Adoption Behaviour of Digital Technologies by Firms: Evidence from South Africa’s Manufacturing Sector

Abstract

The manufacturing industry is vital to the growth and development of most countries. This study investigates the adoption behaviour of digital technologies by manufacturing firms in South Africa. The study employs econometric model to analyze a total of 516 firms in the manufacturing sector. The probit results show that innovation of the firm and firm size have a significant effect on both the current and the expected adoption of digital technologies, whereas capital ownership correlate positively with the current adoption. Likewise, exports and digital-related infrastructure are found to correlate positively with the expected digital technology adoption for customer relations business function. Similarly, study results from the transition probability matrix reveals that a significant persistency pattern is prevalent in all the three business functions of the firms, namely, supplier relations, customer relations and product development. However, product development has considerably higher persistency compared to other types of the firm business functions. Several policies, including supporting business innovation, easy access to core digital tools by firms, developing institutional and systemic support mechanisms, are recommended to encourage firms to engage in digital technology activities.

Keywords

Adoption digital technology persistence manufacturing South Africa

Introduction

The global economy is going through a phase of structural and technological change, due to the growing digitalization of economic activity. The traditional sectors have already been impacted by the business and technological models evolving in this ‘digital economy’, while also giving rise to the whole new ones. This transformation is referred to as the ‘Fourth Industrial Revolution’ (also known as Industry 4.0 and 4IR). This transformation focuses on the digitization of industrial processes (Andreoni et al., 2021). It is characterized by the blurring of boundaries between the physical, digital and biological spheres (Schwab, 2016). Also, it combines advancements in blockchain, 3D printing, the Internet of Things (IoT), robotics, quantum computing and other technologies (Fanoro et al., 2021).

The roots of digitalization have deeply penetrated the economic sector in all its forms. Not only has this profound modernization raised people’s standards of living but it has also improved supply chain management, innovated ways of production and has changed how businesses are conducted (Nasiri et al., 2020). Many parts of the world are experiencing digitalization in different ways, reflecting the range of opportunities it presents. At the same time, reflecting the obstacles that some countries must overcome in order to invest in and successfully adopt these cutting-edge technologies (Andreoni et al., 2021).

About 15.5% of the global GDP in 2016 came from the digital economy. When it comes to the adoption and use of digital technology, significant disparities between and within countries still exist (Cusolito et al., 2020). All economies—including those in Africa—are being transformed by digital technology. The ‘Digital Transformation Plan for Africa 2020–2030’, published recently by the African Union, makes the case that the digital revolution is a key driver of inclusive, sustainable and innovative growth. According to the African Union’s strategic vision, the current period presents a leapfrogging opportunity for the continent and notes that African nations with fewer legacy issues, may be able to implement digital solutions more quickly (Africa Union, 2020).

In terms of economic growth and employment, the manufacturing sector has emerged as the major contributor for developing nations. A competitive advantage exists in the manufacturing sector globally. As a result, nations constantly work to enhance their manufacturing industries through the adoption of cutting-edge technology in order to become and maintain their competitiveness on a worldwide scale (Madonsela et al., 2013).

South Africa’s manufacturing sector makes up 13% of GDP, yet compared to other BRICS nations, it has declined more quickly over the past 20 years (Kreuser & Newman, 2018). In addition, digitalization is taking place, despite a substantial infrastructure and skills gap in the digital sector, and an economy that has already experienced premature deindustrialization. Thus, firms in South Africa are taking advantage of digitalization’s prospects to improve efficiency, innovate processes and integrate supply chains (Andreoni et al., 2021). Digitalization is having an impact on business processes, consumption, innovation, production, and trade, yet the question as to what extent will vary, depending on industries and geographies.

Scholarly investigation into the factors that influence the use of digital technologies has grown recently. Kammerlander et al. (2018) emphasized the significance of how digital technologies connect with how organization members perceive their identities, for instance in terms of consumer expectations. Li et al. (2021) assert that the adoption of digital technology requires organizational attention towards digital transformation. Omrani et al. (2022) argue that the strategic imperatives of digital transformation are digital resources, a digital change-adaptive organizational structure and whether the firm has a digital growth strategy. Other studies (Chen et al., 2015; Yang et al., 2021) emphasize how the pressure from consumers and other stakeholders, as well as competitors, play a part in the business’s choice to adopt digital technologies.

Evidence from empirical studies shows that some scholars have made attempts to understand the various factors influencing the adoption of digital technology by firms (Avenyo et al., 2022; Gholami, 2023; Park, 2019; Vrontis et al., 2022). Other previous studies have focussed on digital technologies adoption and its link to value creation (Vrontis et al., 2022), use of digital technologies, and how it affects firm innovative performance (Usai et al., 2021), nexus between digital technology adoption and knowledge flows (Forman & Zeebroeck, 2019) and impact of digital technologies adoption on firm supply chains (e.g., Yang et al., 2021).

Nevertheless, there is a paucity of empirical research on the adoption behaviour of digital technologies by firms, despite the acknowledged contribution that firms make in alleviating poverty through job creation (Ngo et al., 2020). The article makes significant contributions to the literature of digital technology adoption behaviour in several points. First, it examines the factors that causes both the current adoption of digital technology and the expected adoption of digital technology by firms.

Second, it examines the nexus between the current and the expected adoption of digital technology by firms. Third, it analyses the persistency in digital technology adoption behaviour by firms over time.

Given the situation described above, our study seeks to provide answers to the following questions:

RQ1. What factors cause both the current adoption of digital technology, and the expected adoption of digital technology by firms?

RQ2. What is the nexus between the current and the expected adoption of digital technology by firms?

RQ3. What is the persistency in digital technology adoption behaviour by firms over time?

Therefore, the remainder of the article will be organized as follows: The next section conceptualizes digital technologies on manufacturing firms. The third section explains the methods and data used for the study. After presenting the estimated results in the fourth section, the fifth section will conclude with policy implications.

Literature Review

Theoretical Arguments Related to Digital Technologies and Manufacturing

Digital transformation refers to the use of digital technologies in manufacturing and business process (Yasin et al., 2022). According to Yasin et al. (2022), digital technologies include big data analysis, 3D printing and internet of things. The digital transformation helps the firm to remodel its business model in response to changes in the market system (Li, 2020; Pramanik et al., 2019). Digital transformation has three stages, including digitalization, digitations and transformation (Verhoef & Bijmolt, 2019). The first stage involves firms processing innovation. At the second stage, some specialized functions are employed to influence decisions like whether or not a retailer would switch from the store channel to the online one. The third stage involves firms changing the value creation by applying digital technologies. The goal of digital transformation is not achieved by the activity of a single person, but rather by a variety of programs working in collaboration.

Digital transformation has transformed firms and has enabled them to handle and implement internal and external activities related to collaboration in digital supply chain. Also, it is essential to the external association of the digital supply chain, since it improves customer service by gathering a significant amount of data from numerous sources. Also, it forges strong connections between the many collaborators. The digital transformation makes service providers and customers alike experts in sales and purchase (Crittenden et al., 2019).

