Abstract
Purpose
The purpose of the research is to analyze the factors that determine the intention of small- and medium-sized enterprises (SMEs) to adopt the Metaverse. For this purpose, the analysis of the effort expectancy and performance expectancy of the constructs in relation to business satisfaction is proposed.
Design/methodology/approach
The analysis was performed on a sample of 182 Spanish SMEs in the technology sector, using a PLS-SEM approach for development. For the confirmation of the model and its results, an analysis with PLSpredict was performed, obtaining a high predictive capacity of the model.
Findings
After the analysis of the model proposed in this research, it is recorded that the valuation of the effort to be made and the possible performance expected by the companies does not directly determine the intention to use immersive technology in their strategic behavior. Instead, the results obtained indicate that business satisfaction will involve obtaining information, reducing uncertainty and analyzing the competition necessary for approaching this new virtual environment.
Originality/value
The study represents one of the first approaches to the intention of business behavior in the development of performance strategies within Metaverse systems. So far, the literature has approached immersive systems from perspectives close to consumer behavior, but the study of strategic business behavior has been left aside due to the high degree of experimentalism of this field of study and its scientific approach. The present study aims to contribute to the knowledge of the factors involved in the intention to use the Metaverse by SMEs interested in this field.
Keywords
Citation
Gil-Cordero, E., Maldonado-López, B., Ledesma-Chaves, P. and García-Guzmán, A. (2024), "Do small- and medium-sized companies intend to use the Metaverse as part of their strategy? A behavioral intention analysis", International Journal of Entrepreneurial Behavior & Research, Vol. 30 No. 2/3, pp. 421-449. https://doi.org/10.1108/IJEBR-09-2022-0816
Publisher
:Emerald Publishing Limited
Copyright © 2020, Eloy Gil-Cordero, Belén Maldonado-López, Pablo Ledesma-Chaves and Ana García-Guzmán
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Technological developments have changed the way society operates and how consumers and businesses interact with each other, first through connection to the system (primary Internet-email and e-commerce), then through simple interaction between users (Internet of platforms-Web 2.0), and now (the third era of the internet, the so-called Decentralized Internet) into a future of global connection and immersion in a virtual world (De Regt et al., 2021). This last aspect represents a disruption with the forms of interaction in society, as virtual reality (VR) experiences allow people to spend a lot of time in immersive virtual environments and interact with content that is produced in an alternative reality: the Metaverse. Choi and Kim (2017) define Metaverse systems as a three-dimensional spatial representation based on virtual and augmented reality. These experiences greatly transform the pace at which information is consumed and processed on a daily basis (Mishra et al., 2020), pose major challenges in terms of usability and technological adaptation for companies (Suh and Prophet, 2018), as well as triggering profound controversies regarding the physical, social and psychological implications of immersive technology and the considerations that consumers and companies must take into account (Han et al., 2022).
Based on the above, in the business environment, organizations must assess whether to adopt strategies in the Metaverse environment, taking into account the degree of ease associated with the implementation of a new virtual environment, which is known as effort expectation, and that refers to the cost of resources that would be involved in incorporating a new operating system in a given company (Carvajal et al., 2021; Moriuchi, 2021). In the literature, several authors have related effort expectancy to the difficulty of implementing a new technology (Alam and Uddin, 2018; Nguyen and LE, 2020; Pei-Yee Chin et al., 2020; Xena and Accounting, 2019). In the same way, previous studies have shown that the variable considered as effort expectancy has an impact on the determination of the intention to use a specific tool (Carvajal et al., 2021; Pei-Yee Chin et al., 2020). According to this logic, if a new technology requires less training and implementation effort, its intended use will be higher, so this construct is integrated into our research model.
Following this line of argument, authors such as Kosiba et al. (2022) and Pei-Yee Chin et al. (2020) have concluded in their research that both the expectation of effort and the expectation of results are the main drivers of business behavior toward the use of a technology. Therefore, the intention to use the Metaverse by a company will be affected by the economic effort and the acquisition of knowledge involved in the implementation of this tool in the organization as well as the ability to adapt to new forms of interaction and innovation that derive from its practical application, thus configuring the fundamental predictors incorporated in our research.
In line with the foregoing, previous studies such as those of Mazzarolo et al. (2021), Pozón-López et al. (2019), and Vărzaru et al. (2021) have identified the degree of satisfaction on the part of companies in the use of the internet, social networks and information systems as a significant construct that models their behavioral intention. Consequently, its relevance was taken into account in the design of the applied model.
The global Metaverse market is growing fast. According to Jeon (2021), it is expected to be worth 280bn U.S. dollars by 2025. This is why companies should value the inclusion of Metaverse systems within their value chain. The example par excellence is the step taken by the technology giant Facebook/Meta to be the pioneer in announcing its move to the virtual ecosystem by building a complete alternative world (Zuckerberg and Newton, 2021). A stage is beginning to dawn in which virtual reality assumes a very prominent role in technology-mediated production and consumption experiences worldwide. According to López Díez (2021) large companies have invested significant amounts (US$10bn) in the Metaverse project and are planning to invest even more in future years. In addition, governments around the world are taking into account the opportunities, risks, and policy implications of the Metaverse. The Korean government hopes to play a leading role in the Metaverse industry, where it plans to allocate 9.3tn won (about 51.6bn yuan) to accelerate digital transformation and cultivate new industries, as well as to help SMEs and venture capital firms to build blockchains (Ning et al., 2021). Also the government of Dubai will host the Blockchain Summit, a mutually beneficial initiative for a transition from a regulated crypto-economy to the Metaverse (Warin, 2022). On the other hand, the European Commission has established that at least 20% of each Member State's recovery plan's budget must be spent on digitalization (European Commission, 2020). The government of Spain has implemented a digital kit by taking advantage of the European resources of the Next Generation program for the digitization of companies and SMEs and their inclusion in the Metaverse (Borrás, 2022). But all these investments are taking place without yet knowing whether their investment will be adopted by SMEs in various sectors to integrate the Metaverse as part of their business. Only a few SMEs, such as Cerveza MUR, which has expressed its intention to enter the Metaverse to finance itself outside the traditional channels, and as a new form of entrepreneurship, and Qurable has also shown the same intention, but has had to partner with Maria Cher for its immersion in the Metaverse (Bravo, 2022). Then there are individual entrepreneurs with small SMEs such as the artist Beeple who made the NFT format work “Everyday's: The first 500 days” for use in the Metaverse (Yemenici, 2022). These are just some of the examples of the intended use of the Metaverse by SMEs. This perspective seems to indicate that SMEs will have to decide whether to enter the Metaverse in the coming years and offer their customers the possibility of interacting with their products through the incorporation of this technology.
However, the entry into the Metaverse implies the immersion of the business strategy in a new and practically unknown environment, where customers also represent new segments projected through their avatars (Collins, 2008; Papagiannidis et al., 2017). In this sense, the entry into the Metaverse will not only constitute the adaptation of a business strategy through the adoption of a new technology but will be the beginning of a new form of business behavior based on the observation of an unexplored environment, consumers and competition. So far, the focus on technological adoption by SMEs has been on innovations in specific fields or strategies, such as LAN Technology (Anderson and Schwager, 2004), Social Media Marketing (Dahnil et al., 2014), Open Source Software (D Macredie and Mijinyawa, 2011), Information and Communications Technology (Hashim, 2015; Molinillo and Japutra, 2017), E-commerce (Abdulhakeem et al., 2017), Cloud Computing Services (Skafi et al., 2020), and Blockchain Technology (Sun et al., 2021). To a greater or lesser extent, they have involved modifications or partial adaptations in the strategies of the different companies for the technological integration of advances. However, the Metaverse is the construction of a new environment, an ecosystem to be built, which will be the meeting point of the future for companies and consumers (Toraman, 2022).
With regard to the analysis of SMEs, there have long been calls for research and understanding of the specific problems of SMEs in the face of the avalanche of new technological changes that have been offered (Dahnil et al., 2014). But, above all, this demand has been accentuated by the magnitude and significance that these changes have brought to the reality of SMEs. Compared to large companies, most SMEs perceive the obstacles of implementing new technologies in their business operations as a costly, risky, and complex initiative (Ledesma-Chaves and Arenas-Gaitán, 2022).
However, according to the theory about SMEs, they can benefit from certain characteristics according to their structure when managing new environments and adopting emerging technologies. Examples are their ability to adapt and develop key competencies, become a learning organization, converting risks into opportunities, and their proximity to their environment that enables them to better raise capital for investments (Alberti et al., 2018). More concretely, in an environment under construction such as the Metaverse, SMEs can benefit from elements such as a lesser burden and bureaucracy in developing new procedures, faster decision processes and shorter decision chains, flexibility to adapt quickly to changes, more effective internal communications, and a faster and more adaptive learning capacity (Seville et al., 2015). Therefore, the general objective of this research is to know the intention of SMEs to adopt immersive technology models and develop strategies within the Metaverse, taking into account whether the company's own satisfaction is influenced by effort expectations and performance expectations.
2. Theoretical background
2.1 Strategic behavior of companies in the METAVERSE
In recent years, there have been three major waves of technological innovation, the introduction of personal computers, the internet, and mobile devices. Currently, the fourth wave of computer innovation is developing around spatial and immersive technologies, such as virtual reality (VR) and augmented reality (AR) where it is giving way to a fifth wave where the Metaverse is the protagonist (Azar et al., 2022). The Metaverse is a virtual reality (VR) space using the internet and augmented reality (AR) through avatars and software agents (Joshua, 2017). Meanwhile, the market for freelancers with a knowledge of VR and AR, as part of entrepreneurship in SMEs, will grow much faster than other skills (Choi and Kim, 2017; Loureiro et al., 2020; Su et al., 2020). Emerging issues, challenges, and opportunities related to the adoption of VR and AR technology for companies such as small businesses explain how the entrepreneurial process is affected by the polarization of the job market but also by creation opportunities for smaller companies, as long as they are able to exploit digital technologies for the creation of complementary value and establish their digital presence (Modgil et al., 2022; Sahut et al., 2021).
The fundamental characteristic of Metaverse systems is that they not only provide the consumer with a different or special product or service, but the consumer enjoys the opportunity to interact and participate in the overall experience (Papagiannidis et al., 2013). For example, Italy's top soccer division screened AC Milan vs. Fiorentina within the Nemesis Metaverse, allowing fans to interact within the virtual room of Series A on May 1 (Dwivedi et al., 2022). While it is true that this fact has been taken by companies as part of more advanced marketing strategies, in the Metaverse this becomes fundamental given the existing technological possibilities (Hazan et al., 2022; Hollensen et al., 2022; Hwang and Lee, 2022; Kang, 2022). In the same way, this type of virtual experiences could be used by SMEs in the future. Users can socialize, trade, and even earn income in virtual worlds. This has opened the way to a new way of interacting between companies and users and their representations in virtual worlds (Arakji and Lang, 2008; Schnack et al., 2021; Zackery et al., 2016). Recognizing the economic opportunities that are materializing within the Metaverse, different companies are launching e-commerce initiatives, trying to get a head start on what could become a revolutionary way of doing business online. Organizations such as Toyota, Circuit City, Dell, Sears, and Adidas have opened virtual stores on Metaverse systems in an effort to better understand and connect with consumers, reach new potential customers, and create and increase brand awareness (Arakji and Lang, 2008). The Metaverse has also the potential of improving the access and experience of several services in sectors such as education and healthcare (Dwivedi et al., 2022). An example is the SME The bodymindself™ of Dublin, led by Dr. John Francis Leader, a psychotherapist specializing in experiential learning and mixed reality therapy, developed in collaboration with the Media, Art and Cyberpsychology group of the Psychological Society of Ireland (Leader, 2022). Although some companies have created transactional virtual stores, until now most of them have only allowed avatars to interact with the virtual products and services presented. Without offering them the possibility of buying any product. The initiatives of Balenciaga in collaboration with Epic Games to integrate high fashion into Fortite or Wendy's bet by creating an avatar in Twtich to promote its hamburger brand are examples where companies organize promotional and social activities related to the image itself rather than to specific products. They focus on brand awareness with the aim of generating indirect sales of their products, whether physical or digital, in the real world. Although it is true that today's technology enables virtually all types of transaction in the Metaverse, companies are incorporating the advances gradually, since in their initial implementation there have been difficulties associated with the implementation of the consumer technology (Durukal and Armagan, 2022; Oh, 2021; Papagiannidis et al., 2013). An example of this occurred between 2007 and 2009, in relation to the virtual systems of Second Life, which resulted in an inability to survive in the absence of consumers in the virtual world (Clemons, 2009). Related to the Spanish environment, Gamium is a Spanish start-up company whose commitment is to create a decentralized social Metaverse with a unique digital identity that can be used in other Metaverses or rooms (Gamiun, 2022). In addition, Uttopion, an SME, is the company of the Metaverse through Lanzadera in January 2022, the startup accelerator of the owner of one of the largest Spanish food companies (Lazadera, 2022). According to the company's website, it has sold more than one million euros of plots called “Terras” with prices ranging between 2,000 and 40,000 euros. The Uttopion Metaverse has two communities in Spain, of which 500 plots of each have been put up for sale. The first is Musichood Spain, which in the two years it has been running has held a total of 200 events such as the Oro Viejo festival by DJ Nano, with 4,700 people connected at the same time (Lazadera, 2022) Another example is Virtual Voyagers, one of the few SMEs specialized in virtual environments in Spain, focused on creating large Metaverse projects (Táboas, 2018).
