The role of digital governance in the integration of performance measurement systems uses and Industry 4.0 maturity

ABSTRACT

This study aims to investigate the effect of different types of performance measurement systems (PMSs) use (diagnostic use and interactive use) on Industry 4.0 maturity, examining whether there is a need for digital governance to facilitate the relationship between different types of PMS use (diagnostic use and interactive use) and Industry 4.0 maturity. Although the use of PMSs has been identified as beneficial in the Industry 4.0 context, relatively little research exists on the digital governance that enables firms to lead and control digital processes. The paper posits that digital governance plays an important role in mediating the relationship between PMS use and Industry 4.0 maturity. The data were gathered from 280 small- and medium-sized enterprises (SMEs), which operate in the service and manufacturing industry in Finland. The results demonstrate that different types of PMSs use cannot provide Industry 4.0 maturity alone, so there is a need for digital governance to fuel different types of PMS use, hence leading to Industry 4.0 maturity. However, diagnostic use of PMSs significantly hinders digital governance, while the interactive use of PMSs significantly drives digital governance. Finally, digital governance facilitates Industry 4.0 maturity.

KEYWORDS:

• Industry 4.0
• performance measurement
• performance measurement system
• interactive use
• diagnostic use
• digital governance

1. Introduction

Companies increasingly consider Industry 4.0 as a potential way of gaining a competitive advantage (Frank, Dalenogare, and Ayala 2019; Masood and Sonntag 2020; Tortorella et al. 2020). Industry 4.0 has transformed companies’ interactions with suppliers, stakeholders, and other supply chain members through disruptive technologies and platforms (Dalenogare et al. 2018; Frank, Dalenogare, and Ayala 2019). Although platforms and technologies are crucial in Industry 4.0 maturity, management issues should also be taken into consideration to facilitate business digitalization (Naeem and Garengo 2022; Nudurupati et al. 2022). From a managerial standpoint, the performance measurement system (PMS) is among the most important issues (Garengo, Bititci, and Bourne 2022; Nudurupati, Garengo, and Bititci 2021). Industry 4.0 maturity assists in connecting employees together, monitoring customer behaviors and requirements, and collaborating in the supply chain with different parties (Dalenogare et al. 2018; Nasiri et al. 2020; Porter and Heppelmann 2014; Tao et al. 2018; Tortorella et al. 2020). Thus, Industry 4.0 encompasses both technology- and people-related aspects (Beauchemin et al. 2022; Kayikci et al. 2022; Sahi, Gupta, and Cheng 2020; Tortorella et al. 2020). To make sure that Industry 4.0 maturity will facilitate the above-mentioned issues, a PMS plays a vital role in its management (Kamble et al. 2020).

The purpose of a PMS has changed over time from rational control toward cultural controls and learning (Bititci et al. 2012). This has resulted in research on two types of PMS use: diagnostic and interactive (e.g. Nudurupati et al., 2021; Simons 1994; Smith and Bititci 2017; Tessier and Otley 2012). As a diagnostic system, a PMS can be explained as a group of formal processes that use information to sustain, modify, or optimize patterns in an organizational activity (Henri 2006a; Koufteros, Verghese, and Lucianetti 2014). As an interactive system, a PMS can be defined as a progressive activity demonstrated by communication and active participation of top management, visualizing new ways to manage organizational resources for competitive advantages (Koufteros, Verghese, and Lucianetti 2014). In addition to PMS use, Industry 4.0 maturity may require a comprehensive assessment that enables firms to lead and control the process. This type of digital governance involves digital leadership and access to top managers with full support and initiatives about using digital technology as a new way of working, such as digital solutions, digital networks, and digital platforms (El Sawy et al. 2016).

Few studies have investigated the connection between a PMS and Industry 4.0 (Frederico et al. 2020; Kamble et al. 2020; Naeem and Garengo 2022; Xie et al. 2020). There is some research investigating how Industry 4.0 transforms PMSs, but there is a lack of studies incorporating different uses of PMSs in this context. The majority of the previous studies are literature reviews or case study approaches, which means there is a need for large empirical studies on the effect of different types of PMS use on Industry 4.0. Using empirical evidence is based on SMEs in Finland, the present study contributes to this research gap by studying the effect of digital governance on Industry 4.0 maturity, along with its mediating role on the relationship between the use of PMSs and Industry 4.0 maturity.

Thus, the study’s novelty to manufacturing research streams, especially to Industry 4.0 and smart manufacturing, is emphasized when commonly known that PMSs are the most important management tools of companies and that the existence of emerging technologies at the core of Industry 4.0 increases the complexity of this phenomenon, highlighting the need for a specific approach and method to adopting Industry 4.0. The study also contributes to the literature on performance measurement systems, especially in a manufacturing environment.

