Intention to Adopt Industry 4.0 by Organizations in Colombia, Ecuador, Mexico, Panama, and Peru

This study aims to understand the factors that drive actors belonging to the sector of organizations in Latin America (LA) to adopt Industry 4.0. The proposed model results from the analysis and integration of the technology adoption model (TAM), green information technology adoption model (GITAM), and theory of planned behavior (TPB). To determine the predictive factors for internal organizational actors, the research team surveyed information on organizations belonging to Colombia, Ecuador, Mexico, Panama, and Peru. Information was collected from strategic, tactical, and operational personnel. Data were collected from 499 organizational actors in the productive sector, processed, and analyzed using a structural equation model with the partial least squares technique. The study model explains, first there is an influence of the variables Industry 4.0 perceived ease of use (PEU) and Industry 4.0 perceived utility (PUT) on Industry 4.0 attitude towards use (ATU). Second, there is a positive influence of Industry 4.0 technological context (ICO), Industry 4.0 subjective norm (SNO), Industry 4.0 attitude (ATT), Industry 4.0 attitude towards to use (ATU), and Industry 4.0 attitude behavioral control (BCO) on intention to adopt Industry 4.0 in the organization (IAI). Third, what was not supported is the influence of Industry 4.0 technological context (ICO) on the intention to adopt Industry 4.0 in the organization (IAI). The model results are consistent with those of other studies on technology adoption, and propose a model for Industry 4.0, which is a significant contribution to this study, especially for developing countries.


