologies enhanced the positive impacts of effort expectancy, computer self-efficacy, and perceived enjoyment on behavioral intention.

1. Introduction

1.1. Theoretical Background

Cloud computing plays a pivotal role in modernizing SMEs, providing access to scalable, cost-effective technology that was previously limited to larger organizations; its significance is underscored by enhanced productivity and operational flexibility, which are critical for competitiveness in dynamic markets [1,2]. This accessibility supports SMEs’ growth in a technology-driven economy.

The use of cloud computing by SMEs is essential for their survival and adaptation to evolving market demands. Digital transformation, facilitated by cloud technologies, enables SMEs to reduce costs, improve efficiency, and maintain business continuity, particularly in the context of crises such as the COVID-19 pandemic [3,4].

Cloud computing’s practical value for SMEs lies in its ability to reduce IT costs, streamline operations, and enhance remote work capabilities. SMEs benefit from increased efficiency, faster task execution, and improved collaboration tools, making cloud technology a vital resource for everyday business [5,6]. Cloud computing has revolutionized the technology landscape for small and medium enterprises (SMEs) by making immense computational resources available to them without the excessive capital investment that has traditionally been involved in acquiring such capacities [1]. A recent study by Bello et al. [5] found that usage of the cloud has had a profound effect on SMEs in the USA, due to cases that make operations easier and data management more robust while enabling flexibility tailored for those companies with fewer resources. However, as the use of cloud computing is becoming more widespread, it is also important to continue to identify what drivers influence SMEs when they decide to use (or reject a particular technology [7].

Behavioral intention is a core construct in technology acceptance models and represents the motivational factors that influence a user’s decision to use cloud computing solutions; it is widely regarded as the most immediate predictor of actual system use and is shaped by attitudes, social pressure, and perceived control over the behavior [8]. In the context of TAM- and UTAUT-related frameworks, behavioral intention has been consistently associated with variables such as perceived usefulness, perceived ease of use, effort expectancy, and social influence [9,10,11]. For SMEs, analyzing behavioral intention is particularly relevant, as it reflects employees’ readiness to use digital tools under constraints such as limited IT infrastructure, training, and managerial support. Therefore, understanding and measuring behavioral intention provides critical insight into how cloud technologies can be more effectively deployed within these enterprises.

The COVID-19 pandemic highlighted a novel driver of cloud use, as SMEs relied on cloud technologies to ensure continuity during unprecedented disruptions. This shift emphasizes cloud computing’s resilience in crisis management, marking a unique opportunity for research on technology-driven recovery and adaptation strategies [12,13]. Cloud use was widespread during the global COVID-19 crisis to provide digital continuity solutions for SMEs amid widespread disruption [12]. It was at this time that cloud computing services witnessed the highest surge with respect to Software as a Service (SaaS) and other Infrastructure as a Service models among small enterprises, who are increasingly inclined towards scalable and inexpensive solutions [3]. A survey conducted during the COVID-19 crisis that included small- and medium-sized enterprises stated that they used cloud services since that time, with most expressing higher behavioral intentions for future use [14]. The conjunction of wider knowledge of cloud computing capability and business value, such as cost savings, streamlined communications, and flexibility, seems to be the explanation for this adversarial increase [3]. The pandemic caused small- and medium-sized enterprises to adapt, which was reflected in a slight growth of public cloud model usage, achieving reinforcement for behavioral intention even afterwards [4]. The pandemic further underscored a number of challenges, including data security, application integration, and functionality demands, which, in turn, enhanced the use of cloud computing. At the same time, SMEs in the USA considered these challenges to be solvable, albeit their behavioral intentions towards cloud computing were positively influenced by its benefits [15]. Research shows that while there are some challenges, the barriers to cloud use faced by SMEs in fact appear too modest, as they call for exactly what cloud technologies deliver quickly and effectively: maneuverability and elasticity during times of high disruption [4].

The advancements led to the reconsideration of what determines employees’ intentions with respect to cloud computing, while taking into account how various job roles, organizational positions, and individual competencies can affect those intentions [16]. Therefore, SMEs saw cloud computing as a new technology that could be used to make other technologies more robust and flexible, thereby enabling sustainable digital transformation [13]. Indeed, the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) continuously stress that perceived usefulness and ease of use are important predictors of technology usage [17,18]. Hence, according to Stieninger et al. [19], the major reasons for employees of small and medium enterprises to use cloud services include perceived ease of use and technology leverage. The role of social influence and perceived value, as Ogunde et al. [7] suggest, plays a crucial part in shaping behavioral intentions, whilst peer validation and benefit perception are the main factors that many employees rely upon to make a use decision. Furthermore, computer self-efficacy is seen to be the major determinant for the behavioral intention to use cloud technology in SMEs. An example of this is self-efficacy, meaning the belief that “I have what it takes to use (technologies) effectively,” which is a strong predictor of an individual’s intention to use cloud solutions when studying the work context, where technology-related training resources may not always exist in abundance. More specifically, higher levels of self-efficacy are frequently associated with greater cloud readiness, alongside organizational endorsement and peer effects [20].

While the TAM and UTAUT-2 both aim to explain behavioral intention to use technology, they rely on distinct assumptions and emphasize different aspects of user behavior. The TAM focuses on perceived usefulness and ease of use as primary drivers, which are influenced by factors such as computer self-efficacy, experience, and perceived enjoyment, offering a simplified and well-validated model for predicting behavioral intention. UTAUT-2, on the other hand, introduces constructs such as social influence, price value, effort, and performance expectancy, offering a more comprehensive perspective, particularly for consumer-oriented and organizational contexts. Combining both models provides complementary strengths: the TAM offers a parsimonious structure that is ideal for baseline measurement, while UTAUT-2 allows for contextual flexibility and broader behavioral explanations. Studies have shown that hybrid models combining the TAM and UTAUT-2 result in greater explanatory power across various sectors, including SMEs and e-learning environments [21,22]. Therefore, the integration in this study is not only additive but also strategic, aiming to capture both the cognitive evaluation process from the TAM and the social and experiential dynamics from UTAUT-2. This offers a more holistic framework for understanding the intention to use cloud technology in SMEs during post-pandemic digital transformation.

With regard to the use of the cloud, more experienced employees have been associated with stronger intentions for using and integrating different types of cloud solutions in their work settings. Computer self-efficacy is influenced by computer experience, which can be divided into basic and advanced levels [23]. Lin (2011) [24] found that experience moderates the relationships of key determinants, such as the way in which the user behaves behind a screen. Perceived enjoyment, similarly, is a powerful direct contributor in predicting favorable behavioral intentions, showing that the more an employee perceives cloud technologies to be fun and interesting or enjoyable and useful, the more likely they will be to use them [25]. Flow experience, perceived enjoyment, and interaction affect the intention of a person to use a computer [26]. Previous studies have highlighted the direct effect of effort expectancy on behavioral intention [18,27,28]. Moreover, experience positively moderates the effects of effort expectancy on the behavioral intention to use cloud technology by experienced employees [29]. Longer service experience strengthens the positive effect of effort expectancy on behavioral intention [30].

Behavioral intention to use cloud technology among SME employees is further influenced by factors such as career position and organizational role. The labor force in industries such as finance, insurance, software, and construction has displayed more pronounced behavioral intentions in this respect, perhaps due to their higher exposure to technology [31]. Occupations that closely interface with cloud-supporting equipment, such as building information modeling (BIM) and the Internet of Things, show higher behavioral intention because their skills match up directly. It is suggested in this study that the higher behavioral intentions found among mid-to-top management employees are due to some form of organizational support and readiness for technological change [15].

