Strategic orientation effects on Industry 4.0 base and front-end manufacturing technology adoption

Interesting because: Industry 4.0 technologies consist of a suite of different technologies and are classified as base technologies and front-end manufacturing technologies in the manufacturing context. Manufacturing firms face distinct challenges in adopting these different technologies. Although the importance of the technology levels in Industry 4.0 adoption has been acknowledged, its consideration in prior research is limited. This study addresses the gap and examines how customer, competitor and learning orientations influence Industry 4.0 base and front-end manufacturing technology adoption and the mediation role of technology orientation in these relationships.

Theoretical value: The conceptualisation of Industry 4.0 base and manufacturing front-end technologies as independent constructs deviates from the common practice of combining all Industry 4.0 technologies as a single construct. It recognises the heterogeneity of Industry 4.0 technologies. The findings show that the relationships between customer, competitor and learning orientations on Industry 4.0 base technology adoption are fully mediated by technology orientation. However, the effects of any of those strategic orientations on manufacturing front-end technologies were not statistically significant.

Practical value: For manufacturing firms, the findings emphasise the importance of distributed investments across strategic orientations for Industry 4.0 base technology adoption. Conditions that enable Industry 4.0 base technology adoption do not necessarily enable front-end technology adoption at the same rate of influence. Therefore, manufacturing practitioners need to tailor their technology adoption strategies with the recognition that organisational, technological and environmental enablers of manufacturing front-end technologies differ from those driving base-level technology adoption.

The concept of Industry 4.0, also known as Industrie 4.0 and Industrial Internet of Things (IIoT), originated with a focus on the manufacturing industry and evolved from the mechanisation (second industrial revolution) and automation (third industrial revolution) eras (Lasi et al., 2014). Industry 4.0 is enabled by the Internet of Things (IoT) and is characterised by the integration of cyber-physical systems into manufacturing operations. The emergence of advanced technologies such as additive manufacturing, artificial intelligence, advanced robotics, autonomous systems and big data analytics is prominent in Industry 4.0. These technologies enable mass product customisation without sacrificing economies of scale, predictive planning and maintenance, real-time monitoring for increased productivity and reduced downtime, and data-driven decision making (Oztemel and Gursev, 2020; Ghobakhloo et al., 2021).

As the adoption of Industry 4.0 technologies continues to gain momentum, numerous research studies have emerged investigating firm-level technology adoption from diverse perspectives. Much of this work has focused on the advantages of Industry 4.0 technologies, including increased productivity, resilience, product quality and, more recently, sustainability, including the circular economy (Nakandala et al., 2023, 2024; Oztemel and Gursev, 2020; Ghobakhloo et al., 2021).

Despite the widely acknowledged benefits, firms continue to face significant challenges in adopting Industry 4.0 technologies. Empirical evidence suggests that barriers to digitalisation in manufacturing firms are multi-dimensional encompassing economic, financial, cultural, strategic, behavioural and technological factors (Mishra et al., 2025). The broad range of Industry 4.0 technologies and their various technological complexity levels create further challenges in identifying what technologies suit different firm-level conditions and characteristics and whether all technologies should be treated the same way when we investigate the adoption of Industry 4.0 technologies by firms (Hopkins, 2021).

Industry 4.0 technologies consist of a suite of different technologies with industry-specific applications. In the context of manufacturing, Industry 4.0 technologies are classified as base technologies and front-end manufacturing technologies (Frank et al., 2019). Consideration of the technology level focus in Industry 4.0 research has been recognised. In their exploratory research, Sony et al. (2023) identify that manufacturing firms vary in technological capabilities and argue the importance of considering both base and manufacturing front-end technologies. These two types have specific challenges for manufacturing firms in adoption (Frank et al., 2019). Extending beyond the Industry 4.0 base technologies to capture technology adoption will make research findings more relevant, given their differences in challenges and implications to manufacturing firms; however, consideration of technology levels in investigating technology adoption behaviours of organisations is limited in prior studies. This study thus considers and investigates base and manufacturing front-end technologies as separate, distinct constructs.

Strategic orientations are how firms strategically respond to internal and external environments in conducting business (Zhou and Li, 2010) with technology adoption representing an important strategic choice (Ghobakhloo et al., 2022). They consist of customer and competitor orientations from an external market perspective and learning and technology orientations from an internal organisational perspective (Asare et al., 2011; Narver and Slater, 1990; Chen et al., 2011; Tajeddini, 2011). For manufacturing firms, market and supplier perspectives are required to be integrated within the manufacturing strategy (Oztemel and Gursev, 2020). The alignment between strategic orientations and technology adoption is a requirement driven by internal consistency leading to performance (Lefebvre et al., 1992). The extent to which manufacturing firms realise the benefits of Industry 4.0 technology implementations is contingent on how firms respond to the pressures from the competition and utilise their internal capabilities (Guo, 2025). Despite its importance, empirical research examining the relationships between strategic orientations and technology adoption is limited (Lefebvre et al., 1992).

