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Anton, T.S.; Trupp, A.; Stephenson, M.L.; Chong, K.L. The Technology Adoption Model Canvas. Encyclopedia. Available online: https://encyclopedia.pub/entry/52511 (accessed on 14 May 2024).
Anton TS, Trupp A, Stephenson ML, Chong KL. The Technology Adoption Model Canvas. Encyclopedia. Available at: https://encyclopedia.pub/entry/52511. Accessed May 14, 2024.
Anton, Trevor Shenal, Alexander Trupp, Marcus Lee Stephenson, Ka Leong Chong. "The Technology Adoption Model Canvas" Encyclopedia, https://encyclopedia.pub/entry/52511 (accessed May 14, 2024).
Anton, T.S., Trupp, A., Stephenson, M.L., & Chong, K.L. (2023, December 08). The Technology Adoption Model Canvas. In Encyclopedia. https://encyclopedia.pub/entry/52511
Anton, Trevor Shenal, et al. "The Technology Adoption Model Canvas." Encyclopedia. Web. 08 December, 2023.
The Technology Adoption Model Canvas
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This entry is adapted from the peer-reviewed paper 10.3390/smartcities6060146

The socioeconomic contribution of microbusinesses towards emerging economies is undeniable. This conceptual study proposes the Technology Adoption Model Canvas (TAMC) based on theories such as the Unified Theory of Acceptance and Use of Technology (UTAUT2), Diffusion of Innovation (DOI), and the Business Model Canvas (BMC) alongside four new/emerging variables, making it possible to understand technology adoption through both individual/cognitive and organizational/physical perspectives. 

food service (FS) microbusinesses emerging economies Technology Adoption Model Canvas (TAMC)