The challenges presented by the Metaverse also include the inability of platforms to engage users in an immersive virtual world. Therefore, if we consider the studied SMEs as technology consumers, and considering that there is a technical gap in hardware and software performance compared to user expectations for the Metaverse (Park and Kim, 2022), to provide a sustainable service it is essential to have immersive content that works even with limited hardware and low-resolution software, such as Minecraft (Jaynes et al., 2003; Papagiannidis and Bourlakis, 2010). This study starts from the idea that SMEs should be considered consumers of immersive technology as they have to develop their business strategy for a new market with new consumers. The characteristics of the Metaverse turn its users into other potential consumers (Arakji and Lang, 2008; Papagiannidis et al., 2017) for SMEs that adopt it in their business model, enabling connections between users through visual, auditory, somatosensory, and gustatory senses, while movement and touch are possible in the virtual environment (Kraus et al., 2022). In the same sense, SMEs can use the data obtained to create strategies within the Metaverse world (Heller, 2020; Hollensen et al., 2022; Polas et al., 2022), and it will be possible to segment and offer more personalized and individualized advertising campaigns (Gálvez, 2022) for the development of their strategy and business model.
In addition to the above, according to Xi and Hamari (2021), SMEs are considering the shift to virtual environments, although there are many obstacles to be overcome, including consumer acceptance, the implementation model and information systems. So far, studies have been conducted that have even tried to determine the likelihood of actions taken by avatars in Metaverse systems with respect to virtual shopping and virtual stores, such as the Avatar Business Value (ABV) created by Arakji and Lang (2008), where they analyze a complete decision tree that leads to multiple business strategies. Immersive worlds are virtual spaces that extend our physical universe by adding new dimensions and domains for economic, social and leisure activities (Papagiannidis et al., 2013). The Metaverse system requires the design of a new content world intended to accommodate millions of users: The need arises to design buildings, scenarios, and environments in general (González, 2022) that involve challenges and opportunities for businesses to market their services to potential customers (Collins, 2008). In order to contribute with applied studies on the Metaverse and the decision that SMEs must face in order to be included or not in their value chain, we will present in the following sections an applied study model through the analysis of the effort expectation and performance expectation constructs in relation to business satisfaction.
2.2 Proposed model and justification of hypotheses
The approach taken in recent studies of Metaverse adoption in the consumer domain has been through the Technology Acceptance Model (TAM) (Akour et al., 2022; Almarzouqi et al., 2022; Mostafa, 2022). For the development of the model, the different constructs have been used according to the convenience of the analysis, applying those that were used for technological adoption by consumers and by companies. It should also be noted that, from the point of view of the theory of technological adoption by SMEs, the most convenient constructs were those extracted from the TAM and UTAUT models. These models have already been tested and are believed to be effective for investigating technology acceptance by individuals, although the constructs from the TAM model are more suitable for investigating technology adoption by SMEs (Dahnil et al., 2014). In fact, well-known researchers in the field of adoption have adapted the constructs applied to individuals to be used in SMEs (see Table 1).
Likewise, the theory on SMEs establishes a series of limitations to be taken into account with regard to the adoption of a new technology in the business environment. Based on the theory of the diffusion of innovation (DOI) and the conceptual framework of technology, organization and the environment (TOE) applied to the adoption of the Metaverse, we analyze the different factors to be considered by SMEs in the process. Within the TOE conceptual framework, there are the factors associated with the environment, organization and technology (Hatta et al., 2015). The DOI theory provides three sets of factors associated with the characteristics of managers, characteristics of the internal structure and external characteristics of the organization (Hatta et al., 2015).
2.2.1 Effort expectancy
The degree of ease associated with the use of a product or service is known as the effort expectancy (Venkatesh et al., 2012). There is also the idea of the resource cost of incorporating a new system into their company (Carvajal et al., 2021; Moriuchi, 2021). The literature relates effort expectancy to the difficulty of implementing a new technology (Chua et al., 2018). For this reason, it has been argued that this construct is becoming less important as companies become more familiar with technology (Alam and Uddin, 2018; Nguyen and LE, 2020; Pei-Yee Chin et al., 2020; Xena and Accounting, 2019).
Previous studies show that effort expectancy plays a role in determining the intention to use a tool and has been positioned as the second most important determinant of the intention to use a tool (Carvajal et al., 2021; Pei-Yee Chin et al., 2020). If a new technology requires less effort to understand, the intention to use it will be greater. As it becomes more intuitive and accessible, and as the degree of difficulty decreases, the intention to use it is likely to increase. For this reason, the expectation of effort must be taken into consideration if the intention to use it is to be fostered (Chua et al., 2018; Nguyen and LE, 2020; Rahi et al., 2019; Soliman et al., 2019). Therefore, reducing difficulty can have a large impact on increasing the use of technologies. The literature shows that outcome expectancy and effort expectancy are the main drivers of entrepreneurial behavior toward the use of a technology (Kosiba et al., 2022; Pei-Yee Chin et al., 2020).
The study presented here addresses the intention of SMEs to use the Metaverse, which will depend on the effort in economic resources and knowledge acquisition involved in the implementation of this tool in the company, as well as their business satisfaction, as related to the relationship between the variables: expectations of effort and satisfaction when implementing the Metaverse (Lee and Kim, 2022). In this sense, we formulate the following hypotheses:
effort expectancy significantly influences SMEs' intention to use the Metaverse.
effort expectancy significantly influences SMEs' satisfaction with the use of the Metaverse.
2.2.2 Performance expectancy
The authors Ho and Shafiq (2021), Moriuchi (2021), Lehmann et al. (2020), and Zheng et al. (2018) define performance expectancy as the degree to which the company believes that the use of a product or service will be useful in an organization. In other words, performance expectancy is the confidence that a behavior will lead to certain outcomes which will affect the likelihood that the company will direct its efforts toward that goal (Collado and Evans, 2019; Zheng et al., 2018).
Depending on the company's expectations, it is explained what types of targets they choose (Howardson and Behrend, 2014). The company focuses on goals whose achievements are beneficial, feasible and achievable while improving its performance. Companies direct their behavior toward these goals when they consider that the expected result is sufficiently positive (Lehmann et al., 2019).
Performance expectancy is known as a pillar in different behavioral theories (Grandón et al., 2018; Venkatesh et al., 2012). In all of them, a predictor is considered to explain the behavior. The formation of this estimate, idea or prediction is influenced by the previous positive or negative outcome. That is, after a positive outcome, expectations of success will increase and vice versa (Lehmann et al., 2020; Zheng et al., 2018). Previous studies affirm that performance expectancy is a factor of great importance in predicting the intention to use a system or technology. There is a positive relationship between performance expectancy and intention to use a new technology (Alam and Uddin, 2018; Carvajal et al., 2021; Collado and Evans, 2019; Moriuchi, 2021; Nguyen and LE, 2020; Rahi et al., 2019; Soliman et al., 2019).
This research is based on the idea that investing efforts in the Metaverse leads to positive and beneficial ex-post results. We are not only referring to the economic benefit but also to expectations of improved customer experience, a greater capacity for innovation, an access to new markets and a higher performance of the company's image and popularity, as well as an increased recognition of value by employees (Song et al., 2022). Logically, these expectations are rising as the Metaverse develops and grows and the potential for enterprise participation in immersive environments increases, as organizations already using the Metaverse report positive experiences and overall satisfaction with their experiences using the platform (Arpaci et al., 2022; Lee and Kim, 2022).
Performance expectancy significantly influences SMEs' intention to use the Metaverse.
Performance expectancy significantly influences SMEs' satisfaction with the use of the Metaverse.
2.2.3 Business satisfaction
Following the decision to purchase or use a product or service, the organization may be satisfied or dissatisfied. Satisfaction refers to the degree of conformity with the results obtained after using a new technology (Mazzarolo et al., 2021; Tawafak et al., 2021). The greater or lesser degree of satisfaction arises as a consequence of the subjective comparison between expectations and the final perception.
Numerous studies give a psychological approach to this concept and define it as the sensation of well-being and pleasure obtained from getting what one expects from a product or service (Lie et al., 2019). Due to the aforementioned subjectivity and the incorporation of emotional and/or cognitive elements, it is a difficult concept to handle (Joo et al., 2018; Tawafak et al., 2018). This construct is of vital importance to many industries when it comes to business analysis. The monitoring of business satisfaction is a fundamental aspect for the success of the business against the competition, as it shows the fulfillment of needs, desires and objectives with respect to the original perception of the business (Ahrholdt et al., 2019; Pozón-López et al., 2019). It is also considered one of the key concepts in the development of marketing strategies, as it influences loyalty, fidelity and brand value, favoring the company's market share and profitability (Alzahrani and Seth, 2021; Lie et al., 2019; Prayag et al., 2018).
Satisfaction, therefore, is one of the central elements of behavioral analysis (De Oña, 2021; Wills et al., 1974). Previous studies have pointed to satisfaction in the use of the internet, social networks and information systems as a predictor of behavioral intention, since among the most relevant variables that explain the intention to use a technology is the level of satisfaction with the technology (Mazzarolo et al., 2021; Pozón-López et al., 2019; Vărzaru et al., 2021). On the other hand, satisfaction has been related in the literature to the intention of use when taking into account new technologies without the subjects of the study having interacted with the product, simply by having knowledge of its existence (Mustafa et al., 2022; Tussyadiah, 2016), or having previous experience in a technology that predicts the object of study (Amin et al., 2022; Lee et al., 2022). In the case of this research, it was the experience that companies had with early Metaverse and immersion in virtual reality with different electronic devices (Atzeni et al., 2022) without becoming a total immersion in Metaverse. The latter being the technology that precedes our object of study. Where virtual reality is concerned, the aspects of exploration and using skills to interact with the environment exist by including interactive features enabling the user to explore and control the environment (Sutcliffe, 2016). However, customer satisfaction as a measure for new technology adoption success is limited in scope and treatment and lacks comprehensiveness in capturing the full functionality of the virtual reality (Molla and Licker, 2001). It is considered that the measurement of new technology adoption success by companies should be based on the deployment of people, technology and strategy to accelerate improvement cycles and increase profit margins (Spyns et al., 2002). The demand to develop a new research framework and model to measure Metaverse adoption from the business point of view is becoming more critical. Regarding these, there is a definition proposed by (Wu et al., 2014) for business satisfaction as a measurement of the overall satisfaction that a business has with a new technology system adoption meeting its requirements and expectations. The authors considered that business satisfaction is not customer satisfaction because customers have different research goals and objectives. Business satisfaction focuses on answering how happy businesses are with the process of adopting a new immersive technology.