Recent research diverts from the conventional thinking of independent effects of strategic orientations and argues that no single element independently affects technology adoption and there are interdependencies among them, shaping technology adoption decisions of firms (Sun et al., 2024; Faiz et al., 2024). As they explain, a firm adopting technology hastily when competitor or customer pressure increases without having adequate internal technological capabilities, then such investments are likely to fail. Hence, this study takes a systems perspective to organisations and investigates technology adoption, recognising strategic orientations as interdependent elements.

This study investigates how different strategic orientations of manufacturing firms influence their Industry 4.0 technology adoption levels, focusing on differentiating base technologies from front-end manufacturing Industry 4.0 technologies. The hypothesised model links the market, competitor and learning orientations, Industry 4.0 base and front-end technologies and technological orientation. Based on an anonymous online survey, data were collected from 117 manufacturing firms in Australia and then analysed using the PLS-SEM method. It is found that market, competitor and learning orientations have direct positive effects on the technological orientation and then the technological orientation leads to Industry 4.0 base technology adoption levels. It finds that the effect of technological orientation is stronger on Industry 4.0 base technologies compared to their effects on front-end manufacturing technologies. It is also found that these strategic orientations explain a significant variance of the Industry 4.0 base technology variable. Still, both R² value and the coefficient are small for front-end manufacturing technologies.

The findings of the study show the importance of strategic focus on technology, which shows that manufacturers need to sense opportunities in the technological environment enabled by market, competitor and learning focused strategic orientations. It also finds that different strategic orientations of the manufacturing firms differ in their impact on the Industry 4.0 base and front-end technologies. Such a finding indicates the need for future researchers not to integrate all Industry 4.0 technologies (base and front-end technologies) together to derive conclusions on the ways, benefits or antecedents of Industry 4.0 implementations.

The article first provides a brief literature review leading to hypothesis development and the methodology, including the data collection method and sample characteristics. Next, data analysis and results are presented, followed by a discussion of the results and a conclusion that will also identify the limitations of the study and suggestions for future research.

Strategic orientations focused on in this study consist of both internally and externally oriented perspectives with customer and competitor orientations from the environmental perspective, learning orientation from the organisation perspective and technology orientation from the technological perspective. Market orientation of firms influences their technology-related decisions (Asare et al., 2011). Customer orientation is “the sufficient understanding of one's target buyers to be able to create superior value for them continuously” (Narver and Slater, 1990, p. 21) and Competitor orientation is the firm's understanding of competitors' strengths, weaknesses, capabilities and strategies and the approach to make their products more favourable to customers than their competitors (Schulze et al., 2022). Learning orientation captures a firm's ability to acquire new knowledge and share knowledge within the firm and its commitment to learning (Chen et al., 2011) where learning capabilities enable firms to rapidly respond to technological change (Tajeddini, 2011). Technology-orientation emphasises the importance of interacting with the external technology environment to conduct business (Zhou and Li, 2010) and is defined as “the ability and the will to acquire a substantial technological background and to use it in the development of new products” (Gatignon and Xuereb, 1997). The technology orientation of firms is affected by their internal and external environmental contexts (Zehir et al., 2010; Jeong et al., 2006).

Base technologies provide connectivity and intelligence for front-end technologies and allow front-end technologies to be connected as an integrated manufacturing system (Frank et al., 2019; Marcucci et al., 2022). Cloud computing, Internet of Things, big data and advanced analytics are classified as base technologies (Marcucci et al., 2022; Frank et al., 2019; Maretto et al., 2025; Ramanujan et al., 2023). Front-end manufacturing technologies are smart manufacturing technologies utilised to transform manufacturing operations (Frank et al., 2019; Galizia et al., 2023; Ramanujan et al., 2023). These technologies utilise base technologies that enable connectivity, data storage, management and access and intelligence through analytics (Dalenogare et al., 2018). Many research studies focus on the four Industry 4.0 base technologies (Nakandala et al., 2023; Čater et al., 2021), but there is limited research focus on front-end technologies.