1. Introduction

The increasingly competitive and challenging business climate has made it more difficult for small and micro entrepreneurs to survive, particularly in developing economies [1]. Microbusinesses create jobs, alleviate poverty, and provide the community with more affordable goods and services [2][3]. Many hardships that microbusinesses face are attributed to poor product quality, inadequate knowledge of business management, inefficient production processes, and lack of technological innovations [4]. In general, technology adoption is lower among food service (FS) microbusinesses in developing nations, and the number of businesses that have taken steps towards smartification compared to other industries is modest [5]. This is mainly attributed to their traditional operational approaches and low levels of income making them a part of the ‘informal economy’ [6] as well as factors such as resource limitations, inadequate knowledge, and poor confidence in automation [7]. The slow technology adoption rate has hindered the speed of economic development [8]. Hence, this paper identifies and examines the critical factors that shape smart technology adoption for FS microbusinesses. Micro, small, and medium enterprises (MSMEs) constitute a substantial portion of businesses in developing nations worldwide [9]. However, despite the importance of these microbusinesses, most service-oriented microbusinesses have failed to penetrate new and larger markets beyond their cities. The lack of access to such markets is primarily due to technological, financial, and resource limitations and poor orientation towards business growth [10].
Even though smart technology is perceived to be primarily beneficial, Botezatu [11] emphasizes that ‘smartification’ is an uneven process affecting various socioeconomic actors differently. Smartification represents an opportunity for MSMEs to transform innovatively and sustainably as completing the digital transformation can increase an enterprise’s operational efficiency by 8–10 times [12]. The success of a smart strategy can be measured by the level of participation in the process of ‘smartification’, which is driven by the organizational exploitation of digital facilitators, including Information and Communication Technologies (ICT), the Internet of Things (IoT), cloud technology, knowledge automation, and Information Systems (IS) [13]. These technologies potentially drive substantial economic impacts [11]. As smart technology presents opportunities throughout the entire process of value creation and appropriation, it influences the functional and strategic levels of business operations and their ability to generate new value propositions [14]. In FS microbusinesses, the operators have a growing influence on all business decisions due to the overlap between ownership, management and entrepreneurial roles, and a less formal organizational structure [15]. Hence, within these businesses, smart technology can complement the operator’s capabilities, thereby enhancing the firm’s overall capacity to operate efficiently and effectively [16]. This can be achieved by digitizing time-consuming tasks such as order tracking, order distribution, marketing and sales, and revenue management through the help of smart point of sale (POS) systems, wireless queue systems, website and social media marketing, online delivery, and the use of AI for planning and product innovation. Thus, the process of endowing FS microbusinesses with smart technology can radically alter the services provided to the customer and the internal work environment [16]. In addition, smart technology enables operators to engage in better customizations and improvements in product quality and dissemination, which can drive the overall improvement of products and services [9][17]. Furthermore, smart technology can provide operators with access to training and minimize their dependency on external organizations and governmental institutions.
Notwithstanding the identified benefits, FS microbusinesses in emerging economies have fallen short in keeping up with the ‘smartification’ of their nations [5]. The rapid economic growth resulting from external/global economic dynamics often causes emerging economies to be relatively unstable and decline in the short-term [18]. Compared to developed economies, microbusinesses in developing economies face more vulnerabilities and influence from external factors such as economic development, technological infrastructure, education, technological awareness, legal and institutional regulations, and government interference [19][20]. As a result, these external elements create a whole other perspective in understanding the intrinsic factors proposed through four prominent technology adoption models: Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), Unified Theory of Acceptance and Use of Technology version 2 (UTAUT2) and Diffusion of Innovation (DOI). As these models have been created in the context of developed economies, more research is needed to understand how the external elements of developing nations influence the constructs of these existing technology adoption models. As a consequence of failing to consider the nature of external influences in developing countries, microbusiness have so far been unable to adapt to the technological changes taking place in the market. As a result, many FS microbusinesses have been closing down due to their inability to face interruptions in the supply chain, delays in operations, and constraints in meeting stakeholder requirements, especially during the COVID-19 pandemic [21]. Limited research on the impact of the pandemic on FS microbusinesses in developing nations currently exists [22], as most studies have been conducted in the UK, New Zealand, Australia, and US [23]. Hence, there is a need for more research regarding how these microbusinesses can adapt and innovate in response to such crises while also realizing the need for more sustainable solutions [24].
Furthermore, most of the available smart technologies for FS businesses cater mainly to small, medium, and large-scale businesses and have failed to consider the limitations of micro FS businesses, especially regarding aspects such as complexity, compatibility, affordability, resource availability, and financial constraints. Consequently, the lack of digitalization creates a technology problem in terms of data collection and analysis, which also plays a vital role in the adoption of any future digital processes [25]. Hence, there exists a need for a more suitable framework to evaluate the readiness of such microbusinesses alongside the factors driving/impeding them towards/from adopting smart technology, and potentially guiding them towards embracing a smart business model [26]. To achieve this, a greater focus needs to be placed on the entrepreneur and the business, i.e., the central actors at the micro-level, as any other approach may provide incomplete or misleading results [27].
When looking at the context of service industry microbusinesses, it is important to define the characteristics that set these businesses apart from the large, medium, and small businesses in the context of emerging economies. Regarding the operational scope, microbusinesses usually operate with a narrower scope and less complex infrastructure compared to larger businesses. This means that the adoption of new technology is generally less multifaceted, but the barriers due to limited resources and expertise are often greater. With regard to resource constraints, what distinguishes microbusinesses from larger businesses is the lack of resources, both in terms of financial and human capital. The proposed framework acknowledges these constraints by considering them under the sections of ‘budget cost’ and ‘key resources’. With regard to the influence of human capital, decisions in microbusinesses are often made individually or by a very small team. The proposed constructs of the framework account for this dynamic by emphasizing on individual cognitive factors such as ‘trust’, ‘habit’, and ‘hedonic motivation’, which play a more pronounced role in microbusiness settings.