The COVID-19 pandemic highlighted the progress of firm digitalization, which began several years ago. Under this situation, firms needed to discover a way to conduct business without direct physical contact, in order to adapt to the environment and ensure their survival. Hence, the employment of digital technologies has been a life-saving solution and has opened up new business opportunities. Business firms that have been able to quickly adapt and apply fundamental digital technologies have been able to stay in operation and overcome the new obstacles.

The use of digital technologies can result in many significant benefits for business firms (OECD, 2021). Digitalization has the impact of lowering transaction costs, allowing for better and quicker access to information and communication amongst personnel, suppliers and networks. Furthermore, it can assist firms in integrating into international markets by lowering the costs involved with transit and border operations. Digitalization makes it easier for businesses to access resources, such as those that are increasingly being made available online for government services, financial resources, training and recruitment channels. Also, it encourages innovation and greater access to innovation resources, as well as the opportunity for firms to produce data and analyse their own operations in novel ways to boost performance. Even though digitalization offers the aforementioned benefits, many small and medium-sized enterprises (SMEs) are still trailing behind in adopting digital technologies.

Digital transformation can offer opportunities for growth and competitiveness of manufacturing firms, regardless of their size (Matt et al., 2020). Manufacturing is a key driver of GDP, an engine of economic growth and a mechanism for resistance to economic shocks. Its economic multipliers are high, because of its linkages to the sectors of the economy which are productive. Also, it supports exports and employment, and compared to other sectors the job occupations are more likely to be better-paying, stable and resistant to shocks. These qualities have traditionally led many nations—South Africa included—to prioritize manufacturing as a sector for their national development efforts (SARB, 2020).

The emergence of new business models due to digital transformation is reshaping whole industries and giving firms unmatched opportunity to create value. But, relentless effort is required to unleash that value. This presents South Africa with a once-in-a-lifetime opportunity to fully utilize the benefits of digital technology. South Africa is a nation that deals with a number of issues such as declining productivity and rising unemployment. Nonetheless, there is increasing optimism, as the fourth industrial revolution approaches and political resolve to use digital technology to address the country’s fundamental socio-economic and development concerns. In South Africa, the application of digital technology in important industry areas like financial services, agriculture and manufacturing can generate more than R5 trillion in value for industry and society over the next 10 years (World Bank, 2019).

Theories on the Adoption of Technology

There are various models that may be used to explain how technology is adopted (Van Oorschot et al., 2018). There is a growing body of academic research on technology adoption, leading to the development of several theoretical frameworks, to better comprehend the relationship between the factors that encourage such adoption (Henao & Zapata, 2022). This study considers two adoption models in light of the literature review, Technology Acceptance Model (TAM) model and Technology Organizational Environmental (TOE) framework, based on how well they apply to the adoption of digital technology. We provide a brief explanation of these models.

Technology Acceptance Model

Davis (1989) developed the TAM theory. It is one of the most significant and reasonable theoretical models of how users accept and use technology. It has robust explanatory capability and is simple to comprehend, making it the most widely used model that has been used across many disciplines (Azam et al., 2023; Qin et al., 2020). TAM was created not only to predict user adoption but also to predict user behaviour following some interaction with the system. As a goal-based paradigm, the TAM model is supported and states that embracing innovation as a goal is a good indication of its genuine application (Bryan & Zuva, 2021; Pan & Jang, 2008). According to the TAM theory, behavioural intention is influenced by both how simple a technology is to use and how beneficial it is, which in turn affects how satisfying a technology is for the user. Also, as said by some experts, attitudes can be changed by learning to utilize technology more easily and to better grasp its value (Chen, 2019; Mulyono et al., 2020). Since then, the usage of TAM has been extended to incorporate a number of additional technologies, such as the use of digital technologies and e-learning platforms by students (Sukendro et al., 2020). It positions the ‘perceived usefulness (PU)’ and ‘perceived ease of use (PEOU)’ of the technology, as the key factors of system use (Gangwar et al., 2015).

Technology Organizational Environmental Framework

Tornatzky et al. (1990) developed the TOE Framework, which is in line with Rogers (2003) Diffusion of Innovations Theory. The TOE framework is helpful for studying the uptake and integration of various information technology innovations (Rosario et al., 2011).

The TOE is a useful and adaptable framework for describing adoption behaviour in relation to three types of technological advances: the use of innovations in technical tasks, innovations in business administration and innovations incorporated into a firm’s core business processes (Ramdani & Kawalek, 2007; Swanson, 1994).

The framework is made up of three primary components: environment, organization and technology. Technology is used to describe the features of a piece of technology, such as its functionality, complexity, compatibility with present-day systems and simplicity of use. The term ‘organization’ describes the internal environment in which technology is used and includes elements like the culture, resources, size and structure of the organization. Environment is the term used to describe the external setting in which an organization functions, which includes elements like market conditions, legal constraints and cultural and social norms. TOE has become a widely accepted theoretical viewpoint on the adoption of technology (Zhu, 2004). Consequently, it offers a complete picture of technology user adoption, implementation, challenges anticipated and decision-making elements for business innovation adoption (Gangwar et al., 2015).

Integrating TAM and TOE Frameworks

According to Rosario et al. (2011), it is critical to use an integrated theoretical framework to comprehend the intricate process of technological innovation adoption. This research takes into account the two-technology adoption models—the ‘TAM model’ and the ‘TOE framework’—that have been frequently used in research with an organizational context. Studies on innovation and the adoption of information technology widely use the TAM and TOE framework (Qin et al., 2020).

The TAM and the TOE frameworks have been shown in several empirical studies to play a large, dominant and pertinent role in explaining the adoption of technology; although the models themselves have limits. About 40% of technology use and other variables in the expanded models of TAM are explained by the two TAM constructs, PU and PEOU, which have not yet been precisely defined (Legris et al., 2003). But according to Wang et al. (2010) and Riyadh et al. (2009), the TOE framework is overly general and contains ambiguous major constructions. The TOE framework must therefore be strengthened by being integrated with other models in order to increase the predictive potential of the final model and get beyond some of its specific shortcomings (Qin et al., 2020).

The combination of TAM and TOE could capture adoption practices at multiple levels and combine the benefits of both models. For instance, Gangwar et al. (2015) integrated the TAM and TOE models and used them to investigate the mechanism of cloud adoption at the organizational level. The TOE and TAM models, were combined by Qin et al. (2020), who also examined the aspects of the system that have an impact on how enterprise resource planning is adopted in manufacturing firms.

Hitherto, there is not a universal set of factors that can be used to explain technology adoption across all contexts and technologies. This study uses a methodology that incorporates the TAM and TOE variables found in numerous studies, based on these two models, in order to create an integrated model.

Managerial Insights

Leadership is crucial for encouraging strategic flexibility and producing greater business benefits (Kristianto et al., 2012). According to Matsepe and Van der Lingen (2022), authentic leadership has a substantial impact on how organizations manage technological transformation. Additionally, it was suggested by Schepers et al. (2005) that organizational leadership will affect the adoption of digital technology. Executives who take risks are more inclined to seek growth initiatives that are aggressive, thereby increasing their propensity to support innovative projects and technologies (Czaja & Shark, 1998).