This study is theoretically underpinned by the Technology-Organisation-Environment (TOE) framework from Tornatzky and Fleischer (1990) by taking the strategic perspective on the TOE framework. The TOE framework has been commonly adopted in technology adoption studies in investigating barriers, factors and challenges taking into consideration a firm's technological, organisational and environmental contexts (Ghobakhloo et al., 2022; Chittipaka et al., 2023; Weerabahu et al., 2024; Ganguly, 2024). Technology, organisational and environmental contexts entail technological innovations, intra-organisational characteristics and external conditions respectively in the TOE framework (Ghobakhloo et al., 2022). Previous studies have argued that the TOE framework can comprehensively demonstrate determinants of technology adoption in a single model compared to other models, such as the Technology Adoption Model (TAM) (Davis, 1985) and UTAUT (Venkatesh et al., 2003; Qalati et al., 2022; Wong et al., 2020; Taneja et al., 2023). Moreover, TOE is a firm-centred adoption model and is appropriate for the design of this study, where TAM and UTAUT are individual user-centred models (Ukobitz, 2021). The TOE framework has been used in various technological contexts, including big data analytics, blockchain technology and AI, Industry 4.0 technologies (Mustafa et al., 2023; Ganguly, 2024; Chittipaka et al., 2023; Shahzadi et al., 2024), investigating a range of business functions such as supply chains, digital servitisation, productivity and organisational performance (Shahzadi et al., 2024; Weerabahu et al., 2024; Nugroho et al., 2022).

The utility of the TOE framework in investigating the effects of strategic orientations on technology adoption has been seen in previous empirical studies. For example, Nguyen et al. (2022) investigated the effects of technology and entrepreneurial orientations on digital transformation in online retailing and Qalati et al. (2022) investigated entrepreneurial and customer orientation on social media adoption by small and medium enterprises. Furthermore, Yuan et al. (2025) investigated the effects of learning orientation on AI adoption and Taneja et al. (2023) investigated the effects of sustainable technology orientation on FinTech implementations.

Previous studies on the effects of firm-level strategies identify the importance of the customer, competition, learning and technological orientations for firm performance (Gatignon and Xuereb, 1997; Frambach et al., 2016) and some argue that these orientations are likely to have independent effects on firm performance (Frambach et al., 2016) while others recognise the interdependencies among the TOE elements (Sun et al., 2024; Banerjee and Ma, 2012). Examples for the stream of independent effects of TOE factors include the study by Mustafa et al. (2023) that investigated the direct effects of internal and external factors on Industry 4.0 technology adoption levels and Chen et al. (2024) that investigated the effects of TOE factors and big data analytics and artificial intelligence adoption without recognising their interdependencies among factors.

Alternatively, recent research takes a system perspective to organisations recognising the interdependencies among TOE elements. Hsu and Yeh (2017) investigated the inter-relationships among TOE elements in IoT adoption in the logistics industry and Sun et al. (2024) derived configurations of TOE elements in empirically investigating technology adoption. Furthermore, Faiz et al. (2024) investigated the TOE effects on digital technology adoption recognising the interdependencies between technological and organisational factors. This study follows the latter stream by recognising both direct and interdependent effects of strategic factors on technology adoption in adopting the TOE framework as the theoretical foundation.

Customer-oriented firms are focused on adopting new technologies to meet the needs of their existing markets and customers or expand to new markets (Gangwani and Bhatia, 2024). Increasing customer awareness of new technologies and digital literacy drives firms to focus on updating, creating and adopting new technologies to sustain customer satisfaction. In an exploratory study investigating the influence of market orientations on technology adoption decisions, Asare et al. (2011) argue that attitudes towards technology adoption are shaped by the market orientation in business-to-business technologies, where technology adoption requests from trading partners are highly influenced by their channel orientations. Manufacturing firms are becoming more customer-oriented and prioritising developing innovative solutions addressing customers' specific needs through mass customisation and servitisation strategies (MacCarthy et al., 2003; Atuahene-Gima, 1996).

Mass customisation and servitisation strategies drive technological capability development in firms. Mass customisation requires technology investments primarily in design, manufacturing and administrative technologies (Atuahene-Gima, 1996). Servitisation strategies of manufacturing firms lead to an integrated customer focus, supplementing conventional manufacturing processes to develop and provide services and solutions to their customers, where technology transition is the key enabler of service and product designs (Lee et al., 2022). Digital servitisation of manufacturing firms is about developing new business models that integrate products with high-quality services through the use of digital technologies, meeting customer requirements (Paschou et al., 2020), where the role of Industry 4.0 technologies in digital servitisation has been revealed in previous studies (Weerabahu et al., 2024). Digital Industry 4.0 technologies provide the technological capabilities for rapid responses to dynamic customer demands, meeting their requirements in increasingly changing customer environments (Hsiao et al., 2015). Hence, the following hypothesis is proposed.