Moreover, demographic factors such as education and family responsibility impart a great influence on the behavioral intention to use cloud technology. Employees with postgraduate degrees demonstrate more positive attitudes towards the use of cloud computing, due to their tendency toward proactivity in the use of technology [32]. Moreover, analyses of family dynamics are interestingly showing that people who get married presently tend to gravitate towards technologies that improve efficiency. For example, it is possible to have an improvised behavioral intention due to their connection and interest in superior management [33].

The present study could be viewed as contributing to the advancement of UTAUT-2 by empirically validating its application across a wide range of organizational roles and demographic profiles within SMEs—a dimension that is often underexplored. For instance, Khayer et al. (2020) [34] demonstrated how UTAUT-2 can be effectively merged with the technology–organization–environment (TOE) framework to capture organizational agility and firm performance effects beyond individual-level behavioral intentions. Moreover, this study enriches the UTAUT-2 literature by exploring how the COVID-19 pandemic effectively accelerated the need for cloud use. Few studies have empirically investigated the temporal dynamics of such external shocks in the context of cloud technology intention to use among SMEs.

In conclusion, if SMEs understand the factors affecting the behavioral intention to use cloud computing, they can better strategize technology integration for sustainable growth and resilience in this era of digitalization. Although several studies have examined the factors that affect the behavioral intention to use cloud technologies, very few have examined an integrated model of the behavioral intention to use cloud computing technologies in SMEs in the USA.

1.2. Aim of This Study

The aim of the present study was to examine (a) the attitudes of employees in SMEs in the USA towards the use of cloud computing technologies, (b) the role of the COVID-19 period with respect to the use of cloud computing technologies by SMEs in the USA, (c) the factors that affect the behavioral intentions of employees in SMEs in the USA to use cloud computing technologies, and (d) the role of experience as a moderator to enhance the behavioral intention of employees in SMEs in the USA to use cloud computing technologies. This study contributes to the advancement of the technology acceptance literature by empirically investigating the moderating role of user experience in established models such as TAM-3 and UTAUT-2. While these frameworks have been widely applied, few studies have specifically examined how experience influences the strength of predictors such as effort expectancy, computer self-efficacy, and perceived enjoyment, particularly in the context of SME cloud behavioral intention to use. Prior research has highlighted that experience significantly moderates intention-related relationships in other domains, such as e-learning and digital government [24,30]. By focusing on this interaction, the present study provides a novel analytical perspective that enhances the explanatory depth of the combined TAM–UTAUT framework and offers practical insights for tailoring cloud technology strategies based on user experience levels.

2. Methodology

2.1. Research Design

A primary, cross-sectional, quantitative, correlative study on employees of SMEs in the USA was carried out to examine their views with regard to the use of cloud computing technologies in a specific time period [35]. A quantitative approach was considered to be appropriate, as the variables of this study were measurable [36], and relationships between dependent and independent variables were examined [37]. According to the TAM-2 [38], TAM-3 [39], and UTAUT-2 models [11], the dependent variable of this study is behavioral intention, while the independent variables are (a) factors of the TAM-3 and UTAUT-2 models, (b) the use of cloud technologies, (c) job characteristics, and (d) the demographic characteristics of the employees. In addition, the role of “Experience” as a moderator in the relationship between behavioral intention and factors of the TAM-3 and UTAUT-2 models was examined as depicted in Figure 1.

In order to strengthen the theoretical foundations of this study and contextualize its originality, it is crucial to review how TAM- and UTAUT-based models have been applied in similar contexts, especially in studies focusing on small and medium-sized enterprises (SMEs) and the behavioral intention to use cloud computing.

A comparative study by Anderson and Schwager (2004) [40] on the adoption of wireless LANs in SMEs demonstrated that the UTAUT outperformed the TAM in explaining behavioral intention, due to its ability to incorporate organizational factors and user diversity. Similarly, Khayer et al. (2020) [34] integrated the UTAUT with the technology–organization–environment (TOE) framework to explain the intention to use cloud computing by SMEs in developing countries, emphasizing the roles of trust, absorptive capacity, and data privacy in addition to the UTAUT’s core constructs.

Furthermore, Athambawa et al. (2022) [41] conducted a hybrid TAM-3 and UTAUT-2 analysis of Sri Lankan SMEs to create a secure cloud use model (SCAM). Their study highlights how blending models can increase predictive accuracy, particularly when security and trust play a prominent role in technology decisions.

Comparative findings by Tella et al. (2020) [42] from academic libraries in Nigeria reinforce the value of combining the TAM and UTAUT. They found that integrating constructs from both models—such as perceived security, reliability, and flexibility—yielded 73% explanatory power regarding the behavioral intention to use cloud-based services, suggesting that neither model alone is sufficient in complex institutional environments [42].

In the field of e-government procurement, Soong et al. (2020) [43] applied a dual framework using the TAM and UTAUT to analyze Malaysian SMEs’ behavioral intention to use. The authors confirmed that both models’ core constructs significantly influenced behavioral intentions, and they suggested that policy strategies should account for the overlapping social and technological factors identified by each model [43].

This body of literature reveals several patterns: first, that hybrid models generally outperform single-model frameworks in terms of explanatory power; second, that model effectiveness varies significantly depending on the context (e.g., public vs. private sector and developing vs. developed economy); and third, that UTAUT-2′s extended constructs are particularly relevant for cloud computing environments.

Therefore, the present study’s use of the TAM and UTAUT-2 is not only methodologically grounded but also responds to a growing consensus in the literature regarding the value of integrative theoretical models. This approach is especially valuable in the context of SMEs undergoing post-pandemic digital transformation, where behavioral intention is influenced not only by perceptions of usefulness and ease but also by social norms, external conditions, and economic constraints.

2.2. Questionnaire

As detailed on Appendix A, the demographic characteristics of age, marital status, number of children, education, and region of residence were examined, along with job characteristics such as career and organizational role, annual income, years in current position, and the number of employees in the enterprise. The use of cloud technologies was examined via 8 questions regarding the cloud development and services models, employees in the SMEs before and after the COVID-19 period, the main cloud technologies used in the enterprise, the role of the COVID-19 period in the use of cloud technologies, the primary reasons to use specific service models, and the challenges faced in using cloud technologies during the COVID-19 pandemic.

A combination of the TAM-2 [38] and TAM-3 [39] models was examined using 11 Likert-type questions on a scale from 1 to 7 (1 = disagree strongly, 2 = disagree, 3 = somewhat disagree, 4 = undecided/neutral, 5 = somewhat agree, 6 = agree, 7 = agree strongly), evaluating 3 factors with satisfactory reliability (a > 0.7) according to the Cronbach’s alpha coefficient [44]:

• “Computer self-efficacy” (e.g., “I would complete a job using Cloud technologies if there was no one around to tell me what to do as I go”) (a = 0.705).
• “Experience” (e.g., “I can distinct between IaaS, PaaS and SaaS”) (a = 0.706)
• “Perceived enjoyment” (e.g., “I have fun using Cloud technologies”) (a = 0.701).

These 3 factors were selected because there were identified as the most important significant positive predictors of “Intention to use” in a recent previous study [45]. Although “Subjective norm” (beta = 0.427), “Perceived ease of use” (beta = 0.206), and “Perceived usefulness” (beta = 0.205) were significant predictors of “Intention to use”, they were excluded from the current study, due to overlap reasons, because they are similar to the factors “Social influence”, “Effort expectancy”, and “Performance expectancy” of the UTAUT-2 model (Rev4. Comments 1, 2).

