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This study developed a theoretical model to test the relationship between digital capability and Industry 4.0 (I4.0) and its effect on innovation performance in small and medium-sized enterprises (SMEs).
Design/methodology/approach
The proposed theoretical model was evaluated using partial least-squares structural equation modeling and fuzzy-set qualitative comparative analysis. The data were obtained from a sample of 536 SMEs in Chile.
Findings
The proposed model presented two dimensions of digital capability: management and information and communication technologies (ICTs). Management models composed of enterprise resource planning and customer relationship management systems are essential for optimizing organizational management. Meanwhile, ICTs facilitate the smooth flow of information within an organization, leading to improved efficiency in production processes. I4.0 is encouraged by exposing SMEs to base technologies such as data analytics. These results confirm that I4.0 influences innovation performance.
Practical implications
SME managers should encourage the development of digital capabilities to transition toward I4.0, as this can make SMEs more competitive and innovative in changing and dynamic scenarios.
Social implications
I4.0 adoption and the development of digital capabilities can directly affect employment and national economic growth.
Originality/value
Most studies focus on the organizational factors affecting SMEs’ I4.0 adoption. They do not, however, address the role played by current digital capability in I4.0 technology adoption and its effect on firms’ innovation performance.
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Industries worldwide are undergoing significant transformations owing to the advent of Industry 4.0 (I4.0). I4.0 integrates the latest digital technologies and ensures their interoperability, enabling enterprises to transmit real-time information about their behavior and performance. These technologies facilitate significant improvements in productivity, efficiency, and innovation (Ghobakhloo et al., 2021; Lemstra and de Mesquita, 2023; Proksch et al., 2024). I4.0 technologies represent a crucial shift for organizations seeking to enhance their innovative capacities and compete in increasingly digitized markets.
Small and medium-sized enterprises (SMEs) are considered engines of innovation, growth, and job creation in national economies (Chatterjee et al., 2022; Ibarra-Morales et al., 2020; Verdolini et al., 2018). SMEs can develop new products, organizational forms, and business models (Meijer et al., 2019). Given the importance of SMEs, governments have established public policies that promote their development (Choi and Lim, 2017; Pincheira Varas and Mata, 2023). However, these firms are more vulnerable than large and multinational firms to constantly changing technological environments, which often requires them to adopt I4.0 technologies to remain competitive (Ng et al., 2020; Ur Rahman et al., 2020).
In developing economies, SMEs face additional barriers to adopting these new technologies because of insufficient skills and networks, communication difficulties, limited access to adequate technologies, and limited funding (Hernandez-Pardo et al., 2012; Singh et al., 2010). Given such barriers, adopting I4.0 technologies can offer competitive advantage, enhance other business capabilities, and enable value creation (Gaviria-Marin et al., 2021). Firms implementing digital transformation have reported higher net margins and better performance than the industry average (Woerner et al., 2022). Understanding the factors that influence this adoption process, especially in developing economies, is crucial for improving SMEs’ innovation performance and contributing to the theoretical development of digital capabilities and sociotechnical systems (Teece et al., 1997; Bednar and Welch, 2020).
This study aimed to grasp the relationships between SMEs’ digital capabilities, I4.0 technology adoption, and SMEs’ innovation performance, providing actionable recommendations to improve SMEs’ competitiveness in the context of digital transformation (Singh et al., 2023). We aimed to address the following research questions: (1) Do digital capabilities (information and communication technologies [ICTs] and digital management capabilities) influence the adoption of I4.0 base technologies in SMEs? (2) Does I4.0 base technology adoption influence the adoption of more advanced (front-end) I4.0 technologies? (3) Does adopting I4.0 technologies (base and front-end) affect SMEs’ innovation performance? Based on these questions, we developed the following hypotheses: H1: Digital capabilities (ICTs and digital management capabilities) have a positive effect on I4.0 base technology adoption in SMEs. H2: I4.0 base technology adoption has a positive effect on I4.0 front-end technology adoption in SMEs. H3: The adoption of I4.0 base and front-end technologies has a positive effect on SMEs’ innovation performance.
This study used a mixed-method approach to address these research questions and test the hypotheses. First, partial least-squares structural equation modeling (PLS-SEM) was used to analyze the proposed theoretical model. PLS-SEM is suitable for modeling complex relationships between multiple dependent and independent variables, including unobservable and indirect variables. This method does not impose strict distributional assumptions and has been increasingly used and validated in the social sciences (Cepeda-Carrion et al., 2019; Hair et al., 2019, 2021; Cook and Forzani, 2023). Second, we employed fuzzy-set qualitative comparative analysis (fsQCA) as an asymmetric method to analyze the configurational model. Using this technique, we could identify which conditions are indispensable (or unnecessary) for innovation to occur and which combinations of states are more (or less) important than others (González-Martinez et al., 2023; Pappas et al., 2016). Data for this analysis were obtained from a sample of 536 SMEs in Chile. While SMEs play a vital role in Chile’s economy, they face significant challenges in adopting I4.0 technologies. This approach allowed us to model the relationships between SMEs’ digital capabilities, I4.0 adoption, and innovation performance, offering valuable insights into how digital transformation can improve competitiveness.
Latin American SMEs play a fundamental role in the region’s economic development, job creation, poverty alleviation, and inequality reduction (OECD/CAF, 2019). However, they face severe challenges owing to their low productivity levels, significantly influenced by limited managerial and technological capabilities (OECD/CAF/SELA, 2024). This situation is commonly faced by SMEs in Chile (Carrasco-Carvajal and García-Pérez-de-Lema, 2021). Promoting the digitalization of SMEs is essential for strengthening regional economic resilience and productivity (Ireta-Sanchez, 2023; OECD/CAF, 2019).
Chile stands out in the region for its commitment to digital transformation, driven by its National Digitalization Strategy, development of its infrastructure, digital skills initiatives (OECD/CAF/SELA, 2024), and support for digital trade opportunities. Such opportunities include the Digital Economy Partnership Agreement with Singapore and New Zealand, which focuses on supporting the export of digital products and services by SMEs (Ireta-Sanchez, 2023). Chilean public initiatives have a history of international recognition, as exemplified by the Startup Chile Program (StartupBlink, 2024). Therefore, studying the relationship between digital capability and I4.0 and its effect on the innovation performance of Chilean SMEs can provide insight into how this phenomenon will evolve in Latin America in the coming years.
I4.0 is a concept that refers to the effect of digital technology system innovation on manufacturing production (Marinagi et al., 2023). I4.0 and its technologies are manifested in smart factories and businesses, which improve efficiency and flexibility with increased levels and rates of production, cost reduction, better products, and improved business performance. Furthermore, I4.0 technologies enable integration between suppliers and customers, thereby developing smart value chains (Szász et al., 2020; Duman and Akdemir, 2021). I4.0 technologies and their applications offer innovation opportunities in terms of new business models, processes, and products (Moeuf et al., 2018; Szász et al., 2020; Somohano-Rodríguez et al., 2022). For example, adopting cyberphysical systems facilitates business model innovation (Reischauer, 2018). I4.0 transfers the value of innovation strategies to operational performance (Ghobakhloo et al., 2021). Innovation occurs through outbound, inbound, and coupled processes (Mubarak and Petraite, 2020), and business model innovation can result from industrial digitalization (Müller and Däschle, 2018).