Top management is in charge of resource allocations (Yang, 2008). For innovative ventures to be successful, top managers set up the necessary resources and assistance (Rodríguez et al., 2008). For instance, senior managers give clear instructions and pledges that aid businesses in eliminating ambiguity, efficiently allocate technology resources and define targets that ultimately lead to innovation goals. Therefore, senior management is dedicated to giving businesses all the resources required to support service innovation. Innovations generally entail technology that uses a lot of resources (Hossain et al., 2011). When top management support is strong, the intensity of the direct effect openness of technology adoption grows because it can make up for an absence of having a firm oriented towards embracing technology (Li, 2020).

Empirical Studies on Digital Technology Adoption

Markets and society today require the use of digital technology, and digital transformation is quickly emerging as a major area of business innovation. The complexity of digital transformation, however, prevents businesses from properly understanding and taking advantage of its benefits (Pascucci et al., 2023). The use of digital technologies may give users a long-lasting competitive advantage by setting them up for expansion and market dominance (Akpan et al., 2022). A rise in the demand for digital technologies may be caused by improved IT skills and supplemental human resources that raise the marginal productivity of technological assets (Akpan et al., 2022; Lashitew, 2023). Digital technology can serve as the foundation for dynamic capabilities that allow businesses to reconfigure their business models by reworking outmoded organizational practises and replace them with new, innovative ones (Belitski et al., 2022). By implementing digital technologies, a company increases its productivity, competitiveness and profitability while also ensuring its existence (Papadopoulos et al., 2020). According to Fitriasari (2020), digital technologies also enable SMEs to become more resilient by increasing their capacity to resist the effects of a business interruption and concentrating their efforts on carrying out the required services.

In recent years, conceptual and empirical study on digital technologies has expanded significantly in different fields, and numerous literature reviews have been produced (Pascucci et al., 2023). According to Attencia and Mattos (2022), the models of information technologies adoption in organizations can help us understand the adoption of digital technologies and the crucial elements that influence it. The adoption of information technology is based on several theories used in the field of information systems, including TOE and TAM. This study has extensively discussed the TOE and TAM in earlier sections under literature review. Table 1 presents lliterature on digital technologies adoption from empirical studies.

The use of digital technologies in manufacturing is becoming more and more crucial in the current global economic environment. Digital technologies have been the subject of in-depth research in both academia and industry. Environmental, organizational and technological factors all have a big impact on how quickly digital technology is adopted (Yadegaridehkordi et al., 2018). Digital technology adoption does not, however, always prove successful. Large sums of money are invested in digital transformation by many manufacturing firms, yet the desired commercial benefits are not realized. The gap between strategy development and implementation is a common contributor to failure (Yang et al., 2021). Howbeit, not much is known about how digital technology is being adopted in manufacturing firms for business operations like supplier relations, customer relations and product development. This study aims to understand the adoption behaviour of digital technologies by firms in the context of South Africa.

Table 1.

Literature on Digital Technologies Adoption.

Author	Objective	Sample/Country	Methodology	Contribution
Shahadat et al. (2023)	To investigate how technological, organizational and environmental factors affect digital technology adoption in SMEs sector, in developing nations.	535 managers at senior and medium levels in SMEs enterprises were sampled	Partial least squares structural equation modelling	ICT adoption in SMEs is significantly influenced by competitive pressure, complexity, innovativeness of top management, perceived cost, top management support, relative advantage and government backing.
Cirillo et al. (2023)	To investigating the relationship between technology, skills and the way that labour is organized.	Italian firms	Probit and zero inflated poisson models	Contributions emphasize the value of knowledge acquisition among firm personnel and technological organizational skills.
Mohd Faizal et al. (2022)	To determine whether accountants are ready to adopt digital technology.	380 accountants employed in several industries in Malaysia’s Klang Valley	Quantitative approach	The prediction is supported by performance expectations, optimism and social influence.
Blichfeldt and Faullant (2021)	To develop and put into practice a theoretical foundation for the invention of new goods and services, embracing the use of digital technologies.	747 European manufacturing firms	Exploratory approach	Progressive product and service innovations can be introduced by firms with increased degrees of digital technology adoption.
Vrontis et al. (2022)	To investigate whether and how SMEs can benefit from implementing various contemporary digital technologies.	319 SMEs in India	Partial least squares–structural equation modeling (PLS–SEM) approach	Adopting digital technologies impacts SMEs’ ability to provide social and economic value.
Ofosu-Ampong et al. (2023)	To ascertain the elements that influence the digital population and housing census.	206 participants	Exploratory approach	Digital technology adoption behaviour is substantially influenced by self-efficacy, technological readiness and hedonic incentive.
Bughin et al. (2021)	To establish the relationship that exists between the performance of strategic developing industries and digital technology adoption.	385 enterprises	Theoretical conceptual model approach	Adoption of digital technologies by enterprises is strengthened by digital strategy.
Guo et al. (2020)	To determine whether digitalization has the ability to aid SMEs in efficiently handling public emergencies.	518 Chinese SMEs	Descriptive analysis and correlation analysis	SMEs are better prepared to respond to public crisis because of digitalization. Additionally, digitalization enhances the performance of SMEs.

Methods

Methodology for Finding out the Factors that Cause the Current Adoption of Digital Technologies by Firms

We specify three separate simple probit models, across different business functions (supplier relations, customer relations and product development).

y_{liqt} = \{\begin{matrix} 1 if y_{liqt} = f (α_{0} + X_{iqt} α_{1} + {Industry}_{qt} α_{2} + ε_{iqt}) > 0 \\ 0 otherwise \end{matrix}

(1)

y_{2 i q t} = \{\begin{matrix} 1 if y_{2 i q t} = f (β_{0} + X_{i q t} β_{1} + {Industry}_{q t} β_{2} + μ_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(2)

y_{3 i q t} = \{\begin{matrix} 1 if y_{3 i q t} = f (γ_{0} + X_{i q t} γ_{1} + {Industry}_{q t} γ_{2} + v_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(3)

where y_1iqt, y_2iqt and y_3iqt are dummy variables for supplier relations, customer relations and product development, respectively. They indicate that a firm i of industry q decides to adopt in time t. X_iqt are the factors that determine the firm’s adoption. Industry_qt vector denotes industry classification level of the firm, all α_s, β_s and γ_s are coefficients to be estimated, whereas $ε_{i q t}, μ_{i q t}, and v_{i q t}$ are error terms which follow $N (0, σ_{1}^{2})$ .

Finding out the Factors that Cause the Expected Adoption of Digital Technologies by Firms

We define three distinct simple probit models for the various business functions.

y_{1 i q t + 5} = \{\begin{matrix} 1 if y_{1 i q t + 5} = f (α_{0} + X_{i q t} α_{1} + {Industry}_{q t} α_{2} + ε_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(4)

y_{2 i q t + 5} = \{\begin{matrix} 1 if y_{2 i q t + 5} = f (β_{0} + X_{iqt} β_{1} + {Industry}_{q t} β_{2} + μ_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(5)

y_{3 i q t + 5} = \{\begin{matrix} 1 if y_{3 i q t + 5} = f (γ_{0} + X_{i q t} γ_{1} + {Industry}_{q t} γ_{2} + v_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(6)

where y_1iqt+5, y_2iqt+5 and y_3iqt+5 are dummy variables for supplier relations, customer relations and product development, respectively. They indicate that a firm i of industry q intend to adopt in time t + 5. X_iqt are the factors that determine the firm’s adoption. Industry_qt vector denotes industry classification level of the firm, all α_s, β_s and γ_s are coefficients to be estimated, whereas $ε_{i q t}, μ_{i q t}, and v_{i q t}$ are error terms which follow $N (0, σ_{1}^{2})$ .