I. INTRODUCTION
The continuous and accelerated evolution of information and communication technologies (ICT) and their inclusion in industries and organizations has led to the fourth revolution, Industry 4.0 [1]. The first reference to Industry 4.0 was introduced at the Hannover Industrial Technologies Fair in 2011; Industry 4.0 has now become a global scientific and technological program for the development of industries, especially in industrialized countries [2], [3].
Industry 4.0, on the other hand, integrates production operations systems and modern technologies, especially ICT [4]. This wave of change within organizational ecosystems The associate editor coordinating the review of this manuscript and approving it for publication was Zhaojun Steven Li . influences the strategies, frameworks, and operating models that must be adapted and integrated [5]. Industry 4.0 enables connection information, objects, and people owing to the convergence of the physical and virtual worlds (cyberspace) in the form of cyber-physical systems (CPS); therefore, it enables the transformation of industries and organizations into intelligent environments [6]. The term CPS was coined in the USA in 2006 [7] because of the growing impact of the interactions between interconnected computer systems and the physical world. However, CPS is a thematic axis, not a discipline [4].
The impact of Industry 4.0 is reflected in various scenarios, such as professional, personal, home, cities, organizations, and systems. The Industry 4.0 paradigm has modified the set of skills required for professionals in the labor field [8], and companies cannot implement state-of-the-art technologies if their workforce does not have the skills to use that new technology [9]. Consequently, educators in education, as in the case of universities, are called upon to adopt curricula to develop new professional skills and meet new market requirements [10].
From a personal point of view, self-improvement, openness to change and conservation values significantly affect the inclination toward leadership in the Industry 4.0 workplace [11]. Due to the massive changes that the implementation of Industry 4.0 implies, the behavior of organizational actors must adhere to certain personal values that are emphasized in new challenges [12].
Through Industry 4.0, the transformation of conventional home appliances into IoT-enabled systems has given rise to smart home systems [13]. This new technology allows appliances to provide benefits such as personalization, energy savings, prediction, defect reduction, and quality improvement [14] through feedback, which is a product of the process of large volumes of data stored in the cloud.
The term smart city is the model of new urbanization based on the application of new-generation technologies of Industry 4.0, for the planning, construction, management, integrated industrialization, computerization, modernization, and sustainable development of cities [15]. The development of a smart city promotes new conditions of life, work, health, education, and accumulation of human capital, and captures financial resources for business development [16], [17].
Within organizations, the Industry 4.0 concept requires innovation, and continuous education not only depends on the skills of the staff but also on organizational culture [18]. It has been detected that a large part of research on Industry 4.0 analyzes technical aspects, without giving greater impetus to the management approach, process organization, corporate strategy, work organization, human resource development, and organizational culture [19].
Implementing Industry 4.0, which implies human interaction with the technical part; Industry 4.0, is a system related to humans (socio) and not related to humans (technical) in search of a certain goal; that is, it is a sociotechnical system [20]. Consequently. Industry 4.0 is subject to the perspective and principles of open systems for adaptability in the face of external disturbances in the environment [21].
This hashes with a new paradigm that transforms the world [22]. However, the most significant impact is expected in the corporate and industrial sectors of manufacturing, management, logistics, and marketing [23], where it is necessary to adopt emerging processes and implement effective data management [24]. Industry 4.0 capabilities bring considerable benefits to companies, such as real-time data analysis, product customization, increased visibility, monitoring and control, dynamic product implementation, and improved production [25], contributing to the optimization of operations and significant reduction of costs and leading times [26].
The manufacturing sector represents approximately 15 % of the world's gross domestic product (GDP) [27] and is one of the most relevant activities for generating wealth. Therefore, some of the most advanced economies seek to improve their productivity and efficiency in industrial production by incorporating Industry 4.0 [28]. Nevertheless, it is necessary to clarify that companies adopt Industry 4.0 in three tracks: laggards, emerging industries, and leaders [29]. Some developed countries, such as Germany and the United States, started implementation in 2011 and are currently prominent leaders; the rest are in the laggard and emerging categories.
Only a few organizations implement Industry 4.0 technologies worldwide, especially in developed countries [30]. LA cone countries are no exception to the development of the manufacturing sector, which represents a significant percentage of GDP [31]. Furthermore, scenarios such as competition, short life cycles, new products, and demand, among others, generate new challenges in the industrial field [32]. For this reason, LA companies are struggling to increase their productivity and competitiveness.
In LA, Mexico is considering the route to implement Industry 4.0 [33], not to mention Brazil; however, there is uncertainty about some factors such as cost, knowledge, culture, and training in universities, among others [34]. The problem is that although, in general, many LA countries have made considerable progress in measuring business innovation [35], no broad international initiative has been taken to measure the state of adoption of Industry 4.0 [36]. So far, there has been little comparative evidence on Industry 4.0 efforts and activities in the LA region.
In LA, there are limited studies on adopting Industry 4.0 to help organization improve their efficiency. Although efforts and initiatives have been implemented in isolation, there should be a reference framework for this industry context that allows relating the variables and indicators that lead to a technology adoption process such as Industry 4.0 [37]. This study examines the adaptation of Industry 4.0 in the organizational context, specifically in the industrial context. An initial starting point is identifying the potential drivers, success factors, and barriers of this technological transformation [38].
Technology transfer is understood as the accessible process of knowledge transfer in an organization [39], while adoption is about how we integrate our processes to bring about significant change. In this research, technology adoption was considered to incorporate operational processes such as production, management, and sales. Enabling flexibility, cost, efficiency, quality, and competitive advantage key benefits for the adoption of Industry 4. 0 [40].
The research proposes a model for Industry 4.0 adoption in organizations in LA country contexts. The following research questions are formulated: What are the factors that influence the organization to decide on the adoption of Industry 4.0? What is the appropriate model to reflect the influence of the factors on the adoption of Industry 4.0? In order to answer the questions posed, a review of the literature related VOLUME 11, 2023 to technology adoption models has been used to integrate constructs of TAM, GITAM, and TPB.
A wide variety of models for the adoption of technology are available in the literature; however, for this research, discrimination has been carried out based on what has been stated by several researchers, who explain that the reason for introducing models for the adoption of technology is to direct the intention of use towards the electronic option. Organizations that are not aware of their influence are able to perceive their use.
As explained, Table 1 is structured, in which five models of technology adoption were analyzed based on five factors, thus asserting the TAM, GITAM, and TPB models. From the point of view of several authors, they have a greater feasibility of use for the type of research proposed. These models seek to explain the relationship between technology acceptance and adoption, demonstrating that the perception of usefulness and ease of use are critical factors in the process of technology adoption and the use of systems, becoming a fundamental resource for organizations achieving optimization and improvement in the different areas of the company through an essential role in achieving competitive advantages in the environment. This was validated by a study by Money and Turner [41].
To support the greater elements of judgment, we explain the models indicated below in more detail. TAM is one of the most important models, with almost 20 years of development, and has become the main model to explain the mechanism of information system adoption. This is strongly supported by the theory of action reasoning, which implies that one can realize one's intention to use without any restrictions if one only intends to act [42]. GITAM defines Green IT four different but interrelated perspectives, positing that technology, organization, environmental contextual variables, dynamic dimensions of Green IT readiness, and strong order drivers of green IT can predict the intent, breadth, and depth of the adoption of green IT [50]. Regulatory requirements and legislative actions are likely to play important roles in the adoption of green policies technologies, and may force some companies to accept a technology even if they do not have a strong intention to do so [43], [44]. The theory of planned behavior (TBP) specifies and proposes that attitudes toward certain behavior, the existence of rules, and companies' perceived control are three key antecedents that determine their intention to perform a certain behavior [45]. Table 1 summarizes the analysis of five models of technology adoption and the reasons for their choice by the authors, where they are compared based on five relevant factors, considering those that meet between four and five of them.
The variables described lead the organizations studied to be more efficient in order to stay in the market, and respond to needs and strategies, through the analysis of these variables it is possible to face the challenge of the current market, which is why it is important that Industry 4. 0 continues to support companies to change their mentality in the offer of their products or services, considering the field of modernity as a new technological tool [57]. These variables contribute to organizations to technological innovation in all departments of companies, the use of IT Information Technology allows the total digitization of production areas with fully automated processes, radically improving internal processes, such as production and administration [58].
The factors described lead to the models proposed to be considered by the organizations under study since the use of new technologies supports companies to have a broader panorama, that is, a vision to modernize, leaving aside obsolete technologies. In relation to F1, in recent decades, the TAM has been considered a model of user acceptance of information systems and has been proposed with more extended factors on the acceptance of technology according to technological characteristics, individuals, and organizations [59]. Within F2, it is explained that for the acceptance of the TAM and Green IT models by an individual or company, it addresses personal norms on environmental responsibility and social norms to preserve the environment of the organizations, which are considered important factors, in addition to economic factors such as the reduction of costs through perceived profit [60] For F3, within companies, current technology models represent the ideal pathway as an important step towards the adoption of technology innovation [108]; Organizations currently face problems in attracting customers, which is caused by the use of existing obsolete technology; for this reason, companies are adopting technology innovation as a tool to provide enriching experiences through technology models such as TAM and Green IT, among others [110]; a positive attitude on the part of senior management of organizations towards change is important to create an organizational environment that is receptive to innovation [61]; senior management commitment and support for innovation are particularly important through the alignment of various factors, where coordination between organizational divisions and problem-solving is essential [62].
Regarding the F4 function, conduct and indirect real conduct are attitudinal factors in the use of technology by companies and investors. The application of technological models for online commerce is considered the most common variable in some theories [137], [140]. The F5 function explains that the acceptance of IT is positively influenced by the perceived usefulness through the size and budget of the company, and the usefulness of the use of IT occurs when organizations think about improving their business management through excellent work performance [119], [128].
Finally, the study is based on an analysis of empirical evidence of the relationship between the main factors described by Abroud et al. [120] in Table 1. Hillmer [63], in his research, deepens the analysis by comparing factors in technology acceptance models, where he explains that existing theories and models have produced useful ideas about the cognitive aspects, affective and behavioral responses of companies towards new technological disruptive.
According to Table 2, the variables were selected based on five factors considered fundamental for their veracity. Variables that meet between three and five factors are considered for the model, otherwise, they are not taken into account.
The factors F1 to F5 underpin the proposed variables because they are directly involved in internal and external processes, such as production and administration in an organization [58], through which it is possible to face the challenge of the current market. Therefore, it is important to implement Industry 4.0 it allows companies and their collaborators to change their minds or beliefs about the offers of their products or services. Within the framework of organizational growth and development [64], considering the field of modernity as a new technological tool [57] that pays tribute to the organizational and environmental technological context, allowing companies to have a broader panorama, leaving obsolete technologies to enter the world of innovative technology [65]; in this way, they will be more efficient through better performance at the work level, framed by good practices of conduct and ethics [95].
Finally, factors F1 to F5 were based on the analyses described by Loo et al. [108], Stieninger et al. [218], and Dalvi-Esfahani et al. [220] in their research, as shown in Table 2. They emphasize that they have contributed substantially to the growth of organizations framed in new technologies together with the environmental aspect with appropriate rules of conduct and ethics, which have contributed to this research.
From GITAM, the proposal considers the contextual variable, which we refer to this variable as Industry 4.0 context. From TPB, the proposal considers the variables: attitude, subjective norm, and behavioral control. Moreover, from the TAM model, the following variables are taken into account: perceived ease of use, attitude towards to use, perceived usefulness, and intention to adopt Industry 4.0 in the organization.
The data were collected from organizations in Mexico, Peru, Panama, Colombia, and Ecuador, from 399 organizational actors in industrial sectors, processed and analyzed through a structural equation model using the PLS technique.
In the paper, the subsequent sections that make up this document are organized as follows: section II explains the hypothesis development; section III highlights the research method applied; section IV executes the data analysis and results; followed by their discussion in section V; sections VI and VII consider the research contributions, implications, and conclusions.