The rest of the paper proceeds as follows: section two covers the conceptual framework of the study and hypotheses development. The third section presents the research methodology used. The results are presented in the fourth section. The fifth section discusses the results in light of the literature. Finally, in the sixth section, conclusions with future research possibilities and limitations are discussed.

2. Theoretical background

2.1. Organizational control theory and PMS

The current study builds on organizational control theory, which considers ‘the organization as a dynamic entity operating in an environment that is constantly changing, thus necessitating the basic structure of any control system: measure, compare, analyze, correct, and prevent’ (Bititci et al. 2018). Even though the development of PMS theory has paid attention to, for example, understanding how to keep measurement systems up to date (Melnyk et al. 2014), theorizing the unintended consequences of PMSs (Franco‐Santos and Otley 2018), and using a decision theory perspective on complexity in performance measurement (Alexander, Kumar, and Walker 2018), Bourne et al. (2018) suggest that there remains a compelling need for further theoretical development regarding PMSs. A common feature of prior PMS work is the recognition of two different types of organizational control – technical control and social control (Franco‐Santos and Otley 2018; Okwir et al. 2018; Smith and Bititci 2017) – that need to be studied together in the context of the complexity of the environment in which they operate (Franco‐Santos and Otley 2018). In addition, Ferreira and Otley (2009) have highlighted the need to alter PMSs according to the dynamics of an organization and its environment. Complexity has a significant impact on both PMSs (Melnyk et al. 2014; Okwir et al. 2018) and the mechanisms at play in an organization when PMSs are used (Bourne, Melnyk, and Bititci 2018). It has been reported that organizational control theory could provide the basis for this investigation (Bititci et al. 2018; Bourne, Melnyk, and Bititci 2018; Nudurupati, Garengo, and Bititci 2021). Next, an introduction of PMS and its types of use is presented.

2.2. PMS and its type of use

A PMS is a group of metrics that help quantify the efficiency and effectiveness of actions (Neely 2005). A PMS is critical for companies because it enables managers to have access to a lot of information, which can help them operate successfully in a time of transformation (Koufteros, Verghese, and Lucianetti 2014). PMSs can be used for different purposes, and it has been confirmed that the type of use can modify the outcome of PMS, which has led to an increase in the interest in PMS use (Franco‐Santos, Lucianetti, and Bourne 2012; Guenther and Heinicke 2019). Simons (1994) initially employed both diagnostic and interactive in control systems. After that Tessier and Otley (2012) have revised this view, using diagnostic and interactive systems not as control systems but as a PMS to know how control systems are used.

The diagnostic use of PMSs can be explained as a group of formal processes that use information to sustain, modify, or optimize patterns in an organization’s activity (Henri 2006a; Koufteros, Verghese, and Lucianetti 2014). These processes include monitoring systems, reporting tools, and performance outcomes, which are shared with all staff members at companies. According to Henri (2006a), the diagnostic use of PMSs considers tight controls of the strategies and operations, as well as restricted information flow. The purpose of the diagnostic use of PMSs is manifold, including monitoring, focusing attention, and legitimization (Koufteros, Verghese, and Lucianetti 2014). The monitoring purpose of diagnostic use helps managers track the progress of the company toward goals and monitor the results (Henri 2006b). The focusing attention purpose of diagnostic use enables managers and organizations to focus on relevant issues or set constraints on staff members’ behavior to reach goals (Simons, Dávila, and Kaplan 2000; Widener 2007), as well as direct staff members on challenges in making strategic decisions (Koufteros, Verghese, and Lucianetti 2014). The legitimization purpose of diagnostic use assists in justifying decisions and supporting actions in a way that compares the achieved goals with the targeted ones (Henri 2006b).

The interactive use of PMS can be defined as a progressive activity demonstrated by communication and active participation of top management, visualizing new ways to manage organizational resources for competitive advantages (Koufteros, Verghese, and Lucianetti 2014). The interactive use of PMS enables more searching and learning throughout the organization (Simons 1994). Furthermore, the interactive use of the PMS can lead to dialogue, hence generating new ideas and initiatives (Widener 2007). According to Henri (2006a), the interactive use of PMS provides an opportunity for collecting and sharing information while motivating debate among staff members. The interactive use of PMS is associated with strategy management and learning (Franco‐Santos et al. 2007), more communication and interaction (Guenther and Heinicke 2019), and priority settings (Spekle and Verbeeten 2014).