2. Technology Adoption Model Review

Standard models associated with technology acceptance and adoption are the TRA, TAM, Theory of Planned Behaviour (TPB), UTAUT, UTAUT2, and DOI [28]. Even though these models indicate a growth in research trends on digitalization, a clear gap in the use of such models to explore actual technology adoption continues to exist [29]. The evaluation criteria, dimensions, and constructs are different for various models, and a standardized model is currently absent [30]. Such a model would help understand the actual technology adoption by considering both individual and organizational attributes influencing technology acceptance and use, especially among FS microbusinesses, as such businesses rely mostly on the decisions of individuals and the capacities of their physical resources. Given the current trends in technology adoption, this paper identifies the UTAUT2 and the DOI theory as the two most prominent theoretical models, within the realm of technology acceptance/use, capable of assessing and understanding technology adoption at present, as previously discussed.

2.1. Unified Theory of Acceptance and Use of Technology Version 2 (UTAUT2)

The UTAUT2 introduced by Venkatesh and associates in 2012 [31] is an adoption theory for analysing the determinants of intention to use new technology [32][33]. UTAUT2 was developed to capture and understand the acceptance and use of technology from a consumer perspective by incorporating factors such as ‘hedonic motivation’, ‘price value’ and ‘habit’, thus highlighting seven core factors that influence technology adoption [34]. The UTAUT2, compared to UTAUT, focuses on consumer cognitive aspects, henceforth, classified as controllable or semi-controllable constructs in this study. The UTAUT2 has been proven to be 18 percent more effective in describing the variability of behavioural intention and 12 percent more effective in describing use behaviour than the UTAUT ([34], p. 4). However, the UTAUT2 has its limitations. The most significant criticism of the UTAUT2 is that the model was advanced and was frequently applied in the context of developed economies (with already established technological-based infrastructure) rather than advanced or applied within the developing world [35]. In addition, the UTAUT2 needs to include important indicators covering awareness-raising and low-tech learning when applied within developing economies [35].

2.2. Theory of Diffusion of Innovation (DOI)

The DOI theory, introduced by Rogers in 1962 [36], addresses numerous aspects of innovation by emphasizing the generation (idea), diffusion (movement), and adoption (uptake) of innovations ([37], p. 3). It seeks to explain at what rate and how and why new ideas and technology spread through cultures and the process through which innovation is relayed throughout time to the members of the social system [38]. The DOI theory further investigates the process of diffusion and offers valuable insights into understanding the adoption of various technologies by having variables that account for both the cognitive attributes of individuals and the physical attributes of organizations (consequently classified as semi-controllable constructs hereafter) [39]. However, the theory has been criticized for disregarding external environmental influences such as the economy, the society, and the political arena [40]. A limitation may result from its intention to focus on internal organizational attributes solely. However, this limitation is accounted for in the proposed framework by considering governmental and societal influences and by integrating the theory with the UTAUT2.

