1. INTRODUCTION

Digital transformation has permeated many aspects of contemporary society, and the banking sector is not exception ( González Núñez & Mariné Osorio, 2021). The digitization of financial services is revolutionizing the way consumers interact with banking institutions, altering their consumption processes, as well as their habits and expectations ( Kitsios et al., 2021). Digital transformation in the financial sphere has been characterized by the adoption of technology such as artificial intelligence, data analytics, cloud computing, and mobility ( Camoiras-Rodríguez & Varela-Neira, 2020), innovations that have enabled financial institutions to offer more efficient, personalized, and accessible services ( Tsindeliani et al., 2022); however, this change not only involves the modernization of technological infrastructure in banks but also a cultural and operational adaptation in their administrative processes ( Alam et al., 2023).

Contemporary financial consumers seek effective, efficient, convenient, and secure banking experiences, scenarios in which digital transformation has responded to these demands with the introduction of online services, mobile applications, electronic payments, and virtual assistants (López-Rodríguez & Cardozo-Munar, 2023). Access to real-time financial information and the ability to conduct transactions anywhere has led to a shift not only in customer expectations but also in their consumption dynamics ( Kaur et al., 2021).

Despite the evident benefits, Filotto et al. (2021) have stated that the adoption of digital transformation in the banking sector faces significant challenges. The digital divide, cybersecurity, and resistance to change are obstacles that must be overcome to ensure a successful transition ( Cichosz et al., 2020). Consumer trust in the security of online transactions and data protection is a critical aspect that financial institutions must address to embrace the ever-growing demand for digital services ( Kitsios et al., 2021). This process should not be entrusted to all market segments, as some may view digital transformation as a barrier, which would not only hinder the consumption of emerging banking services but also affect the brand value of banks ( López-Rodríguez & Sandoval-Escobar, 2023; Calvo Dopico et al., 2021).

Digital transformation has influenced how consumers manage their finances, primarily due to the preference for online services, a trend that has reduced reliance on brick-and-mortar branches and has led to a shift towards digital banking models ( Tsindeliani et al., 2022). The customization of supply and the ability to predict individual financial needs are aspects that have redefined the relationship between consumers and banking institutions ( Louw & Nieuwenhuizen, 2020), a process that should be focused on customer experience as a key differentiator in the digital era ( Monroy-Perdomo et al., 2022).

Digital transformation not only requires consumers to adapt to new technologies but also to understand how to use them safely and effectively by promoting financial education, an essential scenario to empower users and ensure that they make informed decisions in the digital environment ( López-Rodríguez & López-Ordoñez, 2022). This consolidates technology acceptance, which is the ability to willingly receive and approve what is offered among all available forms of technology ( Venkatesh et al., 2003); however, this acceptance has also revealed a generational gap when it comes to adopting digital services in the banking sector. While younger generations tend to quickly embrace new technology, older customers may be reluctant to do so ( Parida et al., 2016; Laukkanen, 2016; Khanra et al., 2021), due to both psychological and functional barriers ( Chaouali & Souiden, 2019).

For this reason, this study has been based on the Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), which provides a solid framework that sheds light on which factors influence the adoption of new technology in the financial domain, particularly in the banking sector. Challenges to be tackled regarding technological acceptance for the banking sector are greater integration of advanced technology and a deeper understanding of the psychological and functional factors that can affect consumers when deciding on whether to accept it.

Based on the above, this study addresses the following research question: How do the elements of the UTAUT sway the behavioral intention of account holders for technology use and their actual consumption in the Colombian banking sector? By providing an answer to this question, this study aims to enhance the understanding of various key aspects that the banking sector must consider for its digital transformation to be consolidated considering consumer behavior, thereby strengthening the market orientation of these entities. The next two sections present a literature review accompanied by the formulation of relevant hypotheses that support the validity of the research. After these two sections, the results of the empirical exercise are presented, followed by the Conclusions section, which includes theoretical and practical implications, limitations, and future research directions emerging from this study.