Therefore, we will analyze whether company satisfaction is significant in relation to intention to use the Metaverse:
The satisfaction of SMEs significantly influences the intention to use the Metaverse.
2.2.4 Behavioral intention
Behavioral intention is defined as a company's willingness, in terms of effort and action, to carry out a particular practice (Moriuchi, 2021; Nguyen and LE, 2020). Results from previous studies show that one of the keys to the success of a technology is the intention to use it (Faqih, 2022), as well as the success and growth of a new technology lying to a great extent in the intention (Gunawan and Gunawan, 2019). The literature suggests that there is a strong correlation between behavioral intention and actual behavior (Alam and Uddin, 2018; Gansser and Reich, 2021; Moriuchi, 2021; Soliman et al., 2019). Given the difficulty of measuring the actual behavior of a growing technology, a construct related to intention is often used as an indicator and predictor of behavior in virtual environments (Aburbeian et al., 2022; Huang et al., 2016).
Figure 1 below shows the model proposed for our research.
3. Methodology
The sample of this research is composed of 182 Spanish small and medium-sized enterprises (SMEs) in the technology sector (INE, 2019). Spain has been used as the country for the sample, on the basis of the scales of the tool Culture Compass™ (Hofstede Insights, 2022) being a tool used in the literature (Katz et al., 2022; Masyhuri, 2022; Waters and Day, 2022). It is a tool that has been successfully used in previous analyses of international strategic behavior (Zgodavova et al., 2017), business development (Ahmad and Singh, 2017), technology adoption processes related to satisfaction (Novak et al., 2000), quality in management (Al Mubarak et al., 2017) and virtual worlds and e-learning (Halder, 2019) The Hofstede Culture Compass has proven useful in identifying national trends in performance and behavior concerning management and adoption variables. With Hofstede's Culture Compass, a basic comparison can be made of the general cultural trends in each country, establishing principles and strategic ways of acting on certain dimensions (Zgodavova et al., 2017). Here the Uncertainty Avoidance dimension defines Spain very clearly with a high score of 86 (Díaz-Soloaga and Díaz-Soloaga, 2023). This score has been verified in several studies with SMEs as the object of research (Mhedhbi and Essid, 2022; Somoza, 2023). In this sense, the sample has been made in a conservative environment as an extreme for the propensity to change, since if the main objective of the research is verified in this environment it will be more likely to be so in environments with a lower score. This was determined by Kraus et al. (2012) who showed that SMEs which are innovative in new digital projects perform better in uncertain and nonconservative environments. In this sense, the literature has examined the effects of the environment on SMEs with the implementation of innovative technology (Prajogo and McDermott, 2014).
For the distribution of the sample of companies, the research has tried to be as exhaustive as possible, in order to cover all the subsectors. According to the classification of the National Institute of Statistics (INE), the telecommunications and Information and Communication Technologies sector is divided into five subsectors (manufacturers, commercial industries, telecommunications, programming, and repair), each with its corresponding codes (INE, 2019). Table 2 shows the representativeness of the sample in each of them.
Table 3 shows some demographic data of the companies analyzed, referring to the time since their creation and the current number of employees.
A nonprobabilistic convenience sampling is carried out. This type of sampling implies that not all elements of the population have the same probability of being selected, and it is left to the researcher's judgment as to why. It is widely used in social and business sciences, since heterogeneity in terms of possible characteristics of the elements surveyed makes it necessary to establish research guidelines (Liao et al., 2019). In our case, the convenience of the nonprobabilistic method is based on the need for knowledge on the part of the companies of the Metaverse concept, as well as their positive predisposition to its use. Our specific research requires prior knowledge of immersive reality on the part of the companies that make up the sample. Therefore, prior to the administration of the questionnaire, we contacted the digital strategy managers or CEOs of the company to determine their knowledge of the Metaverse, as well as to confirm their previous experience with virtual reality (VR) processes. This fact was also confirmed later in the fitter questions of the questionnaire, to confirm that the entire useful sample met the knowledge requirement. Subsequently, the questionnaires were distributed online through a platform created for self-administration. The research was conducted between January and April 2022. A total of 197 observations were obtained, of which 15 were discarded as incomplete or null, either because they are incomplete, or because they lack experience with virtual reality processes, since the literature advises eliminating the incomplete sample if the incomplete data are significant (Nelson et al., 1996).
The scales have been adapted from other studies carried out previously and with verified results in their application in samples of companies. Thus, Effort Expectancy and Performance Expectancy have been adapted from Venkatesh et al. (2003), business satisfaction comes from the work of DeLone and McLean (2016), and Behavioral Intention is adapted from the research of Taylor and Todd (1995). We used Smart-PLS 3 software for data processing (Richter et al., 2016). To this end, the reliability and validity of the measurement scales and the structural model were evaluated beforehand (Chin and Dibbern, 2010; Hair et al., 2012). This technique is widely verified in the literature and is particularly interesting in the field of social research with small sample sizes (Wynne w. Chin, 1998). PLS-SEM has been used to test the model and hypotheses for two reasons: firstly, all measurements are treated as estimated Mode A composites (Hair et al., 2019a, b; Henseler, 2017; Rigdon, 2016; Rigdon et al., 2017); and, secondly, because it adopts an explanatory approach (Henseler, 2018). Composites are estimated in Mode A when the indicators that compose the latent variable are correlated. In addition, Mode A composites are the best choice in the case of the sample size being small or medium (Henseler et al., 2009). On the other hand, it can be considered a technique that can handle various forms of construct operationalizations, including reflective measurements and composite models (Carrión et al., 2016).
To measure the items of each variable, a seven-point Likert-type scale was used, where 1 is totally disagree and 7 is totally agree. The variables used in this research are shown in Table 4. This shows the items used and from where they were extracted.
4. Results
For the analysis of the PLS model, we proceed to analyze the reliability and validity of the measurement model, for which we have followed the recommendations in the literature (Fornell and Larcker, 1981; Henseler et al., 2017). In the case of reflective variables, first of all, the individual reliability of the item is ensured. For this purpose, the factor loadings on their own latent variables are examined. These loadings must be higher than the 0.7 proposed in the literature. All the loadings of our reflective constructs are above this indication (Table 4). Secondly, the reliability of the constructs is analyzed using Cronbach's alpha and composite reliability indicators (Table 4). The latter is used due to the limitations of Cronbach's alpha, which takes into account the different values of the external loadings of the indicator variables (Hair et al., 2017). In all cases, our indicators are higher than the required 0.8. In addition, convergent validity has been ensured by analyzing the average variance extracted (AVE). In our case, similarly, all indicators offer levels above the minimum level of 0.5, indicating high levels of convergent validity for the constructs.
And, finally, by means of Heterotrait-Monotrait (HTMT), which is an estimate of the real correlation between two constructs, if they were perfectly reliable (Hair et al., 2017; Henseler et al., 2015) In all cases they offered levels below the threshold of 0.9 (Table 5); even below the 0.85 level set by some authors as more recommendable and conservative (Henseler et al., 2015). Regarding the assessment of the structural model, the model has been subjected to the standardized root mean square residual (SRMR) assessment. Values lower than 0.08 are considered as a good fit. In our case, the SRMR is 0.074, so we consider it a good fit (Hair et al., 2019a, b).
To evaluate the statistical significance level of the path coefficients we used a bootstrapping of 5,000 subsamples (Henseler, 2018). Table 6 shows the results and significance of the direct effects analyzed. Here it can be seen that, of the five hypothesized relationships, three of them are supported (H2, H4 and H5) and two are not (H1 and H3). Of the significant relationships, H2 presents a higher level of intensity (t-value 4.061). Likewise, we also incorporate the variance contribution of the preceding construct in each relationship.
When dealing with entrepreneurial behavior, it is important to analyze the variance contribution of the different constructs, in order to prioritize the development of strategies. Analyzing the R2 of the preceding constructs, we observe the contribution of 0.38 of EXESF over SATIUS, as well as 0.228 of EXRES over SATIUS. In the case of the former, this makes it the most significant construct for the companies, both in terms of its importance and its explanatory contribution. It is also worth highlighting the explanatory contribution of satisfaction in the intention to use the Metaverse by the companies, with 0.41. The general model provides the following variance explanation data (Table 7), reaching almost 55% in the case of intention to use, which indicates the good development of the model.
The results of the mediation analysis provide empirical support for understanding the importance of the satisfaction construct for the companies in the adoption of strategies within the Metaverse (Table 8). The intervention of satisfaction on the part of the companies allows the relationships, which are not directly significant, to become so. We therefore speak of a total mediation by this construct in the model.
Empirical research on companies obtains conclusions and recommendations for their subsequent strategic development based on the models analyzed. Although these models are analyzed according to the established guidelines and their fit is checked, they may present problems in their application in reality. Therefore, our intention is that in a field as novel as the Metaverse, the implications for companies reach the highest level of verisimilitude and practical application. In this sense, we have tested the predictive capacity of the model (Shmueli et al., 2019), i.e., its application for samples other than the one used, through PLSpredict.
The operation of PLSpredict is based on the use of training samples, for which three parameters are determined: the number k, as subsets into which the sample is divided; the number r of repetitions, as the number of times the algorithm will iterate; and, finally, the prediction statistic, to validate the predictive power of the model (Kock and Hadaya, 2018). In our model, according to the 184 sample observations, we have determined a k = 5, so that the remaining samples are significant, as well as a number of repetitions r = 10. This offers a good balance between accuracy and computation time (Witten and Frank, 2002). The setting of the statistic depends on the asymmetric distribution of the prediction errors. If the absolute value of the skewness is less than 1, we will use the RMSE statistic (Shmueli et al., 2019).
We apply PLSpredict to calculate the benchmark Q2predict, which uses the mean value of the variables in a training sample as a prediction of the variables in the reserved sample. A positive Q2predict value indicates that the prediction error of the PLS model is smaller than the prediction error given by the benchmark (Shmueli et al., 2019). Subsequently, a linear regression model is created, which regresses the exogenous indicators, and the estimate is compared with the results of the PLS (sustained) model. The results of the latter should have better (lower) errors than the regression model (see Table 9).
For the dependent variables of interest, it is satisfied that all the
We therefore observe that all the differences are below 0, which allows us to affirm that the model has a high predictive value. As we have indicated, this fact is especially important for an emerging field such as the Metaverse and the strategic implications for companies that derive from it (see Table 10).
5. Discussion
This study has allowed us to answer the question posed in the title of the paper. According to this, SMEs do intend to use the Metaverse as part of their strategy as long as satisfaction acts as a mediator in the process prior to the development of strategies within it. The challenges and perceived opportunities in Spanish SMEs' behavioral intention affect the progress of the companies’ digital entrepreneurship and their implications for policy and practice (Igartua and Ibarra, 2018). The current total investment for digitalizing Spanish SMEs is €4.656bn by 2021–2023 according to the 2025 governmental Digitalization Plan. In addition, the European Parliament Briefing's (Polona et al., 2022) economic studies have predicted that the global Metaverse market will reach €597.3bn by 2030. This is why some stress the importance of the technical solutions and protocols to avoid the risk of some tech companies attempting to shape the emerging Metaverse standards. It seems that businesses that better engage with digital technologies will have a greater influence on business transformation and Metaverse adoption. The efforts of EU policymakers to foster the development of open Metaverse standards enable SMEs an interoperability to build the Metaverse ecosystem but warn that this may lock in developers and limit consumer choice and the creation of competing innovations. In this new framework, SMEs and in particular Spanish SMEs, have little flexibility given their limited capacity to manage the data required to adapt their strategies to market realities (Hirsch et al., 2020). For SMEs digital technology must be initially viewed as an experiment for innovation, which is a differentiating capability needed for long-term success for businesses in the digital age (Cuevas-Vargas et al., 2022; Hussain and Papastathopoulos, 2022; Sultana et al., 2022).