Construct validity was confirmed via confirmatory factor analysis [46], with the factor loadings to be above 0.5 [47] for (a) “Computer self-efficacy” [0.616, 0.754], (b) “Experience” [0.518, 0.757], and (c) “Perceived enjoyment” [0.648, 0.773]. Convergent validity was also confirmed for (a) “Computer self-efficacy” (53.20%), (b) “Experience” (53.30%), and (c) “Perceived enjoyment” (62.59%), using the AVE coefficient to indicate the unidimensional nature of factors [48] with values above 50% (

Table 1. Confirmatory factor analysis (CFA) for the TAM-2 and TAM-3 models.

Items	1. Computer Self-Efficacy	2. Experience	3. Perceived Enjoyment
I would complete a job using Cloud technologies if there was no one around to tell me what to do as I go	0.754
I would complete a job using Cloud technologies if someone showed me how to do it first	0.670
I would complete a job using Cloud technologies if I used similar technologies before	0.642
I would complete a job using Cloud technologies if I had the built-in help facility for assistance	0.616
I can distinct between IaaS, PaaS and SaaS		0.757
I know several Cloud technologies service providers and their services		0.720
I can describe the difference between the concepts of IT outsourcing and Cloud technologies		0.590
I have experience in using Cloud technologies		0.518
I have fun using Cloud technologies			0.773
I find using Cloud technologies to be enjoyable			0.656
The actual process of using Cloud technologies is pleasant			0.648
Variance	19.54%	19.32%	18.27%
Eigenvalue	2.15	2.13	2.01
AVE	53.20%	53.30%	62.59%
Cronbach’s alpha	0.705	0.706	0.701

Notes: Rotation: varimax; method: principal component analysis; KMO: 0.891.

).

The UTAUT-2 model [11] was used—in particular, 16 Likert-type questions on a scale from 1 to 7 (1 = disagree strongly, 2 = disagree, 3 = somewhat disagree, 4 = undecided/neutral, 5 = somewhat agree, 6 = agree, 7 = agree strongly), referring to the following 5 factors that demonstrated satisfactory or high reliability, as well as concurrent and construct validity:

(a)

“Effort Expectancy” (e.g., “I would find it easy to get cloud computing to do what I want it to do”) (a = 0.732, AVE = 55.46%, loadings in [0.635, 0.701]);

(b)

“Performance Expectancy” (e.g., “I find cloud-enabled technologies useful in my daily life”) (a = 0.701, AVE = 62.55%, loadings in [0.657, 0.784]);

(c)

“Behavioral Intention” (e.g., “I intend to continue using cloud technologies in the future”) (a = 0.720, AVE = 64.09%, loadings in [0.617, 0.759]);

(d)

“Price” (e.g., “Cloud technologies are reasonably priced”) (a = 0.710, AVE = 63.31%, loadings in [0.579, 0.797)];

(e)

“Social Influence” (e.g., “People who influence me think that I should use cloud technologies”) (a = 0.704, AVE = 62.85%, loadings in [0.641, 0.785]) (

Table 2. Confirmatory factor analysis (CFA) for the UTAUT-2 model.

Items	1. Effort Expectancy	2. Performance Expectancy	3. Behavioral Intention	4. Price	5. Social Influence
I would find it easy to get cloud computing to do what I want it to do	0.701
My interaction with Cloud technologies would be clear and understandable	0.680
Learning how to apply cloud technologies would be easy to me	0.635
I would find Cloud technologies easy to use	0.635
I find Cloud enabled technologies useful in my daily life		0.784
Using Cloud enabled technologies in my job helps me accomplish things more quickly		0.673
Using Cloud enabled technologies would increase my productivity		0.657
I intend to continue using Cloud technologies in the future			0.759
I will always try to use			0.698
I plan to continue to use Cloud technologies frequently			0.617
Cloud technologies are reasonably priced				0.797
Cloud technologies is a good value for money				0.687
At the current price Cloud technologies provides a good value				0.579
People who influence me think that I should use Cloud technologies					0.785
People whose opinions I value prefer that I use Cloud technologies					0.708
People who are important to me think that I should use Cloud technologies					0.641
Variance	14.85%	12.36%	11.75%	11.68%	11.54%
Eigenvalue	2.38	1.98	1.88	1.87	1.85
AVE	55.46%	62.55%	64.09%	63.31%	62.85%
Cronbach’s alpha	0.732	0.701	0.720	0.710	0.704

Notes: Rotation: varimax; method: principal component analysis; KMO: 0.922.

).

These 5 factors were selected because they were identified as the most important factors for explaining the technology acceptance of cloud computing via the UTAUT2 in a recent study [49]. Moreover, although the factors “Facilitating conditions” and “Hedonic motivation” were selected in the research of [49], they were excluded in the current study, due to overlap reasons, as they are similar to the factors of the TAM-2 and TAM-3 models, “Computer self-efficacy” and “Perceived enjoyment”, respectively.

2.3. Sample, Sampling, and Research Procedure

The inclusion criteria for participation in the current study were as follows: (a) to be an employee in a small–medium enterprise in the USA which uses cloud technology and (b) to be qualified to answer the questionnaire. To satisfy the inclusion criteria, a screening question was used which referred to the kinds of cloud technologies used by the relevant company, with the following possible answers: (1) software applications over the Internet on demand, e.g., file storage/backup, web-based email, and project management tools; (2) renting IT infrastructure—e.g., servers and virtual machines (VMs), storage, networks, and operating systems—from a cloud provider on a pay-as-you-go basis; (3) computing services that supply an on-demand environment for developing, testing, delivering, and managing software applications; (4) on-premises IT infrastructure, e.g., servers, storage, networks, and operating systems; (5) software applications installed on local client machines; (6) separate infrastructure for application development tasks, such as designing, configuring, building, testing, and deployment; (7) none of the above. Only employees who gave (1)–(3) as responses were selected for the survey, while the rest were excluded, as employees who gave (4)–(6) as answers were considered inappropriate, while the employees of companies which do not use cloud technologies (answer 7) were not of interest in the current study.

Then, stratified sampling was used to achieve a representative sample of the study population [36] for SMEs in the USA, using Strata as the cloud services model. The percentages of the sample were similar to those of the population regarding Software as a Service (SaaS) (45.59% for the population and 45.63% for the sample), Infrastructure as a Service (IaaS) (30.42% for the population and sample), and Platform as a Service (PaaS) (23.98% for the population and 23.95% for the sample). Thus, stratified sampling according to the cloud services model was considered successful.