2. LITERATURE REVIEW

It is important to acknowledge that technology has enabled greater efficiency and convenience in the provision of banking services ( Bhatt, 2021; Chaouali & Souiden, 2019). From the automation of administrative processes to the development of online payment systems, technology has simplified and expedited many everyday banking operations ( Laukkanen, 2016), allowing consumers to have better user experiences. However, empirical research has identified that there are barriers to technology acceptance among customers ( Venkatesh et al., 2003).

The increasing reliance on technology in banking services also presents significant challenges, not only for institutions but also for consumers ( Leesakul et al., 2022). In this regard, there is a risk of financial exclusion for those who lack access to technology or do not know how to use it ( Chaouali & Souiden, 2019). As banks close physical branches in favor of online services, those with no internet access or who are not comfortable conducting transactions online may be left behind ( Laukkanen, 2016). This can widen the digital and financial divide, leaving some segments of society without access to essential banking services ( Chung & Liang, 2020).

Colombian financial consumers find themselves immersed in an environment of transformation, marked by the convergence of technology and financial services. Due to the diversity of profiles, ranging from individuals with limited access to financial services to those with a high degree of technological literacy and familiarity, banking institutions that incorporate customer relationship management and market orientation into their corporate philosophy must understand consumer behavior to adapt their offerings to actual demand ( Schulze et al., 2022). With the increasing market penetration of the internet and the proliferation of mobile devices, more and more customers are turning to mobile applications and online platforms to make transfers, check balances, pay bills, and perform other transactions ( Hossain et al., 2020).

Despite this trend toward digitization, certain preferences for human contact and personalized advice persist among Colombian financial consumers, such as in the case of the elderly segment, which is becoming increasingly important due to the investment in the population pyramid, not only regionally but also globally ( López-Rodríguez et al., 2024). This duality in the preferences of these customers presents a challenge for banking institutions, which must balance digital service offers with personalized attention ( Martínez Jiménez, 2021). For this reason, the acceptance and use of technology by the consumer in the Colombian banking sector needs to be analyzed.

UTAUT was proposed by Venkatesh et al. (2003) to integrate various prominent technology adoption theories. These include the Technology Acceptance Model ( Davis, 1989), the Extended Technology Acceptance Model ( Venkatesh & Davis, 2000), the Revised Technology Acceptance Model ( Venkatesh & Bala, 2008), and the Socio-Cognitive Technology Acceptance Model ( Thompson et al., 1994), among others.

The idea of the UTAUT theory is to explain and predict the acceptance and use behavior of technology in various contexts ( Venkatesh et al., 2016). It consists of four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. Additionally, it includes four moderating factors: age, gender, experience, and voluntariness, which are related to predicting the behavioral intention to use technology and actual technology use, mainly in organizational contexts ( Venkatesh et al., 2016).

Performance expectancy, as defined by Fernández Morales et al. (2015), refers to the level of benefit that users believe that they will reap from using an information system. According to Puspitasari et al. (2019), it also corresponds the degree to which using certain types of technology offer benefits when certain activities are carried out, based on the perception of how the technology will perform with a probabilistic and subjective estimation, involving associating a specific action or performance with one or more outcomes ( Yu et al., 2021). In this regard, it is proposed that:

1. H1: Performance expectancy predicts users’ behavioral intention and actual use of technology in the Colombian banking sector.

Effort expectancy represents the subjective and probabilistic relationship between one`s effort and the likelihood of one`s successfully performing an action or task ( Maillet et al., 2015). According to Morales et al. (2015), it is also elated to the usability of a particular system. This component supports the consumer's perception regarding how easy or difficult it is to use technology in consumption processes, or to learn how to use it, which, in turn, impacts a user’s intentions ( Puspitasari et al., 2019). Based on the above, it is proposed that:

1. H2: Effort expectancy predicts users’ behavioral intention and actual use of technology in the Colombian banking sector.