It is clear that a PMS is a complex phenomenon, and how current business trends are impacting PMSs is not yet understood (Nudurupati, Garengo, and Bititci 2021). Thus, there is a need for research to enhance our understanding about the connection between PMSs and digital transformation, which is referred to in the present study as Industry 4.0 maturity, along with examining whether changes to PMS use can be made in a proactive manner; the present study explores the facilitating role of digital governance. Next, the literature is reviewed to identify current knowledge on PMSs and digital transformation, especially as it is related to Industry 4.0 maturity and digital governance; the literature review serves as the basis to develop hypotheses.

2.3. Industry 4.0

The concept of Industry 4.0 originated in Germany in 2011, having the aim of supporting production development within cutting-edge technologies (Kagermann et al. 2013). Since then, this concept has been presented and developed in different countries with different terms, such as ‘smart manufacturing’, ‘Made in China 2025’, and ‘Future of manufacturing’ (Kusiak 2018; Liao et al. 2017). All these terminologies have a consensus that Industry 4.0 is the fourth industrial revolution caused by emerging technologies (Frank, Dalenogare, and Ayala 2019; Masood and Sonntag 2020) and by changes in the way of working (Frank, Dalenogare, and Ayala 2019; Sahi, Gupta, and Cheng 2020; Tortorella et al. 2020). Industry 4.0 has changed the interactions among suppliers, stakeholders, and supply chains both internally and externally, developing interaction in supply chains through digital platforms and connectivity (Dalenogare et al. 2018; Frank, Dalenogare, and Ayala 2019). Furthermore, Industry 4.0 involves smart working, where smart approaches have been used to get business done in value chains (Frank, Dalenogare, and Ayala 2019). Smart approaches include using emerging technologies to connect with different parties and innovative business models for different types of interactions in the value chain (Dalenogare et al. 2018; Masood and Sonntag 2020). Thus, connectivity among different parties plays an important role in Industry 4.0 (Müller, Buliga, and Voigt 2018).

Industry 4.0 can be considered in customer connectivity in the way that it tracks and monitors customer behaviors and requirements (Porter and Heppelmann 2014; Tao et al. 2018). Furthermore, Industry 4.0 provides a superior user experience for customers through advanced technologies like virtual reality and digital twins (Tao et al. 2018). Additionally, Industry 4.0 can be involved in the new ways of operating and collaborating in supply chains with different parties (Dalenogare et al. 2018; Nasiri et al. 2020). Through digital platforms, Industry 4.0 connects employees with each other and facilitates training, remote working, and operating with customers (Tortorella et al. 2020). Thus, Industry 4.0 encompasses both technology-related and people-related aspects (Sahi, Gupta, and Cheng 2020; Tortorella et al. 2020), and the contribution of Industry 4.0 can be seen in different domains and industries (Tortorella et al. 2020). It is worth mentioning that the existence of emerging technologies at the core of Industry 4.0 enhances the complexity of this phenomenon, highlighting the need for a specific approach and method to adopt Industry 4.0 (Frank, Dalenogare, and Ayala 2019; Masood and Sonntag 2020; Tortorella et al. 2020).

As one of the most recent trends and competitive advantages for companies, Industry 4.0 needs a specific approach to adopt (Frank, Dalenogare, and Ayala 2019; Masood and Sonntag 2020; Tortorella et al. 2020). Frank et al. (2019) concentrated on Industry 4.0 technologies in manufacturing companies and proposed two main layers in the Industry 4.0 pattern: front-end technologies and based technologies. Front-end technologies are the main concern within operational and market requirements, including smart manufacturing, smart product, smart supply chain, and smart working, while base technologies (e.g. IoT, big data, cloud services, analytics) enable connectivity and smartness for front-end technologies. These two layers can complete each other; however, companies still need to enhance their ability in big data analysis. As mentioned by Tortorella et al. (2020), companies with organizational learning capabilities can adopt Industry 4.0 quicker because Industry 4.0 is built on advanced interconnected technologies and an integrated environment while also having knowledge sharing. Thus, companies that want to adopt Industry 4.0 need to update the communication and process of information exchange in a way that is compatible with emerging technologies. According to Masood and Sonntag (2020), a lack of financial resources, shortage of knowledge, and limited technology consciousness have been listed as the main challenges for SMEs in Industry 4.0 maturity. Furthermore, they also mentioned a lack of a clear method for evaluating Industry 4.0 technologies against the requirements of SMEs (Culot et al. 2020; Elibal and Özceylan 2022; Trotta and Garengo 2019).