Examining the Nexus between the Current, and the Expected Adoption of Digital Technology by Firms

A probit model will be employed and is expressed as follows:

y_{i q t + 5} = \{\begin{matrix} 1 if y_{i q t + 5} = f (y_{t} δ + Z_{i t} ρ + ε_{i q t}) > 0 \\ 0 otherwise \end{matrix}

(7)

where i indicate firm i of industry q. y_iqt+5 is a dummy variable and indicate that a firm i of industry q intend to adopt digital technology in time t+5. y_t is a binary variable which assumes the value 1 if firm i adopts digital technology at time t. Z_it is a vector of control variables, and ε_iqt is the error term.

Exploring the Ways in Which Firms Make Transitions Between Two States in Terms of Digital Technology Adoption

The transition probability matrix (TPM) will be employed to find out the existence of persistence in digital technology adoption among firms. TPM provides valuable information for analyzing persistence given that it assesses the likelihood that a firm will change states within a given time period. In this study, we investigate the firms’ current status as either digital technology adopters or non-adopters at a particular point in time.

TPM is formulated as follows:

Transition Probability Matrix = [\begin{matrix} p_{11} & p_{12} & \dots & p_{1 d} \\ p_{21} & p_{22} & \dots & p_{2 d} \\ ⋮ & \dots & \dots & ⋮ \\ p_{d 1} & p_{d 2} & \dots & p_{d d} \end{matrix}]

(8)

where

p_{i j} = P (Y_{t + 5} = j ∣ Y_{t} = i)

(9)

p_ij is the likelihood that vector Y will move from state i to state j between time t and time t+5. Y is made up of a number of variables that measure digital technology adoption across business functions. p_ij are unknown parameters and can be estimated as ${\hat{p}}_{i j} = \frac{n_{i j}}{n_{i}}, n_{i}$ represents the overall number of state i at time t, whereas n_ij represents the number of transitions from state i at time t to state j at time t+5. The persistence in digital technology adoption exists and is strong if the sum of the diagonal elements of matrix p_ij with i = j are greater than 50% (Vu & Nguyen, 2022). Following Tavassoli and Karlsson (2015), we can also calculate the unconditional state dependence digital technology adoption as follows:

Unconditional state dependence = p_{i j} - p_{i j} = P (Y_{t + 5} = j ∣ Y_{t} = j) - P (Y_{t + 5} = j ∣ Y_{t} = i)

(10)

The unconditional state dependence shows the proportion of the adoption (or non-adoption) probability at time t+5 that can be explained by the distinction between adopters (non-adopters) versus being non-adopters (adopters) at time t. It is unconditional since it does not factor in any specific characteristics (observed or unobserved) of a firm.

Data Sources

The main source of data for this study is the ‘digitalisation and skills in industry’ survey. This survey was conducted in March 2021, by The Department of Trade, Industry and Competition (DTIC), the Sector Education and Training Authorities (SETAs) and the University of Johannesburg (UJ). The survey is a first of its kind in South Africa, covering 516 manufacturing firms.

The aim of the survey is to gain an understanding of the levels of digital technologies adoption, level of technological and digital skill in South Africa’s manufacturing firms. Senior representatives of the firms were selected to participate as respondents because of their knowledge and experience in the manufacturing sector. The survey gathered data on firms adoption trends, both current and future, across four major business functions: customer relations, production management, product development and supplier relationship. Also, data were gathered on employment, innovation, firm-level characteristics and firms exports between the 2017–2018 and 2019–2020 fiscal years.

Results and Discussions

Descriptive Statistics

Before discussing the empirical results, this section will discuss the features that characterize our data. Table 2 presents some descriptive statistics of our variables. From our sample, about 30% of the firms are micro (sales of less than R10 million per financial year), about 30% are small (sales between R11 and R50 million per financial year), and medium-sized (sales between R51 and R250 million per financial year) and large firms (sales above R250 million per financial year) make up about 26% and 14%, respectively. Additionally, our data indicate that about 5% of the firms are using digital technologies for supplier relations, whereas 2% and 3% of firms use digital technologies for customer relations and product development, respectively. Also, 37% of firms said they will use digital technologies in the next 5–10 years for supplier relations. Expected adoption rates of digital technologies are 11% and 7% for customer relations and product development, respectively.

The average age of a firm is 29 years, meaning that the majority of the firms in the survey were established for a long time. On average, most firms had 76 number of employees. Besides, the firms in the survey, about 51% of them innovate, about 44% export, and on capital ownership, 86% were owned by the locals. Last but not least, when adopting digital technologies, 51% of the firms take into account the importance of digital-related infrastructure (such as availability of high-end broadband networks) to their firm.

Table 2.

Descriptive Statistics

Variable	Descriptions	Mean	Std. Dev.	Min	Max
Supplier relations (y_t)	Dummy = 1 if firm’s main way to communicate with suppliers (to place orders) is through real-time monitoring of orders and logistics of suppliers (e.g., computer-managed inventory systems), 0 otherwise.	0.048	0.215	0	1
Supplier relations (y_t+5)	Dummy = 1 if firm’s main way to communicate with suppliers (to place orders) is through real-time monitoring of orders and logistics of suppliers (e.g., computer-managed inventory systems) in 5–10 years, 0 otherwise.	0.368	0.483	0	1
Customer relations (y_t)	Dummy = 1 if firm’s primary method of managing relationships and communication with customers is through automation of the sales force using customer databases or, monitoring and management of the customer with Artificial Intelligence and use of Big Data Analytics, 0 otherwise.	0.017	0.131	0	1
Customer relations (y_t+5)	Dummy = 1 if firm’s primary method of managing relationships and communication with customers is through automation of the sales force using customer databases or, monitoring and management of the customer with Artificial Intelligence and use of Big Data Analytics in 5–10 years, 0 otherwise.	0.11	0.314	0	1
Product development (y_t)	Dummy = 1 if firm’s primary method of managing their product development process is through integrated product data management systems (e.g., Product Data Management) or Virtual reality applications and virtual manufacturing and assembly simulation tools, 0 otherwise.	0.031	0.173	0	1
Product development (y_t+5)	Dummy = 1 if in 5–10 years, the technology firms would use in the development of products is integrated product data management systems (e.g., Product Data Management) or Virtual reality applications and virtual manufacturing and assembly simulation tools, 0 otherwise.	0.068	0.251	0	1
Firm age	Continuous variable indicating the total number of years a firm has been in operation.	28.716	19.923	1	100
Firm size	Dummy = 1 if the firm is large (sales valued at more than R250 million per financial year), 0 otherwise.	0.145	0.352	0	1
No. of employees	Continuous variable indicating the total number of employees a firm had in 2019/2020 fiscal year.	76.115	59.822	2	550
Innovation	Dummy = 1 if the firm made significant improvements to products/introduced new production process between 2017–18 and 2019–20 financial years, 0 otherwise.	0.509	0.501	0	1
Exports	Dummy = 1 if the firm exports in 2019–20 financial year, 0 otherwise	0.436	0.497	0	1
Capital ownership	Dummy = 1 if the capital ownership of the firm is foreign-owned.	0.082	0.275	0	1
Digital related infrastructure	Dummy = 1 if the firm considers characteristics related to digital infrastructure (e.g., availability of high-end broadband networks) as relevant in the adoption of digital technologies, 0 otherwise.	0.505	0.5	0	1
Industry	A categorical variable that shows the 20 South African industrial classifications of the firms in our sample.	-	-	1	20