3. New/Emerging Variables

Recent studies have proposed new variables and improvements to the UTAUT, UTAUT2, and DOI models concerning technology acceptance and adoption [32][33][41][42]. These studies suggest the inclusion of exogeneous variables, namely, ‘trust’ [43], ‘perceived risk’ [42], ‘perceived security’, and ‘government support’ [32][33], to examine the determinants of the intentions of technology acceptance and adoption by consumers in an era of pandemic recovery and Industry 4.0. Thus, similar to most of the constructs of the UTAUT2, the constructs of ‘trust’, ‘perceived risk’, and ‘perceived security’ will be classified as controllable constructs due to their individual/cognitive nature, while ‘government support’ will be classified as an uncontrollable construct as individuals and their businesses cannot influence it.
The construct of ‘trust’ is added to the proposed model as it was identified to have a strong influence on technology adoption in the study conducted by Ariwiati [43]. The users’ trust in smart technology can be seen to play a vital role in their adoption decisions as it can shape their overall perception of smart technologies and also inform other constructs such as ‘perceived risk’ and ‘perceived security’. Hence, ‘trust’ is an important construct in understanding users’ perceptions towards smart technology and in identifying the factors contributing to their reasons to trust or distrust smart technology, which can later influence their motivation to adopt smart technology.
The construct of ‘perceived risk’ is added to the proposed model as its influence on technology adoption was identified to be of great significance in a recent study conducted during the pandemic [42]. With the increase in use of smart technology throughout the pandemic, it was evident that the risk of scammers and hackers too had increased, and people have become increasingly aware of these risks. Hence, moving forward, it is important to consider the risk perceptions of the users when looking at smart technology adoption.
The construct of ‘perceived security’ is added to the proposed model as it coincides with the construct of ‘trust’ and ‘perceived risk’. Similar to the construct of ‘perceived risk’, ‘perceived security’ is a construct that emerged as important during the pandemic. The construct was found to be of importance in recent studies conducted by Moorthy et al. [32] and Najib et al. [33], where it was made evident that people have become increasingly concerned about the safety of using smart technology and now factor in the security associated with the technology when deciding whether or not to adopt smart technology.
The construct of ‘government support’ is added to the model as the influence of the authorities on microbusiness operators to adopt technology has been increasing [32][33], especially after the impact of the pandemic. In developing nations, in particular, the local authorities can be seen to have a greater influence on the (in)ability of microbusinesses to engage in innovation. Hence, the influence of the government via (in)actions in providing financial support, training, infrastructure support, etc., needs to be considered when studying smart technology adoption, especially in developing economies.

4. Business Model Canvas (BMC)

The BMC is touted as one of the most suitable models to measure business potential. It broadly applies to new businesses, existing businesses, and non-profit organizations [44]. The BMC is an intuitive and easy-to-use visual chart for representing a firm’s logic and its way of organizing its operations for creating, delivering, and capturing value and shows how a firm creates and delivers value for customers and how it captures profits [45]. The BMC comprises nine crucial elements (Table 1) that should be considered when evaluating business performance and readiness for change [44][46]. The BMC has been frequently used to address/explain three phenomena: (a) e-business and the use of information technology in organizations; (b) strategic issues, such as value creation, competitive advantage, and firm performance; and (c) innovation and technology management [47].
Table 1. List of constructs.
UTAUT2 DOI New/Emerging BMC
Performance Expectancy (PE)
[31]		 Compatibility (CM)
[36]		 Perceived Risk (PR)
[42]		 Value Proposition
[48]
Effort Expectancy (EE)
[31]		 Complexity (CX)
[36]		 Perceived Security (PS)
[32][33]		 Key Partners
[48]
Societal Support (SS)
[31]		 Relative Advantage (RA)
[36]		 Government Support (GS)
[32][33]		 Key Activities
[48]
Facilitating Conditions (FC)
[31]		 Observability (OB)
[36]		 Trust (T)
[43]		 Key Resources
[48]
Habit (HA)
[31]		 Trialability (TR)
[36]		   Budget Cost
[48]
Hedonic Motivation (HM)
[31]		      
Price Value (PV)
[31]		  
However, the model has been criticized for its sole focus on creating economic value and neglecting social and environmental values [45][49]. The model has been revised numerous times to facilitate various forms of research. Timeus et al. [45] created a CMC, Diana [49] developed the TLBMC, and Ghazinoory et al. [46] utilized the PEST framework and Porter’s Market Forces to incorporate an environmental dimension into the BMC. Similarly, as the TAMC focuses on technology acceptance and adoption, specific components of the BMC (customer segments, customer relationships, distribution channels, and revenue streams) have been omitted as they are considered to have little to no influence on technology adoption. Nevertheless, since the BMC focuses on the organization’s overall structure [49], it enables the incorporation of the constructs of technology adoption theories and other emerging constructs in the formulation of a cumulative framework focused on exploring and assessing the readiness of FS microbusinesses towards smart technology adoption.

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Subjects: Urban Studies; Hospitality, Leisure, Sport & Tourism
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Trevor Shenal Anton
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Alexander Trupp
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Marcus Lee Stephenson
,
Ka Leong Chong
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Update Date: 08 Dec 2023
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