I4.0 integrates the physical production and manufacturing of parts with advanced software and cybernetworks (Bajic et al., 2020). It encompasses decentralized production and supply chain design (Mohelska and Sokolova, 2018). The popularity of I4.0 in companies stems from its facilitation of production processes and the environmental protection it offers (Moktadir et al., 2018). I4.0 enhances digitalization and makes it possible to produce while consuming fewer natural resources (Cricelli and Strazzullo, 2021). Companies of all sizes can adopt these technologies to create, deliver, and capture value during their production processes (Ge et al., 2020). It is expected that I4.0 will efficiently transform production processes, leading to significant increases in productivity (Hwang and Kim, 2021). As it drives economic transformation (Chen et al., 2022), its implementation will nevertheless be challenging for governments.
Digital society technologies offer unlimited competencies that translate into competitive advantages and improvements in organizational performance (Bhardwaj et al., 2021). To remain competitive, SMEs must be prepared to develop digital competencies (Drydakis, 2022). Against this background, I4.0 presents excellent opportunities for SMEs but also poses challenges in terms of financing and development (Jayashree et al., 2021). While there is an increasing adoption of I4.0 technologies in SMEs, they face postadoption problems owing to a lack of skills in using these advanced technologies (Marrucci et al., 2023). Implementing I4.0 varies according to the type of company and industry (Ortt et al., 2020). Its adoption and sustainability rely on the company’s ICTs, management, teamwork, and external support (Jayashree et al., 2022). ICTs are excellent indicators of the capacity to modernize and compete in a globalized environment (Rivera-Trigueros and Olvera-Lobo, 2021). In the context of I4.0, adopting ICTs and digitization, and then using them to create value for consumers, suppliers, and the company itself, is regarded as a dynamic third-order capability called “digital capability” (Wang and Ahmed, 2007; DeLone et al., 2018; Heredia et al., 2022; Proksch et al., 2024).
Firms must undergo digital transformation to exploit new opportunities and adapt to I4.0 (Vuksanović et al., 2020). However, the adoption of new technologies poses various difficulties (Saridakis et al., 2018). The main obstacles for SMEs include financial constraints, weak networks, a lack of knowledge (Choi and Lim, 2017), a lack of evidence of technological benefits, and delays in transformation processes (Hwang and Kim, 2021). In particular, a lack of resources and talent for executing and managing digital transformation are among the main challenges SMEs face (Brodeur et al., 2023). This situation highlights the need to analyze I4.0 adoption from a digital capability perspective and its effects on SMEs’ innovation performance.
Over the past few decades, Chile has positioned itself as a regional leader in economic growth, creating a fertile environment for SMEs (Castillo-Vergara and García-Pérez-de-Lema, 2020). As major job creators and contributors to national production, SMEs hold significant value for Chile’s economic and social life (Carrasco-Carvajal et al., 2023). Despite the high number of SMEs in Chile, there is an asymmetrical relationship between these organizations and large companies because of their position in the supply chain (Basco and Calabrò, 2016). Moreover, I4.0 adoption remains incipient in SMEs. According to data from the Chilean Ministry of Economy (Minecon, 2024), while four out of five large companies have a website, only two out of five SMEs have one. In addition, only 22% of large companies and 13% of SMEs engage in e-commerce, either buying or selling, with sales constituting the majority of electronic transactions. E-commerce represents up to 22% of the total sales of large companies and only 6% of those of SMEs. In terms of management software, 77% of large companies use enterprise resource planning (ERP) systems compared with only 22% of SMEs, and only 21% of large companies and 5% of SMEs use customer relationship management (CRM) systems.
This study contributes to the literature in several ways. First, we developed and validated a theoretical model that explores how digital capabilities (divided into managerial and ICT capabilities) affect the adoption of core and advanced I4.0 technologies, as well as the effect on innovation performance, which has not been previously examined in SMEs. Most research investigating this type of implementation involves large organizations or multinationals (Mittal et al., 2018, 2020). Methodologically, this study employed a mixed-method approach that included quantitative analysis using PLS-SEM and qualitative analysis using fsQCA, offering a comprehensive and in-depth analysis of the investigated relationships.
The rest of this paper is organized as follows: 2. Theoretical framework and hypothesis development, 3. Method, 4. Results, 5. Discussion, and 6. Conclusions and implications.
2. Theoretical framework and hypothesis development
While new technology adoption significantly affects businesses, especially SMEs, challenges remain in I4.0 adoption. For example, an analysis of 5,706 companies in 30 European countries revealed that investment in digitalization is crucial for balancing financial performance, and this effect varies based on the size and geographic location of the company (Lastauskaite and Krusinskas, 2024). The productivity benefits derived from technological investment are particularly notable in companies with low productivity (Borowiecki et al., 2021). According to McKinsey Global Institute (2017), automation could boost global productivity growth by 0.8–1.4% annually; they also note that digital technology adoption still varies widely among companies of different sizes. Approximately 80% of large companies have adopted a broad range of advanced digital technologies. By comparison, only about 60% of medium-sized companies and 40% of small companies have implemented digital solutions to optimize their operations and improve productivity. This includes the use of ERP and CRM systems (Calvino et al., 2024; World Bank, 2024).
I4.0 and its technologies are manifested in smart factories and businesses, which achieve improved efficiency and flexibility with higher levels and rates of production, cost reductions, better products, and improved business performance. Furthermore, I4.0 technologies enable integration between suppliers and customers, thereby developing smart value chains (Szász et al., 2020; Duman and Akdemir, 2021). I4.0 is related to the Internet of Things (IoT), cyberphysical systems, ICTs, enterprise architecture, and enterprise integration (Lemstra and de Mesquita, 2023). I4.0 is divided into front-end and base technologies. Front-end technologies include intelligent manufacturing, smart products, smart supply chains, and smart work; base technologies include cloud computing and big data (Frank et al., 2019). These technologies have the potential to generate competitive advantages in both domestic and global markets (Castelo-Branco et al., 2019). The front-end technologies of I4.0 are intelligent systems used as flexible lines for almost all production processes, with real-time information provided by the base technology (Javaid et al., 2020). The base technology is expected to offer promising transformative solutions for the operations and functions of many existing industrial systems within companies (Xu et al., 2018). The key element that characterizes this new industrial stage is the profound change in the connectivity of manufacturing systems owing to the integration of base technology and front-end technology systems (Dalenogare et al., 2018).
We used two complementary theoretical approaches to analyze SMEs’ I4.0 adoption and its effect on innovation performance. These include “sociotechnical system theory,” which is related to the analysis of technology acquisition in organizations (Bednar and Welch, 2020; Sony and Naik, 2020), and a “dynamic capabilities” perspective, which facilitates the analysis of new technology adoption (Mitrega et al., 2018). Such capabilities, when described as digital technology acquisition, are called digital capabilities (Heredia et al., 2022; Proksch et al., 2024). These theoretical approaches are developed in this section. Moreover, our hypotheses are supported by specific studies, such as “Digital Capabilities and Industry 4.0,” “Industry 4.0 Technologies,” and “Industry 4.0 and Innovative Performance.” These relationships are presented in our proposed theoretical model, as shown in Figure 1.