Estimates of the Factors that Cause the Current Adoption of Digital Technologies and the Expected Adoption of Digital Technologies by Firms

Table 3 presents the probit estimates of the determinants of digital technology adoption by firms. The results shows that innovation of the firm is statistically significant and positively correlated with both the current and the expected digital technology adoption for supplier relations and product development business functions. The results imply that innovative firms are more likely than non-innovative ones, to use both existing and future digital technologies, with respect to supplier relations, by 5.7% and 15.5%, respectively. Additionally, innovative firms have a 7.2% and a 14.5% higher likelihood of adopting both existing and future digital technologies for product development than non-innovative ones. The most likely explanation is that innovation is essential to bringing novelty to firms existing product lines or processes. Subsequently, firms would want to adopt digital technologies in a bid to boost productivity and allow them to produce more with the same input mix.

Table 3.

Probit Estimates of the Current Adoption of Digital Technologies and the Expected Adoption of Digital Technologies by Firms.

	(1)	(2)	(3)	(4)	(5)	(6)
Model	Probit
	Supplier relations(y_t)	Customer relations(y_t)	Product development(y_t)	Supplier relations(y_t+5)	Customer relations(y_t+5)	Product development(y_t+5)
Age of the firm	0.000	0.000	0.000	–0.026	0.000	–0.001
	(0.000)	(0.001)	(0.000)	(0.071)	(0.000)	(0.001)
Size of the firm	0.103^**	–0.013	0.052	0.396^***	0.056	0.106
	(0.079)	(0.044)	(0.060)	(0.095)	(0.074)	(0.085)
No. of employees	0.000	0.000	0.000	0.000	–0.001	0.000
	(0.000)	(0.000)	(0.000)	(0.001)	(0.000)	(0.000)
Innovation	0.057^***	0.009	0.072^**	0.155^**	0.011	0.145^***
	(0.039)	(0.039)	(0.044)	(0.079)	(0.052)	(0.058)
Exports	0.012	0.000	0.005	–0.026	0.094^***	0.018
	(0.020)	(0.000)	(0.018)	(0.071)	(0.047)	(0.034)
Capital ownership	0.046	0.037^*	–0.136	–0.078	0.118	–0.031
	(0.078)	(0.197)	(0.077)	(0.161)	(0.109)	(0.041)
Digital-related infrastructure	–0.009	0.006	–0.024	–0.038	0.097^**	0.023
	(0.017)	(0.031)	(0.017)	(0.062)	(0.042)	(0.0309)
Number of obs.	300	131	281	292	300	300
LR χ²	10.23	10.44	25.19	43.63	16.78	22.99
Prob > χ²	0.2491	0.2355	0.0007	0	0.0188	0.0034
Pseudo R²	0.1085	0.2143	0.2392	0.109	0.653	0.1206

Note: Standard errors appear in parentheses. The other coefficients are marginal effects (dy/dx).

Asterisks represent level of statistical significance: ***p ≤ 1%; **p ≤ 5%; *p ≤ 10%.

Besides from the results, firm size has a positive effect with both the current and future adoption across the supplier relations business function. The results imply that large firms are more likely to adopt digital technologies compared to small, micro or medium firms. A probable explanation is that large firms are more likely to have larger market influence, manufacture more complex items, and have the flexibility to adopt these technologies in order to stay competitive.

Exports shows a significant and positive effect on future adoption, for customer relations business function, at 1% level. According to this positive correlation, firms that engage in exports are more likely to use digital technologies, in the next 5–10 years, by a factor of 9.4%. The possible explanation may be that exports can boost turnover and innovation of firms, thus enabling them to adopt.

Furthermore, characteristics related to digital infrastructure (e.g., availability of high-end broadband networks) has a significant and positive effect on the expected digital technology adoption for customer relations. However, it does not have any effect on current adoption. This suggests that firms will take into account the importance of their firm’s digital-related infrastructure when deciding whether to use digital technologies, in the next 5–10 years.

Capital ownership has a positive and significant relationship with current adoption, on the customer relations business function. This suggests that the use of digital technologies is significantly different between foreign and domestic firms. From the results, foreign firms are more inclined than domestic firms to use more of digital technologies by a factor of 3.7%.

Estimates of the Nexus Between the Current and the Expected Adoption of Digital Technologies by Firms

The probit estimates in Table 4 shows that current adoption of digital technology is related to the expected adoption of digital technology. This relationship is positive and significant at 1% level. From the marginal effects, firms that are now adopting digital technologies have a 25% higher likelihood of adopting similar technologies in future (i.e., in the next 5–10 years) than firms that are not currently adopting.

Table 4.

Probit Estimates of the Nexus between the Current and the Expected Adoption of Digital Technology by Firms.

	(1)
Model	Probit
	Expected Adoption
	(y_t+5)
Current adoption (y_t)	0.24537^***
	(0.05075)
Age of the firm	4.87E-06
	(0.00028)
Size of the firm (large)	–0.05453
	(0.06926)
No. of employees	–4.9E-05
	(0.00039)
Innovation	0.063319
	(.04815)
Exports	0.056523
	0.05393
Capital ownership	–0.02965^***
	(0.09201)
Digital-related infrastructure	0.063319
	(0.04815)
Number of obs.	300
LR χ²	55.14
Prob > χ²	0.0000
Pseudo R²	0.1744

Note: Standard errors appear in parentheses. The other coefficients are marginal effects (dy/dx).

Asterisks represent level of statistical significance: ***p ≤ 1%; **p ≤ 5%; *p ≤ 10%.

Analyzing the Persistency in Digital Technology Adoption Behaviour by Firms Over Time

Table 5 shows the transition probabilities and the persistence pattern in digital technology adoption of manufacturing firms across the various business functions. It is evident from the table that in all the three business functions, there is a significant persistency pattern that is prevalent. This is so because the diagonal elements exceed 50%. Furthermore, 95% of firms under supplier relations persisted not to adopt in the subsequent period, while 5% shifted to adopt. Moreover, the probability of firm adopting in time t + 5 is about 58 higher for adopters than non-adopters in time t (58 = 63–5). Under customer relations, 89% of the adopters in one year persisted in adopting in the subsequent year, while 11% did not. The probability of firms adopting in time t+5 is about 87 higher for adopters than non-adopters in time t. In conclusion, compared to other types of business functions, product development business function has considerably higher persistency (higher state dependence) in adopting digital technologies, among the various business functions of the firm.