Data were selected from a Pollfish survey conducted in February of 2022. Pollfish is a commercial online survey platform that uses the random device engagement (RDE) methodology to recruit participants through mobile apps in real time. This method ensures natural sampling by engaging verified users through their devices, without relying on panels or pre-selected pools. This approach allows for rapid data collection from real users in their natural digital environments, minimizing traditional survey panel biases and enhancing the ecological validity of responses. Participants are screened according to customized targeting criteria set by the researcher (e.g., country, employment type, and age group), and their responses are monitored for quality through attention checks and completion rates. This platform allows researchers to collect high-quality, large-scale survey data in a short timeframe, and it is widely used in academic and commercial research settings. The survey was distributed to users engaged with mobile apps and websites, presenting the survey as an optional interaction within their current activity. Specific targeting criteria were applied to reach the desired population of SME employees in the USA. These criteria included the following: (a) geographic targeting: limited to respondents located within the USA, including all regions; (b) demographic filters: to align with the target audience of SME employees. Moreover, Pollfish emphasizes respondent anonymity through its RDE approach, where no direct personal identifiers are collected or linked to responses. The platform employs several automated quality checks to ensure data integrity, including (a) fraud detection algorithms to identify and disqualify suspicious responses (e.g., speedsters, flatliners, and gibberish answers); (b) duplicate ID prevention through the use of mobile ad IDs and other device identifiers to prevent the same respondent from taking the survey multiple times; and (c) in-survey quality checks, such as consistency checks within the questionnaire to identify inattentive or disengaged respondents. Responses failing these checks were automatically excluded from the final dataset (Pollfish, 2025).

Considering the sample size, at least 300 employees were needed to perform confirmatory factor analysis [47]. Moreover, a power analysis via G Power indicated that the appropriate sample size for the accomplishment of multiple linear regression with 14 independent variables (this was the model with the highest number of independent variables) was at least 194 participants, setting f2 = 0.15, a = 0.05, and power = 0.95 [35].

The sample consisted of 626 employees, most of whom were 25–44 years old (59.9%, N = 375), high school (27.16%, N = 170) or university graduates (34.19%, N = 214), married (47%, N = 290) or living with a partner (14.26%, N = 88), and had at least one child (55.63%, N = 346). Considering their region of residence in the USA, 38.05% (N = 156) lived in the South, 22.68% (N = 93) in the Midwest, 19.76% (N = 81) in the West, and 19.51% (N = 80) in the Northeast (

Table 3. Demographic characteristics of employees in SMEs in the USA.

Characteristic	Category	Ν	%
Age	18–24	88	14.06
	25–34	196	31.31
	35–44	179	28.59
	45–54	89	14.22
	55+	74	11.82
Marital status	Single	181	29.34
	Living with partner	88	14.26
	Married	290	47.00
	Separated	20	3.24
	Divorced	30	4.86
	Widowed	8	1.30
Number of children	0	276	44.37
	1	102	16.40
	2	157	25.24
	3	56	9.00
	4+	31	4.99
Education	Elementary school	1	0.16
	Middle school	38	6.07
	High school	170	27.16
	Vocational technical college	78	12.46
	Postgraduate	125	19.97
	University	214	34.19
Region	Midwest	93	22.68
	Northeast	80	19.51
	South	156	38.05
	West	81	19.76

).

The majority of the employees had an up to mid-level role in their organization (67.33%, N = 410); worked in the sectors of healthcare and social assistance (14.22%, N = 89), finance and insurance (13.26%, N = 83), software (12.78%, N = 80), education (12.62%, N = 79), or construction (11.02%, N = 69); had 3–15 years of experience in their current position (61.34%, N = 384); and earned USD 25,000–99,999 annually (60.53%, N = 368). Most of the SMEs included in this research had 26–500 employees (74.44%, N = 466) (

Table 4. Job characteristics of employees in SMEs in the USA.

Characteristic	Category	Ν	%
Career	Education	79	12.62
	Finance and insurance	83	13.26
	Healthcare and social assistance	89	14.22
	Information services and data	47	7.51
	Government and public administration	16	2.56
	Manufacturing (computers and electronics)	27	4.31
	Manufacturing (other)	41	6.55
	Software	80	12.78
	Construction	69	11.02
	Transportation and warehousing	22	3.51
	Shipping distribution	31	4.95
	Information (other)	22	3.51
	Telecommunications	15	2.40
	Utilities	5	0.80
Income	USD < 25,000	71	11.68
	USD 25,000–49,999	129	21.22
	USD 50,000–74,999	110	18.09
	USD 75,000–99,999	129	21.22
	USD 100,000–124,999	67	11.02
	USD125,000–149,999	45	7.40
	USD 150,000+	57	9.38
Organizational role	Lower-level	121	19.87
	Lower–mid-level	166	27.26
	Mid-level	123	20.20
	Mid–top-level	58	9.52
	Top-level	141	23.15
Number of employees	1	26	4.15
	2–5	43	6.87
	6–10	31	4.95
	11–25	60	9.58
	26–50	89	14.22
	51–100	134	21.41
	101–250	104	16.61
	251–500	139	22.20
Years in current position	0–2	103	16.45
	3–5	150	23.96
	6–10	125	19.97
	11–15	109	17.41
	16–20	67	10.70
	21+	72	11.50

).

2.4. Data Analysis

Data analysis was carried out using IBM SPSS 26. Means and standard deviations were used to estimate the levels of agreement for the factors of the TAM-2, TAM-3, and UTAUT-2 models. Categorical variables referring to the use of cloud technologies are presented with percentages and frequencies. Significance in inferential statistics was set at 5% [50].

McNemar’s test was used to compare the percentages of cloud deployment and services models before and after the COVID-19 pandemic [50]. The normality of the dependent variable “Behavioral intention” was not accepted using the Shapiro–Wilk test [51]. Thus, the correlations between independent variables and “Behavioral intention” were examined using the following non-parametric tests: (a) Spearman’s test [52], to examine linear relationships with scale or ordinal variables; (b) the Mann–Whitney test [53], to compare the levels of behavioral intention between 2 independent samples; and (c) the Kruskal–Wallis test [54], to compare the levels of behavioral intention between 3 or more independent samples.

The predictive power of the independent variables with respect to “Behavioral intention” was examined using 4 multiple linear regression models for each sector: (1) TAM-3 and UTAUT-2 factors, (2) use of cloud technologies, (3) job characteristics, and (4) demographics. Only independent variables that presented significant correlations with the “Behavioral intention” were entered in the multiple regression models. The categorical variables were transformed to a dichotomic form before being entered in the models. The adjustment of the models was tested via the AdjR2 coefficient. The multiple regression models were tested for multicollinearity using the VIF (<3) coefficient [50]. The role of “Experience” as a moderator in the relationship between behavioral intention and factors of the TAM-3 and UTAUT-2 models was examined using Model 1 from the work of Hayes [55].

While multiple linear regression was selected for the current analysis due to its interpretability and suitability for the sample size, we acknowledge that the relationships between the TAM and UTAUT-2 constructs are theoretically complex and potentially mediated. Structural equation modeling (SEM) has been widely used in similar studies due to its ability to estimate latent variables and account for measurement error [11].

SEM provides several advantages, including the capacity to test both measurement and structural models simultaneously, thereby offering a more holistic view of construct relationships [55]. Given this study’s focus on direct predictive relationships rather than full mediation modeling, multiple regression remains a statistically valid and methodologically appropriate choice [47].