Sociotechnical systems theory, developed in the 1950s and the 1960s, concerns the interaction between people and technology in organizations (Das and Jayaram, 2007). It aims to balance social (human-related) and technical (non-human-related) systems within organizations, achieving a common goal without prioritizing one system over the other (Whitworth, 2009). I4.0 aligns with this framework, as it requires integrating advanced digital technologies with human elements in the workplace.
Sony and Naik (2020) proposed integrating sociotechnical systems theory with horizontal, vertical, and end-to-end integration to sustain I4.0. Similarly, Yu et al. (2023) emphasized that incorporating technologies such as artificial intelligence (AI) from a sociotechnical perspective can enhance an organization’s competitive advantage while improving employee work experience. Lundgren et al. (2023) and Sony and Naik (2020) further suggested that I4.0, which requires a specialized workforce and human interaction, will remain critical.
Although relatively new, sociotechnical systems theory provides a useful framework for understanding how digital capabilities (both human and technological) influence I4.0 adoption in SMEs. SMEs in developing economies can benefit from understanding the balance between their limited technical resources and human capital when adopting advanced technologies.
The theory of dynamic capabilities (Teece et al., 1997) explains how organizations can adapt to rapidly changing environments by reconfiguring their internal competencies. This theory is closely related to the human dimension of sociotechnical system theory, as it emphasizes the role of human capital in organizational transformation. Dynamic capabilities enable firms to innovate, optimize resources, and penetrate markets by building internal competencies (Martin and Bachrach, 2018; Schilke et al., 2017).
In the context of I4.0, dynamic capabilities are relevant because they enable firms to adopt and manage new technologies. AL-Kathib et al. (2023) highlighted how dynamic capabilities—namely, sensing, seizing, and reconfiguring—positively influence I4.0. Díaz-Chao et al. (2021) noted that integrating dynamic capabilities with I4.0 technologies leads to greater economic returns and sustainability, whereas Lepore et al. (2023) emphasized the role of external collaborations in supporting I4.0 adoption.
Dynamic capabilities are even more important for SMEs given their limited resources, especially in developing economies. SMEs must leverage dynamic capabilities to identify technological opportunities (sensing), seize them, and reconfigure their operations to effectively integrate these technologies. Understanding how SMEs mobilize these dynamic capabilities, particularly in digital transformation, is crucial for explaining their ability to adopt I4.0 technologies.
I4.0 represents the convergence of operational and information technologies (Chung et al., 2022). The literature suggests that solid digital capabilities are essential for organizations to achieve successful digital transformation. Specifically, ICTs and digital management capabilities are critical for creating an integrated and interconnected system that supports adopting I4.0 technologies (Antony et al., 2023).
Companies with solid digital capabilities often have an established technological infrastructure that facilitates the incorporation of I4.0 technologies (Antony et al., 2023). Furthermore, these capabilities support the implementation of more advanced technologies such as automation and AI, ultimately improving operational efficiency and competitive advantage (Ghobakhloo and Ching, 2019). For example, firms implementing robust ERP and CRM systems can better manage resources, optimize processes, and prepare for complex digital transformations.
It is essential to include SMEs in developing countries when analyzing I4.0 because of the specific barriers they face in terms of limited infrastructure, resource scarcity, and financial constraints, which hinder advanced technology adoption (Karuppiah et al., 2023). Several studies have noted that SMEs in emerging economies such as Chile face similar challenges that limit their ability to implement I4.0 technologies, requiring adaptations in technology maturity models that reflect their realities (Mittal et al., 2018). In addition, technological and human resource barriers are seen as salient factors affecting these companies. This means that the experiences of developing countries can help contextualize findings in Chile as representative of the obstacles faced by SMEs in general in this type of technological transition (Pech and Vrchota, 2020). Collaboration with external entities such as universities and technology providers allows these companies to access the resources and knowledge needed for digital transformation; this validates the relevance of including Chilean SMEs as representatives of such challenges in the analysis of I4.0 (Lepore et al., 2023).
Such capabilities are critical for SMEs, given their resource limitations and challenges in adopting advanced technologies. Developing strong digital capabilities enables SMEs to better position themselves in adopting I4.0, thereby improving efficiency and competitiveness. By developing robust digital capabilities, SMEs can facilitate the incorporation of I4.0 base technologies. Furthermore, these capabilities support the implementation of more advanced technologies such as automation and AI, ultimately improving operational efficiency and competitive advantage (Ghobakhloo and Ching, 2019).
In this context, dynamic capabilities theory is particularly important for SMEs in developing economies. These firms face greater resource constraints and must rely on their ability to sense, seize, and reconfigure resources to adopt new technologies. The dynamic capabilities framework helps explain how SMEs can leverage their existing digital capabilities to transition to I4.0 technologies and improve their innovation performance.
In developing economies, many SMEs face significant barriers to digital transformation because of financial constraints and limited technological infrastructure (Hernandez-Pardo et al., 2012). However, SMEs with solid digital capabilities (ICTs), such as digital platforms, big data analytics, simulation tools, and communication systems, are better positioned to adopt I4.0. These ICT capabilities provide the foundational infrastructure for SMEs to digitize their operations and incorporate advanced technologies (Jayashree et al., 2022). For example, adopting ERP and CRM systems helps SMEs optimize their processes and improve data management (Ghobakhloo and Ching, 2019), facilitating their initial steps toward I4.0 integration. Considering this, we propose the following hypothesis:
H1a. Digital capabilities (ICTs) positively affect I4.0 base technology adoption.
In addition to ICT capabilities, digital management capabilities such as automation, robotics, and advanced data collection technologies are critical for the successful adoption of I4.0 base technologies. These capabilities allow SMEs to integrate software and hardware solutions to automate processes, collect data, and improve decision-making (Somohano-Rodríguez et al., 2022). In developing economies, SMEs with more robust digital management capabilities are more likely to overcome the common barriers to advanced technology adoption and incorporate I4.0 technologies into their operations (Kee et al., 2023). Given the limited technological infrastructure of many SMEs, the ability to manage and deploy these advanced technologies is key to achieving digital transformation. Based on this background, we formulate the following hypothesis:
H1b. Digital management capabilities positively affect I4.0 base technology adoption.
I4.0 represents a fundamental shift in how manufacturing activities and product delivery are conducted. Frank et al. (2019) classified such technologies into two layers: “front-end technologies” and “base technologies.” Front-end technologies focus on transforming manufacturing activities and enhancing product delivery via real-time monitoring, automation, and advanced robotics. By contrast, base technologies provide the foundational connectivity and intelligence required to support front-end systems. Key examples of base technologies include cloud computing and data analytics (big data), which drive information processing by enabling greater data integration and utilization (Battistoni et al., 2023).
Firms require a scalable and flexible infrastructure to adopt I4.0, particularly those that are supported by cloud computing. Cloud computing offers adjustable resources that can be scaled up or down depending on the organization’s needs, making it essential for implementing I4.0 (Zahoor and Al-Tabbaa, 2020). Moreover, cloud computing facilitates the integration of front-end technologies by providing the necessary computational power to monitor and optimize manufacturing processes in real time (Rodríguez-Espíndola et al., 2022). Cloud capabilities also support remote access to resources and data, which are crucial for effectively deploying advanced technologies such as AI and robotics (Li and Zhang, 2021).