Competition pressures influence technology focus in firms, where the adoption of new technologies or advanced technologies by competitors creates pressure on firms to do the same or better for business survival (Uddin and Jayaram, 2025). As argued by Gatignon and Xuereb (1997), any firm focusing on responding to competition should have a strong technological orientation. Schulze et al. (2022) reported empirical evidence for the effects of proactive competitor orientation on technology orientation for rapid response to competitors' actions in their study of firms engaging in business-to-business transactions. Uddin and Jayaram (2025) also empirically confirmed the effects of competitive pressures on the focus of automation in the manufacturing operations in their investigation of garment manufacturing firms. Therefore, firms' competitor orientation drives firms to respond to competition and improve their performance with a strategic focus on developing advanced technological capabilities.

Learning orientation represents the commitment to seek new knowledge and insights, enabling their transformation (Yuan et al., 2025). Learning philosophy is defined as “a pervasive set of organisation-wide understanding about learning, thinking, acquiring, transferring, and using knowledge in the firm to innovate” (Siguaw et al., 2006). Absorptive capacity of organisations is about acquiring, assimilating, transforming and exploiting knowledge (Zahra and George, 2002), where Haro-Domínguez et al. (2007) show that learning and knowledge management with absorptive capacity development positively influence technology acquisitions in engineering consultancy firms. Figueiredo and Piana (2021) argue that learning strategies drive technology upgrading capabilities in mining firms. Further, organisational learning and its output and knowledge are considered antecedents of innovation (Martín-de Castro et al., 2011). They stated that an organisation considering enhancing technical innovation should adopt organisational learning practices. When firms are capable of learning new knowledge and innovation, they are able to respond to technological changes, adapt to changing technological environments and adopt new technologies (Tajeddini, 2011). Hence, organisations need to be able to continuously renew their knowledge to deal with dynamic changes in the environment.

Technological capabilities have been known as a primary factor of competitiveness for firms in recent times, where those who develop, adopt, and adapt advanced technologies have advantages over other firms. Successful integration of the production process, information technologies and techniques refers to the digital manufacturing system or Industry 4.0(I4). The Internet of Things (IoT), Big Data Analytics (BDA), Augmented Reality (AR), Cyber-Physical Systems(CPS), Cloud Computing (CC), Additive Manufacturing (AM), Advanced Robotics are considered as key I4 technologies that enhance the efficiency and responsiveness of manufacturing systems (Kamble et al., 2020; Oztemel and Gursev, 2020).

While some studies have examined technology orientation at the same level as the other orientations (customer, competitor), others argue for technology orientation having more importance than the others or as a mediator (Adiguzel et al., 2025). For firms seeking competitive superiority, it is essential to adopt a strong technological orientation (Gatignon and Xuereb, 1997). Empirical evidence supports its mediating role in the relationship between competitor orientation and innovation performance (Schulze et al., 2022) and between market orientation and I4.0 adoption (Gangwani and Bhatia, 2024). Moreover, technology orientation is argued to be more important than other strategic orientations for high firm performance in uncertain times (Gatignon and Xuereb, 1997). For example, the importance of digital capabilities for resilience and sustainability performance is empirically evident (Nakandala et al., 2024; Oztemel and Gursev, 2020; Ghobakhloo et al., 2021). Simply put, firms primarily compete on their technological capabilities in today's uncertain and dynamic industry environment.

Technology-oriented firms invest in new technologies and attempt to surpass the capabilities of competitor firms by investing in technologies to develop low-cost or superior products (Hakala and Kohtamäki, 2010; Gatignon and Xuereb, 1997). They are receptive to new technologies and sense, seize and adapt new technologies to improve their product quality, process efficiency, planning and resilience capabilities. They are more aware of the benefits of technology investments, thus are more inclined to adopt Industry 4.0 technologies (Gangwani and Bhatia, 2024). Weerabahu et al. (2024) report in their empirical study that manufacturing firms need to focus on developing a digital strategy before considering other barriers to technology adoption in their digital servitisation attempts. Nugroho et al. (2022) reported empirical evidence for the positive effects of technology orientation on information technology adoption capabilities of firms in Indonesia and Singapore. Manufacturing firms with a strategic orientation towards technology increasingly adopt Industry 4.0 technologies for increased proactive planning, predictive maintenance and real-time monitoring for decision-making (Oztemel and Gursev, 2020; Ghobakhloo et al., 2021).

Hence, the following hypotheses are proposed. Figure 1 presents the research model.

This study collected data from managers and executives of manufacturing firms in Australia based on an anonymous online survey. The importance of Australia as the research context for this study is supported by the increasing government attention towards manufacturing. For example, recent government initiatives promoting and supporting advanced manufacturing include the $15 billion investment in the National Reconstruction Fund (https://www.nrf.gov.au/) and the announcement of $22.7 billion investment in the “Future Made in Australia” initiative (https://futuremadeinaustralia.gov.au/), which shows the recognition of manufacturing industry development.