The analysis of the results has allowed us to confirm the proposed hypotheses, as well as to reject others, and thus to obtain interesting conclusions on the behavior of the companies. The information obtained helps to understand what aspects companies prioritize when deciding whether to allocate resources to this virtual environment. Firstly, the relationship between effort expectancy and intention to use is not significant. This result is in line with those obtained by (Alam and Uddin, 2019) in their analysis of the adoption and implementation by companies of the ERP (enterprise resource planning) system. However, it contrasts with the data obtained in the study of Subawa et al. (2020) on the uptake of e-commerce in SMEs, and that carried out by Nguyen and LE (2020) about the intention to apply management accounting in Vietnamese firms, in which they concluded that the effort expectancy construct is a good predictor of intention to use it. The fact that the development of strategies within the Metaverse requires a comprehensive and novel adaptation for the firm (Schumacher, 2022), rather than the adoption of a technological system or a management program, could explain to some extent the disparity in these results.
When Hypothesis 2 is fulfilled, it can be affirmed that, on the contrary, the expectation of the effort required does significantly influence the companies' satisfaction with the use of the Metaverse. This result is consistent with that obtained by Lim et al. (2018) in the study where they compile the factors that influence online repurchase. In addition, the research presented here also relates to the predictions of Chan et al. (2010). These authors confirm in their model that the expectation of effort influences satisfaction with the use of applications and other technologies imposed by the government.
In relation to outcome expectancy, it can be affirmed that it does not have a significant impact on intention to use, since Hypothesis 3, which establishes a relationship between both variables, is not fulfilled. This result differs from that obtained in studies on education conducted by Özlem (2022) and Sahin (2008), which state that the expectation of results does relate to the intention of use when it comes to integrating technological systems in the field of teaching, and those obtained by Keong et al. (2012) and Wongsabsin (2021) in their research on the implementation of resource planning in companies. This seems to indicate that companies in the development of strategies within the Metaverse do not pay attention in this first phase of the technology to the possible results to be obtained but that other variables determine their behavior.
However, with Hypothesis 4, it is corroborated that outcome expectancy has a significant impact on firm satisfaction. These results are consistent with previous studies on online tools. On the one hand (Alruwaie et al., 2020), in their analysis of the use of e-government services, and, on the other hand (Song and Deng, 2019), confirm this relationship of variables in the field of virtual learning communities.
Finally, by fulfilling Hypothesis 5, this research confirms, as suggested by the literature, that the relationship between satisfaction and intention to use is significant. When deciding their approach to immersive systems, companies take into account the level of satisfaction that they will get from them. The same is true of several previous studies, such as the one carried out by Garg and Sharma (2020), in which satisfaction is revealed as the most significant predictor of the intention to use e-learning courses for employees in India. The same conclusions are drawn from the study by Xiao and Warkentin (2021) which also confirms this relationship with respect to the use of information technologies.
5.1 Academic, social, and business implications
This study has interesting implications for various stakeholders. We address the gap in studies related to SMEs' business adaptation to rethinking the opportunity which can be leveraged from the new cutting edge of technology and how to develop their strategic assets and capabilities toward the Metaverse virtual world. Understanding the circumstances and reasons for the behavioral intention of using the Metaverse system that facilitates digital entrepreneurship is of interest to academic research and guides business practice, as well as public policies aiming to support this phenomenon, given its positive impacts in terms of job creation and economic growth.
From an academic perspective, this work represents one of the first studies that places SMEs as a central object of analysis regarding the adoption of the Metaverse. The behavior of companies in relation to the technological adoption of total immersive realities will imply important social changes, both from the development of their own business and from the point of view of their social responsibility. This research extends the existing theory on technological adoption in firms by introducing and adapting the satisfaction construct. The incorporation of the satisfaction construct is useful with respect to effort and performance expectations to address, from an academic point of view, the position of firms in the future and contribute to the need for empirical studies applied to the business environment in relation to Metaverse adoption. Once the implementation of the Metaverse is more widespread, the concept of satisfaction will be analyzed longitudinally, obtaining comparable results across companies and sectors, thus opening up an interesting field of future research. Therefore, this research serves as a starting point for academics and researchers to propose new models and avenues of research.
From a social perspective, the research is a relevant contribution. There is no doubt that the Metaverse, in addition to constituting a new technology for companies, will have unprecedented repercussions in the social sphere, given the plurality of areas in which there will be different uses and users (education, health, tourism) and the need for political regulations that focus on principles of responsible research and innovation, as well as incorporating the social benefit in relation to jurisprudence, labor relations, taxation, and social participation itself, among many other fields. The model reflects the fact that companies are currently observing reality, that of both consumers and competitors. SMEs show a predisposition toward Metaverse technology adoption through satisfaction in relation to expectations of achieving a balance between the company's needs and objectives. Therefore, the evolution of both sets in the Metaverse will be decisive in confirming the current behavior of companies or, failing that, in deriving future strategies that have not yet been considered. However, as we have already mentioned, it is essential for companies interested in developing strategies within the Metaverse to observe the behavior of the social environment.
From a political perspective, the considerable increase in public investment for the adoption and introduction of companies in digital technologies and, specifically, in the regulation of Metaverse systems, places this research as a starting point to develop policies related to the operability of SMEs in the adoption of immersive technology. It does do knowing what the reality of companies and their considerations regarding their resources and capabilities is. SMEs require practical approaches from researchers to seize the potential of the Metaverse. Recent studies describe how new technological advances have significantly optimized data analysis processes, making it profitable for companies of all sizes to invest (Liu et al., 2020). In addition, SMEs can access multiple initiatives to ask for basic information, network, scale-up and updates in relevant technology or training funding opportunities (Sanchez-Hughet et al., 2022). The model studied in this research indicates that SMEs propose a process of observation of reality, establishing a balance between the resources that they have and those necessary to operate in the Metaverse. The results indicate that SMEs are concerned about the effort to be made, translated into the cost of economic and non-economic resources that the adoption of the Metaverse may entail. Public policies should analyze the tangible and intangible resource needs of companies in order to economize and make the most of public resources. This research highlights the need for public policies tailored to the SME context for the development of specific strategies in the Metaverse. This will imply that the different strategies of public administrations should be aimed at reducing the adverse economic effects on SMEs and the costs associated with technological adoption.
The model analyzed presents important and novel implications for business. In the SMEs environment, the intention of companies to venture into the Metaverse is an original and necessary approach as companies must consider whether to adapt their business model to this new reality and define the challenges and opportunities that the adoption of immersive technology would mean for the creation of business value. The proposed research model presents a dichotomous perspective in terms of the questions that companies should ask themselves in the process of adopting the Metaverse: What effort do I have to make to develop a strategy in the Metaverse? And what do I expect to obtain by developing a strategy in the Metaverse? The model proposes that both questions be asked simultaneously, as this will help to better determine the development strategy of companies in virtual environments. Companies organize their resources and capabilities, prioritizing which decisions to make and how to accommodate to the Metaverse adoption strategy from an integral vision, in relation to perceived satisfaction and their expectations in the technological adoption process, valuing the effort to be made and the performance to be developed.
Related to the above, entry into the Metaverse is a complex environment change for SMEs, as evidenced by the need to be satisfied with their decision. Therefore, prior to entry, the company must develop the corresponding digital and physical adaptation mechanisms to overcome the obstacles of access and operability in this new environment. This will increase its chances of success. Each SME will have to determine the degree of satisfaction of its strategy, as this will be the key that balances the efforts made, as well as the possible expected returns. At this early stage of Metaverse development, an SME must feel comfortable entering the Metaverse. This “comfort” will require, in business terms, data, analysis and information. This satisfaction can be associated at the company level with the reduction of uncertainty and knowledge of the new environment, so it will be necessary to implement systems for analyzing the environment in terms of the behavior of competitors in the Metaverse, the development of new strategies, consumer studies, and the impact on the environment, among other issues.
Furthermore, with regard to the organization and mobilization of the resources necessary for the adoption of the Metaverse, and given that the expectation of effort is the construct with the greatest impact on the model, its assessment must be carefully carried out by the SME before considering entry into the Metaverse. Therefore, the SME must carry out an adequate resource analysis and determine the necessary investment for the development of the strategy in the new environment. The necessary prioritization of tasks within all business processes must start with this in the case of the Metaverse.
All the necessary conditions for entrepreneurship are present in the Metaverse: a new technology with development options, the interest of the market and the presence of entrepreneurs who want to invest in it. As a result, the creation of companies in virtual worlds opens up new and unknown possibilities in the future, precisely because of the special global characteristics of the Metaverse. The issues derived from this research open up possibilities in the organization of resources for the entrepreneurship process of SMEs and start-ups. The possibilities of the Metaverse provide new companies with the ability to interact with a wider number of consumers, where an optimal level of feedback will provide companies with greater flexibility to adapt their products more quickly and efficiently than others already established in the market, also producing specific offers for the virtual world. The branding process will as well be accelerated by the transformation of the consumer's accessibility experience, starting from different premises and strategic designs, which must be defined and elaborated in the construction of the virtual business world.
In particular, for start-ups the market intelligence system will be more effective for targeting, and more advanced communication and marketing techniques will be used in general. It is also possible that these companies will face the challenge of new dynamic capabilities, network building systems or customer relationships as part of a new social reality. The consumer's flow in the face of a reality that is more adapted to his or her needs may have an impact on new business strategies. In general, the creation of value by new companies will involve a very different process from the one we know today.
These advantages will not be without new and complex challenges for start-ups. The Metaverse, apart from different entrepreneurial premises, will involve knowledge on the part of managers for which there is currently no complete experimental basis. Strategies will have to be implemented in a social, economic and legal environment under construction. While large companies are so far considering the Metaverse as an option to operate in the near future, developing plans to participate in immersive systems, the levels of investment currently required are far beyond the capacity of SMEs, which may not have the knowledge or technology necessary for its implementation. The level of access to the Metaverse may not be the same for all countries or economic organizations, thus limiting the number of consumers and companies. New sectors and companies will appear, which will manage aspects such as new products and services to organize a new environment, new IT platforms, forms of authentication, security, identity and digital presences, among other related aspects.
6. Conclusions
This research has been able to respond to the established objectives. The information obtained has resolved the questions raised about the attitude of companies toward the Metaverse. According to the results of this analysis, the expectation of effort and the expectation of results do not directly influence the intention to use the Metaverse, but they do so indirectly through satisfaction. At the present time, neither the expected return on investment nor the cost of resources involved in incorporating this new technology into the SME's business is a priority for the companies' objectives. The main priority is the satisfaction of the team and of the company as a whole: the pleasure and well-being of investing in a new technology and the fact that it meets expectations, as well as this being adjusted to the resources and capabilities present in SMEs. It can be deduced that, being in the initial phase, the companies mainly take into account being part of a new project and promoting aspects such as corporate image, innovation or even the interest in not being late for this new opportunity. SMEs want to be in the Metaverse, but do not clearly define what for.
The practical value of this study lies in offering SMEs a vision of how to approach access to the Metaverse, considering their own resources, capabilities and strategic development process. The model allows SMEs to order and prioritize their decision process in order to enter this new environment with a specific application. Based on this information, the level of satisfaction of the SMEs will be the main factor that will increase investment and, therefore, the Metaverse will grow to a greater or lesser extent. Its development and expansion would be a breakthrough for technology and society as a whole. The combination of the real world and the virtual world presents infinite possibilities with the ultimate goal of creating an interactive world in all areas: social relations, academia, videogames, property, art, entertainment, etc. It offers many interesting opportunities in different areas of society, including the business world. Technology is advancing and developing very fast, and SMEs are reacting in this sense: they are open to incorporating and backing the Metaverse to be at the forefront of this evolution.