A. CLOUD TECHNOLOGY
The cloud is a virtual storage space on the Internet that is not the same as the Internet because the cloud is only a part of the Internet [71]. It has the most powerful computing architecture, allowing large amounts of data collected from systems, devices, equipment, and sensors to be stored on remote servers and has hardware, software, and networked Internet infrastructure. Cloud systems allow access and retrieval of large amounts of data in real-time [72].

B. THE INTERNET OF THINGS (IoT)
Is a centralized control system that communicates and interacts with different equipment and systems and is the set of sensors, instruments, and autonomous devices connected through the internet to industrial applications [73]. This network allows data collection, analysis, and production optimization, increases efficiency, and reduces manufacturing and service delivery costs [74]. Furthermore, through IoT, a collaboration between enterprises can be enabled by improving functionality and business capabilities [75].

C. BIG DATA
It is a concept that encompasses large volumes of data, both structured and unstructured. This is a complex and large amount of data that none of the traditional data management tools are capable of storing or processing efficiently. Big data uses large volumes of data to improve efficiency and productivity [76]. It helps organizations gather value from large volumes of data to improve efficiency and process performance, customize products, increase flexibility, and enable fast and real-time decision-making [77].

D. SIMULATION
Processing and data collection from big data and cloud systems can be used as a source of a virtual model to analyze all possible scenarios related to product design, development, and production [78]. Simulations are widely used in business modeling to leverage available real-time data and simulate real-world work in a virtual ecosystem [79]. In addition, processes can be tested and optimized through simulations even before implementing changes in a real scenario [69].

E. AUTONOMOUS ROBOTS
Are used in many areas, including manufacturing, logistics, e-commerce, and training [80]. Robots and humans have strengths and limitations, working safely together provides a better-quality product with high precision in less time. The goal of robotics and Industry 4.0 is to improve productivity, generate high-quality products at a low price, and meet customer expectations [81].

F. ADDITIVE MANUFACTURING AND THREE-DIMENSIONAL (3D) PRINTING
Are used to produce objects layer by layer in three dimensions. Functionality in Industry 4.0 is widely used for batch production of scaled and customized products [82]. The quality of materials is vital for efficient additive manufacturing, as some processes can process a more comprehensive range of materials than others [83].

G. AUGMENTED REALITY (AR)
In Industry 4.0, a wide variety of services can be implemented, such as the design of a production line, physical infrastructure, and maintenance schemes, among others, through the use of mobile devices or remote control equipment [84]. AR helps identify and avoid design errors in the early stages of the development process, reduces the number of physical prototypes, saves time and cost for companies [85]; AR is considered a valuable tool for improving and accelerating the development of products and processes in many industrial applications [86]. Although AR is in its early stages; however, in the future, organizations will use it to improve their organizational processes and decision-making [87].

H. BUSINESS INTELLIGENCE (BI)
Involves the provision of technology platforms to collect, store, analyze, and present data obtained from different sources in the organization to support decision-making [88]. In the current competitive environment within the business context, BI has become a vital tool at strategic, tactical, and operational levels. This technology acts as a key and strategic factor for the organization because it provides decision-makers with timely and reliable information to respond to situations that may arise in the company, such as entering new markets, cost analysis, and profitability of a product line [89].

I. CYBERSECURITY
In the context of Industry 4.0, is critical because of the likely increase in security threats [90]. The main elements associated with cybersecurity are the assets involved, system vulnerabilities, cyber threats, and risks within industrial contexts, where physical systems are connected through the Internet. The successful implementation of cybersecurity measures consists mainly of the prevention of internal and external attacks in industries, but in an integrated manner [91].
The main characteristics of Industry 4.0 are collaboration and system integration, both horizontal and vertical [92]. In horizontal integration, ICT are used to exchange information between different actors within a network [93], whereas, in vertical integration, ICT are integrated at different hierarchical levels of the organization covering control, production, operations, and management [94].
The main drivers and barriers to adopting the Industry 4.0 paradigm come from the literature review and are located within several dimensions: organizational, technical, and ethical strategies [95].
Regarding technology adoption, some frameworks consider aspects related to adopting new technologies [96]; these factors are fundamental since they enable investment decisions to be made [97]. For Sperber [98], adopting new technologies is a function of managers' awareness, organizational culture, and problems that encourage risk. The literature also identifies antecedents such as sector, size of the organization, level of complexity, and availability of scarce resources for technology adoption [99].
TAM, it is one of the most popular models cited in research on user acceptance of technology [100]. TAM aims to predict user acceptance and highlight drawbacks before users interact with the system [101]. In fact, for TAM, the acceptance of any technology is fundamentally affected by the user's perception of the use and usefulness of the technology [102]; as it contains two primary constructs that identify it: perceived ease of perceived usefulness and perceived ease of use, which are used in numerous contexts [103].
In GITAM, there are dimensions called Green IT enablers (drivers), which could influence the adoption process of Green IT. Therefore, it is necessary to resort to the theory of motivation to identify these enablers [104].
TPB, emphasizes the importance of attitudinal components in behavior prediction and explanation [105]. Several authors believe that using the TPB as a framework can describe much of the intention and future behavior in the study of environmental behavior [106]. In contrast, Yuriev et al. [107], Loo et al. [108], TPB has limitations in predicting human behavior. Therefore, it is advisable to incorporate more variables according to the context analyzed, demonstrating the flexibility of PTB to adapt.