3. Results

3.1. Attitudes Towards the Use of Cloud Computing Technologies

The levels of “Behavioral intention” were above average (M = 4.93, SD = 1.50). The employees somewhat agreed that they intended (M = 4.98, SD = 1.94) and planned (M = 4.94, SD = 1.84) to continue using cloud technologies in the future and would always try to use them (M = 4.85, SD = 1.83). “Performance expectancy” was somewhat supported by the employees (M = 4.78, SD = 1.52) in terms of increases in productivity (M = 4.85, SD = 1.85), the speed of accomplishing tasks (M = 4.81, SD = 1.97), and usefulness (M = 4.66, SD = 1.94). Employees in SMEs in the USA were optimistic about their “Computer self-efficacy” (M = 4.78, SD = 1.38) regarding using cloud technologies if they had similar experience (M = 4.89, SD = 1.90), someone showed them how to use the technology (M = 4.84, SD = 1.94), and they had technical assistance (M = 4.79, SD = 1.87). “Effort expectancy” was also supported (M = 4.68, SD = 1.38) in terms of ease of use (M = 4.76, SD = 1.83) and the level of understandable interaction (M = 4.75, SD = 1.81). “Perceived enjoyment” was rated above average (M = 4.68, SD = 1.50), and the employees stated that the use of cloud technologies is pleasant (M = 4.68, SD = 1.50). The employees seemed to be satisfied regarding the “Price” (M = 4.64, SD = 1.45), as they believed that cloud technologies provide good value (M = 4.72, SD = 1.86) and are worthwhile for their monetary cost (M = 4.69, SD = 1.76). “Social influence” in relation to use of cloud technologies was rated above average regarding the influence of people whose opinions the employees value (M = 4.72, SD = 1.92), as was “Experience” in using cloud technologies (M = 4.85, SD = 1.96) (

Table 5. Attitudes of employees in SMEs in the USA towards the use of cloud computing technologies.

Factor	M	SD
Behavioral intention	4.93	1.50
I intend to continue using cloud technologies in the future	4.98	1.94
I plan to continue to use cloud technologies frequently	4.94	1.84
I will always try to use cloud technologies	4.85	1.83
Performance expectancy	4.78	1.52
Using cloud-enabled technologies would increase my productivity	4.85	1.85
Using cloud-enabled technologies in my job helps me accomplish things more quickly	4.81	1.97
I find cloud-enabled technologies useful in my daily life	4.66	1.94
Computer self-efficacy	4.78	1.41
I would complete a job using cloud technologies if I used similar technologies before	4.89	1.90
I would complete a job using cloud technologies if someone showed me how to do it first	4.84	1.94
I would complete a job using cloud technologies if I had the built-in help facility for assistance	4.79	1.87
I would complete a job using cloud technologies if there was no one around to tell me what to do as I go	4.59	2.03
Effort expectancy	4.68	1.38
I would find cloud technologies easy to use	4.76	1.83
My interaction with cloud technologies would be clear and understandable	4.75	1.81
I would find it easy to get cloud computing to do what I want it to do	4.61	1.81
Learning how to apply cloud technologies would be easy for me	4.60	1.95
Perceived enjoyment	4.68	1.50
The actual process of using cloud technologies is pleasant	4.77	1.82
I have fun using cloud technologies	4.68	1.84
I find using cloud technologies to be enjoyable	4.59	2.03
Price	4.64	1.45
At the current price, cloud technologies provide good value	4.72	1.86
Cloud technologies are good value for money	4.69	1.76
Cloud technologies are reasonably priced	4.49	1.83
Social influence	4.58	1.50
People whose opinions I value prefer that I use cloud technologies	4.72	1.92
People who influence me think that I should use cloud technologies	4.56	1.85
People who are important to me think that I should use cloud technologies	4.45	1.91
Experience	4.51	1.44
I have experience in using cloud technologies	4.85	1.96
I know several cloud technologies service providers and their services	4.62	1.95
I can describe the difference between the concepts of IT outsourcing and cloud technologies	4.33	1.95
I can distinguish between IaaS, PaaS, and SaaS	4.24	2.03

).

The primary reasons to choose the service models were their ease of deployment (43.45%, N = 272), cost (42.49%, N = 266), faster implementation (40.58%, N = 254), and better features/functions (39.62%, N = 248) (

Table 6. Use of cloud technologies in SMEs in the USA.

Variable	Category	N	%
Main cloud technologies used in your enterprise	Software applications Renting IT infrastructure—servers and virtual machines; computing services	318	50.80
		156	24.92
		152	24.28
Starting using cloud technologies due to the COVID-19 pandemic	No	253	40.42
	Yes	373	59.58
The primary reasons your enterprise chose one of the following service models	Faster implementation	254	40.58
	Better features/functions	248	39.62
	Easier to deploy	272	43.45
	Scalability	158	25.24
	Cost	266	42.49
	Did not require IT resources	165	26.36
	COVID-19	181	28.91
	Other	16	2.56
Challenges you faced with using cloud technologies during the COVID-19 pandemic	Data and application integration	222	35.46
	Getting features/functions we need	215	34.35
	Performance	226	36.10
	Not enough customization options	148	23.64
	Interoperability/portability	139	22.20
	Difficulty migrating	171	27.32
	Managing costs	150	23.96
	Downtime	162	25.88
	Security	190	30.35
	Other	21	3.35

).

3.2. The Role of the COVID-19 Period

Most SMEs in the USA (59.58%, N = 373) started to use cloud technologies due to the COVID-19 pandemic. The main challenges faced in using cloud technologies during the COVID-19 pandemic were the performance (36.10%, N = 226), the data application and integration (35.46%, N = 222), and the needed functions (34.35%, N = 215). Half of the SMEs in the USA use software applications (50.80%, N = 318) as cloud technology (Table 6).

After the COVID-19 pandemic, there was a significant increase in the use of SaaS (64.22% vs. 42.81%, p < 0.001) and IaaS (42.81% vs. 36.42%, p = 0.018) and a decrease in the use of PaaS (33.71% vs. 58.31%, p < 0.001) (

Table 7. Comparisons before and after the COVID-19 pandemic regarding the use of cloud development and services models by SMEs in the USA.

Variable	Before	After	X2	p-Value
Private	42.33%	38.66%	2.616	0.106
Public	49.68%	53.19%	2.100	0.147
Hybrid	27.80%	27.32%	0.024	0.877
Community	24.28%	25.08%	0.090	0.764
Software as a Service	42.81%	64.22%	50.253	<0.001
Infrastructure as a Service	36.42%	42.81%	5.633	0.018
Platform as a Service	58.31%	33.71%	66.127	<0.001

).

3.3. The Role of “Experience” as a Moderator

Experience moderated the relationships between “Behavioral intention” and “Effort expectancy” (t = 3.125, p = 0.002), “Perceived enjoyment” (t = 2.693, p = 0.007), and “Computer self-efficacy” (t = 3.413, p < 0.001). “Effort expectancy” explained 10.5% of the variance in “Behavioral intention” for low–medium “Experience” and 39% for high “Experience”. “Perceived enjoyment” explained 9.8% of the variance in “Behavioral intention” for low–medium “Experience” and 22.9% for high “Experience”. “Computer self-efficacy” explained 12.8% of the variance in “Behavioral intention” for low–medium “Experience” and 38.1% for high “Experience” (

Table 8. Role of “Experience” as a moderator in the relationship between “Behavioral intention” and TAM and UTAUT-2 factors in SMEs in the USA.

Interactions	t	p	Coeff.	LCI	UCI	R2 (Low–Medium Experience)	R2 (High Experience)
Effort expectancy x experience	3.125	0.002	0.076	0.028	0.123	10.5%	39.0%
Price x experience	1.163	0.245	0.026	−0.018	0.071	13.0%	31.2%
Social influence x experience	0.904	0.367	0.021	−0.024	0.065	13.2%	34.0%
Perceived enjoyment x experience	2.693	0.007	0.059	0.016	0.101	9.8%	22.9%
Computer self-efficacy x experience	3.413	<0.001	0.074	0.032	0.117	12.8%	38.1%

Notes: moderator: experience; dependent variable: behavioral intention.