Table 5.

Transition Probabilities.

		Innovation Status at t+5
Types of Business Function	Adoption Status at t	NON-ADOPT (%)	ADOPT (%)	Unconditional StateDependence (%)
Supplier relations	NON-ADOPT	95	5	58
	ADOPT	37	63
Customer relations	NON-ADOPT	98	2	87
	ADOPT	11	89
Product development	NON-ADOPT	97	3	90
	ADOPT	7	93

Theoretical Implications

This study has made theoretical contributions to the existing literature. For instance, the results of the current study show that while firms may benefit individually from adopting digital technology, they may also benefit more economically and socially, which would speed up productivity and business growth. Additionally, the current study suggests a theoretical framework that integrates the TAM and TOE theories. This integration categorizes important driving forces of digital technologies adoption by firms. This comprehensive theoretical framework combines the novel feature of firm adoption of digital technology, by taking into account environmental, organizational, technological facets, as well as the prediction of firm adoption and behaviour. The study also significantly contributes to the literature by determining the adoption of technologies, particularly the adoption of digital technologies by firms in developing countries, namely South Africa. As far as the author is aware, no prior studies have looked at the factors causing adoption (current and future) of digital technology, the nexus between the current and the expected adoption of digital technology, and the persistency in digital technology adoption behaviour by firms over time in South Africa.

Managerial Implications

The findings of this study have practical implications for managers and/or owners of the firm. It is impossible to overstate the significance of digital transformation for a firm to prosper in this era, because it has a profound impact on business performance. As a result, the essential skill set is expected of managers to handle digital transformation. The management must determine whether their business is equipped to take advantage of the opportunities brought about by the adoption of digital technologies. For business firms to fully benefit from new technologies, their structures and processes must be adjusted. Additionally, managers need to develop a thorough business strategy that enables their firms to employ digital technology in a strategic way to get the intended outcomes. Placing a high priority on culture and strategy enables organizations to foster a growth-oriented environment that improves the customer experience and promotes long-term success. Managers of firms should set up sufficient training for their staff so that they can use these technologies properly, and are encouraged to abide by the environmental obligations that firms must observe. This will support firms in developing economic and social values, consequently improving performance. Decision-making processes can be streamlined via digital transformation, which enables advanced data collecting and processing. Lastly, managers must support staff innovation ideas, facilitate employee learning and offer chances for development and progress in order to anticipate change and ensure business continuity.

Policy Implications

The findings of this study have various policy implications. As a result, this study makes the following recommendations to policy makers and the relevant stakeholders. First, the need to support business innovation, and the provision of innovative digital solutions, through innovation policies and research in the field of digital technologies. Second, policymakers should ensure that other firms (e.g., micro, small and medium) besides large ones, can access and take advantage of the available core digital tools. It is also crucial that this strategy is reinforced by industry- and function-specific approaches that support the technology that are most pertinent to the individual firms. Third, investment in high-end broadband networks should be a priority for policy action since the availability of digital-related infrastructure is a critical enabler of digital technology adoption among firms. Lastly, policy intervention should aim to develop institutional and systemic support mechanisms to encourage both domestic and foreign firms to engage in technology activities.

Conclusion

The purpose of this article is to examine the adoption behaviour of digital technologies by manufacturing firms in South Africa. Our empirical results via probit estimates show that innovation of the firm and firm size have a significant and positive effect on both current and future adoption of digital technologies. On the other hand, capital ownership has a positive and significant relationship with current adoption, implying that foreign firms are more inclined than domestic firms to more use of digital technologies. Also, exports and digital-related infrastructure have a significant and positive effect on expected digital technology adoption for customer relations business function. Furthermore, the current adoption of digital technology is positively related to the expected adoption of digital technology. This implies that firms that are now adopting digital technologies have a higher likelihood of adopting similar technologies in future (i.e., in the next 5–10 years) than firms that are not currently adopting. The transition probability matrix was employed and it revealed that a significant persistency pattern is prevalent in all the three business functions (supplier relations, customer relations and product development).

This study offers a number of theoretical, managerial and policy implications. On the theoretical part, the results of the study show that while firms may benefit individually from adopting digital technology, they may also benefit more economically and socially, which would speed up productivity and business growth.

On the managerial part, the essential skill set is expected of managers to handle digital transformation. Management must determine whether their business is equipped to take advantage of the opportunities brought about by the adoption of digital technologies.

Lastly, on the policy side, there is need to support business innovation, and the provision of innovative digital solutions, through innovation policies. Finally, investing in high-end broadband networks should be a priority for policy action since the availability of digital-related infrastructure is an essential facilitator of digital technology adoption among firms.

Limitations and Future Research

The current study has offered some implications, but it is not without limitations. The findings of the study are based on a small sample of 516 manufacturing firms in South Africa, making it difficult to generalize the findings with the larger population. Future research should examine the data from more respondents so that the conclusions drawn from them can be safely generalized. Besides, the results are dependent on cross-sectional data, which leads to causality flaws in the relationships between the variables. Future research may rely on longitudinal studies to fix these flaws and aid in understanding how the forces driving digital technology have changed over time.

Footnotes

Acknowledgment

The author is grateful to the journal’s anonymous referees for their beneficial suggestions to improve the quality of the article. Usual disclaimers apply.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Kiru Sichoongwe

References

Africa Union. (2020). The digital transformation strategy for Africa (2020–2030) . https://au.int/en/documents/20200518/digital-transformation-strategy-africa-2020-2030.

Akpan

I. J.

, Udoh

E. A. P.

, & Adebisi

(2022). Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic. Journal of Small Business and Entrepreneurship , 34(2). https://doi.org/10.1080/08276331.2020.1820185

Andreoni

, Barnes

, Black

, & Sturgeon

(2021). Digitalization, industrialization, and skills development. In Structural transformation in South Africa (pp. 261–285). Oxford University Press. https://doi.org/10.1093/oso/9780192894311.003.0012

Attencia

, & Mattos

(2022). Adoption of digital technologies for asset management in construction projects. Journal of Information Technology in Construction , 27. https://doi.org/10.36680/j.itcon.2022.030

Avenyo

E. K.

, Bell

J. F.

, & Nyamwena

(2022). Determinants of digital technologies’ adoption in South African manufacturing: Evidence from a firm-level survey [Working Paper 13] . www.competition.org.za. https://www.uj.ac.za/wp-content/uploads/2021/10/sarchi-wp-2022-13-avenyo-e-k--bell-j-f--nyamwena-j-october-2022.pdf

Azam

N. A. J.

, Kabiraj

, Shaoyuan

, & Azam

M. I. A.

(2023). Adoption of TAM to measure consumer’s attitude towards self-service technology: Utilizing cultural perspectives as a moderator variable. Global Business Review . https://doi.org/10.1177/09721509221142373

Belitski

, Guenther

, Kritikos

A. S.

, & Thurik

(2022). Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses. Small Business Economics , 58(2). https://doi.org/10.1007/s11187-021-00544-y

Blichfeldt

, & Faullant

(2021). Performance effects of digital technology adoption and product & service innovation–A process-industry perspective. Technovation , 105. https://doi.org/10.1016/j.technovation.2021.102275

Bryan

J. D.

, & Zuva

(2021). A review on TAM and TOE framework progression and how these models integrate. Advances in Science, Technology and Engineering Systems Journal , 6(3), 137–145. https://doi.org/10.25046/aj060316

10.