III. HYPOTHESIS DEVELOPMENT
The research hypothesis is a logical relationship between two or more variables expressed in a statement. The research hypotheses are presented in the following sections. To do this, each of the variables from literal A to H is studied, and the relationship between the variables is justified based on theory. A hypothesis was proposed at the end of each literal. In organizations, the current technological system is one of the essential measures to consider the ease of use of new technology [109]. An organization's technological competence is related to its personnel's technical competence through development and training, leading to the ease of use or adoption of new technologies. According to Ramdani at al. [110], the management of organizations can intervene positively in the use of new technologies by expressing a broad vision and fortifying value through the company.
Companies currently face problems in attracting customers, the cause of which is the use of existing technology. Therefore, organizations are looking for new technologies that provide enriching experiences for the market [111]. The opportunities offered by the increasing use of technology in the business world are changing the competitiveness of organizations in the market [112]. On the other hand, for Ghobakhloo et al. [113] only some organizations with sufficient economic resources will have the facility to use the new technology of their choice.
With the increasing ease of technology, materials, energy, essential knowledge, and other factors to develop them will inevitably grow at the same rate [114], [115]. Industry 4.0 is currently known as Industry 4.0, for the ease of digitization in production processes within an organization [116]. For Hofmann and Rüsch [117], the ease of use of the term Industry 4.0, is aligned to describe the concept of digitization of the company, fully automated production processes integrated through a supply chain, which will allow the reduction of labor through cost minimization.
Davis [46] and Abroud et al. [118] explain that perceived ease of use is understood as the degree to which a person assumes that the use of a system would involve less mental and physical effort. Therefore, the organization perceives it as user friendly, giving rise to a favorable attitude towards use. In addition, individuals and companies that demonstrate their identity also have positive intentions regarding the ease of use of technology [119].
An investor's perception of the ease of use of, for example, an online stock trading system could generate a positive attitude towards the use of this system [120]. The technology introduced in banking through web services has degrees of perception of great innovation based on its ease of use [121], [122]. Fichman [123] explained technical characteristics such as relative advantage, complexity, compatibility, observability, and probability. This originates from the ease of use of technology adoption.
Fichman [123], Doh et al. [125], Gupta et al. [126], and Pernici et al. [127], have current views on the feasibi-lity of using technology to improve the efficiency and sustainability of organizations. Industry 4.0 could contribute to the ease of optimal use of technology to efficiently manage organizational activities with reasonable degrees of sustainability [128]. Therefore, the hypothesis contains the following postulate, H1: Industry 4.0 perceived ease of use (PEU) has a positive impact on Industry 4.0 attitude towards to use (ATU). According to Kim et al. [129], technology acceptance is positively influenced by perceived usefulness across organizational size and budget. Perceived usefulness occurs when investors believe that using an Internet trading platform improves their job performance. By contrast, Chan and Lu [130] explain that when it comes to online stock trading, perceived usefulness is defined as the extent to which an investor can use Internet technology to his or her advantage, leaving aside the traditional actions of trading platforms. Thus, it is essential to note that some research has VOLUME 11, 2023 confirmed the importance of perceived usefulness in influencing investors' attitudes toward stock trading [131], [132].
Some studies consider attitude to mediate the direct relationship between perceived usefulness and intention to adopt an online securities trading platform [118]. According to Deci and Ryan [133], attitude is an intrinsic motivation towards adoption, resulting from perceived enjoyment or usefulness in adopting the new technology. Furthermore, Wallace and Sheetz [134] explained that research models exist to evaluate the adoption of software measures, meaning that the effects of perceived usefulness and ease of use are of great importance for designing and developing practical measurement programs that can lead to higher-quality software.
The validity and reliability of perceived usefulness and perceived ease of use variables in TAM have been supported by several investigations, including [135], [136]. Olsson et al. [137], explain that the increase in the growth of augmented reality (AR) applications can be attributed to perceived usefulness and positive consumer experience. Despite the increase in the use of AR technology in the industry, the expected response change in the adoption of AR in a company's value chain may be inadequate. For Gefen and Straub [138], several investigations have determined that attitude toward the use of a self-reported technology system indicates that perceived usefulness plays an important role in determining future effects.
Moore and Benbasa [139] indicated that many studies have attempted to understand the predictors of perceived usefulness related to technology and information adoption. However, some researchers interpret perceived usefulness as multidimensional and may be conceptually too broad to be applied in practice. Regression analyses suggested that perceived ease of use may be a causal antecedent of perceived usefulness. Thus, ease of use operates through usefulness [116]. Hall and Fenton [140] explained that attitude towards using software measures is linked to organizational goals, addressing beyond evaluations focused on superficial characteristics as a measurement scale. Therefore, the hypothesis is as follows: H2. Industry 4.0 perceived utility (PUT) has a positive impact on Industry 4.0 attitude towards to use (ATU).

C. INDUSTRY 4.0 ATTITUDE TOWARDS TO USE (ATU)
Attitudes towards the use of technology by investors through online trading are the most common variables among some theories, considering an estimation of direct behavioral intention and actual indirect behavior [141], [142]. Some researchers have provided significant evidence on the effect of the attitude towards use by investors whose direction is the intention to use technology as a support for online trading [143], [144]. For Ming-Chi [145], an investor has a positive attitude toward using technological tools, such as the internet for stock trading to accept this technology to trade stocks that are favorable. According Taylor and Todd [146], a person has a positive attitude towards using a given technological tool when this goes in a positive direction towards the use that subject will make of the technology.
Alden at al. [147] explained how information and communication technology is expanding rapidly and widely throughout the world, and the attitudes of businesses and customers towards this technology market are becoming increasingly globalized. As those who plan to turn to computers more frequently, the global community is more likely to adopt usage attitudes toward new technologies and advanced products [46]. According to Chen and Huang [148], companies and individuals tend to adopt attitudes toward using new technologies to express their identities, even when unsatisfied with a technological innovation that requires improvement, which means that potential customers or consumers have a more assertive global identity and a broad view of new technology as a way to build a psychological connection with the global community.
Primary users are more enthusiastic about their attitudes towards using new products. They can overcome any problem with new technologies [149]. Chen and Huang [148] showed that a consumer's intention to adopt home robot products is influenced by their usage attitude toward the technology, considering costs and benefits, and their usefulness. In developed and technologically advanced countries, AR technology's adoption and usage attitudes for e-commerce are in advanced stages [150]. In contrast, for Kumar et al. [151], despite the positive attitude toward using the technology, the adoption of AR by e-commerce organizations is still marginal.
Cheng, et al. [152] obtained similar findings when they evaluated the effects of perceived web security on attitudes towards internet banking and trust, usefulness, and perceived security. Other studies have considered internet banking using TAM; internet banking is relevant because research efforts confirm that accuracy, security, network speed, ease of use, user involvement, and convenience are critical to the attitudes toward internet banking adoption [153]. In contrast, Fenton and Neil [154] asserted that using TAM in software measures, in the technological context, accepts that easierto-use measures are more likely to be adopted. Our model includes ease of use and is consistent with TAM. We model it as a construct and sub-dimension of perceived usefulness [132]. Therefore, the hypothesis contains the following postulate. H3. Industry 4.0 attitude towards to use (ATU) has a positive impact on intention to adopt industry 4.0 in the organization (IAI). Industry 4.0 focuses on creating smart factories [38] using emerging technologies such as big data, analytics, the internet of things, virtual reality, additive manufacturing, and cloud computing [155]. It also focuses on the implementation of robotic systems to obtain CPS [38], [156] and a human component interface that leads to manufacturing systems with economic, environmental, and socially sustainable approaches [157] that contribute to the different sectors of a country. Thus, it is known as the fourth industrial revolution framed in the digital revolution [158], [159] and its contribution to industrial production [96] which is emerging from the improvement of networks, contributes to all areas within organizations.
The strength of Industry 4.0 which is directly related to the emergence of digital manufacturing [160], refers to intelligent networks between industry units, mobility in processes, and flexibility in operations [5], thus achieving integration between internal and external customers, suppliers, and above all in the adoption of innovative business models that allow for greater profitability [161], [162]. Since in recent years, factors such as increasing national and international competition, market volatility, product demand, and unforeseen situations such as the global pandemic have presented serious challenges for companies and their life cycles, the need to improve the quality of their products and services has become more critical than ever.
On the other hand, the rapid technological progress has increased the number of potential businesses with greater opportunities and new trends [163] such as digitalization, internet of things, internet of services, and CPS increasingly relevant [164]. Thus, Industry 4.0, has gained significant importance in companies worldwide [165]. Implementing Industry 4.0, technologies in a manufacturing environment indicate that recognizing digital opportunities, especially in manufacturing products and processes, requires deep collaboration with innovation to internalize firm and sector-specific inadequacies and create reciprocal security among stakeholders [166]. Therefore, the hypothesis contains the following postulate: H4. Industry 4.0 technological context (ICO) has a positive impact on the intention to adopt industry 4.0 in the organization (IAI).