7. Limitations and future research lines
Studies on the Metaverse linked to SMEs are scarce in the literature. Therefore, as the field develops, it will be possible to include in the discussion and in the theoretical structure of the research a larger amount of literature specifically associated with this area, so that the frameworks of the research can be better established, as well as the extension and comparison of the results.
The Metaverse is a nascent technology, in its early stages of adoption and development. As such, it is possible that new developments and strategies will enable SMEs to enter it in a different way than how it is currently conceived. Therefore, although the model is useful to know a first strategic approach, future research should expand this according to new developments in the area.
Although the Spanish technology sector has world-renowned companies, it is true that its size is small, since the country's economic base is centered on other elements. For this reason, it would be advisable to replicate the study in countries with a greater tradition and power in their technology companies sector.
Figures
Factors to be considered by SMEs in the metaverse adoption process
| End users | Need for training in the use of new technological tools |
| Modification of workers' attitudes toward technological change | |
| Lack of technical knowledge, combined with a low appreciation of the benefits associated with the Metaverse | |
| Organizational | Ease and rationality in the exchange of information in the new structure |
| Influence and proper management of managers with respect to the availability of resources, money, time and talent to tackle Metaverse projects | |
| Technological | Correct identification of the costs derived from the Metaverse as well as the return on investment |
| Credibility of the new technology | |
| Compatibility with other businesses associated with the organization's environment | |
| Management | Metaverse adoption process fully determined by top management, affecting day-to-day decision making |
| Their involvement will determine the level of commitment and participation of the rest of the employees | |
| Training of senior management on the Metaverse, to encourage their support | |
| Need for a positive, innovative and tech-savvy attitude about the Metaverse | |
| Business Environment | Company perception of the Metaverse as a tool of competitive necessity |
| Adoption of an innovative or reactive attitude | |
| Need for market readiness to adopt Metaverse technology | |
| Culture, globalization and market trends |
Source(s): Author's own creation
Distribution of the sample according to ICT sectors
| Sector | CNAE codes | Sample |
|---|---|---|
| Manufacturers | 2611; 2612; 2620; 2630 | 19.23% |
| Commercial Industries | 4651; 4652 | 15.93% |
| Telecommunications | 6110; 6120; 6130; 6190 | 26.37% |
| Programming | 6201; 6202; 6203; 6209 | 32.42% |
| Repair | 9511; 9512 | 6.04% |
Source(s): Author's own creation
Characteristics of the analyzed companies
| Time since the creation of the company | |
|---|---|
| 0–2 years | 23.63% |
| More than 2 years up to 5 years | 32.97% |
| More than 5 years–10 years | 31.32% |
| More than 10 years | 12.09% |
| Current number of employees | |
|---|---|
| Between 0 and 20 employees | 26.92% |
| Between 21 and 50 employees | 30.77% |
| Between 51 and 100 employees | 18.68% |
| Between 101 and 150 employees | 14.29% |
| More than 150 employees | 9.34% |
Source(s): Author's own creation
Scales and measurement model
| Variables | Items | Loadings | AVE | Composite reliability | Cronbach's alpha | Rho_A |
|---|---|---|---|---|---|---|
| Effort expectancy | 0.715 | 0.882 | 0.811 | 0.890 | ||
| The use of the Metaverse is clear and understandable for my business | 0.890 | |||||
| I don't think I will have any problems using the Metaverse in my business | 0.764 | |||||
| I find the Metaverse useful for my business | 0.878 | |||||
| Performance expectancy | 0.660 | 0.847 | 0.736 | 0.851 | ||
| Using the Metaverse allows me to make queries or transactions for my business faster | 0.913 | |||||
| Using the Metaverse increases the quality of the products or services offered by my company | 0.919 | |||||
| My company can access the Metaverse from any device | 0.849 | |||||
| Business satisfaction | 0.888 | 0.959 | 0.936 | 0.944 | ||
| I believe that the use of the Metaverse has value in the business environment | 0.895 | |||||
| I believe that the use of the Metaverse has many advantages for my company | 0.970 | |||||
| I believe that it is worth using the Metaverse in my business activity | 0.960 | |||||
| Behavioral intention | 0.913 | 0.969 | 0.952 | 0.953 | ||
| I intend to use the Metaverse for the development of my business activity | 0.956 | |||||
| I intend to use some of the Metaverse services for the development of my business activity | 0.968 | |||||
| My intention is to do more and more of my business activity through the Metaverse | 0.943 |
Source(s): Author's own creation
Heterotrait-monotrait ratio (HTMT)
| EXESF | EXRES | IUS | SATIUS | |
|---|---|---|---|---|
| EXESF | ||||
| EXRES | 0.749 | |||
| IUS | 0.692 | 0.707 | ||
| SATIUS | 0.814 | 0.832 | 0.772 |
Note(s): EXESF: Effort expectancy; EXRES: Performance expectancy; IUS: Behavioral intention; SATIUS: business satisfaction
Source(s): Author's own creation
Effects of the construct on endogenous variables
| Effects on variables | Path coefficient | Confidence intervals (95%) | Significance of effect (P-value) | R2 of the dependent construct | |
|---|---|---|---|---|---|
| 5% CIlo | 95% Cihi | ||||
| EXESF → IUS (H1) | 0.139 | −0.163 | 0.462 | 0.386 | 0.090 |
| EXESF → SATIUS (H2) | 0.499 | 0.257 | 0.738 | 0.000 | 0.380 |
| EXRES → IUS (H3) | 0.096 | −0.193 | 0.374 | 0.507 | 0.060 |
| EXRES → SATIUS (H4) | 0.312 | 0.039 | 0.576 | 0.023 | 0.228 |
| SATIUS → IUS (H5) | 0.558 | 0.312 | 0.746 | 0.000 | 0.410 |
Note(s): EXESF: Effort expectancy; EXRES: Performance expectancy; IUS: Behavioral intention; SATIUS: business satisfaction
Source(s): Author's own creation
Explained variance of the model
| R2 | R2 adjusted | |
|---|---|---|
| IUS | 0.559 | 0.542 |
| SATIUS | 0.608 | 0.598 |
Source(s): Author's own creation
Significance analysis of direct and indirect effects
| Direct effect | 95% confidence interval | t value | Significance (p < 0.05) | |
|---|---|---|---|---|
| Effort expectancy → Behavioral intention | 0.139 | (−0.163; 0.462) | 0.868 | No |
| Performance expectancy → Behavioral intention | 0.096 | (−0.193; 0.374) | 0.664 | No |
| Indirect effect | 95% confidence interval | t value | Significance (p < 0.05) | |
|---|---|---|---|---|
| Effort expectancy → Behavioral intention | 0.278 | (0.107; 0.464) | 3.063 | Si |
| Performance expectancy → Behavioral intention | 0.174 | (0.019; 0.340) | 2.169 | Si |
Source(s): Author's own creation
Choice of statistic for Q2predict
| Q2predict | Asymmetry | Statistician | |
|---|---|---|---|
| IUS1 | 0.358 | −0.121 | RMSE |
| IUS2 | 0.334 | −0.105 | RMSE |
| IUS3 | 0.367 | −0.162 | RMSE |
| SATIUS3 | 0.522 | 0.365 | RMSE |
| SATIUS2 | 0.521 | −0.114 | RMSE |
| SATIUS1 | 0.480 | 0.167 | RMSE |
Source(s): Author's own creation
Differences between PLS model and linear regression model (LM)
| PLS model | Linear regression model | Differences | |
|---|---|---|---|
| RMSE | RMSE | ||
| IUS1 | 1.382 | 1.436 | −0.054 |
| IUS2 | 1.418 | 1.477 | −0.059 |
| IUS3 | 1.315 | 1.394 | −0.079 |
| SATIUS3 | 1.120 | 1.211 | −0.091 |
| SATIUS2 | 1.156 | 1.157 | −0.001 |
| SATIUS1 | 1.116 | 1.126 | −0.010 |
Source(s): Author's own creation
References
Abdulhakeem, I., Edwards, H. and McDonald, S. (2017), “E-commerce adoption in developing countries SMEs: what do the prevailing theoretical models offer US?”, (Paper presentation), ICOEC 18th-20th September, Putrajaya, Malaysia.
Aburbeian, A.M., Owda, A.Y. and Owda, M. (2022), “A technology acceptance model survey of the metaverse prospects”, AI, Vol. 3 No. 2, pp. 285-302, MDPI.
Ahmad, M. and Singh, R. (2017), “An empirical study on ease of doing business with specific reference to brics nations”, Pranjana: The Journal of Management Awareness, Vol. 20 No. 1, pp. 57-63, Integrated Academy of Management And Technology.
Ahrholdt, D.C., Gudergan, S.P. and Ringle, C.M. (2019), “Enhancing loyalty: when improving consumer satisfaction and delight matters”, Journal of Business Research, Vol. 94, pp. 18-27.
Akour, I.A., Al-Maroof, R.S., Alfaisal, R. and Salloum, S.A. (2022), “A conceptual framework for determining metaverse adoption in higher institutions of gulf area: an empirical study using hybrid SEM-ANN approach”, Computers and Education: Artificial Intelligence, Vol. 3, 100052, Elsevier.
Al Mubarak, H., Al Alawi, M. and Razzaque, A. (2017), “The importance of national cultural dimensions influence on quality management performance”, The international Journal of Business Research, Vol. 17 No. 3, doi: 10.18374/IJBR-17-3.1.
Alam, M.S. and Uddin, K.M.K. (2018), “End users' behavioral intention to use an enterprise resource planning (ERP) system: an empirical explanation of the UTAUT model”, The Comilla University Journal of Business Studies, Vol. 5, pp. 73-86.
Alam, M.S. and Uddin, M.A. (2019), “Adoption and implementation of enterprise resource planning (ERP): an empirical study”, Journal of Management and Research (JMR), Vol. 6 No. 1, p. 2019.
Alberti, F.G., Ferrario, S. and Pizzurno, E. (2018), “Resilience: resources and strategies of SMEs in a new theoretical framework”, International Journal of Learning and Intellectual Capital, Vol. 15 No. 2, pp. 165-188, Inderscience Publishers (IEL).
Almarzouqi, A., Aburayya, A. and Salloum, S.A. (2022), “Prediction of user's intention to use metaverse system in medical education: a hybrid SEM-ML learning approach”, IEEE Access, Vol. 10, pp. 43421-43434, IEEE.
Alruwaie, M., El-Haddadeh, R. and Weerakkody, V. (2020), “Citizens’ continuous use of eGovernment services: the role of self-efficacy, outcome expectations and satisfaction”, Government Information Quarterly, Vol. 37 No. 3, doi: 10.1016/j.giq.2020.101485.
Alzahrani, L. and Seth, K.P. (2021), “Factors influencing students' satisfaction with continuous use of learning management systems during the COVID-19 pandemic: an empirical study”, Education and Information Technologies, Vol. 26 No. 6, pp. 6787-6805, Springer.
Amin, R., Hossain, M.A., Uddin, M.M., Jony, M.T.I. and Kim, M. (2022), “Stimuli influencing engagement, satisfaction, and intention to use telemedicine services: an integrative model”, Healthcare, Vol. 10, p. 1327, MDPI.
Anderson, J.E. and Schwager, P.H. (2004), “SME adoption of wireless LAN technology: applying the UTAUT model”, Proceedings of the 7th Annual Conference of the Southern Association for Information Systems, Vol. 7, pp. 39-43.
Arakji, R.Y. and Lang, K.R. (2008), “Avatar business value analysis: a method for the evaluation of business value creation in virtual commerce”, Journal of Economic Commerce Research, Vol. 9, pp. 1-14.