In operationalising the research model, the measures of the four independent constructs of customer, competitor, technology and learning orientations were developed from the strategic management literature. Customer orientation was measured using a four-item scale by following Zhou and Li (2010), which evaluated if the participant firm's competitive advantage was based on understanding their customers' needs, and if their business objectives are driven primarily by customer satisfaction, and if they assess customer satisfaction and pay attention to after-sales service. The measure for competitor orientation was adopted from Narver and Slater (1990). They assessed the level of integration of information about the products and strategies of competitors, collection and analysis of competitors' activities, management collaborative actions on sharing competitors' information and the possession of knowledge of competitors’ capabilities. Four survey items for the learning orientation were based on Chen et al. (2011) and Siguaw et al. (2006). Those items captured the firm's learning philosophy towards employees' willingness to learn and share new knowledge and the firm encourages employees to resolve problems innovatively. The measures of technology orientation were adapted from Zhou and Li (2010). They included four items on the use of sophisticated technologies, technological innovations and the acceptance of research-based technology innovations. All the survey items had a measurement scale of seven points, ranging from strongly disagree to strongly agree on a 7-point Likert scale.

The measures of the dependent constructs of Industry 4.0 base and front-end technologies were adapted from Frank et al. (2019). The four Industry 4.0 base technologies of cloud computing for storage and data management, the use of the internet to access data from remote sensors and control physical objects in the surrounding environment, cyber-physical systems and advanced data analytics (e.g. simulation, optimisation, regression) have been commonly used in numerous studies (Marcucci et al., 2022; Frank et al., 2019; Maretto et al., 2025; Ramanujan et al., 2023; Nakandala et al., 2023) where the manufacturing front-end technologies had limited presence in Industry 4.0 technology measures. The base Industry 4.0 technologies were extended to include front-end manufacturing technologies following Frank et al. (2019), and respondents were asked to rate the level of implementation in their firms.

The respondent selection criteria were embedded in the online survey to exclude micro and small firms with less than 20 employees, those not in managerial positions and not in technology-related functions in their firms. Since this study required strategy-level data, the key respondents were selected to hold managerial positions in manufacturing firms. The focus on medium to large firms was based on the rationale that they are more resourced to invest in Industry 4.0 technologies than micro and small firms. The data were collected by working with a reputable survey agency with a large panel of manufacturing firms in Australia.

Participants representing 117 manufacturing firms responded to the survey. 68% of respondent firms had 20–200 employees, and the remaining had more than 200 employees. About 50% of the sample firms had annual revenue between AU$20m and AU$500m, and another 35% reported annual revenue of less than AU$20m. Over 96% of firms had been in business for more than five years, and 50% were in business for over 20 years. 38% of respondents were CEOs, CIOs and COOs holding senior management positions. Twenty-six percent had functional-level senior management positions, and others were executives or managers. All these respondents were managers of technology-related functions such as engineering, technology, operations, production/manufacturing, and supply chain or C-level managers with overall business management responsibilities. This project received Western Sydney University ethics approval (ref: the approval ID is H14686).

The dataset was tested for multivariate outliers (Tabachnick et al., 2007) and normality using skewness and Kurtosis values. Exploratory Factor Analysis was conducted to test the scales for the front-end technologies using SPSS version 30. Confirmatory factor analysis was then conducted in the PLS-SEM method using Smart PLS version 4.1.1.4 to confirm the reliability and validity of all scales. The I4.0 front-end technologies construct was conceptualised as a higher-order reflective-reflective construct (I4.0- Front-end) with two lower-level constructs of conventional (Automation) and advanced manufacturing technologies proposed under the smart factory concept (Smart Factory). The higher-order construct was tested using the disjoint two-stage approach (Becker et al., 2023), where the model was first tested without the higher-order construct for construct reliability and validity. Cronbach's alpha was higher than the threshold of 0.7 (Nunally and Bernstein, 1978) and AVE had a minimum of 0.593, higher than the threshold of 0.5 (Fornell and Larcker, 1981). The latent scores for the two constructs of Smart Factory and Automation were then generated from the model, added to the data file and used as indicators of the higher-order construct of I4.0-Front-end with all other constructs with measured variables in the second stage.