For Maillet et al. (2015), social influence is used to measure the impact of perceived influence from a user's social circle, whether it be friends or family, on the use of technology. According to Fernández Morales et al. (2015), it is also referred to the extent to which an individual perceives the use of an information system as important to those around him/her. It is one’s ability to develop or change one’s mind, feelings, attitudes or behavior because of interaction with others that is perceived as being similar ( Yu et al., 2021). Therefore, it is proposed that:

1. H3: Social influence predicts users’ behavioral intention and actual use of technology in the Colombian banking sector.

Facilitating conditions represent consumers perceptions of available resources and customer to carry out the actions they wish to perform, focusing on access to the resources they need to use new technology ( Al-Mamary, 2022). Puspitasari et al. (2019) have indicated that these are supportive criteria reflecting the extent to which an individual perceives that the organization is being able to handle new technology. They can be considered objective factors that allow consumers to perform the tasks that they need ( Almogren, 2022). In this regard, it is proposed that:

1. H4: Facilitating conditions predict users’ behavioral intention and actual use of technology in the Colombian banking sector.

Behavioral intention to use is a key component that represents the willingness of users to incorporate technology into their consumption processes, so it is a fundamental predictor of actual use of technology behavior ( Chao, 2019); therefore, the higher the intention to use technology, the greater the likelihood that it will be coupled and used Venkatesh et al., 2016).

It is pertinent to highlight that while usage intention predicts users’ initial willingness to adopt technology, actual usage reflects the practical and sustained implementation of technology in their daily lives ( Chao, 2019); hence, actual use o technology refers to when, in real situations, they have adopted it in their consumption processes ( Williams et al., 2015), making it a measure of effective and continuous technology use in their everyday lives ( Arfi et al., 2021).

The relationship between behavioral intention and consumers’ actual use of technology is a phenomenon that has been studied in various fields, including psychology, sociology, and marketing. It refers to the disparity between what people say they will do or think they will do regarding the coupled and use of a type of technology, and what they do ( Chan et al., 2023; Leesakul et al., 2022). This is known as perceived complexity, which emerges when they intend to use a new kind of technology then ultimately perceive it to be too complex to use ( Chung & Liang, 2020).

Another aspect related to behavioral intention and actual use of technology is framed within barriers of resistance to technological innovation ( Laukkanen, 2016). The challenges of technology acceptance which the banking sector seeks to overcome are greater integration of advanced technology and a deeper understanding of the psychological and functional factors that convince consumers to adopt it ( Chaouali & Souiden, 2019). While functional barriers are represented by various obstacles that may hinder the use of new technology by individuals in consumption processes ( Antioco & Kleijnen, 2010), psychological barriers refer to cognitive and motivational processes that prevent specific action or behavior modification from occurring ( Bercht, 2019).

Taking the above into account, Figure 1 presents the theoretical model addressed in this research.

3. MATERIALS AND METHODS

This body of research has been consolidated through an empirical study that adopted a quantitative, descriptive, and causal approach. To achieve the study objectives, an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were used, later complemented with a Structural Equation Modeling (SEM) framework, following the recommendations of Hair et al. (2021).

3.1. The participants

The study involved 556 financial consumers and virtual banking users in Colombia aged between 18 and 65 (M = 35.01; SD = 11.38). Of these, 52.9% were men and 47.1% were women. Non-probabilistic convenience sampling was used, resulting in 497 effective surveys. The instrument was applied anonymously, with participants being informed about the research objectives, confidentiality of information, non-remuneration for their participation and adherence to bioethical principles, which formed the first component of the survey and which was provided to participants in digital format. The fieldwork was conducted in the city of Bogotá, the capital of Colombia, between November and December 2023.

3.2. The instrument

The instrument used was divided into three parts: the first part consisted of informed consent; the second presented 5 items that allowed for the demographic characterization of the participants; the other part located the elements associated with the UTAUT that have been decisive for the analysis of consumer behavior in the banking sector, as proposed by Venkatesh et al. (2003). These elements are facilitating conditions, performance expectancy, effort expectancy, and social influence. In addition, this section explains how the intention of behavioral conduct and the actual use of technology was measured. All the constructs were measured with observed variables with a 5-point Likert scale, with 1 being "totally disagree" and 5 "totally agree".