Arpaci, I., Karatas, K., Kusci, I. and Al-Emran, M. (2022), “Understanding the social sustainability of the Metaverse by integrating UTAUT2 and big five personality traits: a hybrid SEM-ANN approach”, Technology in Society, Vol. 71, 102120, Elsevier.
Atzeni, M., Del Chiappa, G. and Mei Pung, J. (2022), “Enhancing visit intention in heritage tourism: the role of object‐based and existential authenticity in non‐immersive virtual reality heritage experiences”, International Journal of Tourism Research, Vol. 24 No. 2, pp. 240-255, Wiley Online Library.
Azar, T., Barretta, R. and Mystakidis, S. (2022), Metaverse, Mdpi.com, doi: 10.3390/encyclopedia2010031.
Borrás, P. (2022), “SMEs and the metaverse”, Expansión, available at: https://www.eleconomista.es/opinion/noticias/11907834/08/22/Las-pymes-en-el-metaverso.html (accessed 19 October 2022).
Bravo, R. (2022), “Metaverses, NFT and the new hope for SMEs to beat the crisis”, Expansión.
Carrión, G.C., Henseler, J., Ringle, C.M. and Roldán, J.L. (2016), “Prediction-oriented modeling in business research by means of PLS path modeling: introduction to a JBR special section”, Journal of Business Research, Vol. 69 No. 10, pp. 4545-4551, Elsevier.
Carvajal, K., Cataldo, A., Ampajo, M. and Guiovanna, P.-A. (2021), “Factores que influyen en la aceptación de dispositivos inteligentes insertables en el cuerpo”, Ingeniare. Revista Chilena de Ingeniería., Vol. 29, pp. 184-198.
Chan, F.K.Y., Thong, J.Y.L., Venkatesh, V., Brown, S.A., Hu, P.-H. and Tam, K.Y. (2010), “Modeling citizen satisfaction with mandatory adoption of an e-government technology”, Journal of the Association for Information Systems, Vol. 11 No. 10, pp. 519-549.
Chin, W.W. (1998), “The partial least squares approach to structural equation modeling”, Modern Methods for Business Research, Vol. 295 No. 2, pp. 295-336.
Chin, W.W. and Dibbern, J. (2010), “An introduction to a permutation based procedure for multi-group PLS analysis: results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA”, in Handbook of Partial Least Squares, Springer, pp. 171-193.
Choi, H. and Kim, S. (2017), “A content service deployment plan for metaverse museum exhibitions—centering on the combination of beacons and HMDs”, International Journal of Information Management, Vol. 37 No. 1, pp. 1519-1527, Elsevier.
Chua, P.Y., Rezaei, S., Gu, M.L., Oh, Y.M. and Jambulingam, M. (2018), “Elucidating social networking apps decisions: performance expectancy, effort expectancy and social influence”, Nankai Business Review International, Vol. 9 No. 2, pp. 118-142, Emerald Group Publishing.
Clemons, E.K. (2009), “The complex problem of monetizing virtual electronic social networks”, Decision Support Systems, Vol. 48 No. 1, pp. 46-56.
Collado, S. and Evans, G.W. (2019), “Outcome expectancy: a key factor to understanding childhood exposure to nature and children's pro-environmental behavior”, Journal of Environmental Psychology, Vol. 61, pp. 30-36.
Collins, C. (2008), “Looking to the future: higher education in the metaverse”, Educause Review, Vol. 43 No. 5, pp. 50-52.
Cuevas-Vargas, H., Aguirre, J. and Parga-Montoya, N. (2022), “Impact of ICT adoption on absorptive capacity and open innovation for greater firm performance. The mediating role of ACAP”, Journal of Business Research, Vol. 140, pp. 11-24, Elsevier.
D Macredie, R. and Mijinyawa, K. (2011), “A theory-grounded framework of Open Source Software adoption in SMEs”, European Journal of Information Systems, Vol. 20 No. 2, pp. 237-250, Springer.
Dahnil, M.I., Marzuki, K.M., Langgat, J. and Fabeil, N.F. (2014), “Factors influencing SMEs adoption of social media marketing”, Procedia-Social and Behavioral Sciences, Vol. 148, pp. 119-126, Elsevier.
De Oña, J. (2021), “Understanding the mediator role of satisfaction in public transport: a cross-country analysis”, Transport Policy, Vol. 100, pp. 129-149, Elsevier.
De Regt, A., Plangger, K. and Barnes, S.J. (2021), “Virtual reality marketing and customer advocacy: transforming experiences from story-telling to story-doing”, Journal of Business Research, Vol. 136, pp. 513-522, Elsevier.
DeLone, W.H. and McLean, E.R. (2016), “Information systems success measurement”, Foundations and Trends® in Information Systems, Vol. 2 No. 1, pp. 1-116.
Díaz-Soloaga, P. and Díaz-Soloaga, A. (2023), “Forced telecommuting during the COVID-19 lockdown: the impact on corporate culture in Spain and Kazakhstan”, Corporate Communications: An International Journal, Vol. 28 No. 2, pp. 193-212.
Durukal, E. and Armagan, E. (2022), “Metaverse ve pazarlamaya etkileri”, İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, Vol. 11 No. 3, pp. 1890-1909.
Dwivedi, Y.K., Hughes, L., Baabdullah, A.M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M.M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C.M.K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D.P., Gustafsson, A., Hinsch, C., Jebabli, I., Janssen, M., Kim, Y.-G., Kim, J., Koos, S., Kreps, D., Kshetri, N., Kumar, V., Ooi, K.-B., Papagiannidis, S., Pappas, I.O., Polyviou, A., Park, S.-M., Pandey, N., Queiroz, M.M., Raman, R., Rauschnabel, P.A., Shirish, A., Sigala, M., Spanaki, K., Tan, G.W.-H., Tiwari, M.K., Viglia, G. and Wamba, S.F. (2022), “Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy”, International Journal of Information Management, Elsevier, Vol. 66, 102542.
European Commission (2020), “Recovery plan for Europe”, European Commission, available at: https://ec.europa.eu/info/strategy/recovery-plan-europe_en (accessed 03 December 2022).
Faqih, K.M.S. (2022), “Factors influencing the behavioral intention to adopt a technological innovation from a developing country context: the case of mobile augmented reality games”, Technology in Society, Vol. 69, 101958, Elsevier.
Fornell, C. and Larcker, D.F. (1981), Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics, Sage Publications Sage CA, Los Angeles, CA.
Gálvez, Y. (2022), “Qué Es el metaverso: guía práctica sobre todo lo que necesitas saber sobre el futuro del metaverso y La transformación virtual de Empresas”, Independently published.
Gamiun (2022), “The center of Metaverse”, available at: https://gamium.world/
Gansser, O.A. and Reich, C.S. (2021), “A new acceptance model for artificial intelligence with extensions to UTAUT2: an empirical study in three segments of application”, Technology in Society, Vol. 65, 101535, Elsevier.
Garg, S. and Sharma, S. (2020), “User satisfaction and continuance intention for using e-training: a structural equation model”, Vision, Vol. 24 No. 4, pp. 441-451, Sage Publications India Pvt.
González, M.G. (2022), “Implementación y análisis de la tecnología Blockchain y su implicación fundamental en el desarrollo de un metaverso descentralizado”, (Final Degree Proyect) Universidad Politécnica de Madrid.
Grandón, E.E., Aravena Ibarra, A., Araya Guzman, S., Ramirez-Correa, P. and Alfaro-Perez, J. (2018), “Internet de las Cosas: factores que influyen su adopción en Pymes chilenas”, Proceedings of the CISTI.
Gunawan, J. and Gunawan, F.E. (2019), “Factors that influence employees' intention to use enterprise social media as knowledge sharing media”, CommIT (Communication and Information Technology) Journal, Vol. 13 No. 2, pp. 79-89.
Hair, J.F., Sarstedt, M., Ringle, C.M. and Mena, J.A. (2012), “An assessment of the use of partial least squares structural equation modeling in marketing research”, Journal of the Academy of Marketing Science, Vol. 40 No. 3, pp. 414-433.
Hair, J.F. Jr., Hult, T.M., Ringle, C.M., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrión, G.A. and Roldán Salgueiro, J.L. (2017), Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM), Segunda Ed, Publishing, S.
Hair, J.F. Jr, Hult, G.T.M., Ringle, C.M., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrion, G.A. and Roldan, J.L. (2019a), Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM), Segunda Edition, OmniaScience Scholar, Sage Publications, Barcelona.
Hair, J.F., Risher, J.J., Sarstedt, M. and Ringle, C.M. (2019b), “When to use and how to report the results of PLS-SEM”, European Business Review, Vol. 31 No. 1, pp. 2-24.
Halder, S. (2019), “Virtual learning through hofstede's individualism and collectivism cultural dimension: an international student's perspective”, E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Association for the Advancement of Computing in Education (AACE), pp. 942-948.
Han, D.-I.D., Bergs, Y. and Moorhouse, N. (2022), “Virtual reality consumer experience escapes: preparing for the metaverse”, in Virtual Reality, Springer, pp. 1-16.
Hashim, J. (2015), “Information communication technology (ICT) adoption among SME owners in Malaysia”, International Journal of Business and Information, Vol. 2 No. 2.
Hatta, N.N.M., Miskon, S., Ali, N.M., Abdullah, N.S., Ahmad, N., Hashim, H., Alias, R.A. and Maarof, M.A. (2015), “Business intelligence system adoption theories in SMES: a literature review”, ARPN Journal of Engineering and Applied Sciences, Vol. 10 No. 23, pp. 18165-18174.
Hazan, E., Kelly, G., Khan, H., Spillecke, D. and Yee, L. (2022), “Marketing in the metaverse: an opportunity for innovation and experimentation”, McKinsey Quarterly, available at: https://www.mckinsey.com/business-functions/growth-marketing-and-sales/our-insights/marketing-in-the-metaverse-an-opportunity-for-innovation-and-experimentation
Heller, B. (2020), “Watching androids dream of electric sheep: immersive technology, biometric psychography, and the law”, Vanderbilt Journal of Entertainment and Technology Law, Vol. 23, p. 1, HeinOnline.
Henseler, J. (2017), “Bridging design and behavioral research with variance-based structural equation modeling”, Journal of Advertising, Vol. 46 No. 1, pp. 178-192.
Henseler, J. (2018), “Partial least squares path modeling: quo vadis?”, Quality and Quantity, Vol. 52 No. 1, pp. 1-8, Springer.
Henseler, J., Ringle, C.M. and Sinkovics, R.R. (2009), “The use of partial least squares path modeling in international marketing”, New Challenges to International Marketing, Vol. 20 No. Emerald Group Publishing Limited, pp. 277-319, Emerald Group Publishing.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115-135, Springer.
Henseler, J., Hubona, G. and Ray, P.A. (2017), Partial Least Squares Path Modeling: Updated Guidelines, Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications, Springer, doi: 10.1007/978-3-319-64069-3_2.
Hirsch, S., Mishra, A., Möhring, N. and Finger, R. (2020), “Revisiting firm flexibility and efficiency: evidence from the EU dairy processing industry”, European Review of Agricultural Economics, Vol. 47 No. 3, pp. 971-1008, Oxford University Press.
Ho, R. and Shafiq, A. (2021), “Promocode: an examination of the outcome expectations in shopping apps”, International Journal of Electronic Marketing and Retailing, Vol. 12 No. 2, pp. 111-132.
Hofstede Insights (2022), “Culture Compass”, available at: https://www.hofstede-insights.com/product/compare-countries/%0A (accessed 2 May 2020).
Hollensen, S., Kotler, P. and Opresnik, M.O. (2022), “Metaverse–the new marketing universe”, Journal of Business Strategy, Vol. 44 No. 3, pp. 119-125.
Howardson, G.N. and Behrend, T.S. (2014), “Using the Internet to recruit employees: comparing the effects of usability expectations and objective technological characteristics on Internet recruitment outcomes”, Computers in Human Behavior, Vol. 31, pp. 334-342, Elsevier.