E. INDUSTRY 4.0 ATTITUDE BEHAVIORAL CONTROL (BCO)
Technologies and their accelerated advancement over the years, together with innovation, do not leave the intention of consumer behavior towards the use of technologies unnoticed [137]. With the application of the TPB model, the effects of usefulness and ease of use on intention were mediated by attitude. Therefore, it is essential to analyze the effects of these antecedents on behavioral intentions. Cortés et al. [167], as well as Onar et al. [159], found that self-identity can increase predictive validity, considering individual differences, personality traits, and characteristics of users in organizations, which will determine the attitude and intentions of technology adoption [168]. Another aspect to consider, according to Bisoyi and Das [169], is that user leadership greatly influences the adoption behavior of new products.
The scope and implications of technological initiatives worldwide are still difficult to quantify [170], [171]. However, Industry 4.0 optimizes production and transformation systems, shortens the development cycle of new products, reduces manufacturing costs, and allows fully integrated and automated production processes [167], [172]. Furthermore, these will provide information that can be accessed globally in real time through the internet and various mobile devices, thus facilitating the creation of cooperation and collaboration networks [173], positively changing the attitude of the members of the organization, and most importantly, reaching timely decision-making and having greater control of activities over time.
People's satisfaction or behavior towards existing systems and the shift from centralized to decentralized production supported by new technologies have become a challenge for organizations. As emphasized by Mittal et al. [174], resistance to change was an important aspect that hindered the success of integrated manufacturing by processors in the 1990's [175]. The Rejection simultaneously with the motivation for change emanates from two circumstances: lack of skills or satisfaction with the existing system [176]. Therefore, the motivation and socialization of the benefits of the application of information technologies should be focused on improving the performance and behavioral attitude of the organization's members and making it satisfactory.
According to Ajzen and Fishbein [177], an attitude towards behavior is an evaluative judgment of marked hanges in an object. It is conceived as a predisposition learned to respond positively or negatively to an object [152] attitudes are shaped by the repertoire of dogmas relative to an object [178]. This Attitude can be determined by emotional factors, previous experiences, or preceding information from the environment, which defines whether the behavior will be executed [179]. Opinions are conceived as consequences of performing a particular behavior [180].
Therefore, the hypothesis is as follows: H5. Industry 4.0 attitude behavioral control (BCO) has a positive impact on intention to adopt industry 4.0 in the organization (IAI). The subjective norm represents the influence exerted by society in general and by the individuals that the person considers necessary to him [181]; i.e., it accumulates the regulated beliefs of the particular subject at the moment of setting his behavior [182]. On the other hand, it is believed that behavior is made explicit by behavioral intention and that this, in turn, is manifested through attitudes towards behavior and the subjective norm [174]. The two elements, in turn, are manifested on normative bases insofar as they represent retained information about objects.
Ajzen and Fishbein [177] considered that attitude and subjective norms do not have the same weight in predicting behavior, depending on the person and the situation, as they can have very different results on behavioral intention. [183]. According to research by Chen and Huang [148], subjective norms weaken the effect of attitude on adoption intentions [157]. Therefore, word-of-mouth and coercion from collaborators could be vital communication tools to convince consumers to adopt technologies in their homes and businesses [184].
The following hypothesis is proposed: H6. Industry 4.0 subjective norm (SNO) has a positive impact on the intention to adopt industry 4.0 in the organization (IAI). Many extended and competing models have been formulated using TAM and information technology (IT) acceptance research. For example, Kim et al. [185] emphasized that attitude toward technology use fully mediates the effects of salient beliefs on behavioral intention when the attitude is strong, while it partially mediates when attitude is weak. A similar idea can be found in Iyer et al. [186], who argued that some authors accepted virtual learning environments in China, while others added standard TAM constructs with an attitude towards system use. This idea constitutes an extension of that proposed by Sampat et al. [187], who refers to TAM as a model describing an individual's acceptance of information technology.
According to cognitive dissonance theory, individuals who like consistency in their beliefs (dogmas, papers, and performance) when there is a lack of agreement will change their attitude to resolve dissonance [188]. On the other hand, attitude is considered a critical antecedent of technology use and adoption. Two hedonic and utilitarian attitudes were examined to determine how they influenced satisfaction with technology. The former may not lead to use but influence satisfaction, whereas utilitarianism leads to use and satisfaction [189]. This view is supported by Sampat and Sabat [190], who state that perceived usefulness and perceived ease of use have a positive effect on attitude leading to continued intention and satisfaction with continued intention to use e-Government services; however, attitude and satisfaction, together with perceived usefulness and self-efficacy, can support users' intention.
Wang et al. [191] established that leaders' attitude mediate agricultural information technology (AIT), technological factors and intention to adopt. However, to our knowledge, no scholars have tested the mediating effect of executives' attitudes on technology adoption in an organizational context. Additionally, attitudinal and motivational states are essential for predicting innovation. There are examples of innovation models derived from motivational models [192]. Attitude refers to a person favorably or unfavorably feeling toward the adopting a specific technology. However, it is not the only factor that determines usage because it is influenced by system performance which leads to the intention to use and adopt the technology. The attitude to use and intention to accept are endogenous factors that previous studies have shown to have an effect on the presence of attitude toward the intention to use new technologies. Several studies support that attitude affects intention to use. Reference [193] similarly, perceived ease of social network use is positively related to attitude towards social network use [187].
In this context, there is a model called the binary choice model that understands how attitudes influence the decision to purchase. It studies the adoption at the individual level according to the antecedents of hedonic and utilitarian attitudes, and is used to determine adoption and usage behavior [189].
Therefore, the hypothesis is as follows H7. Industry 4.0 attitude (ATT) has a positive impact on intention to adopt industry 4.0 in the organization (IAI).