The applied instrument obtained an overall Cronbach's Alpha of α=0.935, the validation of the theoretical constructs being shown in Table 1. This internal consistency indicates that the items of the instrument were consistently and reliably correlated with each other, generating relevant implications for the quality and interpretation of EFA, CFA, and SEM, which are less likely to be affected by random errors.

3.3. Procedure and data analysis

To provide psychometric support for the instrument, an exploratory factor analysis (EFA) followed by a confirmatory factor analysis (CFA) was conducted. In the EFA, the Kaiser-Meyer-Olkin (KMO) test was used to assess the adequacy of the data for factor analysis. Subsequently, Bartlett's test of sphericity was performed to determine if there was a significant correlation structure among the variables studied. The extraction method used was principal axis factoring, with a Varimax rotation, in order to obtain a solution of correlated factors and identifying structures for the set of observed variables and group them into latent factors ( Grieder & Steiner, 2022).

For the CFA, the maximum likelihood (ML) method was employed due to the ordinal nature of the items on the measurement scale ( Chen et al., 2015). The model fit was evaluated using the chi-square test ( ${\chi }^2$), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR), where values below 0.05 indicated a good fit, and values between 0.05 and 0.08 referred to an acceptable fit ( Li, 2016). Additionally, the Comparative Fit Index (CFI) and the Tucker-Lewis Index (TLI) were considered, with values above 0.95 indicating a good fit ( Li, 2016).

The Omega coefficient (ω) and the Alpha coefficient (α) were reviewed to assess the internal consistency of the scale and were deemed adequate, as they were above 0.80 ( Hayes & Coutts, 2020). The average variance extracted (AVE), which represents the average amount of variation that a latent construct can explain in the observed variables to which it is theoretically related, was calculated. This indicator should be greater than 0.50 ( Dos Santos & Cirillo, 2023). The KMO test was presented for each item, which allowed the adequacy of the sample to be analyzed that supported the relevance of the factorial analysis conducted, with values close to 1.0, indicating that a factorial analysis may have been useful with the data available ( Chen et al., 2015).

Subsequently, a SEM model was developed using the ML estimation method, which produces estimators that are asymptotically efficient, meaning that they have minimal variances in large samples ( Maydeu-Olivares, 2017), the development of this analysis of the information was conducted using the open-source software JASP, which is characterized by implementing methods such as frequentist statistical analysis, Bayesian analysis, and regression analysis ( Huth et al., 2023).

4. RESULTS

To identify the underlying structure of the variables in the instrument used in the study, an EFA was conducted, including both the dependent variables (BI, ATU) and the independent variables (PE, EE, SI, FC), so that each factor could be revealed separately. Bartlett's test was analyzed, which was observed to be statistically significant ( ${\chi }^2$ = 2783.254, p < 0.001), allowing for the consideration of factorial extraction. The KMO test was performed to whether the total sample was suitable enough to support the relevance of this analysis, yielding a value of 0.922, as shown in Table 2.

Regarding the RMSEA, which explains the amount of unexplained variance, a value of 0.071 was obtained, indicating a good fit for the data. The TLI, which compares the fit of the proposed model to a null model in degrees of freedom, reached a value of 0.921, identifying a very good fit for it. As for the CFI, which evaluates the goodness of fit of a model to the observed data, the result was 0.965, determining an excellent fit of the model.

Based on the solution provided by the EFA, which utilized principal axis factoring with a Varimax rotation, five latent factors were revealed, explaining 65.7% of the cumulative variance. This finding suggests a complex yet significant underlying structure in the data, providing a solid foundation for the relationships between dependent and independent variables to be better understood.

A combination of the two dependent variables was included, resulting in an initial factor termed "behavioral intention and actual use of technology (BIA)," which accounted for 19.2% of the explained variance, comprising six items. This factor focuses on behavioral intention and actual use of technology.