).

3.4. Predictors of “Behavioral Intention”

The TAM-3 and UTAUT-2 models explained 47% of the total variance in “Behavioral intention”. Significant predictors included “Performance expectancy” (beta = 0.194 ***), “Effort expectancy” (beta = 0.133 **), “Price” (beta = 0.127 **), “Social influence” (beta = 0.189 ***), “Perceived enjoyment” (beta = 0.083 *), and “Computer self-efficacy” (beta = 0.167 ***) (

Table 9. Multiple regression analysis with the dependent variable “Behavioral intention” in SMEs in the USA.

N/A	Model	Independent Variable	B	Beta	t	p	VIF
1	TAM and UTAUT-2	(Constant)	0.629	-	3.327	0.001	-
		Performance expectancy	0.192	0.194	5.241	<0.001	1.605
		Effort expectancy	0.144	0.133	3.191	0.001	2.029
		Price	0.132	0.127	3.211	0.001	1.830
		Social influence	0.188	0.189	5.075	<0.001	1.615
		Perceived enjoyment	0.083	0.083	2.160	0.031	1.743
		Computer self-efficacy	0.177	0.167	4.148	<0.001	1.890
2	Use of cloud technologies	(Constant)	3.682	-	29.820	<0.001	-
		Public changes during COVID-19	0.002	0.078	2.131	0.033	1.032
		SaaS changes during COVID-19	0.002	0.090	1.876	0.061	1.791
		IaaS changes during COVID-19	0.003	0.121	2.689	0.007	1.568
		PaaS changes during COVID-19	−0.001	−0.066	−1.376	0.169	1.761
		Software applications	0.397	0.133	3.525	<0.001	1.096
		Started using cloud technologies due to COVID-19	0.490	0.161	4.342	<0.001	1.062
		Faster implementation	0.620	0.204	5.230	<0.001	1.173
		Better features/functions	0.384	0.126	3.260	0.001	1.150
		Easier to deploy	0.088	0.029	0.771	0.441	1.119
		Cost	0.382	0.126	3.304	0.001	1.132
		Data and application integration	0.080	0.025	0.652	0.515	1.178
		Getting features/functions we need	0.066	0.021	0.557	0.578	1.107
		Performance	−0.073	−0.023	−0.590	0.555	1.203
		Difficulty migrating	0.074	0.022	0.600	0.549	1.059
3	Job characteristics	(Constant)	4.285	-	33.572	<0.001	-
		Career	0.331	0.110	2.727	0.007	1.055
		USD 150,000+	0.272	0.053	1.230	0.219	1.202
		Mid–top-level/top-level	0.616	0.193	4.570	<0.001	1.155
		251–500 employees	0.328	0.091	2.207	0.028	1.113
		3–20 years in current position	0.253	0.075	1.896	0.058	1.015
4	Demographics	(Constant)	4.554	-	50.029	<0.001
		35–44	0.261	0.079	1.974	0.049	1.046
		Married	0.058	0.019	0.456	0.649	1.193
		1–2 children	0.282	0.093	2.227	0.026	1.148
		Postgraduate	0.757	0.203	4.799	<0.001	1.178

Notes: Model 1: F (6619) = 91.626, p < 0.001, R² = 47% SE = 1.09. Model 2: F (14,611) = 11.660, p < 0.001, R² = 21.1%, SE = 1.34. Model 3: F (5585) = 12.378, p < 0.001, R² = 9.8%, SE = 1.44. Model 4: F (4608) = 12.833, p < 0.001, R² = 7.8%, SE = 1.44.

).

The use of cloud technologies explained 21.1% of the total variance in “Behavioral intention”. Significant predictors included “Public change during COVID-19” (beta = 0.078 *), “IaaS change during COVID-19” (beta = 0.121 **), “Software applications” (beta = 0.133 ***), “Started using cloud technologies due to COVID-19” (beta = 0.161 ***), “Faster implementation” (beta = 0.204 ***), “Better features/functions” (beta = 0.126 **), and “Cost” (beta = 0.126 **) (Table 9).

Job characteristics explained 9.8% of the total variance in “Behavioral intention”. “Career” was a significant predictor (beta = 0.110 **). Levels of “Behavioral intention” were higher for employees with careers in finance and insurance (M = 5.05), software (M = 5.13), construction (M = 5.22), information services and data (M = 5.43), and information (other) (M = 5.09). In addition, significant predictors included “Mid–top-level/top-level” organizational role (beta = 0.193 ***) and working in an SME with “251–500 employees” (beta = 0.091 *) (Table 9).

Demographics explained 7.8% of the total variance in “Behavioral intention”. Significant predictors included being aged “35–44” (beta = 0.079*), having “1–2 children” (beta = 0.093 *), and having a “Postgraduate” educational level (beta = 0.203 ***) (Table 9).

4. Discussion

Employees of SMEs in the USA presented positive attitudes towards the use of cloud computing technologies. In particular, they stated that the use of cloud technologies is pleasant and that they intend to continue using cloud technologies in the future, and they mentioned a few benefits arising from the use of such technologies, such as (a) increased productivity, (b) higher speed of task completion, (c) ease of use, (d) highly understandable interactions, (e) better features/functions, and (f) reduced costs. Perceived usefulness and innovation attitudes significantly impacted behavioral intentions towards cloud use [16]. Perceived ease of use and usefulness, along with behavioral control and subjective norms, significantly impact behavioral intention to use with respect to cloud computing [17]. Reduced costs of IT infrastructure and maintenance, improved communication, and business continuity are key drivers [1]. Moreover, employees of SMEs in the USA believe that they have adequate computer self-efficacy and experience to use cloud technologies. Employees’ perceptions of cloud computing services in SMEs are influenced by factors such as perceived benefits, communication processes, and overall user experience [20]. Self-efficacy, defined as the belief in one’s ability to use technology effectively, plays a crucial role in the behavioral intention to use IT technologies in SMEs [56]. Other factors that contribute to positive attitudes toward the use of cloud technologies include their reasonable price and social influences. Cost-effectiveness, often referred to as price value, is a significant driver [7,57]. The social norms surrounding cloud computing services positively affect perceived ease of use, perceived usefulness, and willingness to use these services [6].

The COVID-19 pandemic positively affected the behavioral intention to use cloud technologies. Of the SMEs considered here, 59.58% started using cloud technologies due to COVID-19. Moreover, the SMEs that used cloud technologies due to the COVID-19 pandemic indicated higher behavioral intentions. The COVID-19 pandemic has significantly impacted small and medium enterprises (SMEs), prompting a shift towards the use of cloud computing. This transition has been driven by the need for digital adaptation and business continuity. The pandemic primarily led to the migration of basic services to the cloud, with advanced systems being transferred by a number of SMEs [4].

Half of the SMEs in the USA currently use on-demand software applications over the Internet, including file storage/backup, web-based email, and project management tools. In particular, after the COVID-19 pandemic, there was a 21.41% increase in SaaS (Software as a Service) cloud services and a 6.39% increase in IaaS (Infrastructure as a Service) cloud services. The increase in SaaS and IaaS cloud services after the COVID-19 pandemic led to a 24.60% decrease in PaaS cloud services. The results indicate that SMEs increased their use of SaaS and IaaS cloud services after the COVID-19 pandemic, and this led to higher future behavioral intentions. Thus, enterprises in the USA seem to be satisfied by their tendency to use SaaS and IaaS cloud services since the pandemic. According to the literature, SMEs have increasingly embraced Software as a Service (SaaS) and Infrastructure as a Service (IaaS) models due to their cost-effectiveness and ease of implementation [3].