The digitization of manufacturing processes generates large volumes of data, requiring sophisticated tools to process and extract valuable insights. Big data analytics allows businesses to manage and make sense of vast datasets in terms of volume, variety, value, velocity, and veracity (Li and Zhang, 2021). Base technologies, such as cloud computing and big data analytics, facilitate the connection and interaction of front-end technologies, creating a fully integrated manufacturing system that allows for more efficient production, enhanced supply chain management, and the development of new products and processes (Mappadang and Yuliansyah, 2021). These improvements ultimately lead to better decision-making and enhanced operational performance (Dalmarco and Barros, 2018; Tsang et al., 2022).
Given the crucial role of base technologies in supporting and enabling the implementation of front-end technologies, we propose the following hypothesis:
H2. I4.0 base technologies positively affect adopting I4.0 front-end technologies.
I4.0, which not only enhances production efficiency but also plays a crucial role in driving innovation, is essential for the survival and performance of organizations in competitive markets (Gallo et al., 2021; Brand et al., 2021). Innovation involves navigating uncertainty and complex interdependencies and requires companies to improve their information processing capabilities and foster internal and external cooperation. I4.0 technologies are vital for addressing these challenges, particularly in highly dynamic environments, where rapid adaptability is crucial (Somohano-Rodríguez et al., 2022; Kumar and Bhatia, 2021).
One way I4.0 technologies support innovation is through their ability to generate, process, and analyze vast amounts of data. Implementing base technologies such as big data and cloud computing enhances a company’s ability to collect, integrate, and utilize diverse data sources at unprecedented speeds (Niebel et al., 2019). These base technologies serve as the foundation that supports connectivity across various activities in the value chain, improves coordination, and facilitates better decision-making processes through real-time data processing, simulations, and decentralized operations (Sullivan et al., 2023). By enhancing a firm’s capacity to process information, base technologies enable the development of new ideas and innovative products, services, and business models (Ibarra et al., 2018; Sarbu, 2020; Somohano-Rodríguez et al., 2022; Yousaf et al., 2021).
The role of I4.0 base technologies in driving innovation can be observed through their capacity to improve information processing and operational flexibility. Base technologies such as cloud computing and big data analytics provide firms with the infrastructure to gather, store, and analyze large datasets from across the organization and its environment. This ability allows firms to identify trends, optimize processes, and quickly make informed decisions, which is essential for fostering innovation (Frank et al., 2019; Li and Zhang, 2021). Empirical evidence from studies such as Niebel et al. (2019) indicates that companies leveraging big data are more likely to innovate and exhibit higher innovation intensity than those not using big data. By increasing the availability of relevant data and the efficiency with which they are processed, base technologies enhance a firm’s ability to innovate, making them critical enablers of innovation performance.
H3a. I4.0 base technologies positively affect innovation performance.
In addition to base technologies, front-end technologies, such as automation, advanced robotics, and IoT, further enhance innovation performance by facilitating the seamless integration of digital processes throughout the organization. These technologies connect various value chain components, allowing real-time data exchange and decision-making, ultimately improving operational efficiency and responsiveness to market changes (Sullivan et al., 2023). For example, IoT applications allow firms to sense, seize, and reconfigure resources in response to new opportunities or challenges, thereby enhancing their innovation capacity (Sullivan et al., 2023).
Front-end technologies such as IoT, advanced robotics, and automation significantly enhance a firm’s innovation performance by creating a highly responsive and flexible operational environment. Through IoT-enabled systems, firms can more effectively sense customer needs, technological possibilities, and market shifts, enabling them to more rapidly innovate. For example, IoT facilitates the real-time monitoring and adaptation of production processes, whereas advanced robotics and automation streamline operations and reduce time to market for new products and services (Sullivan et al., 2023; Atif et al., 2021). Integrating front-end and base technologies creates a feedback loop that continuously improves a firm’s innovation capacity. Empirical studies such as Sarbu (2020) have further validated that firms adopting I4.0, particularly in the service sector, experience higher product innovation intensity than traditional firms.
Research on SMEs’ I4.0 adoption in developing countries has revealed that Chile represents a broader phenomenon, reflecting the conditions under which these firms move toward digitization despite structural and economic constraints (Karuppiah et al., 2023). Experiences from India and other developing countries suggest that, with the right support in terms of policy and training, SMEs can overcome initial barriers to technology implementation and benefit from incremental growth in infrastructure and organizational skills (Pech and Vrchota, 2020). The proposal of a “level 0” in I4.0 maturity models specifically addresses this gradualism, helping to capture the progressive process of technological adoption in countries with conditions similar to those of Chile (Mittal et al., 2018). Additionally, studies such as Lepore et al. (2023) have underscored the importance of incoming open innovation for SMEs in countries with limited resources, such as Chile, to access advanced technologies, consolidating the argument that Chilean data reliably represent the I4.0 adoption phenomenon in developing countries.
Such technologies enable firms to respond rapidly to market changes and develop new products and services, thereby enhancing their overall innovation performance.
The theoretical model shown in Figure 1 outlines the constructs and their hypothesized relationships.
3. Method
3.1 Sample and data collection
We invited 12,000 firms randomly selected from the Chilean SME directory to participate in the study (https://basededatoschile.cl/). Of these companies, 2,233 (19%) started the survey process and 536 (4%) completed it. The sample comprised 536 Chilean SMEs, each with headquarters in the country’s metropolitan region and employing 10–250 individuals, selected following the Oslo guideline set forth by the European Commission (OECD/Eurostat, 2018). The data were gathered via an online questionnaire completed by individuals in key positions in the company (e.g. chief executive officer, department manager, deputy manager) or other knowledgeable people designated by the company. To minimize heterogeneity and selection bias, we adopted a stratified random sampling approach across different sectors (Bird and Wennberg, 2016). Fieldwork was conducted from November 2022 to January 2023. A summary of the sample is presented in Table 1. The questionnaire was designed to minimize social desirability bias by avoiding terms related to success, emphasizing that they were not right or wrong, and ensuring data anonymity and confidentiality (Sexton, 2022). The process was developed based on the Act of the Ethics Committee for Project 11,220,339, dated May 9, 2022, with which the principal investigator was affiliated.
3.2 Measures
A seven-point Likert scale was used in the study, where 1 indicated “Never,” and 7 meant “Very often.” The Likert scale shows symmetry over an intermediate category; therefore, it can be approached as an interval-level measure and used in PLS-SEM (Astrachan et al., 2014). Regarding the constructs of technologies and I4.0, the questions concerned the degree of application of various technologies. An organization’s achievement level compared with that of its competitors was established for performance. Table 2 presents the indicators and their respective factor weights. These scales have been previously used in the context of Spanish, Brazilian, and Danish SMEs. The constructs were tested as mode A compounds. The survey translation process followed several steps to ensure accuracy and proper understanding of the items. A researcher translated the original items into Spanish. Then, two other researchers reviewed the translations and adjusted the items as needed for clarity and understanding. This process ensured the coherence and quality of the translation. After the final translation was completed, a pilot test was conducted with five entrepreneurs to assess the clarity and relevance of the items in the specific context of this study. We used the feedback and suggestions received during the pilot test to make additional adjustments to the survey, thus ensuring its suitability for the target population and research objectives.