Huang, H.-M., Liaw, S.-S. and Lai, C.-M. (2016), “Exploring learner acceptance of the use of virtual reality in medical education: a case study of desktop and projection-based display systems”, Interactive Learning Environments, Vol. 24 No. 1, pp. 3-19, Taylor & Francis.
Hussain, M. and Papastathopoulos, A. (2022), “Organizational readiness for digital financial innovation and financial resilience”, International Journal of Production Economics, Vol. 243, 108326, Elsevier.
Hwang, R. and Lee, M. (2022), “The influence of music content marketing on user satisfaction and intention to use in the metaverse: a focus on the spice model”, Businesses, Vol. 2 No. 2, pp. 141-155, MDPI.
Igartua, J.I. and Ibarra, D. (2018), “Ready for industry 4.0—a study of SMEs in Spain”, International Conference on Industrial Engineering and Operations Management, Springer, pp. 195-203.
INE (2019), “Demografía armonizada de Empresas año 2017”, available at INEbase: https://www.ine.es/en/ (accessed 13 June 2022).
Jaynes, C., Seales, W.B., Calvert, K., Fei, Z. and Griffioen, J. (2003), “The Metaverse: a networked collection of inexpensive, self-configuring, immersive environments”, Proceedings of the Workshop on Virtual Environments 2003, pp. 115-124.
Jeon, J.-E. (2021), “The effects of user experience-based design innovativeness on user-metaverse platform channel relationships in South Korea”, Journal of Distribution Science, Vol. 19 No. 11, pp. 81-90, Korea Distribution Science Association.
Joo, Y., So, H. and Kim, N. (2018), “Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs”, Computers and Education, Vol. 122, pp. 260-272.
Joshua, J. (2017), “Information bodies: computational anxiety in neal stephenson's snow crash”, Interdisciplinary Literary Studies, Vol. 19 No. 1, pp. 17-47, Pennsylvania State University Press.
Kang, H.-R. (2022), “A case study on metaverse marketing of jewelry brand”, Journal of Digital Convergence, Vol. 20 No. 1, pp. 285-291, The Society of Digital Policy and Management.
Katz, I.M., Rauvola, R.S., Rudolph, C.W. and Zacher, H. (2022), “Employee green behavior: a meta‐analysis”, Corporate Social Responsibility and Environmental Management, Vol. 29 No. 5, pp. 1146-1157.
Keong, M.L., Ramayah, T., Kurnia, S. and Chiun, L.M. (2012), “Explaining intention to use an enterprise resource planning (ERP) system: an extension of the UTAUT model”, Business Strategy Series, Vol. 13 No. 4, pp. 173-180.
Kock, N. and Hadaya, P. (2018), “Minimum sample size estimation in PLS-SEM: the inverse square root and gamma-exponential methods”, Information Systems Journal, Vol. 28 No. 1, pp. 227-261.
Kraus, S., Rigtering, J.P., Hughes, M. and Hosman, V. (2012), “Entrepreneurial orientation and the business performance of SMEs: a quantitative study from The Netherlands”, Review of Managerial Science, Vol. 6 No. 2, pp. 161-182, Springer.
Kosiba, J.P.B., Odoom, R., Boateng, H., Twum, K.K. and Abdul-Hamid, I.K. (2022), “Examining students’ satisfaction with online learning during the Covid-19 pandemic-an extended UTAUT2 approach”, Journal of Further and Higher Education, Vol. 46 No. 7, pp. 988-1005.
Kraus, S., Kanbach, D.K., Krysta, P.M., Steinhoff, M.M. and Tomini, N. (2022), “Facebook and the creation of the metaverse: radical business model innovation or incremental transformation?”, International Journal of Entrepreneurial Behavior and Research, Vol. 28 No. 9, pp. 52-77, Emerald.
Lazadera (2022), “Uttopion”, available at: https://lanzadera.es/proyecto/uttopion/
Leader, D.J.F. (2022), “The bodymindself”, available at: https://jfl.com/
Ledesma-Chaves, P. and Arenas-Gaitán, J. (2022), “Dynamic innovation capabilities and their impact on export performance in times of economic crisis”, Revista Brasileira de Gestão de Negócios, Vol. 24, pp. 351-365, SciELO Brasil.
Lee, U.-K. and Kim, H. (2022), “UTAUT in metaverse: an ‘ifland’ case”, Journal of Theoretical and Applied Electronic Commerce Research, Vol. 17 No. 2, pp. 613-635, Multidisciplinary Digital Publishing Institute.
Lee, B.-K., Lee, E.H. and Lee, T.D. (2022), “The effect of E-Government website evaluation on user satisfaction and intention to use: the mediating role of warmth and competence judgment on government”, Information, Communication and Society. doi: 10.1080/1369118X.2022.2041701.
Lehmann, A.I., Brauchli, R. and Bauer, G.F. (2019), “Goal pursuit in organizational health interventions: the role of team climate, outcome expectancy, and implementation intentions”, Frontiers in Psychology, Vol. 10, doi: 10.3389/fpsyg.2019.00154/full.
Lehmann, A., Brauchli, R., Jenny, G., Füllemann, D. and Bauer, G. (2020), “Baseline psychosocial and affective context characteristics predict outcome expectancy as a process appraisal of an organizational health intervention”.
Liao, H., Shi, Y., Liu, X., Shen, N. and Deng, Q. (2019), “A non-probabilistic model of carbon footprints in remanufacture under multiple uncertainties”, Journal of Cleaner Production, Vol. 211 Nos 9-10, pp. 1127-1140.
Lie, D., Sudirman, A., Efendi and Butarbutar, M. (2019), “Analysis of mediation effect of consumer satisfaction on the effect of service quality, price and consumer trust on consumer loyalty”, International Journal of Scientific and Technology Research, Vol. 8 No. 8, pp. 421-428.
Lim, X., Aw, E. and Teoh, K. (2018), “Factors influencing repurchase intention in online shopping context: the mediating role of satisfaction”, Journal of Applied Structural Equation Modeling, Vol. 2 No. 1, pp. 29-43.
Liu, Y., Soroka, A., Han, L., Jian, J. and Tang, M. (2020), “Cloud-based big data analytics for customer insight-driven design innovation in SMEs”, International Journal of Information Management, Vol. 51, 102034, Elsevier.
López Díez, J. (2021), “Metaverse: year One. Mark Zuckerberg's video keynote on Meta (October 2021) in the context of previous and prospective studies on metaverses”, Pensar La Publicidad: Revista Internacional de Investigaciones Publicitarias, Vol. 15 No. 2, pp. 299-303.
Loureiro, S.M.C., Guerreiro, J. and Ali, F. (2020), “20 years of research on virtual reality and augmented reality in tourism context: a text-mining approach”, Tourism Management, Vol. 77, 104028, Elsevier.
Masyhuri, M. (2022), “HRM comparative performance and practices within MNCs' subsidiaries in Kazakhstan”, Eurasian Journal of Economic and Business Studies, Vol. 1 No. 63, pp. 31-61.
Mazzarolo, A.H., Mainardes, E.W. and Innocencio, F.A. (2021), “Antecedents and consequents of user satisfaction on Instagram”, Marketing Intelligence and Planning, Vol. 39 No. 5, pp. 687-701, Emerald Group Holdings.
Mhedhbi, K. and Essid, M. (2022), “National cultural dimensions and adoption of the international financial reporting standard (IFRS) for small and medium-sized entities (SMEs)”, The International Journal of Accounting, Vol. 57 No. 01, 2250004, World Scientific.
Mishra, A.R., Mardani, A., Rani, P. and Zavadskas, E.K. (2020), “A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures”, Journal of Cleaner Production, Vol. 272, 122807, Elsevier.
Modgil, S., Dwivedi, Y.K., Rana, N.P., Gupta, S. and Kamble, S. (2022), “Has Covid-19 accelerated opportunities for digital entrepreneurship? An Indian perspective”, Technological Forecasting and Social Change, Vol. 175, 121415, Elsevier.
Molinillo, S. and Japutra, A. (2017), “Organizational adoption of digital information and technology: a theoretical review”, Bottom Line, Vol. 30 No. 1, pp. 33-46, Emerald Group Publishing.
Molla, A. and Licker, P.S. (2001), “E-commerce systems success: an attempt to extend and respecify the Delone and MacLean model of IS success”, J. Electron. Commer. Res., Vol. 2 No. 4, pp. 131-141, Citeseer.
Moriuchi, E. (2021), “An empirical study of consumers' intention to use biometric facial recognition as a payment method”, Psychology and Marketing, Vol. 38 No. 10, pp. 1741-1765, John Wiley and Sons.
Mostafa, L. (2022), “Measuring technology acceptance model to use metaverse technology in Egypt”, Revista de Investigación Financiera y Empresarial, Vol. 23 No. 3, pp. 118-142, جامعة بورسعيد، کلية التجارة.
Mustafa, S., Qiao, Y., Yan, X., Anwar, A., Hao, T. and Rana, S. (2022), “Digital students’ satisfaction with and intention to use online teaching modes, role of big five personality traits”, Frontiers in Psychology, Vol. 13, doi: 10.3389/fpsyg.2022.956281.
Nelson, P.R.C., Taylor, P.A. and MacGregor, J.F. (1996), “Missing data methods in PCA and PLS: score calculations with incomplete observations”, Chemometrics and Intelligent Laboratory Systems, Vol. 35 No. 1, pp. 45-65, Elsevier.
Nguyen, H.Q. and Le, O.T.T. (2020), “Factors affecting the intention to apply management accounting in enterprises in Vietnam”, Journal of Asian Finance, Economics and Business, Vol. 7 No. 6, pp. 95-107.
Ning, H., Wang, H., Lin, Y., Wang, W., Dhelim, S., Farha, F., Ding, J. and Daneshmand, M. (2021), “A survey on metaverse: the state-of-the-art, technologies, applications, and challenges”, ArXiv Preprint ArXiv:2111.09673.
Novak, T.P., Hoffman, D.L. and Yung, Y.-F. (2000), “Measuring the customer experience in online environments: a structural modeling approach”, Marketing Science, Vol. 19 No. 1, pp. 22-42, INFORMS.
Oh, J.-H. (2021), “A study on factors affecting the intention to use the Metaverse by applying the extended technology acceptance model (ETAM): focused on the virtual world Metaverse”, The Journal of the Korea Contents Association, Vol. 21 No. 10, pp. 204-216, The Korea Contents Association.
Özlem, K. (2022), “Factors affecting the behaviors of teachers towards technology integration teaching via distance education during COVID-19 pandemic: a path analysis”, International Journal of Curriculum and Instruction, Vol. 14 No. 1, pp. 814-843.
Papagiannidis, S. and Bourlakis, M.A. (2010), “Staging the new retail drama: at a metaverse near you!”, Journal of Virtual Worlds Research, Vol. 2 No. 5, pp. 425-446.
Papagiannidis, S., Pantano, E., See-To, E.W.K. and Bourlakis, M. (2013), “Modelling the determinants of a simulated experience in a virtual retail store and users' product purchasing intentions”, Journal of Marketing Management, Vol. 29 Nos 13-14, pp. 1462-1492.
Papagiannidis, S., Pantano, E., See-To, E.W.K., Dennis, C. and Bourlakis, M. (2017), “To immerse or not? Experimenting with two virtual retail environments”, Information Technology and People, Vol. 30 No. 1, pp. 163-188, Emerald Publishing.
Park, S.-M. and Kim, Y.-G. (2022), “A Metaverse: taxonomy, components, applications, and open challenges”, Ieee Access, Vol. 10, pp. 4209-4251, IEEE.
Pei-Yee Chin, C., Raymond Choo, K.-K., Yee Chin, P., Evans, N. and Zhechao Liu, C. (2020), “Understanding factors influencing employees' consumptive and contributive use of enterprise social networks”, Springer Science+Business Media, Vol. 22 No. 6, pp. 1357-1376, Springer.