Furthermore, after the COVID-19 pandemic, there was a 3.51% increase in public cloud development models among SMEs, and this tendency to use public deployment models was linked with higher future behavioral intentions. The results indicate a small tendency of enterprises to use public cloud development models since the COVID-19 pandemic, which has led to higher behavioral intentions for small–medium enterprises. Research indicates that the COVID-19 pandemic has influenced the use of cloud computing and entrepreneurial behavior among small and medium enterprises (SMEs). While some SMEs increased their use of cloud services to ensure business continuity during the pandemic [4], the overall impact on cloud use varied, largely due to medium-sized enterprises showing a significant increase. A study by Theby [14] emphasized the role of public cloud computing as a response tool during COVID-19, discussing how the flexibility, resilience, and collaboration inherent in cloud services led to a surge in use among public services, as these technologies enabled continuity and adaptability in essential operations. This use demonstrated a significant increase in behavioral intention to continue using cloud solutions beyond the pandemic, supporting future use as part of regular operations on a daily basis.

Regarding the challenges faced in using cloud technologies during the COVID-19 pandemic, those mentioned most frequently were data and application integration, the need for features and functions, performance, and security. However, these challenges did not negatively affect the behavioral intentions of the employees. The general conclusion is that enterprises successfully faced the challenges of using cloud technologies during the COVID-19 pandemic, which accelerated cloud use among enterprises, particularly for ensuring business continuity and enabling remote work [4].

Factors of the TAM-3 and UTAUT-2 models explained most of the variance in behavioral intention. Behavioral intention was mostly affected by perceived enjoyment, computer self-efficacy, effort expectancy, performance expectancy, price, and social influence. In the context of studies that tested factors affecting behavioral intention, in the application of the TAM and UTAUT models to understand intention to use different technologies, it has been indicated that there were consistent results on the direct effects of performance expectancy and effort expectancy [18]. In the context of SMEs, effort expectancy directly influences behavioral intention to use technology [28]. In some cases, performance expectancy was identified as the most powerful factor [58]. Chatzoglou et al. [59] identified enjoyment as a direct predictor of intention to use technology. Studies show that perceived enjoyment positively influences employees’ pro-environmental behaviors [25] and their intention to use technology [60] and that enjoyment contributes to positive attitudes and behavioral intentions [61], while also confirming the significance of perceived usefulness, ease of use, enjoyment, and self-efficacy in predicting behavioral intentions to use workplace technology. Evidence has consistently affirmed that computer self-efficacy correlates positively with intentions to use technology. Usman et al. [62] pointed out that computer self-efficacy exhibited a positive influence on behavioral intentions towards technology. Studies show that a particular type of social presence is important in the process of forming an intention to use cloud computing technologies. Employees measuring high in terms of social influence have been found to exhibit a higher behavioral intention to use cloud services [63,64]. Furthermore, in SMEs, previous studies have indicated that there are predictors of behavioral intention to use cloud computing, including performance expectancy, effort expectancy, social influence, and price value [27].

Experience moderates the relationships between behavioral intention and effort expectancy, perceived enjoyment, and computer self-efficacy. Thus, the linear positive relationships between behavioral intention and effort expectancy, perceived enjoyment, and computer self-efficacy were higher for SME employees with more experience. The relationship between effort expectancy and behavioral intention to use technology is influenced by various factors. Experience has been found to moderate this relationship, with longer service experience strengthening the positive effect of effort expectancy on behavioral intention [30]. Perceived usefulness and ease of use influence the behavioral intention to use technology, with self-efficacy affecting these factors [29]. Computer self-efficacy is influenced by computer experience, which can be divided into basic and advanced levels [23]. Research suggests that user experience and perceived enjoyment play significant roles in behavioral intention. Lin [24] found that experience moderates the relationships of key determinants, such as the way in which the user behaves behind a screen. Flow experience, perceived enjoyment, and interaction affect the intention of a person to use a computer, for several reasons [26].

Career affected the behavioral intentions of employees in SMEs in the USA regarding the use of cloud computing technologies. Higher levels of behavioral intention were found for employees with careers in finance and insurance, software, construction, information services and data, and information (other). Behavioral intention varies across industries, with finance and information services showing higher frequency of use rates for cloud computing [17]. A study by Tarhini et al. [31] highlighted that job position significantly impacts behavioral intention (BI) to use cloud technologies. Career roles in higher technology sectors, such as software, show elevated BI due to greater technological awareness and skill alignment with cloud features [31]. In the construction field, a study by Bello et al. [5] found that cloud computing is increasingly accepted by professionals, as it enables enhanced collaboration and operational efficiency. However, the diffusion curve remains steep, with positive BI linked to roles that directly engage with cloud-compatible tools, such as building information modeling (BIM) and the IoT. This higher BI among project managers and engineers in the construction sector highlights how career roles interact with the use of cloud technology [5].

The behavioral intention to use cloud technologies was higher for enterprises with 250–500 employees. Organizational factors such as the number of employees, establishment year, and sector influence the perceptions and usage of cloud computing [65].

Moreover, higher behavioral intention to use cloud technologies was observed for employees with mid–top- or top-level organizational roles. The results indicate that employees with responsibilities in SMEs are keener to use cloud technologies. Factors influencing cloud behavioral intention to use include organizational support, technological readiness, and environmental factors [15].

Demographic characteristics affected the behavioral intention to use cloud technologies. In particular, employees with a postgraduate education level indicated higher levels of behavioral intention in all enterprises. Research shows that enterprises with more educated employees show greater engagement in corporate social responsibility activities [32].

Furthermore, employees who were married or had 1–2 children presented higher behavioral intention to use cloud technologies. Due to their considerable family obligations, it is possible that employees seek ways to enhance their time performance. Family roles and workplace factors influence employees’ intention to use new technologies such as cloud computing. Married employees with children show higher family role identification, which can positively impact leadership behaviors at work, such as taking new initiatives [33].

Moreover, employees aged 35–44 years presented higher behavioral intention to use cloud technologies. This age range is compatible with the medium experience, which was correlated with higher behavioral intention as well as with higher family obligations. While age does not appear to differentiate behavioral intention, work position and work classification do show significant differences [31], meaning that training and experience in cloud computing positively affect its perceived usefulness and ease of use.

5. Conclusions

The present study examined (1) the attitudes of employees in SMEs in the USA towards the use of cloud computing technologies, (2) the role of the COVID-19 pandemic with regard to the use of cloud computing technologies by SMEs in the USA, (3) the factors that affect the behavioral intention to use cloud computing technologies among employees of SMEs in the USA, and (4) the role of experience as a moderator to enhance the behavioral intention to use cloud computing technologies among employees of SMEs in the USA.

Considering the first aim of this research, employees of SMEs in the USA presented positive attitudes towards the use of cloud computing technologies. Regarding the second aim, the COVID-19 pandemic positively affected the behavioral intention to use cloud technologies in SMEs. Furthermore, SMEs in the USA were satisfied by their tendency to use SaaS and IaaS cloud services models after the pandemic, as well as by the use of public cloud development models. As far as our third research aim is concerned, behavioral intention to use cloud technologies was linked with higher performance and effort expectancy, price, perceived enjoyment, computer self-efficacy, and social influence to use cloud technologies. Higher behavioral intention to use cloud technologies was observed among employees who (a) had a mid–top- or top-level role in enterprises; (b) worked in finance and insurance, information services/data, construction, or software; (c) worked in an SME with 250–500 employees; (d) had a postgraduate level of education; (e) were 35–44 years old; and (f) had family obligations. As for the fourth research aim, higher experience with the use of cloud technologies enhanced the positive impact of effort expectancy, computer-self efficacy, and perceived enjoyment on behavioral intention.