3.3 Data analysis
We used PLS-SEM to analyze the proposed theoretical model using the data from our sample of SMEs. This allowed us to simultaneously model and estimate complex relationships among multiple dependent and independent variables, including unobservable and measured indirect variables, without imposing assumed distributional values; this approach is increasingly used and validated in the social sciences (Baier-Fuentes et al., 2023; Cepeda-Carrion et al., 2019; Hair et al., 2019, 2021; Cook and Forzani, 2023). The SmartPLS package (v.4.0.8.5) was used to test our research model (Ringle et al., 2022).
The measurement model (outer model) evaluated the observed variables and constructs (latent variables) while the structural model (inner model) evaluated the relationships between the latent variables (hypotheses) (Hair et al., 2019). Both models were evaluated using the same statistical tests. Internal consistency signals construct reliability while convergent validity indicates which indicators represent an underlying construct (Hair et al., 2016). For the measurement model, we selected items for each construct with factor loadings above 0.700. Regarding the composite reliability of the constructs, we accepted Cronbach’s alpha, rhoA, and rhoC values greater than 0.700. For convergent validity, AVE ≥0.500. Discriminant validity was evaluated with the heterotrait–monotrait ratio (HTMT) statistic ≤0.9. Regarding multicollinearity, “collinearity statistics” (variance inflation factor) were ≤3.0 for all variables. The model fit indicator was SRMR ≤0.08 (0.10), and the exact fit test was based on bootstrap d_ULS ≤ HI95 ≤ HI99 and d_G d_ULS ≤ HI95 ≤ HI99 (Hair et al., 2019, 2021; Cook and Forzani, 2023).
PLS-SEM was applied in a series of steps. First, we fit the structural model using a bootstrap procedure (10,000 subsamples), followed by bootstrap-based exact fit tests on the estimated model. Second, the measurement model was evaluated by analyzing the saturated model. Finally, a structural model was evaluated (Cepeda-Carrion et al., 2019; Fassott et al., 2016).
We followed the established guidelines for applying fsQCA (Pappas et al., 2016; Pappas and Woodside, 2021). First, the original values of the seven-point Likert scale were recalibrated; a value of 1 was translated into a fuzzy value of 0. By contrast, values of 4 and 7 were calibrated as 0.5 and 1, respectively.
Once all the variables were calibrated, the fuzzy-set algorithm was executed, and a truth table was generated. The truth table was evaluated based on the frequency and consistency values. Frequency indicates the number of observations for each possible combination; in samples greater than 150, a minimum threshold of three observations is suggested. Meanwhile, consistency measures the degree to which the cases correspond to the theoretical relationships expressed in a solution, establishing a threshold of 0.75. Cases with PRI consistency scores below 0.5 were considered inconsistent and were therefore not included in the final solutions.
4. Results
Regarding company size, 89% of the companies in the dataset had less than 50 employees, and 11% had more than 50 employees. Regarding company age, 2% were under two years old, 24% were between two and five years old, 23% were between six and 10 years old, and the rest were older than 10 years. Regarding CEO age, CEOs were under 50 in 40% of the companies and over 50 in the remaining 60%.
4.1 Measurement model (outer model)
The model met all established requirements. The SRMR value of the saturated model was 0.07, below the value of 0.08 recommended in the literature, providing empirical evidence for operationalized constructs (Henseler et al., 2016). The model met the construct reliability requirement, as the Dijkstra–Henseler indicator (ρ), Cronbach’s alpha coefficient, and composite reliability all exceeded the minimum values (Table 3). The AVE values were above the 0.5 threshold, achieving convergent validity. Finally, all variables achieved discriminant validity, as the Fornell–Larcker criterion was satisfied and the bootstrap-based confidence interval for the HTMT value (Table 3) met the threshold value.
4.2 Structural model (inner model)
Table 4 gives the algebraic sign evaluation, path coefficient magnitude, one-tailed Student’s t-value with −1 degrees of freedom, p-value, and confidence intervals to evaluate the statistical significance of the path coefficients, along with the f2 values. This analysis was performed by bootstrapping 10,000 samples. In addition, the table presents the explained variance (R2) for each dependent variable. The model tested several hypotheses related to digital capabilities and innovation performance in the context of I4.0. The results support H1a, indicating that digital capabilities (ICTs) have a positive and significant influence on I4.0 base technologies (β = 0.443, t = 8.597, p < 0.001), with an effect size f2 of 0.165, suggesting a moderate influence. Similarly, H2 was supported, demonstrating a strong relationship between I4.0 base and front-end technologies (β = 0.546, t = 14.344, p < 0.001), with an effect size f2 of 0.425. Regarding innovation performance, both base (H3a: β = 0.218, t = 3.137, p = 0.001) and front-end (H3b: β = 0.144, t = 2.076, p = 0.019) technologies exhibited significant positive effects. Overall, the dimensional models accounted for about 27% of the variance in I4.0 base technologies, 29.7% in front-end technologies, and 10% in innovation performance, reflecting the relevance and effect of technological and managerial factors in I4.0. The variance explained for I4.0 base technologies was 23%, explained by the contribution of digital capabilities and 4% by digital management capabilities. Regarding innovation performance, 60% of the contribution came from base technologies, and the remaining 40% came from front-end technologies.
In addition to direct effects, we assessed the indirect effects of digital management capabilities and ICTs via I4.0 base and front-end technologies on innovation performance. Although the indirect effect of digital management capabilities via base technologies on innovation performance did not reach statistical significance (β = 0.025, t = 1.543, p = 0.061), other indirect pathways showed positive and significant results. Specifically, the effect of digital capabilities (ICTs) via both base and front-end I4.0 technologies on innovation performance was significant (β = 0.097, t = 2.843, p = 0.002), as was the pathway including all interactions from digital capabilities to innovation performance (β = 0.035, t = 1.919, p = 0.027). Figure 2 shows the results of the structural model.
We analyzed endogeneity bias following Hult et al. (2018) and included instrumental variables (control variables) in our model toward the dependent variable (innovation performance). Four control variables were used to measure the potential for endogeneity: firm operating years, industry, firm size, and manager age. All path coefficient estimates were near zero and insignificant after running the bootstrap procedure with 10,000 replications.
Table 5 presents the results in terms of predictive power. The obtained R2 values were acceptable. For the out-of-sample indicators, the PLSpredict procedure was run with 17 folds and 10 repetitions (Shmueli et al., 2019). First, we checked that all indicator values were positive (PLSpredict MV). We completed the evaluation by examining the CVPAT results for the target constructs following Capeau et al. (2024). Overall, the analysis showed that the LM model offered a lower loss and better predictive fit for most constructs, especially for front-end technologies and innovation performance, where the linear model presented significant differences in loss compared with the PLS model. Although the PLS model was valid, these results suggest that the LM model provided a slightly higher overall predictive capacity for this specific case.
4.3 fsQCA solutions
Table 6 presents an intermediate solution with sufficient configuration and acceptable consistency (>0.8) and coverage (>0.2) to achieve innovation performance. The results indicated an overall solution coverage of 0.842, suggesting that the four solutions covered a substantial percentage of the results.