Regarding the independent variables, PE was the second factor, explaining 16.7% of the variance, composed of four items. The third factor, FC, accounted for 10.9% of the variance, comprising four items. EE was the fourth factor, justifying 9.1% of the variance, with three items. Finally, the fifth factor, SI, attributed 8.2% of the variance and consisted of three items. The factor loadings of the five emerging latent factors are presented in Table 3

To support the results obtained in the EFA, a CFA was conducted. This was done to address its weaknesses ( Thompson, 2004) and maintain the phases of structural model construction ( Escobedo et al., 2016). The maximum likelihood factorial method (ML) was employed, which allows for estimating the model parameters in such a way as to maximize the probability of observing the actual data based on those parameters. ( Liang & Luo, 2020).

Table 4 presents the overall fit indices of the CFA, allowing the fit of the proposed model with the observed data to be evaluated and its underlying structure to be better understood, with a favorable outcome found in this study. The results indicate that the observed factors of the test confirmed the theoretical factorial structure, which subsequently enabled the development of the structural model.

The data from the latent variables and their respective items showed a general Omega Coefficient (ω) of 0.936, and an Alpha Coefficient (α) of 0.930. Table 5 presents the same indicators independently for each of the resulting latent variables from the EFA, along with the unstandardized factor loadings for each item, the KMO test, and the standard error, revealing satisfactory results for each subscale.

For the final stage of the study, and to construct and evaluate the structural model, the ML estimation method was used. The first element considered for the model fit to be evaluated was the chi-square to degrees of freedom ratio ( ${\chi }^2$/df) where the following results were obtained: ${\chi }^2$ (163.940) / df (94) = 1.74, meeting the parameters proposed by Escobedo et al. (2015), who stated that values below 3 are considered acceptable. Table 6 presents other fit indices for the proposed structural model to be analyzed, which were favorable for the empirical exercise, while Figure 2 displays the structural model of the UTAUT Theory in users’ behavioral intention and actual use of technology in the Colombian banking sector.

The integration of "behavioral intention" and "actual use of technology" as a specific construct (dependent variable) in this research implies having a grasp of the dynamic relationship between consumers’ willingness to adopt the technology and their actual behavior when interacting with it in relation with products in the Colombian banking sector. This relationship is fundamental for understanding the technology adoption process and provides valuable information for designing effective implementation and promotion strategies for technology in the banking sector.

Table 7 presents the hypothesis test, where two out of the four proposals were accepted. Regarding H1 and H3, it was inferred that performance expectancy and social influence did not predict users’ behavioral intention and actual use of technology in the Colombian banking sector and were, therefore, rejected. H2 and H4 were accepted because effort expectancy and facilitating conditions did predict them.

5. CONCLUSIONS

The objective of this article was to provide empirical evidence of the relationship between the elements of the UTAUT, with customers’ behavioral intention to use technology and their actual use of it in the banking sector. The results showed that both effort expectancy, referring to an individual's perception of the amount of effort required to adopt and use new technology, and facilitating conditions, representing the extent to which consumers perceive that resources exist to facilitate their experience with technology, predict this theory when it comes to banking. However, performance expectancy and social influence as relevant components of UTAUT neither had a significant impact as a predictor on the behavioral intention of consumers to real use technology.

These results have significant implications for the commercial management of banking entities, particularly in terms of strengthening their market orientation. This challenge for contemporary organizations requires the consolidation of integrated marketing, understood as the ability to integrate other management areas into the marketing function, primarily to collectively meet customer expectations. The findings align with previous research indicating that the UTAUT model is suitable for evaluating technology adoption, intention, and consumption in the banking sector ( Patil et al., 2020; Samartha et al., 2022), as well as for studying customer satisfaction ( Marinković et al., 2020; Bhatt, 2021).

Examining the links between the UTAUT theory and behavioral intention and actual use of technology reaffirms the importance not only of intention in consumer adoption studies in the banking sector but also of usage behavior. This is a distinguishing factor of this research since, unlike most studies that have focused on intention, it emphasizes these two elements as dependent variables, Called Behavioral Intention and Actual Use of Technology," when EFA was applied.