H. INTENTION TO ADOPT INDUSTRY 4.0 IN THE ORGANIZATION (IAI)
Industry 4.0 has driven analytical capabilities in the company over the last decade through innovation in all areas of the organization, business models, and the ability of organizations to become an element of competitive advantage in almost all sectors [194], [195]. For Arnold et al. [162], Bauer et al. [196], the main characteristics related to Industry 4.0, such as real-time capacity, interoperability, vertical and horizontal integration of productive systems through ICT systems, are considered the current challenges that organizations must face to remain competitive in the market, owing to the growing demand and the demand of customers in relation to product and service innovation.
Discussion and initiatives point to digital transformation in organizations. Thus, Germany was the first country to introduce digitization of Industry 4.0. However, term expanded worldwide, so the United States of America focused on smart manufacturing through the adoption of Industry 4.0 in almost all organizations, followed by smart Japan and Korea [6], [65]. Therefore, the authors, Fleisch, et al. [197] and Iivari et al. [198], explain that technology directed to digital industries is becoming very relevant for traditional companies as sales increase. Consequently, the adoption of Industry 4.0 driving organizations to change their mentality to improve their products and services.
Companies within the technology world are highly competitive in having a better position in adopting Industry 4.0, therefore, are more prepared for Industry 4.0 [199]. By contrast, Nguyen and Luu [200] found that the perception of a better customer relationship has a significant effect on the adoption of Industry 4.0, and that efforts by organizations aimed to increase the perceptions of factors on the adoption of Industry 4.0. The relevance of technology contributes to its success, which is defined as the effective use of Industry 4.0.
For Neugebauer et al. [201], Industry 4.0 technology should be a priority in the new manufacturing era, calling it smart factory or smart manufacturing. The vision of Industry 4.0 is real-time digitization with added value. Therefore, it is considered to be the most revolutionary and evolutionary. On the other hand, for Nick and Pongrácz [202], Dassisti et al. [203], Industry 4.0 is supported by nine pillars, these being: advanced manufacturing; additive manufacturing; augmented reality; simulation; horizontal and vertical integration; industrial internet; cloud; cybersecurity; and big data analysis; however, the challenge that organizations face is to understand their processes, procedures and philosophy, in order to take advantage of the potential of technology, as some theorists always ask, with Industry 4.0 ''how do we get from where we are now to where we want to be?

IV. RESEARCH METHODOLOGY A. INSTRUMENT
To develop the questionnaire, indicators were generated through a review of the theoretical framework (see Appendix A). Items referring to demographic information about the organization and informants were also inserted, and all items of the instrument were evaluated on a scale from 0 to 4, with the following parameters: disagree entirely, partially disagree, neither agree nor disagree, partially agree, and completely agree.
Initial feedback was provided by 10 experts who refined the instrument. Based on this, the questions were consolidated to maintain consistency in the responses of the informants. The profile of the experts is explained in the Table 3, where they appear, academic degree, type of work carried out, positions, university, and country.

B. POPULATION AND SAMPLE
The characteristics of the proposed model were used to determine the sample, that is, eight constructs and four constructs that point to the dependent variable. According to the Partial Least Square (PLS) model, the sample size for the model is calculated by multiplying ten by the last number, that is, 10 x 4 = 40. Despite this, the 80% power analysis [204] is applied, requiring in the end: 40 + 32 = 72 cases.
Data were captured between November 2021 and February 2022 and an online questionnaire was administered using Google Docs. The survey link was sent by email, and after four months, 499 refined surveys were completed. The study involved stakeholders from organizations in LA countries in the following proportions: Colombia 25.25%, Ecuador 23.05%, Mexico 22.04%, Peru 20.24%, and Panama 9.42%.
The sectors of manufacturing, education, commerce, services and others, support the advancement and development of a state, include organizations with the capacity to absorb Information Technologies. These new technologies are enabling ever higher levels of production efficiency [205], they are intermediate measures, for consumer surplus and economic growth [206]. Companies choose development strategies that lead to the achievement of economic objectives. In this sense, these sectors are those whose needs are framed by the adoption of quality technology, in order to be more competitive and thus contribute to the progress of their countries.  Table 4, illustrates the demographic composition of the respondents. As explained in the previous paragraph, the research focused on some Latin American countries, whose demographic information was extremely important for the strengthening of the study, where factors such as the number of employees, exceeding 300, of the qualities in their greatest number are male, each of them having a relevant position within the market; another important factor is their level of higher education, highlighting this as a priority for organizational efficiency; finally, it is shown that most of the registered companies have little seniority with staff ranging from 35 to 55 years. These factors are essential to strengthen research, as these organizations are friendly with the use of modern technology.

V. DATA ANALYSIS AND RESULTS
The model was measured using the PLS technique in Smart PLS 3.3.3. The model is analyzed in two parts: the measurement model and the structural model [207], and is useful when there are limitations in the sample size [208].

A. MEASURE MODEL
Cronbach's alpha, composite reliability, average variance extracted (AVE), and discriminant validity were determined. The measurement model results are shown in Figure 2. The Cronbach's alpha of the indicators, except for ICO4, is greater than 0.7; this implies, according to Carmines and Zeller [209], that they are valid. Table 5 contains the detailed values of the loadings for each indicator where ICO4 should be revised, eliminated or restructured in the text of the question The reliability of the constructs was analyzed using composite reliability and Cronbach's alpha. A value higher than 0.7 is acceptable [210]; in Table 6, the constructs have a satisfactory level of internal consistency reliability, with values higher than 0.7. The convergent validity of each construct was analyzed with the value of the average variance extracted (AVE); if the value is greater than 0.5, it is acceptable according to Fornell and Larcker [211]; in Table 6, the AVE value was greater than 0.5 for all constructs.
Discriminant validity analysis was performed based on Fornell and Larcker [211], which determines whether the value of the square root of the mean extracted variance is greater than the interconstruct correlations. For the present model, although for most of the constructs the square root of AVE is greater than the correlation between them, this condition is not 100% fulfilled in all cases.
For example, it is below the constructs of ATT and ICO. Therefore, it cannot be concluded that the model meets the discriminant validity criterion and that the latent variables are differentiated (see Table 7). However, to strengthen the discriminant validity analysis, a cross-loading check is also performed, which validates that each indicator is correlated with its latent variable rather than with others. Therefore, it was not necessary to reconsider the adequacy of the model, as shown in Table 8. The measurement model is validated after analyzing its parameters, implying that the instrument is statistically valid and reliable and that the theory is supported [212].