5. Discussion
Our measurement model and structural model analysis results provided robust evidence for statistically significant relationships among the variables (i.e. digital capabilities [ICTs], management digital capabilities, I4.0 base technologies, and I4.0 front-end technologies) and their effects on the innovation performance of Chilean SMEs. The reliability and convergent validity coefficients of the latent variables suggest that our constructs are reliable and valid for measuring the theoretical dimensions they represent. Importantly, we also addressed potential endogeneity biases by including instrumental variables as control variables. The results indicated no significant endogeneity bias in the model, thus strengthening the internal validity of our findings. In terms of methods, this study contributes to strengthening quantitative approaches to analyzing digital capabilities, I4.0, and innovation performance. We presented and validated scales to measure the aforementioned variables, particularly in the context of emerging economies such as Chile. We also showed that PLS-SEM allows for analyzing multiple complex relationships, such as those investigated in this study. Moreover, PLS-SEM can be a methodological strategy when working with developing topics since it does not require imposing assumed distributional values and can include both unobserved and directly observed variables (Hair et al., 2019, 2021; Cook and Forzani, 2023).
Our proposed analytical model highlights the importance of two critical digital capability dimensions: management and ICTs. In management, ERP and CRM systems have emerged as fundamental pillars for optimizing organizational management. These systems significantly reduce costs, improve interorganizational communication, and enhance worker efficiency and decision-making effectiveness (Brixner et al., 2020). This improved efficiency is crucial for I4.0, where the seamless integration of physical and cybernetic manufacturing components is essential (Bajic et al., 2020). Meanwhile, ICTs play a vital role in ensuring the efficient circulation of information within organizations, resulting in more agile and effective production processes (Edquist et al., 2021). This synergy between management and ICTs facilitates I4.0 adoption, which is instrumental for organizational transformation toward this new paradigm, providing empirical support for H1a and H1b.
Beyond technological implementation, our findings underscore the relevance of technological support and organizational management in the adoption of advanced systems (Skafi et al., 2020). This intersection between management and technology suggests that a successful transition to digital work environments and I4.0 goes beyond the mere acquisition of new technologies (Sievers et al., 2021). This entails carefully integrating these technologies with innovative and adaptive management practices, which may involve incorporating additional systems to maximize operational efficiency and competitiveness in the market.
Furthermore, we found that I4.0 base technologies positively affected I4.0 front-end technologies, suggesting that a solid technological infrastructure facilitates the implementation of more advanced manufacturing and smart supply chain technologies. Technological integration can enhance operational efficiency and provide companies with a solid foundation for product and process innovation. The adoption of I4.0 base technologies, such as big data, plays a transformative role in SMEs, acting as a catalyst for organizational agility and technological innovation (Khayer et al., 2021). Such technologies are fundamental for incorporating new technologies into an organization, offering a scalable and flexible infrastructure that adapts to changing business needs (Ponomaryova and Sergy, 2021). Such flexibility is essential for effectively implementing front-end technologies, optimizing processes through efficient monitoring, and providing remote access to critical resources and data. In addition, base technologies reduce the complexity of information and technology systems through a range of self-managed technologies available in the virtual infrastructure, simplifying the adoption and integration of new solutions within existing operations (Arvanitis and Loukis, 2020).
Meanwhile, the rise of digitization, driven by these base technologies, generates large volumes of data, which, through big data analytics, enable companies to efficiently process and extract valuable insights (Battistoni et al., 2023). This ability to handle and analyze vast datasets enhances decision-making and innovation, creating opportunities to transform production processes by integrating I4.0. The interconnection of base technologies with front-end technologies facilitates a fully integrated manufacturing system, thereby enhancing manufacturing processes, supply chains, and the development of new products and processes (Mappadang and Yuliansyah, 2021). These findings support H2, emphasizing the positive influence of I4.0 base technologies on the adoption and effectiveness of front-end technologies, underscoring the crucial role of technological support and organizational management in this transformation process.
These results underscore the transformative role of I4.0 technologies in fostering innovation among SMEs. These technologies not only enable decision-makers to materialize innovative ideas but also establish a bridge toward the effective execution of these ideas into tangible products and services (Sullivan et al., 2023). The mediating influence of front-end technologies is strong as they enhance the effect of base technologies on innovation performance. This synergy between base- and front-end technologies creates an ecosystem conducive to business model reinvention and agile adaptation to unpredictable markets and dynamic environments (Tamvada et al., 2022).
Integrating I4.0 technologies in SMEs enhances data processing capacity and speed while increasing the diversity of available data sources. This wealth of information facilitates internal and external collaboration among companies, which is fundamental to value co-creation and innovation (Müller et al., 2018). Furthermore, the environment enabled by I4.0 promotes training activities and digital skill development, which are crucial for testing and adopting new technologies and creating an organizational culture that fosters participation in and commitment to innovation (Niebel et al., 2019).
Adopting I4.0 technologies drives innovation and contributes to sustainability and resource efficiency. Implementing innovative products and intelligent services resulting from optimized processes and informed decisions positively affects energy sustainability and resource efficiency. This, in turn, enhances organizational performance, reduces costs, increases profits, and improves customer and employee loyalty through optimal recruitment processes and enriched work practices (Khayer et al., 2021).
The evidence supporting H3a and H3b reinforces the notion that I4.0 base and front-end technologies are essential catalysts for innovation performance in SMEs. These technologies not only directly enhance innovation capability but also establish conditions for a culture of continuous innovation, adaptability, and sustainability (Atif et al., 2021). Our findings showed that, although not all indirect paths reached statistical significance, the pathways in which digital capabilities (ICTs) were integrated with I4.0 technologies demonstrated a positive and significant effect on innovation performance.
Using fsQCA, we sought to understand how different combinations of digital capabilities and I4.0 technologies related to firms’ innovation performance. Unlike the traditional linear correlation approach, fsQCA identifies multiple causal paths, which is particularly useful for understanding how different configurations of resources and capabilities lead to innovative outcomes. In this case, the four configurations suggested that innovation performance does not depend on a single combination of factors but can be achieved through different paths.
Solution 1 suggests that innovation performance can be achieved by implementing I4.0 base technologies (core condition) and addressing the absence of digital management capabilities (core condition). This implies that investment in I4.0 base technologies is sufficient for some companies to enhance innovation, even without robust digital management capabilities. This approach can be useful for companies that, instead of relying on advanced digital systems such as ERP or CRM to innovate, focus on improving their processes using basic industrial technologies such as automation or data analytics. For example, a small factory can improve innovation by optimizing its production lines using sensors and real-time data without implementing complex digital management systems.
In Solution 2, innovation performance is associated with the absence of I4.0 front-end technologies (peripheral condition) and digital capabilities (core). This suggests that companies can generate innovation in some cases by prioritizing the development of general digital capabilities rather than relying on specific I4.0 front-end technologies such as advanced user interfaces or direct interaction systems. For companies associated with services and commerce with high digital capabilities, this combination seems sufficient to carry out innovations without the need for front-end technologies. The absence of these technologies does not hinder innovation performance, indicating that digital capabilities are of considerable importance to this innovative path.