Polas, M.R.H., Afshar Jahanshahi, A., Kabir, A.I., Sohel-Uz-Zaman, A.S.M., Osman, A.R. and Karim, R. (2022), “Artificial intelligence, blockchain technology, and risk-taking behavior in the 4.0 IR metaverse era: evidence from Bangladesh-based SMEs”, Journal of Open Innovation: Technology, Market, and Complexity, Vol. 8 No. 3, p. 168, MDPI.
Polona, C.A.R., André, M.T. and Maria, N. (2022), Metaverse: Opportunities, Risks and Policy Implications, EPRS: European Parliamentary Research Service, Belgium, available at: https://policycommons.net/artifacts/2476871/metaverse/3498933/ (accessed 21 May 2023).
Pozón-López, I., Kalinic, Z., Higueras-Castilllo, E. and Liébana-Cabanillas, F. (2019), “A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC)”, Interactive Learning Environments, Vol. 28 No. 8, pp. 1003-1021, Routledge.
Prajogo, D. and McDermott, C.M. (2014), “Antecedents of service innovation in SMEs: comparing the effects of external and internal factors”, Journal of Small Business Management, Vol. 52 No. 3, pp. 521-540, Taylor & Francis.
Prayag, G., Hassibi, S. and Nunkoo, R. (2018), “A systematic review of consumer satisfaction studies in hospitality journals: conceptual development, research approaches and future prospects”, Journal of Hospitality Marketing and Management, Vol. 28 No. 1, pp. 51-80, Routledge.
Rahi, S., Othman Mansour, M.M., Alghizzawi, M. and Alnaser, F.M. (2019), “Integration of UTAUT model in internet banking adoption context: the mediating role of performance expectancy and effort expectancy”, Journal of Research in Interactive Marketing, Vol. 13 No. 3, pp. 411-435, Emerald Group Holdings.
Richter, N.F., Cepeda-Carrion, G., Roldán Salgueiro, J.L. and Ringle, C.M. (2016), “European management research using partial least squares structural equation modeling (PLS-SEM)”, European Management Journal, Vol. 34 No. 6, pp. 589-597.
Rigdon, E.E. (2016), “Choosing PLS path modeling as analytical method in European management research: a realist perspective”, European Management Journal, Vol. 34 No. 6, pp. 598-605.
Rigdon, E.E., Sarstedt, M. and Ringle, C.M. (2017), “On comparing results from CB-SEM and PLS-SEM: five perspectives and five recommendations”, Marketing: ZFP – Journal of Research and Management, Vol. 39 No. 3, pp. 4-16.
Sahin, I. (2008), “From the Social-Cognitive Career Theory perspective: a College of Education faculty model for explaining their intention to use educational technology”, Journal of Educational Computing Research, Vol. 38 No. 1, pp. 51-66.
Sahut, J.-M., Iandoli, L. and Teulon, F. (2021), “The age of digital entrepreneurship”, Small Business Economics, Vol. 56 No. 3, pp. 1159-1169, Springer.
Sanchez-Hughet, C., Aramendia-Muneta, M.E. and Erro-Garcés, A. (2022), “Seizing opportunities in Europe: a roadmap for efficient big data implementation in Spanish SMEs”, Digital Policy, Regulation and Governance, Vol. 24 No. 5, pp. 463-478.
Schnack, A., Wright, M.J. and Elms, J. (2021), “Investigating the impact of shopper personality on behaviour in immersive Virtual Reality store environments”, Journal of Retailing and Consumer Services, Vol. 61, 102581, Elsevier.
Schumacher, P. (2022), “The metaverse as opportunity for architecture and society: design drivers, core competencies”, Architectural Intelligence, Vol. 1 No. 1, pp. 1-20, Springer.
Seville, E., Van Opstal, D. and Vargo, J. (2015), “A primer in resiliency: seven principles for managing the unexpected”, Global Business and Organizational Excellence, Vol. 34 No. 3, pp. 6-18, Wiley Online Library.
Shmueli, G., Sarstedt, M., Hair, J.F., Cheah, J.H., Ting, H., Vaithilingam, S. and Ringle, C.M. (2019), “Predictive model assessment in PLS-SEM: guidelines for using PLSpredict”, European journal of marketing, Vol. 53 No. 11, pp. 2322-2347.
Skafi, M., Yunis, M.M. and Zekri, A. (2020), “Factors influencing SMEs' adoption of cloud computing services in Lebanon: an empirical analysis using TOE and contextual theory”, IEEE Access, Vol. 8, pp. 79169-79181, IEEE.
Soliman, M., Karia, N., Moeinzadeh, S., Shamimul, M. and Mahmud, I. (2019), “Modelling intention to use ERP systems among higher education institutions in Egypt: UTAUT perspective”, International Journal of Supply Chain Management, Vol. 8 No. 2, pp. 429-440.
Somoza, A. (2023), “Voluntary sustainability assurance in small and medium‐sized entities: the role of country origin in Europe”, Business Strategy and the Environment, Vol. 32 No. 1, pp. 462-483.
Song, H. and Deng, H. (2019), “What influences user satisfaction in virtual learning communities? The China experience”, 2018 8th International Conference on Education and Management (ICEM 2018), 22-24 March, Atlantis Press, pp. 186-190.
Song, Y., Zhang, M., Hu, J. and Cao, X. (2022), “Dancing with service robots: the impacts of employee-robot collaboration on hotel employees' job crafting”, International Journal of Hospitality Management, Vol. 103, 103220, Elsevier.
Spyns, P., Oberle, D., Volz, R., Zheng, J., Jarrar, M., Sure, Y., Studer, R. and Meersman, R. (2002), “OntoWeb-a semantic Web community portal”, International Conference on Practical Aspects of Knowledge Management, Springer, pp. 189-200.
Su, Q., Zhou, F. and Wu, Y.J. (2020), “Using virtual gifts on live streaming platforms as a sustainable strategy to stimulate consumers' green purchase intention”, Sustainability, Vol. 12 No. 9, p. 3783, MDPI.
Subawa, N., Widhiasthini, N. and The, C.M. (2020), An Empirical Study of E-Marketplace Acceptance in MSMEs under the Constructs of Effort Expectancy, Social Influence and Facilitating Condition Factors (Conference paper), The 6th International Conference on E-Business and Applications, February 10th-12th, doi: 10.1145/3387263.3387288.
Suh, A. and Prophet, J. (2018), “The state of immersive technology research: a literature analysis”, Computers in Human Behavior, Vol. 86, pp. 77-90, doi: 10.1016/j.chb.2018.04.019.
Sultana, S., Akter, S. and Kyriazis, E. (2022), “How data-driven innovation capability is shaping the future of market agility and competitive performance?”, Technological Forecasting and Social Change, Vol. 174, 121260, Elsevier.
Sun, W., Dedahanov, A.T., Shin, H.Y. and Li, W.P. (2021), “Using extended complexity theory to test SMEs' adoption of Blockchain-based loan system”, PloS One, Vol. 16 No. 2, e0245964, Public Library of Science San Francisco, CA.
Sutcliffe, A. (2016), “Designing for user experience and engagement”, in Why Engagement Matters, Springer, pp. 105-126.
Táboas, A.M. (2018), “Visual immersion and art studies: augmented reality adoption in art practice and edutainment”, CUICIID 2018, p. 135.
Tawafak, R., Romli, A. and Arshah, R. (2018), “Continued intention to use UCOM: four factors for integrating with a technology acceptance model to moderate the satisfaction of learning”, IEEE Access.
Tawafak, R.M., Malik, S.I., Mathew, R., Ashfaque, M.W., Jabbar, J., AlNuaimi, M.N., ElDow, A. and Alfarsi, G. (2021), “A combined model for continuous intention to use e-learning system”, International Journal of Interactive Mobile Technologies, Vol. 15 No. 3, pp. 113-129.
Taylor, S. and Todd, P.A. (1995), “Understanding information technology usage: a test of competing models”, Information Systems Research, Vol. 6 No. 23, pp. 144-176.
Toraman, Y. (2022), “User acceptance of metaverse: insights from technology acceptance model (TAM) and planned behavior theory (PBT)”, EMAJ: Emerging Markets Journal, Vol. 12 No. 1, pp. 67-75.
Tussyadiah, I.P. (2016), “Factors of satisfaction and intention to use peer-to-peer accommodation”, International Journal of Hospitality Management, Vol. 55, pp. 70-80, Elsevier.
Vărzaru, A.A., Bocean, C.G., Rotea, C.C. and Budică-Iacob, A.F. (2021), “Assessing antecedents of behavioral intention to use mobile technologies in e-commerce”, Electronics, Vol. 10 No. 18, p. 2231.
Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003), “User acceptance of information technology: Toward a unified view”, MIS Quarterly, Vol. 27 No. 3, pp. 425-478.
Venkatesh, V., Thong, J.Y.L. and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology”, MIS Quarterly, Vol. 36 No. 1, pp. 157-178.
Warin, T. (2022), “The future of business on the internet”, California Review Management, July 5.
Waters, T. and Day, M.J. (2022), “Thai menschenbild: a study of Chinese, Thai, and international students in a private Thai university as measured by the national survey of student engagement (NSSE)”, Humanities and Social Sciences Communications, Vol. 9 No. 1, pp. 1-12, Palgrave.
Wills, T.A., Weiss, R.L. and Patterson, G.R. (1974), “A behavioral analysis of the determinants of marital satisfaction”, Journal of Consulting and Clinical Psychology, Vol. 42 No. 6, p. 802, American Psychological Association.
Witten, I.H. and Frank, E. (2002), “Data mining: practical machine learning tools and techniques with Java implementations”, Acm Sigmod Record, Vol. 31 No. 1, pp. 76-77.
Wongsabsin, S. (2021), “Determinants of enterprise resource planning (ERP) systems adoption in Thai private companies”, Journal of Marketing and Management, Vol. 8 No. 2, pp. 118-135.
Wu, M., Gide, E. and Jewell, R. (2014), “The EBS management model: an effective measure of e-commerce satisfaction in SMEs in the service industry from a management perspective”, Electronic Commerce Research, Vol. 14 No. 1, pp. 71-86, Springer.
Xena, P. and Accounting, R.R. (2019), “Adoption of e-payment to support small medium enterprise payment system: a conceptualised model”, International Journal of Accounting, Finance and Business (IJAFB), Vol. 4 No. 18, pp. 33-41.
Xi, N. and Hamari, J. (2021), “Shopping in virtual reality: a literature review and future agenda”, Journal of Business Research, Vol. 134, pp. 37-58.
Xiao, S. and Warkentin, M. (2021), “User experience, satisfaction, and continual usage intention of IT: a replication study in China”, Transactions on Replication Research, Vol. 7, pp. 1-15.
Yemenici, A.D. (2022), “Entrepreneurship in the world of metaverse: virtual or real?”, Journal of Metaverse, Vol. 2 No. 2, pp. 71-82.
Zackery, A., Shariatpanahi, P., Zolfagharzadeh, M.M. and Pourezzat, A.A. (2016), “Toward a simulated replica of futures: classification and possible trajectories of simulation in futures studies”, Futures, Vol. 81, pp. 40-53, Elsevier.
Zgodavova, K., Hudec, O. and Palfy, P. (2017), “Culture of quality: insight into foreign organisations in Slovakia”, Total Quality Management and Business Excellence, Vol. 28 Nos 9-10, pp. 1054-1075, Taylor & Francis.
Zheng, F. and Jin, X. (2018), “The effect of person-job and person-supervisor fit on job satisfaction”, OPCIÓN, Vol. 34, pp. 882-900, J.-O.R.de C.H. 2018, U.
Zuckerberg, M. and Newton, C. (2021), “Verge interview-zuckerberg on Facebook metaverse”, e-publications @Marquette, available at: https://epublications.marquette.edu/zuckerberg_files_videos/index.2.html