In testing the measurement model with the higher order construct of I4.0-Front-end, construct reliability and convergent validity of constructs were tested with Cronbach's alpha (α) and the average variance extracted (AVE) values that were found to be 0.769 and 0.593 minimum, respectively. They are higher than the recommended minimum values of 0.7 for Cronbach's alpha (Nunally and Bernstein, 1978) and 0.5 for AVE (Fornell and Larcker, 1981). For all constructs, composite reliability values have a minimum of 0.853, which indicates the internal consistency of the constructs as shown in Table 1. The construct validity and reliability values for the lower-order constructs of I4.0-Front-end are given in Table A1 in the  Appendix. In assessing discriminant validity, following Fornell and Larcker (1981) and heterotrait-monotrait (HTMT) ratio of correlations. The highest HTMT value was 0.770 and lower than the cut-off value of 0.85 (Henseler et al., 2016). These tests confirmed the discriminant validity, especially the two novel constructs of base technologies and front-end manufacturing technologies, which are found to be unique constructs as shown in Table 2.

The respondent firms had varying degrees of Industry 4.0 technology implementations. Cloud computing has the highest implementation rate in base technologies (Mean = 5.368, D = 1.344). AI for production planning, virtual commissioning, robots (industrial robots, autonomous guided vehicles or similar), and AI for predictive maintenance and automatic nonconformity identification were at the lowest end of the front-end manufacturing technology implementation in the sample firms. The data set must be tested for common method bias since both the dependent and independent variables were tested by the same respondents using a single survey. Harman's one-factor test resulted in a single-factor contribution of 44.771%, which is less than 50% (Podsakoff et al., 2003), showing that common method bias is not a major concern for this dataset. Following the measurement model testing, hypothesis testing was conducted using the PLS-SEM method. The model fitness value was tested using the standardised root mean square residual (SRMR) value at 0.055, indicating a good model fit level (Henseler et al., 2014).

In the structural model testing, the hypothesised model shown in Figure 1 was tested, and the results are shown in Table 3.

In testing the relationships among the customer orientation, competitor orientation, learning orientation and technology orientation, the relationship between customer orientation and technology orientation was positive and significant, supporting H1 (β = 0.260, p < 0.05), between competitor orientation and technology orientation was positive and significant supporting H3 (β = 0.290, p < 0.05) and between learning orientation and technology orientation was positive and significant (β = 0.285, p < 0.05) supporting H5. The direct effects of customer, competitor and learning orientations on technology adoption for both base and manufacturing front-end technologies were not statistically significant. The relationship between technology orientation and Industry 4.0 base technology adoption was positive and significant (β = 0.344, p < 0.05), but the effects of technology orientation on manufacturing front-end technologies were not statistically significant. R² values of dependent constructs, as in Figure 2, show that the model explains 53.6% of the variance in the technology orientation construct, 49.8% of the variance in the Industry 4.0 base technology construct but only 5.5% of the variance in the Industry 4.0 front-end manufacturing technology construct.

The Q² (predictive relevance) values of technology orientation, industry 4.0 base technologies, were at 0.492 and 0.388, respectively; thus, they were larger than zero. However, Q² value was lower than zero for Industry 4.0 front-end manufacturing technologies. Hence, the model has predictive relevance for the Industry 4.0 base technology adoption but not for front-end manufacturing technologies. The f² statistic values show that technology orientation has an acceptable level of effect size on Industry 4.0 base technologies with f² = 0.109, but a very weak effect size on Industry 4.0 front-end manufacturing technologies with f² = 0.021. All path coefficients were tested by controlling for the number of employees, firm age and annual revenue, where none had significant effects on Industry 4.0 front-end manufacturing and base technologies.

The study finds that customer, competitor and learning orientations have positive effects on technology orientation, with roughly equal effect levels indicating that they interact harmoniously. The direct effects of competitor, customer and learning orientations on Industry 4.0 technology adoption were not empirically confirmed. This finding aligns with the TOE framework (Tornatzky and Fleischer, 1990), which states that technological, organisational and environmental factors influence technology and innovation in firms. More specifically, it deviates from the conventional approach to the TOE framework that treats TOE elements as independent and contributes to the literature that recognises the interdependencies among the elements (Sun et al., 2024) by empirically confirming the effects of competitor, customer and learning orientations on technology orientation for Industry 4.0 technology adoption.

The study did not find empirical evidence for the direct effects of customer, competitor and learning orientation on technology adoption for both base technology and manufacturing front-end technology adoptions. Similar to our findings, some past studies have reasoned for the lack of empirical evidence of the direct effects of TOE strategic elements on technology adoption. For example, Persaud et al. (2021) argue that industry competition is more important than firm-level competition in their technology and innovation performance in investigating 18 Canadian industries. Nugroho et al. (2022) argue that market orientation could be more influential on e-commerce or e-business capabilities instead of information technology adoption capabilities, as they found no empirical evidence of market orientation on the latter. Mustafa et al. (2023) argue that market pressures moderate the effects of internal factors on Industry 4.0 adoption for sustainability. Wang et al. (2024) argue that firm-level network structures are important in learning orientation and the differential effects of network types and structures in facilitating the commitment to adopt digital technologies.