For Solution 3, innovation performance is obtained by combining the absence of I4.0 front-end technologies (peripheral) and the presence of digital management capabilities (central). This configuration is interesting because it shows that innovation performance can be achieved when strong management is in place without relying on I4.0 front-end technologies. In this case, companies can focus on effectively managing their digital capabilities, which is sufficient for innovation without significant investment in front-end technologies. This can be relevant in sectors in which interaction with end users is not critical for innovation. Instead, the key factor is the management and optimization of internal digital processes.
In Solution 4, innovation performance is achieved through the presence of I4.0 base technologies (core) and digital capabilities (peripheral). This combination suggests that companies can focus on implementing I4.0 base technologies, supported by additional digital capabilities, to achieve innovation performance. Core technologies play a central role in innovation, and digital capabilities reinforce this. Companies that opt for this combination are likely to have a higher level of digital maturity and are more efficient and effective in managing innovation.
From a theoretical perspective, we contribute to discussions about the role and measurement of digital capability as a specific type of dynamic capability in I4.0 adoption, from the evolutionary perspective of digital capabilities focused on competitive advantage through innovation (Teece et al., 1997; Salvato and Vassolo, 2018) rather than behavior and best practices (Eisenhardt and Martin, 2000). We also contribute to the discussion of sociotechnical systems theory in implementing I4.0 (Sony and Naik, 2020). Specifically, this study raises questions about the training needs of SMEs’ work teams in digital technology adoption, initially and then later in more advanced I4.0. This facilitates the adoption of these technologies and raises questions regarding the effects of adopting these technologies in organizations, such as aspects related to organizational culture, leadership, communication, and remote work. In addition, this study contributes to the discussion of the determinants of innovation in firms, especially Latin American SMEs, which face a challenging environment characterized by political and economic instability, informality, and corruption. These SMEs also have low levels of innovation, clear sectoral differences, and low ICT adoption (Geldes et al., 2017a; Heredia Pérez et al., 2019; WIPO, 2023).
In Latin America, especially in Chile, SMEs play a crucial role in economic development and job creation. However, they face significant challenges, such as a lack of access to financing and the limited adoption of advanced technologies. Our findings underscore the importance of addressing these digital gaps and promoting I4.0 technology adoption in Chilean SMEs. By enhancing their digital capabilities and adopting innovative technologies, SMEs can improve their productivity, efficiency, and innovation capacity, thereby contributing to their sustainable growth and ability to compete in global markets.
6. Conclusions and implications
This study found that digital capabilities (ICTs) (e.g. use of the Web, telework, intranet, digital platforms, and simulation tools) and digital management capabilities (ERP and CRM) positively influenced I4.0 base technology adoption (big data, data intelligence) in SMEs. This implies that developing this specific type of dynamic capability facilitates I4.0 base technology adoption. In addition, adopting I4.0 base technologies positively affects the development of I4.0 front-end technologies (autonomous robots, additive manufacturing or 3D printing, augmented reality or virtual reality, AI warehouses). Moreover, base and front-end technologies are positively related to SME innovation performance.
These findings highlight the importance of front-end technology penetration in SMEs. Such technologies enhance an organization’s management capabilities and positively affect production processes. I4.0 digital transformation offers new possibilities for SMEs to digitally transform their operations, enabling them to produce services and products at competitive prices and penetrate new markets (Stentoft et al., 2021). Dynamic data-driven business models based on cloud computing replace conventional models, and the data produced are used for informed decision-making. The technologies associated with I4.0 allow SMEs to transform their production models, reduce costs, improve interorganizational communication, refine management practices, and create innovative opportunities that add value to their production processes. However, adopting these technologies requires the development of digital and technological capabilities, especially in emerging economies, whose innovation processes have specificities in terms of firms’ internal and external factors (Heredia Pérez et al., 2019; 2022).
Furthermore, I4.0—in which production processes achieve integration and connectivity through front-end technologies, is considered a stage of the Industrial Revolution. However, SMEs’ adoption of these technologies faces barriers that hinder I4.0 integration. Limited adoption capabilities arise from economic constraints and skill deficiencies, necessitating supportive government policies. Governments need to take an active role in promoting I4.0 among organizations to stimulate the economy and promote economic growth.
Studies have used sociotechnical systems theory to understand SMEs’ technological diffusion processes. For example, Oni and Papazafeiropoulou (2008) used a sociotechnical framework to examine broadband use in SMEs and identify perception gaps among the social groups involved, highlighting the importance of considering social and technical factors in the diffusion process.
SMEs in developing economies operate in highly informal contexts, where personal relationships and trust networks are fundamental to business (Geldes et al., 2017b; Heredia Pérez et al., 2019). By recognizing the importance of social aspects, sociotechnical system theory encourages interventions that leverage these networks rather than weaken them, promoting the creation of more sustainable solutions aligned with local practices and sociocultural contexts (Smith et al., 2011).
From an academic perspective, we contribute to the integration of sociotechnical system theory with I4.0 implementation in SMEs (Sony and Naik, 2020) and the development of the dynamic capabilities approach. This highlights the role of digital capabilities in the adoption of I4.0 technologies, which improves SMEs’ innovation performance (Salvato and Vassolo, 2018; Lepore et al., 2023; Sullivan et al., 2023).
This research also contributes to the literature by providing decision-makers with the conditions needed to adopt I4.0 and its effects on organizations, particularly SMEs. Specifically, we propose paths for improving innovation performance by developing digital capabilities to adopt I4.0 technologies. Additionally, for policymakers, our results highlight the need to develop policies and programs to promote I4.0 adoption and digital capabilities in SMEs given their positive effects on innovation performance.
With regard to implications, from a theoretical perspective, there is a need for more in-depth studies of this type. New research questions arise pertaining to the types of leadership that facilitate I4.0 technology adoption; the changes in organizational culture that these new technologies will generate; the effects of political, economic, and technological contexts; and the progress of ICTs in each country, especially in Latin American economies where they are less developed. Additionally, our analysis complements the systemic approach to innovation as an innovation ecosystem.
Regarding policy and business perspectives, this study highlights the need to develop policies that promote I4.0 technology adoption in SMEs. Policies should consider sectoral differences, diversity, and the experience of companies, especially in Latin America (Amaya et al., 2024; Sánchez-Báez et al., 2024). It is also necessary to promote innovation ecosystems that facilitate I4.0 technology adoption by considering the actors who facilitate, promote, and undertake the development of related systemic digital platforms. Finally, our findings have concrete implications for SMEs, for whom digital capability development and I4.0 adoption will improve competitiveness and innovation.