5.1. Limitations and Future Research

The use of the Pollfish RDE approach allowed for efficient and broad data collection, providing insights from an organically engaged user base. However, a potential limitation inherent in this methodology is the non-probability sampling nature, which, while beneficial for speed and access to a diverse mobile audience, may require careful consideration when generalizing findings. The results refer mainly to SMEs in the healthcare and social assistance, finance and insurance, software, education, and construction sectors in the USA with 26–500 employees. Moreover, this study did not examine all factors of the TAM-2, TAM-3, and UTAUT-2 models.

Future researchers could repeat similar studies in different countries in Europe, examining all of the factors of the TAM-2, TAM-3, and UTAUT-2 models. Future studies should also address the scalability and replicability of the current findings by testing the model in different continents and at different levels of digital maturity. The inclusion of firms from emerging markets or under-represented industries could significantly improve the generalizability of the insights. This is especially crucial in the current post-pandemic context, where SMEs face accelerated digitization but uneven access to technological resources. Research could also examine how external shocks (e.g., COVID-19) act as moderators or disruptors within TAM-/UTAUT-based behavioral intention models [66]. In addition to extending the geographical scope of SMEs, future research should explore longitudinal designs to assess how cloud behavioral intentions evolve over time and under changing economic or policy conditions.

As the present study used multiple regression analysis, future investigations could apply structural equation modeling (SEM) to account for latent variable measurement errors and complex mediating relationships among constructs in the TAM and UTAUT-2 [55]. Methodologically, future research could adopt mixed-methods approaches to validate quantitative findings with richer qualitative data, allowing for a better appreciation of contextual barriers such as vendor dependency, data security, or lack of internal IT capacity. These challenges have been highlighted across multiple reviews and should be addressed through targeted policy and support frameworks. Comparative sectoral analysis, for example, across construction, fintech, and healthcare SMEs could further illuminate sector-specific drivers and inhibitors of cloud behavioral intention to use cloud technology [5].

Moreover, researchers could build on recent models that link cloud behavioral intention to use with sustainable consumer advocacy and responsible digital transformation strategies. In particular, studies could examine how SMEs align cloud-based operations with broader social values and digital ethics, especially in socially driven platforms such as social commerce [34]. Incorporating constructs such as consumer trust, digital sustainability, and organizational agility into extended TAM/UTAUT frameworks would deepen the understanding of value-based behavioral intention to use cloud technology. By addressing these limitations, future research could contribute to more resilient and inclusive frameworks for SMEs’ digital transformation.

5.2. Theoretical, Managerial, and Social Implications

This study offers several important theoretical implications. By integrating elements from both the TAM-3 and UTAUT-2 frameworks, the results confirmed that a hybrid model improves the explanatory power of behavioral intention in the context of cloud computing’s use in SMEs. Specifically, variables such as computer self-efficacy, perceived enjoyment, effort expectancy, and experience as a moderator extend existing theoretical constructs by contextualizing them within pandemic-driven digital transitions. This supports the findings of the prior literature emphasizing the role of experience and enjoyment in shaping technology-related behavior in SMEs [25,26]. The findings further contribute to the theoretical discourse by showing how post-COVID-19 dynamics serve as moderating forces that affect intention to use technology constructs such as performance expectancy and social influence, echoing calls for more crisis-sensitive technology models [13]. Moreover, this study enhances our understanding of cloud computing in specific job sectors, thereby supporting domain-specific extensions of UTAUT-2, which have been underexplored in SME research [11].

From a practical standpoint, this study provides valuable insights for SME decision-makers, policymakers, and cloud service providers. For SME managers, understanding that behavioral intention is strongly influenced by enjoyment, computer self-efficacy, social influence, and price value perception can guide the design of training and incentive programs to increase cloud use rates.

As shown in recent work on the transformative power of cloud computing for SMEs, shocks such as the COVID-19 pandemic can serve as accelerators of digital change, but they can also expose structural vulnerabilities. The research highlights the need to bridge the digital gap by supporting SMEs—especially in resource-limited contexts—with funding, infrastructure, and digital literacy initiatives [4].

Overall, these implications underscore the value of context-aware, theory-informed approaches to understanding the behavioral intention to use cloud technology in SMEs, particularly in a rapidly evolving post-pandemic landscape.

5.3. Recommendations for Stakeholders

In addition to the theoretical, managerial, and social implications already discussed, this study proposes tailored recommendations for key stakeholder groups involved in SMEs’ digital transformation efforts:

Managers should also focus on enhancing user confidence and promoting positive experiences with cloud platforms, especially among less experienced employees with lower computer self-efficacy, educational levels, and organization roles. This could be achieved through targeted upskilling and onboarding strategies. Cloud service providers can leverage these insights by designing user-centric solutions. Features emphasizing usability, intuitive interfaces, and real-time collaboration may significantly boost behavioral intention to use cloud technology among SMEs. Emphasizing the enjoyment of using cloud tools, as well as building confidence through hands-on experience, can enhance behavioral intention to adopt cloud technologies. Creating a culture that values the development of digital skills can foster smoother cloud transitions [19].

Vendors should focus on delivering simple, cost-effective, and scalable cloud solutions that address the unique operational challenges of SMEs. Prioritizing intuitive interfaces, collaboration features, and strong user support can increase perceived ease of use and enjoyment—key drivers of intention to use. Clear value-for-money propositions and flexible service models are also critical [5,6].

Governments and public bodies should implement targeted cloud use support schemes, such as subsidies for technology investment, tax incentives, and free digital training programs. Government-backed cloud use programs, particularly those responding to public health or economic crises, could significantly enhance resilience and technological readiness. Cloud strategies must also address infrastructure inequalities to ensure that all SMEs—especially those in rural or under-resourced areas—can participate in digital innovation [4].

Summarizing, in order to enhance the levels of behavioral intention to use cloud technologies, stakeholders should consider the following: (1) enhancing levels of performance expectancy with market campaigns focused on the benefit of productivity increase; (2) enhancing levels of effort expectancy with (a) market campaigns that focus on the benefit of ease of use and (b) cloud technology applications which make the user experience easier; (3) decreasing the cost of cloud technology or defining a reasonable price; (4) increasing the perceived enjoyment of the user of cloud technologies; (5) increasing the computer self-efficacy of employees via training in the use of cloud technologies; (6) advertising the use of cloud technology on social media platforms in order to enhance levels of social-influence.

Market campaigns should focus on (1) SMEs in finance and insurance, information and data services, construction, and software with higher numbers of employees; (2) professionals who hold a mid- to top-level role in SMEs and who have a master’s degree and are 35–44 years old and married. The current study indicates practical insights for tailoring cloud technology strategies based on users’ experience levels. By enhancing effort expectancy, perceived enjoyment, and computer self-efficacy, in particular, for employees with higher experience of using cloud technologies, it is possible to increase their behavioral intentions. On the other hand, for employees with lower experience of using cloud technologies, intensive mandatory use in the first months of use is proposed in order to enhance their experience and consequently their behavioral intentions.