The findings reveal that the presence of learning orientation from the organisation perspective (O element) and customer orientation and competitor pressures from the market environment perspectives and (E elements), without the technology perspectives (T element), is likely to not succeed in Industry 4.0 technology adoption in manufacturing firms. This study thus confirms the importance of technology strategy orientation for Industry 4.0 technology adoption in a manufacturing setting by confirming statistically significant and positive effects on Industry 4.0 base technology and Industry 4.0 front-end manufacturing technology adoption rates. These results align with previous studies that identified the lack of digital strategy as a key barrier to Industry 4.0 adoption (Raj et al., 2020; Guo, 2025). They extend the argument of Tortorella et al. (2022) on the importance of manufacturing strategy for Industry 4.0 adoption, which combines with an important technology strategy orientation. Weerabahu et al. (2024) also argued for the importance of having a digital strategy as an influential factor reducing barriers to digital servitisation in manufacturing forms, where this study shows the critical role technology orientation plays in technology adoption in manufacturing firms.

The findings of this study report that the effects of strategic orientations differ between base and manufacturing front-end technologies. Investigating different technologies, technology levels and categories has been limited in the literature, with a few exceptions reported recently. Rikalovic et al. (2021) investigated the challenges faced in implementing different Industry 4.0 technology categories in predicting, controlling, maintaining and integrating manufacturing processes and reported different challenges in different technological categories. Different layers of Industry 4.0 technologies were studied in the manufacturing context by Frank et al. (2019), where base technologies and front-end technologies were introduced, and their different implementation levels were empirically investigated. Benitez et al. (2025) investigated the effects of Industry 4.0 technologies on lean practices and reported different effects of base and front-end technologies. Teruel et al. (2025) investigated the effects of digital technologies, considering base and front-end technologies as distinct variables and reported their different effects on firm growth in the manufacturing, services, construction and infrastructure sectors. Our findings contribute to this stream of Industry 4.0 literature that identifies base and front-end technology as different types by reporting the empirically validated differential effects of strategic orientations on the two distinct technology types in the manufacturing sector.

This study adopted the approach to test Industry 4.0 base technologies and manufacturing front-end technologies as distinct constructs and showed the importance of not combining them in testing specific effects on them. The research model testing results show a very low value for R² for the manufacturing front-end technology adoption variable, identifying that only 5.5% of the variation of the variable is explained by the model. However, the model explains 49.8% of the base technology adoption variable, where Industry 4.0 base technologies provide connectivity, computing infrastructure and data-driven intelligence for implementing front-end technologies (Čater et al., 2021). This finding confirms the utility of the proposed categorisation of Industry 4.0 technologies to base and manufacturing front-end technologies for manufacturing firms (Hopkins, 2021; Frank et al., 2019).

The effects of technology orientation on base technologies are significant and higher than on manufacturing front-end technologies. Hence, this study shows that even though the technological orientation of the firm seems necessary, other factors beyond the strategic orientations of firms influence the adoption of manufacturing front-end technologies categorised as automation and smart factory technologies, including artificial intelligence, additive manufacturing, autonomous lines, M2M communication, robots and virtual commissioning, etc. A research model only with strategy orientations is not adequate for validating the determinants of manufacturing front-end technology adoption in manufacturing firms; there are other factors such as the availability of capital and resources, supply chain and operations readiness and dynamics, workforce readiness and their profile as well as social capital (Raj et al., 2020; Aquino et al., 2023; Kim and Dey, 2016; Martinsuo and Luomaranta, 2018; Agostini and Nosella, 2020) for advanced manufacturing technology investments.

The differential effects of strategic orientations on base and front-end technologies can be understood by considering the distinct challenges associated with their adoption and implementation. For base technologies, most barriers stem from external environmental factors that often require macro-level responses. For instance, cloud computing adoption is reportedly challenged by the lack of regulations and standards, as well as concerns regarding loss of data control and low reliability and provider-level service quality issues (Al-Hujran et al., 2019). Big data analytics adoption faces challenges around heterogenous data types, including acquisition, reliability, scalability of storage and data mining capabilities (Dai et al., 2020). Similarly, IIoT adoption is constrained by integration issues between information and operations technologies and connectivity limitations (Kumar and Iyer 2019). There are shared concerns related to data privacy and security across cloud computing, data analytics and IIoT, further hindering their adoption.