5.1. Theoretical implications of the research

It should be made clear that the UTAUT theory has been validated and applied in various research scenarios, which supports its theoretical relevance. Therefore, its applicability in specific empirical settings allows for research to be conducted that generates solid knowledge about consumer behavior in the banking sector ( Al-Mamary, 2022; Arfi et al., 2021; Chao, 2019; Patil et al., 2020; Yu et al., 2021; López-Rodríguez et al., 2024).

It should also be highlighted that the UTAUT theory integrates psychological and social factors, recognizing that the decision to adopt technology is not solely based on individual aspects ( Theis et al., 2018). Therefore, components of UTAUT such as performance expectancy and social influence let both personal perceptions and social influences in technology adoption decision-making be considered ( Koenig-Lewis et al., 2015).

Mbama and Ezepue (2018) reported that the UTAUT Theory addresses both positive expectations, represented by performance expectancy, and negative ones, standing for effort expectancy, providing a holistic approach to the user experience. This reflects the complexity of consumer behavior and underscores the need to understand both positive and negative perceptions that can affect technology adoption ( Chaouali & Souiden, 2019).

The facilitating conditions of the UTAUT theory highlight the importance of the organizational when technology adoption, Gutierrez et al.’s (2015) suggestion which emphasizes the significant role that service providers play in ensuring that end-users receive a proper value proposition that strengthens the consumption of financial and banking services. This implies that consumer behavior is not only determined by individual factors but also by the organizational context that provides resources and support ( Shaikh & Karjaluoto, 2015).

UTAUT focuses on initial adoption and even addresses the continuous use of technology, which implies considering temporal dynamics in consumer behavior ( Dauda & Lee, 2015). The factors influencing initial consumer intention may differ from those affecting the decision to continue using financial technology over time ( Petersen et al., 2015).

5.2. Practical implications of the research

Highlighting the practical implication of this research, the study of the UTAUT Theory in the banking sector not only helps understand behavioral intention and actual use of technology y financial consumers but also provides valuable insights for continuous innovation. Among these, user experience design stands out through the implementation of intuitive interfaces. Understanding the expectation of effort can lead to the creation of user-friendly interfaces in banking applications and platforms, reducing perceived complexity that can enhance technology acceptance.

The development of marketing strategies associated with the existence of tangible benefits influences performance expectancy, which should focus on clearly communicating how technological solutions enhance efficiency, security, and accessibility for customers. These strategies should be complemented with training programs and specific technological development to address consumer perceptions and concerns.

From a social influence management perspective, it is pertinent to promote positive feedback. Such influence plays a significant role in integrated marketing communications. Therefore, activities such as testimonials, reviews, and referral programs can contribute to building a positive perception in the brand community of banking entities.

The facilitating conditions highlight the importance of the organizational environment in enhancing technological infrastructure, not only in business processes but also in administrative activities. This involves providing adequate resources and structuring internal policies that support the adoption and continuous use of financial technologies for the benefit of the consumer.

5.3. Limitations and future research directions

As limitations of the research, it should be highlighted that mediator variables were not used, which could have explained the relationship between the elements of the UTAUT, with bank customers’ behavioral intention to use technology and their actual consumption of it. It would be wise to use such mediator variables in future research to holistically recognize the behavior of consumers in your occupation.

Another limitation is the cross-sectional design of the study and its sample size; in light of this, it would be highly recommended for future research to perform experimental longitudinal studies with larger samples, as it would enrich the empirical results and the literature review of consumer behavior in the banking sector.

As future avenues for research, it is suggested to study the UTAUT Theory for different market segments, particularly across different generations, identifying which financial consumer behavior is characteristic of Baby Boomers, Generation X, Millennials, and Generation Z. The physical and psychological features of each of these generations require banking entities to generate, develop, and deliver their value propositions in a customized way. The study of the UTAUT Theory enables companies not only to anticipate consumption trends but also to design strategies that align with the expectations and preferences of each generation, thereby driving long-term success in the market.

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Author notes

^acelopez@uniminuto.edu^bluirodriguezca@unal.edu.co

IR