Construction of buildings for residential use (36)
30
6
49.97
13.87%
13.42 Years
11.11%
22.80%
72.22%
Production of alcoholic and non-alcoholic beverages (8)
8
0
15.25
26.48%
14.62 Years
25.00%
46.25%
87.50%
Prepared food processing (42)
35
7
43.90
25.25%
14.36 Years
23.81%
35.39%
78.57%
Other Manufactures (46)
40
6
31.33
23.96%
17.87 Years
21.74%
36.83%
69.56%
Food manufacturing (25)
21
4
29.72
36.94%
13.72 Years
40.00%
51.48%
84.00%
Printing and printing-related service activities (11)
11
0
16.55
23.86%
17.45 Years
45.45%
39.55%
81.81%
Installation of machinery and industrial equipment (71)
69
2
16.46
12.56%
11.94 Years
18.31%
26.82%
67.61%
Industrial Assemblies (25)
22
3
27.96
14.30%
13.68 Years
16.00%
21.84%
64.00%
Construction (12)
11
1
19.83
11.24%
10.00 Years
27.27%
22.75%
81.81%
Services (242)
Administration and accounting (10)
9
1
42.90
21.26%
15.40 Years
30.00%
47.70%
54.54%
Other services (99)
92
7
29.67
21.79%
12.82 Years
34.34%
36.45%
55.56%
Commercial services (61)
55
6
26.54
24.28%
13.91 Years
29.51%
32.91%
78.69%
Education and Training (20)
12
6
51.55
40.67%
23.05 Years
35.00%
45.05%
10.00%
Logistics services (15)
13
2
46.00
17.64%
18.40 Years
13.33%
31.53%
73.33%
HR Services (24)
12
3
37.50
35.69%
15.92 Years
29.17%
46.25%
66.67%
Services to the mining industry (13)
11
2
43.84
19.79%
19.15 Years
23.07%
32.31%
84.61%
Source(s): Authors’ own work
Indicators and composite
Composite
Indicators
Indicators
Source
Digital management capabilities
In the company, we use ERP (Enterprise Resource Planning System) technologies
0.874
Somohano-Rodríguez et al. (2022)
In the company, we use CRM (Customer Relationship Management) technologies
0.913
Digital capabilities
In the company we use telework
0.682
Somohano-Rodríguez et al. (2022)
In the company we use intranet
0.758
In the company, we use digital platforms
0.800
In the company, we use simulation tools
0.759
Industry 4.0 base technologies
In the company, we use big data – data intelligence
1.000
Frank et al. (2019), Stentoft et al. (2021)
Industry 4.0 front-end technologies
In the company, we use autonomous robots
0.795
Frank et al. (2019), Stentoft et al. (2021)
In the company, we use Additive Manufacturing or 3D Printing
0.747
In the company, we use augmented reality or virtual reality
0.829
In the company, we use artificial intelligence
0.843
Innovation performance
Ability to introduce new products and services to the market better than competitors
0.768
Castillo-Vergara (2020)
Quality of new products and services introduced
0.737
Increased sales generated by new products
0.765
Increased sales generated by modified products
0.766
Efficient delivery processes in and out of the work environment
0.766
Improved processes to save costs and time
0.781
Simplification of operations by focusing on better organizational practices
0.807
Employee motivation to be more creative
0.771
Improved employee qualification
0.817
Improved teamwork
0.798
Increased promotion opportunities for employees through innovation
0.810
Source(s): Authors’ own work
Reliability, convergent validity, and discriminant validity values
Cronbach’s
Composite reliability
HTMT
Fornell-Larcker
Composite
Alpha
Rho A
AVE
DMC
ICTs
BT
FeT
IP
DMC
ICTs
BT
FeT
IP
>0.70
>0.70
>0.70
>0.50
>0.85
Digital management capabilities (DMC)
0.749
0.764
0.888
0.798
0.893
Digital capabilities (ICTs)
0.742
0.751
0.838
0.564
0.817
0.617
0.751
Industry 4.0 base technologies (BT)
0.445
0.590
0.388
0.512
1.000
Industry 4.0 front-end technologies (FeT)
0.820
0.839
0.880
0.647
0.288
0.547
0.558
0.237
0.440
0.520
0.804
Innovation performance (IP)
0.936
0.938
0.945
0.610
0.256
0.411
0.273
0.266
0.220
0.343
0.266
0.239
0.781
Source(s): Authors’ own work
Structural model
Paths
R2 industry 4.0 base technologies obs: 0.270
R2 industry 4.0 front-end technologies obs: 0.297
R2 innovation performance obs: 0.103
Coeff
t-value
p-value
Confidence intervals
f2
Supported
H1a: Digital capabilities (ICTs) → Industry 4.0 base technologies
0.443
8.597
0.000
[0.362; 0.531]
0.165
Yes
H1b: Digital management capabilities → Industry 4.0 base technologies
0.115
2.059
0.020
[0.027; 0.211]
0.011
Yes
H2: Industry 4.0 base technologies → Industry 4.0 front-end technologies
0.546
14.344
0.000
[0.486; 0.611]
0.425
Yes
H3a: Industry 4.0 base technologies → Innovation performance
0.218
3.137
0.001
[0.116; 0.340]
0.037
Yes
H3b: Industry 4.0 front-end technologies → Innovation performance
0.144
2.076
0.019
[0.035; 0.263]
0.016
Yes
Indirect effects
Digital management capabilities → Industry 4.0 base technologies → Industry 4.0 front-end technologies
0.603
2.052
0.020
[0.015; 0.115]
Yes
Digital management capabilities → Industry 4.0 base technologies → Innovation performance
0.025
1.543
0.061
[0.005; 0.057]
No
Digital capabilities (ICTs) → Industry 4.0 base technologies → Industry 4.0 front-end technologies
0.242
6.741
0.000
[0.189; 0.307]
Yes
Digital capabilities (ICTs) → Industry 4.0 base technologies → Innovation Performance
0.097
2.843
0.002
[0.050; 0.160]
Yes
Industry 4.0 base technologies → Industry 4.0 front-end technologies → Innovation performance
0.079
2.023
0.022
[0.019; 0.147]
Yes
Digital capabilities (ICTs) → Industry 4.0 base technologies → Industry 4.0 front-end technologies → Innovation performance
0.035
1.919
0.027
[0.008; 0.068]
Yes
Digital management capabilities → Industry 4.0 base technologies → Industry 4.0 front-end technologies → Innovation performance
0.009
1.350
0.089
[0.001; 0.022]
No
Note(s):t-values in parentheses. Bootstrapping 95% confidence intervals bias-corrected in square brackets (based on n = 10,000 subsamples)
Source(s): Authors’ own work
Predictive power
R2
R2
f2 effect size
PLS predict LV scores
CVPAT LV
Composite
Adjust
BT
FeT
IP
Q2predict
PLS-SEM_RMSE
LM_RMSE
Average PLS
Average IA
Dif
t-value
p-value
Digital management capabilities (DMC)
0.011
Digital capabilities (ICTs)
0.165
Industry 4.0 base technologies (BT)
0.273
0.270
0.425
0.037
0.262
0.862
0.676
3.011
3.037
−0.026
1.266
0.206
Industry 4.0 front-end technologies (FeT)
0.298
0.297
0.016
0.154
0.926
0.673
2.398
2.285
0.113
2.243
0.025
Innovation performance (IP)
0.103
0.100
0.073
0.968
0.754
2.264
2.208
0.056
1.808
0.071
Source(s): Authors’ own work
fsQCA results
This research was funded by the Agencia Nacional de Investigación y Desarrollo: FONDECYT Iniciación (No: N 11220339; recipient MCV).
Management area: management
Ethics aspects: According to the approval record of the ethics committee of Alberto Hurtado University within the framework of project 11220339.
References
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Further reading
Batra, S. (2024), “Exploring the application of PLS-SEM in construction management research: a bibliometric and meta-analysis approach”, Engineering Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/ECAM-04-2023-0316.
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Gong, C. and Ribiere, V. (2021), “Developing a unified definition of digital transformation”, Technovation, Vol. 102, 102217, doi: 10.1016/j.technovation.2020.102217.
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