Bughin

, Kretschmer

, & Van Zeebroeck

(2021). Digital technology adoption drives strategic renewal for successful digital transformation. IEEE Engineering Management Review , 49(3). https://doi.org/10.1109/EMR.2021.3098663

11.

Chen

(2019). The impact of expatriates’ cross-cultural adjustment on work stress and job involvement in the high-tech industry. Frontiers in Psychology , 10. https://doi.org/10.3389/fpsyg.2019.02228

12.

Chen

D. Q.

, Preston

D. S.

, & Swink

(2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems , 32(4). https://doi.org/10.1080/07421222.2015.1138364

13.

Cirillo

, Fanti

, Mina

, & Ricci

(2023). The adoption of digital technologies: Investment, skills, work organisation. Structural Change and Economic Dynamics , 66, 89–105. https://doi.org/10.1016/J.STRUECO.2023.04.011

14.

Crittenden

A. B.

, Crittenden

V. L.

, & Crittenden

W. F.

(2019). The digitalization triumvirate: How incumbents survive. Business Horizons , 62(2). https://doi.org/10.1016/j.bushor.2018.11.005.

15.

Cusolito

A. P.

, Lederman

, & Peña

(2020). The effects of digital-technology adoption on productivity and factor demand firm-level evidence from developing countries [Working Paper 9333]. http://www.worldbank.org/prwp. https://ideas.repec.org/p/wbk/wbrwps/9333.html

16.

Czaja

S. J.

, & Shark

(1998). Age differences in attitudes toward computers. Journal of Cemnlology: Psychological Sciences , 53(5). https://academic.oup.com/psychsocgerontology/article/53B/5/P329/572303

17.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems , 13(3). https://doi.org/10.2307/249008

18.

Fanoro

, Božanić

, & Sinha

(2021). A Review of 4IR/5IR Enabling Technologies and Their Linkage to Manufacturing Supply Chain. In Technologies (Vol. 9, Issue 4). https://doi.org/10.3390/technologies9040077

19.

Fitriasari

(2020). How do small and medium enterprise (SME) survive the COVID-19 outbreak? Jurnal Inovasi Ekonomi , 5(02). https://doi.org/10.22219/jiko.v5i3.11838

20.

Forman

, & Zeebroeck

(2019). Digital technology adoption and knowledge flows within firms: Can the Internet overcome geographic and technological distance? Research Policy , 48(8). https://doi.org/10.1016/j.respol.2018.10.021.

21.

Gangwar

, Date

, & Ramaswamy

(2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management , 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065

22.

Gholami

(2023). Investigating Adoption of Digital Technologies in Construction Projects . Licentiate dissertation, Linköping University Electronic Press. https://doi.org/10.3384/9789180750257.

23.

Guo

, Yang

, Huang

, & Guo

(2020). The digitalization and public crisis responses of small and medium enterprises: Implications from a COVID-19 survey. Frontiers of Business Research in China , 14(1). https://doi.org/10.1186/s11782-020-00087-1

24.

Henao-Ramírez

A. M.

, & López-Zapata

(2022). Analysis of the factors influencing adoption of 3D design digital technologies in Colombian firms. Journal of Enterprise Information Management , 35(2), 429–454. https://doi.org/10.1108/JEIM-10-2020-0416

25.

Hossain

M. D.

, Moon

, Kim

J. K.

, & Choe

Y. C.

(2011). Impacts of organizational assimilation of e-government systems on business value creation: A structuration theory approach. Electronic Commerce Research and Applications , 10(5), 576–594. https://doi.org/10.1016/J.ELERAP.2010.12.003

26.

Kammerlander

, König

, & Richards

(2018). Why do incumbents respond heterogeneously to disruptive innovations? The interplay of domain identity and role identity. Journal of Management Studies , 55(7). https://doi.org/10.1111/joms.12345

27.

Kreuser

C. F.

, & Newman

(2018). Total factor productivity in South African manufacturing firms. South African Journal of Economics , 86. https://doi.org/10.1111/saje.12179.

28.

Kristianto

, Ajmal

, Tenkorang

R. A.

, & Hussain

(2012). A study of technology adoption in manufacturing firms. Journal of Manufacturing Technology Management , 23(2), 198–211. https://doi.org/10.1108/17410381211202197

29.

Lashitew

A. A.

(2023). When businesses go digital: The role of CEO attributes in technology adoption and utilization during the COVID-19 pandemic. Technological Forecasting and Social Change , 189. https://doi.org/10.1016/j.techfore.2023.122324

30.

Legris

, Ingham

, & Collerette

(2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management , 40(3). https://doi.org/10.1016/S0378-7206(01)00143-4

31.

(2020). The digital transformation of business models in the creative industries: A holistic framework and emerging trends. Technovation , 92–93. https://doi.org/10.1016/j.technovation.2017.12.004

32.

, Wu

, Cao

, & Wang

(2021). Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility. Journal of Business Research , 122. https://doi.org/10.1016/j.jbusres.2019.10.036

33.

Madonsela

N. S.

, Mbecke

, & Mbohwa

(2013). Improving the South African manufacturing sector’s competitiveness through the adequate use of ICT. International Conference on Information Technology and Computer Systems Engineering (ITCSE’2013) Nov. 27–28.

34.

Matsepe

N. T.

, & Van der Lingen

(2022). Determinants of emerging technologies adoption in the South African financial sector. South African Journal of Business Management , 53(1). https://doi.org/10.4102/sajbm.v53i1.2493

35.

Matt

D. T.

, Modrák

, & Zsifkovits

(2020). Industry 4.0 for SMEs: Challenges, opportunities and requirements . Palgrave Macmillan. https://doi.org/10.1007/978-3-030-25425-4.

36.

Mohd Faizal

, Jaffar

, & Mohd nor

A. S.

(2022). Integrate the adoption and readiness of digital technologies amongst accounting professionals towards the fourth industrial revolution. Cogent Business and Management , 9(1). https://doi.org/10.1080/23311975.2022.2122160

37.

Mulyono

, Syafei

W. A.

, & Kusumaningrum

(2020). Analisa Tingkat Penerimaan Pengguna Terhadap Aplikasi SIMPUS dengan Metode Technology Acceptance Model (TAM). JOINS (Journal of Information System) , 5(1). https://doi.org/10.33633/joins.v5i1.3277

38.

Nasiri

, Ukko

, Saunila

, & Rantala

(2020). Managing the digital supply chain: The role of smart technologies. Technovation , 96–97. https://doi.org/10.1016/j.technovation.2020.102121

39.

Ngo

Q. T.

, Doan

N. P.

, Thi Tran

T. H.

, & Nguyen

T. D.

(2020). Technology adoption strategies in the supply chain: The case of vietnamese young small and medium-sized enterprises. Journal of Open Innovation: Technology, Market, and Complexity , 6(2). https://doi.org/10.3390/JOITMC6020037.

40.