B. STRUCTURAL MODEL
The structural model was assessed based on the weight and magnitude of the relationships between different variables using the R 2 index, f 2 effect, standardized path coefficients β, and bootstrapping analysis. R 2 determines the predictive power of the model; values greater than 0.67, 0.33, and 0.19, denoted as substantial, moderate, and weak, respectively, are considered feasible [205]. Table 9 lists values that ensured the percentage of construct variability, thereby confirming the predictive characteristics of the model. On the other hand, f 2 identifies the impact on a dependent construct of a variable, if f 2 > 0.35, it implies large size; 0.15 < f 2 ≤0.35 implies medium effect and 0.02 < f 2 ≤0.15, represents small effect size (see Table 10). f 2 can be seen as an indicator for which the latent variable predictor has a small, medium, or large effect at the structural level and quantifies the proportion of variance of the dependent variable that is explained by the set of predictor variables [213]. Table 10   . The fact that the five variables have a low impact on the IAI variable is related to the context of the informants, as they are organizations that belong to a group of developing countries where inconveniences and challenges are perceived in the implementation of the industry.

C. HYPOTHESIS TESTING
Concerning the standardized path coefficients β, whose objective is to measure the relevance of path relationships, those that reach at least a value of 0.2 are considered significant [214]. Unfortunately, in Table 11, which contains the path coefficients between the variables, two values (0.029 and 0.060) do not exceed the minimum value of 0.2, which is why the model should be reorganized from a structural point of view.
Bootstrapping analysis is a resampling procedure that treats an observed sample as representative a population. Figure 3 shows the bootstrap values of the model. In addition, this analysis allows the testing of hypotheses by calculating the standard error of the parameters and the Student's t-values; in this area, the indicators whose Student's t-values are greater than 1.96 are considered significant [215]. Table 11 shows the relationships between the model's constructs through standardized beta paths, standard error, Student's t-value, significance level, and acceptance or rejection of the hypothesis.
The study tested the following hypotheses: Hypothesis one suggests that Industry 4.0 perceived ease of use (PEU), has a positive impact on Industry 4.0 attitude towards to use (ATU). Table 11 highlights the positive and median influences of PEU on ATU (β = 0.203, t-value = 3.168, p <0.01), supporting this hypothesis.
Hypothesis two suggests that Industry 4.0 perceived utility (PUT) has a positive impact on Industry 4.0 attitude towards to use (ATU). Table 11 highlights the positive and significant influence of PUT on ATU (β = 0.762, t-value = 22.380, p <0.001), thus, supporting this hypothesis.
Hypothesis three suggests that Industry 4.0 attitude towards to use (ATU) have a positive impact on intention to adopt Industry 4.0 in the organization (IAI). Table 11 highlights a positive and significant influence of PUT on ATU (β = 0.203, t-value = 4.496, p <0.001), thus, supporting this hypothesis.
Hypothesis four suggests that Industry 4.0 technological context (ICO) has a positive impact on intention to adopt Industry 4.0 in the organization (IAI). Table 11 highlights a positive and lower significance influence of ICO on IAI (β = 0.060, t-value = 2.006, p <0.05), the hypothesis is supported.
Hypothesis five suggests that Industry 4.0 attitude behavioral control (BCO) has not a positive impact on intention to adopt Industry 4.0 in the organization (IAI). Table 11 highlights the negative influence of BCO on IAI (β = 0.029, t-value = 0.928). Therefore, this hypothesis is not supported.
Hypothesis six suggests that Industry 4.0 subjective norm (SNO) has a positive impact on the intention to adopt Industry 4.0 in the organization (IAI). Table 11 highlights a positive and significant influence of SNO on IAI (β = 0.354, t-value = 6.608, p <0.001); thus, this hypothesis is accepted.
Hypothesis seven suggests that Industry 4.0 attitude (ATT) has a positive impact on the intention to adopt Industry 4.0 in the organization (IAI). Table 11 highlights a positive and significant influence of ATT on IAI (β = 0.540, t-value = 8.725, p <0.001), the hypothesis is supported VI. DISCUSSION H1: The model analysis demonstrates the positive influence and impact of PEU on ATU. Ease of use goes hand in hand with the attitude towards use. Many companies are currently on the way to the search for new technologies in order to be more competitive in the market. Some organizations' economic factors do not impede positive attitudes towards green IT. It is important to note that the ease of use of Industry 4.0 aligns with companies' objectives in LA. This allows digitization production processes with fully automated processes, thus reducing labor and costs within organizations [81]. The ease of use of Industry 4.0, through the efficient incorporation of technology, allows companies to maintain their attitude towards this use, thus allowing them to efficiently manage organizational activities, leading to more competitiveness and sustainability in the national, LA, and global markets [126].
H2: This study highlights the positive and significant influence of PUT on ATU. Therefore, companies currently perceive the adoption of technology as a perceived utility, in the sense that companies make use of cutting-edge technology, leaving aside without taking into consideration traditional technologies; thus, they regain the importance importance of the application of Industry 4.0 for organizational processes, directly influencing investors attitudes towards stock trading [129], [130]. Therefore, it is essential to emphasize that companies' objectives are currently oriented towards using software that supports better organizational management. In short, the perceived usefulness of Industry 4.0 influences the attitude toward use when an investor or company believes that using the Internet trading platform improves work performance [118].
H3: The research shows that ATU directly influences IAI. IT is essential to highlight the importance of organization's positive attitude towards the use of modern technological tools to improve processes at the enterprise level, favoring the intention to accept this technology to be more efficient in the market. Some studies show the critical effect of the attitude towards the use by organizations and investors whose intention is to use technology as a support for online commerce [142], [143], [144]. Current information systems and communications are expanding rapidly worldwide. The attitudes of use by companies, investors, and customers towards this new technological market, are becoming increasingly globalized, so those who plan to address computers more frequently are more likely to maintain the intention to adopt new technologies and advanced products such as Industry 4.0 in organizations worldwide [46], [147].
H4: Predominates a positive influence and has lower significance for ICO than for IAI. This coupled with the dizzying technological progress that has motivated the birth of new businesses with more significant opportunities and modern trends [163] such as digitization, the Internet of Things, the Internet of Services, and CPS [164] has become increasingly timely, which is why Industry 4.0 has gained significant importance in organizations worldwide. [165]. This is why it is known as the fourth industrial revolution framed in the VOLUME 11, 2023   digital revolution [158], [159] and especially its contribution to industrial production [93], which is born from the improvement of networks, contributing to all areas within organizations. The strength of Industry 4.0 is directly related to the emergence of digital manufacturing [160].
H5: Table 11 highlights the negative influence of BCO on IAI. Ricci et al. [166] argued that attitude towards behavior is conceived as a learned predisposition to respond positively or negatively to an object [152]. This is why behavioral control towards Industry 4.0 is shaped by the repertoire of beliefs relative to the object, which does not influence the intention  to adopt new technologies in organizations [187] which emotional principles, past experiences can relate, or through preceding information from the environment that specifies whether the behavior will be executed or not. [179] Therefore, opinions are conceived as consequences of performing a particular behavior [180]. Chen and Huang [148] determined that self-identity can increase predictive validity by considering individual differences, personality traits, and characteristics of organizational users. These will determine the attitude and intentions of adoption of Industry 4.0 in the organization [157]; another aspect to consider, according to Bisoyi and Das [169], is that user leadership greatly influences the adoption behavior of new products.
H6: Table 11 highlights the positive and significant influence of SNO on IAI. Thus, the subjective norm represents the predominance exerted by society in general, which the individual considers vital for him [181]; that is, it accumulates the regulated beliefs of the particular subject at the moment of setting his course of action. [182] On the other hand, it is believed that behavior is made explicit by behavioral intention and that this, in turn, is manifested by attitudes towards behavior and the subjective norm [174]. These two elements, in turn, are manifested by normative bases because they represent the information retained about the objects. Ajzen and Fishbein [177] considered that attitude and subjective norms do not have the same weight in predicting behavior, depending on the person and situation, since they can have very different results in behavioral intention. [183] According to research by [162], subjective norms weaken the effect of attitude on adoption intentions [182] therefore, word-of-mouth and coercion from collaborators could be strong communication tools to convince consumers to adopt technologies in their homes and organizations [197].
H7: Table 11 highlights the positive and significant influence of ATT on IAI. Wang et al. [191] determine that managers' attitudes have a mediating effect on AIT, technological components, and intention to adopt. However, the mediating effect of firm managers' attitudes on technology adoption in an organizational context has not yet been tested. Since attitudinal and motivational states play an essential role in the prediction of innovation, there is an influence on the adoption of Industry 4.0 as modern and efficient technology for internal processes at the enterprise level. [192]. Attitude refers to a person or organization's optimistic or negative feelings towards adopting a specific technology. However, it is not the only factor that determines usage as it is influenced by the system performance that leads to the intention to use and adopt technology. Attitude and intention to accept are the endogenous factors; previous studies have shown the effect of Attitude related to the influence of the adoption of new technologies and claim that Attitude has a positive effect on the intention to adopt Industry 4.0 in the organization [193]. Similarly, perceived ease of use of social networking through current technology is positively related to attitude [187]. In short, the attitude toward the use of cutting-edge technology companies worldwide is influenced by the efficiency it provides in administrative and productive processes aimed at the quality, competitiveness, and sustainability of organizations.