In contrast, the adoption and implementation of front-end technologies are primarily driven by technology-centric and internal organisational factors. Automation technologies such as ERP, MES and AM face challenges including excessive customisation requirements, change management issues, integration with legacy systems, reliance on internal IT infrastructure, poor internal data quality, limited managerial capabilities, high implementation costs, cultural resistance to change and lack of technical skills and knowledge (Momoh et al., 2010; Titu and Stan, 2024; Martinsuo and Luomaranta, 2018). Supply chain readiness and material quality have been identified as external challenges for AM (Martinsuo and Luomaranta, 2018). Similarly, smart factory technologies face adoption barriers specific to their application. For example, preventive maintenance using AI is constrained by interoperability problems, data consolidation and quality issues, and broader strategic challenges (Sakthi et al., 2025). Human–robot collaboration technologies face challenges related to the issues associated with resource utilisation, robot functionality, multilingual capabilities and the effectiveness of human–machine communication (Inkulu et al., 2022).

This study empirically validates the effects of strategic orientations on Industry 4.0 technology adoption in manufacturing firms. Given that the study found no significant direct effects of customer, competitor and learning orientations on Industry 4.0 technology adoption, it argues that the focus on market dynamics, including customers and competitors, as well as internal learning capabilities, will require interactions with technology strategy for Industry 4.0 technology adoption. Hence, the findings show the importance of the technology orientation of firms for adoption of Industry 4.0 base and manufacturing front-end technologies. It further shows that the determinants of manufacturing front-end technologies extend beyond strategic orientations of firms.

The theoretical contribution of this study to Industry 4.0 literature is bifold. First, this study introduced base and manufacturing front-end technologies as independent constructs, deviating from the common practice of combining all Industry 4.0 technologies under the broad construct of Industry 4.0 technologies as seen in previous studies (Agostini and Nosella, 2020; Tortorella et al., 2022). This paves the way for future studies to investigate the antecedents, barriers and implications by considering these two types of Industry 4.0 technologies as distinct constructs. It provides empirical evidence for the differential effects of firm technological orientations on base and manufacturing front-end technologies, confirming the utility of distinct constructs. Hence, the heterogeneity of Industry 4.0 technologies needs to be recognised, and the front-end manufacturing technology requirements are important to be considered in research studies (Hopkins, 2021; Frank et al., 2019). Secondly, it deviates from the conventional approach of testing the effects of independent TOE elements (Chittipaka et al., 2023; Hubenova et al., 2024) to interdependent relationships among TOE elements (Schulze et al., 2022; Gangwani and Bhatia, 2024; Sun et al., 2024; Faiz et al., 2024), showing the importance of the technology strategy and confirming the interdependencies of TOE elements on technology adoption, extending the utility of the TOE framework in technology adoption studies.

The practitioners in manufacturing firms are informed of the importance of technology orientation, meaning technological innovation capabilities, acceptance and the drive towards integrating advanced technologies for superior products are important strategic capabilities influencing Industry 4.0 adoption. It further emphasises the importance of considering the interdependencies among strategic orientations for technology adoption. In particular, the role of customer, competitor and learning orientations in positively influencing technology orientation, thereby base technology adoption. Practitioners are guided towards the need to account for such interdependencies and the importance of distributed investments across strategic capabilities for technology adoption. In addition, the different effects of technology orientation on Industry 4.0 base and front-end manufacturing technologies were evident in the study. Hence, it is important for practitioners to understand that the adoption of Industry 4.0 base technologies and front-end technologies has different antecedents. Various other factors beyond strategic orientations influence the adoption of front-end technologies in Industry 4.0 relative to base technologies. This indicates that conditions that enable Industry 4.0 base technology adoption do not necessarily enable front-end technology adoption at the same rate of influence. Therefore, practitioners need to tailor their technology adoption strategies with the recognition that organisational, technological and environmental enablers of manufacturing front-end technologies differ from those driving base-level technology adoption.

The limitations of the study are known. Even though the study found empirical evidence for the relationships between strategic orientations and Industry 4.0 adoption, it did not explore why the firms' focus on customers, competitors and learning does not directly influence Industry 4.0 technology adoption and how those strategic orientations lead to technology orientation. In addition, the data set is limited to manufacturing firms in Australia, so extended research in other geographies could empirically confirm the generalisability of the findings. reported in this study. While a few other previous studies reported various configurations of internally and externally oriented strategic orientations (Banerjee and Ma, 2012; Sun et al., 2024), our knowledge of why market and supply factors have limited direct influence on technology adoption is still limited. It is also important to note the contingencies of characteristics specific to firms and sectors, such as the target markets of firms (business-to-customer or business-to-business modes) and the degree of competition in the industry (e.g. high competition in the fashion industry relative to the wood and paper products industry). Further research could investigate different technology development trajectories of manufacturing firms taken to extend their Industry 4.0 technologies from base technologies to more advanced manufacturing technologies and how base and front-end technologies interact in the digital transformation of manufacturing firms.