Ofosu-Ampong

, Asmah

, Kani

J. A.

, & Bibi

(2023). Determinants of digital technologies adoption in government census data operations. Digital Transformation and Society . https://doi.org/10.1108/DTS-11-2022-0056

41.

Omrani

, Rejeb

, Maalaoui

, Dabic

, & Kraus

(2022). Drivers of digital transformation in SMEs. IEEE Transactions on Engineering Management . https://doi.org/10.1109/TEM.2022.3215727

42.

Pan

M. J.

, & Jang

W. Y.

(2008). Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications industry. Journal of Computer Information Systems , 48(3).

43.

Papadopoulos

, Baltas

K. N.

, & Balta

M. E.

(2020). The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice. International Journal of Information Management , 55. https://doi.org/10.1016/j.ijinfomgt.2020.102192

44.

Pascucci

, Savelli

, & Gistri

(2023). How digital technologies reshape marketing: Evidence from a qualitative investigation. Italian Journal of Marketing . https://doi.org/10.1007/s43039-023-00063-6

45.

Pramanik

H. S.

, Kirtania

, & Pani

A. K.

(2019). Essence of digital transformation—Manifestations at large financial institutions from North America. Future Generation Computer Systems , 95. https://doi.org/10.1016/j.future.2018.12.003.

46.

Qin

, Shi

, Lyu

, & Mo

(2020). Using a tam-toe model to explore factors of building information modelling (Bim) adoption in the construction industry. Journal of Civil Engineering and Management , 26(3), 259–277. https://doi.org/10.3846/jcem.2020.12176

47.

Ramdani

, & Kawalek

(2007). SME Adoption of Enterprise Systems in the Northwest of England. In: McMaster

, Wastell

, Ferneley

, DeGross

J.I.

(Eds.) Organizational Dynamics of Technology-Based Innovation: Diversifying the Research Agenda . TDIT 2007. IFIP International Federation for Information Processing (vol. 235). Springer, Boston, MA. https://doi.org/10.1007/978-0-387-72804-9_27

48.

Riyadh

A. N.

, Akter

, & Islam

(2009). The adoption of e-banking in developing countries: A theoretical model for SMEs. International Review of Business Research Papers , 5(6). https://doi.org/10.1016/j.technovation.2007.10.003

49.

Rodríguez

N. G.

, Pérez

M. J. S.

, & Gutiérrez

J. A. T.

(2008). Can a good organizational climate compensate for a lack of top management commitment to new product development? Journal of Business Research , 61(2), 118–131. https://doi.org/10.1016/J.JBUSRES.2007.06.011

50.

Rogers

E. M.

(2003). Diffusion of innovations (5th ed.). Free Press.

51.

Rosario Oliveira Martins

, Maria Araújo da Silva Muggli

, Oliveira

, & Fraga Martins

(2011). Literature review of information technology adoption models at firm level. The Electronic Journal Information Systems Evaluation , 14, 110.

52.

SARB. (2020). What price level data can tell us about pricing conduct in South Africa South African Manufacturing A situational analysis . https://ideas.repec.org/s/rbz/oboens.html

53.

Schepers

, Wetzels

, & de Ruyter

(2005). Leadership styles in technology acceptance: Do followers practice what leaders preach? Managing Service Quality , 15(6). https://doi.org/10.1108/09604520510633998

54.

Schwab

(2016). The fourth industrial revolution: What it means and how to respond . World Economic Forum. Www.Weforum.Org.

55.

Shahadat

M. M. H.

, Nekmahmud

, Ebrahimi

, & Fekete-Farkas

(2023). Digital technology adoption in SMEs: What technological, environmental and organizational factors influence SMEs’ ICT adoption in emerging countries? Global Business Review . https://doi.org/10.1177/09721509221137199

56.

Sukendro

, Habibi

, Khaeruddin

, Indrayana

, Syahruddin

, Makadada

F. A.

, & Hakim

(2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during COVID-19: Indonesian sport science education context. Heliyon , 6(11). https://doi.org/10.1016/j.heliyon.2020.e05410

57.

Swanson

E. B.

(1994). Information systems innovation among organizations. Management Science , 40(9). https://doi.org/10.1287/mnsc.40.9.1069

58.

Tornatzky

L. G.

, Fleischer

, & Chakrabarti

A. K.

(1990). The processes of technological innovation . Lexington Books.

59.

Usai

, Fiano

, Messeni

P. A.

, Paoloni

, Farina

B. M.

, & Orlando

(2021). Unveiling the impact of the adoption of digital technologies on firms’ innovation performance. Journal of Business Research , 133. https://doi.org/10.1016/j.jbusres.2021.04.035.

60.

Van Den Berg

, & Van Der Lingen

(2019). An empirical study of the factors affecting the adoption of mobile enterprise applications. South African Journal of Industrial Engineering , 30(1), 124–146. https://doi.org/10.7166/30-1-1992

61.

Van Oorschot

J. A. W. H.

, Hofman

, & Halman

J. I. M.

(2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change , 134, 1–21. https://doi.org/10.1016/J.TECHFORE.2018.04.032

62.

Verhoef

P. C.

, & Bijmolt

T. H. A.

(2019). Marketing perspectives on digital business models: A framework and overview of the special issue. International Journal of Research in Marketing , 36(3), 341–349. https://doi.org/10.1016/j.ijresmar.2019.08.001.

63.

Vrontis

, Chaudhuri

, & Chatterjee

(2022). Adoption of digital technologies by SMEs for sustainability and value creation: Moderating role of entrepreneurial orientation. Sustainability (Switzerland) , 14(13). https://doi.org/10.3390/su14137949

64.

N. H.

, & Nguyen

N. M.

(2022). Development of small-and medium-sized enterprises through information technology adoption persistence in Vietnam. Information Technology for Development , 28(3). https://doi.org/10.1080/02681102.2021.1935201

65.

Wang

Y. M.

, Wang

Y. S.

, & Yang

Y. F.

(2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change , 77(5). https://doi.org/10.1016/j.techfore.2010.03.006

66.

World Bank. (2019). South-Africa-digital-economy-diagnostic . The World Bank Group. www.worldbankgroup.org

67.

Yadegaridehkordi

, Hourmand

, Nilashi

, Shuib

, Ahani

, & Ibrahim

(2018). Influence of big data adoption on manufacturing companies’ performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change , 137, 199–210. https://doi.org/10.1016/J.TECHFORE.2018.07.043

68.

Yang

(2008). Unravelling the link between knowledge integration and new product timeliness. Technology Analysis and Strategic Management , 20(2). https://doi.org/10.1080/09537320701711256

69.

Yang

, Fu

, & Zhang

(2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change , 169. https://doi.org/10.1016/j.techfore.2021.120795

70.

Yasin

, Sadia

, Hali

S. M.

, & Iqbal

(2022). Digitalization and firm performance: Mediating role of smart technologies. Journal of Tianjin University Science and Technology , 55(4). https://doi.org/10.17605/OSF.IO/8FNDC.

71.

Zhu

(2004). The complementarity of information technology infrastructure and E-commerce capability: A resource-based assessment of their business value. Journal of Management Information Systems , 21(1). https://doi.org/10.1080/07421222.2004.11045794