VII. RESEARCH CONTRIBUTIONS AND IMPLICATIONS A. THEORETICAL CONTRIBUTION
The work demonstrated that the proposed model is consistent with the data. There is theoretical evidence of the results by identifying the variables that influence the intention of organizational actors to adopt Industry 4.0, as well as the fundamental role played by the integrating role of GITAM, TPB and TAM. The positive relevance of the influence of

B. PRACTICAL CONTRIBUTION
Organizations worldwide, especially in LA, are currently focused on acquiring knowledge through the digitalization of the company, which will radically improve internal processes such as production and administration, leading reduced costs and, in turn, high standards of quality and efficiency, a goal pursued by every business person. Industry 4.0 is being inserted into this modern world, whose purpose is to offer cutting-edge technology to the industry manufacturing and services sectors. The opportunities provided by technology within the world of organizations are leading to more productive, competitive, and sustainable development in the current market, increasing regional, national, and international development. This study contributes to organizational actors in determining the critical factors in adopting Industry 4.0 and the support for sound decision-making in this regard.

C. RESEARCH LIMITATIONS
Information was collected from 499 organizational actors of the productive sector belonging to strategic, tactical, and operational levels in Colombia, Ecuador, Mexico, Panama, and Peru, where limitations were detected, such as the capacity to expand to a greater number of countries due to logistical difficulties and contact with the informants. For the authors, the fact that the information was collected through virtual channels can be considered a limitation to a certain extent because it prevented a personalized approach to resolving concerns about the indicators on the part of the respondents. However, the lack of research on adopting Industry 4.0 in LA did not contribute to the feedback results.

D. CONTRIBUTIONS TO THE SDGS
The Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda aim to ensure a more sustainable future for all nations; the 17 SDGs form an action plan designed to help nations achieve a more sustainable future. The research proposed in the context of the 17 SDGs, contributes with central and substantial contributions to SDG-7 ''Industry, Innovation and Infrastructure'', and indirectly to SDG-11  ''Sustainable cities and communities'', to SDG-13 ''Climate action '' and SDG-7 of ''Clean and affordable energy''

E. FUTURE PROJECTS
Future studies on Industry 4.0 adoption should consider other potential technology adoption drivers. It is possible to include additional internal variables in the proposed model, such as organizational culture, the beliefs and values of organizational actors, innovation, and knowledge management. It is also feasible to consider the adoption of Industry 4.0, that is, inter-institutional Industry 4.0, among organizations as the scope of this study. Another variable that can be analyzed is the outsourcing of Industry 4.0, which is based on the principal-agent theory. The proposed model can be applied in organizational environments of different natures and geographical latitudes, and to validate the indicated topic, it is necessary to develop new research processes.
It is expected that this research will be a reference for future studies, which will allow companies in different LA countries to incorporate Industry 4.0 as state-of-the-art technology, especially in information and communication, thus improving both internal and external processes of the organizations, and continuing to position the business sector of goods and services within the global market.

VIII. CONCLUSION
The research validated the generation of a model for the adoption intention of Industry 4.0 in the context of LA organizations; the main research questions have been answered. The model complies with the parameters that validate the measurement model, implying that it is reliable and that the instrument applied is statistically valid. These indicators significantly contribute to the latent variables. However, the model validated six of the seven hypotheses from a structural perspective. In addition, the number of hypotheses fulfilled depends on the context in which the model is applied. It may be that in another organizational environment or region, a greater or lesser number of hypotheses may be supported.
The selection of variables in the TAM, GITAM, and TPB models will lead to the intention to adopt Industry 4.0 within the organizations studied, allowing them to be more efficient through quality production and management, leveraging them to face competitive challenges within the market.
Organizations at the Latin American level maintain a positive attitude towards the adoption of Industry 4.0, from the point of view of the attitude and behavior of their employees, business utility, and the technological context in which they operate.
The intention to adopt Industry 4.0, expressed by organizations, is a necessity and will affect commitment to technological updating, changes in worker attitude, commitment, and leadership at the strategic, tactical, and operational levels.
This work contributes to the lack of knowledge about technology adoption strategies, especially in Industry 4.0, within organizations in developing countries, where there is a lack of knowledge on how to do it. Overcoming this gap will allow the organization to be more efficient and profitable.

APPENDIX A
See Table 12.