Based on the previously presented literature, the implementation of Industry 4.0 necessities a comprehensive approach meaning not only technological aspects but also considering strategies, workforce competencies, cultural change, and the development of awareness and readiness to implement the transformation (Rafael et al. 2020; Santos and Martinho 2020; Wagire et al. 2021). Numerous maturity models have been presented to support companies in this transformation (e.g. Angreani, Vijaya, and Wicaksono 2020; Schumacher, Erol, and Sihn 2016; Akdil et al. 2018). According to the Oxford English Dictionary, the concept of maturity refers to ‘state of being complete, perfect, or ready’ (Simpson and Weiner 1989). Thus, Industry 4.0 maturity refers the extent to which an implementing entity is capable to implement Industry 4.0. Thus, the Industry 4.0 maturity models support the need for a comprehensive approach by allowing to identify of a target value for different dimensions covered by the model, and to define the strategy to proceed from the current situation to the desired one (Rafael et al. 2020; Schumacher, Erol, and Sihn 2016; Akdil et al. 2018).

2.4. Digital governance

Governance is defined as the available rights of the staff members, as well as the practices and decision-making processes that employ resources to achieve companies’ goals (O’Mahony and Bechky 2008). Governance aims to assess an issue, possible knowledge and skill sets, technologies, and networks to address the issue before then setting the governance framework that will enhance the potential for successful solutions (Nickerson and Zenger 2004). Because currently handling digital transformation is complex and has conflicting results (Sousa-Zomer, Neely, and Martinez 2020; Vial 2019) and the critical element in governance is handling possible conflict (O’Mahony and Bechky 2008), to achieve positive outcomes in digital transformation, digitality will need to be governed using new criteria (Sama, Stefanidis, and Casselman 2021).

Digital governance is a comprehensive assessment that enables firms to lead and control digital processes. Especially the digitalization of manufacturing has opened several opportunities to monitor, control, optimize and manage different types of knowledge intensive processes to enhance real-time decision-making supports and problem solving (Cheng and Bateman 2008; Katchasuwanmanee, Bateman, and Cheng 2016). Handling digital processes through digital governance involves digital leadership and the access to top managers with the full support and initiatives about using digitalization and digital technology as a new way of working, such as digital solutions, digital networks, and digital platforms (El Sawy et al. 2016). Additionally, the governance of digitalization is not possible without employees’ support and eagerness to learn digital skills and resources (Chen 2017; Sia, Soh, and Weill 2016).

3. Hypotheses development and research model

3.1. Hypothesis development

3.1.1. Type of PMS use and industry 4.0 maturity

Previous studies have shown that PMSs are beneficial in the Industry 4.0 context (Duman and Akdemir 2021; Frederico et al. 2020; Xie et al. 2020). Horváth and Szabó (2019) suggest that increased managerial emphasis on PMSs drives the maturity of Industry 4.0. In their study, the PMS covers increased control and permitting real-time performance measurement. Samaranayake et al. (2017) show a process-related PMS, covering, for example, flexibility in production and stability of the process, hence fostering technological readiness to the maturity of Industry 4.0. Because the diagnostic use of a PMS can be explained as a group of formal processes that use information to sustain, modify, or optimize patterns in an organizational activity (Henri 2006a; Koufteros, Verghese, and Lucianetti 2014), this type of a PMS can foster Industry 4.0 maturity by improving process flexibility and stability (c.f. Samaranayake, Ramanathan, and Laosirihongthong 2017). Similarly, the study of Kamble et al. (2020) supports the diagnostic use of a PMS in Industry 4.0 maturity; they examine smart manufacturing systems, finding that manufacturing goals and performance targets may guide the maturity of Industry 4.0. These targets require careful monitoring and related improvement. Frederico et al. (2020) study the maturity of Industry 4.0 in supply chains and consider a PMS as a crucial managerial viewpoint. Because the monitoring purpose of diagnostic use helps managers track the progress of the company toward goals and monitor the results (Henri 2006b), it is likely to drive the maturity of Industry 4.0, which includes expectations for efficiency, integration, transparency, and customer’s satisfaction, among others (Frederico et al. 2020).

Regarding the interactive use of a PMS, Klovienė and Uosytė (2019) state that a PMS can provide a rapid reactive ability, which is crucial in the maturity of Industry 4.0; transformations in technology, business environment, and organizational processes force developing a PMS to fit the context of Industry 4.0. Because the interactive use of a PMS enables more searching and learning throughout the organization (Simons 1994), as well as dialogue and generating new ideas and initiatives (Widener 2007), it is likely to drive Industry 4.0 maturity. Based on the above evidence, we believe that both the diagnostic and interactive use of a PMS contributes to Industry 4.0 maturity. Thus, the following hypotheses are set:

H1. The type of PMS use is positively associated with Industry 4.0 maturity

H1a. Diagnostic use of a PMS is positively associated with Industry 4.0 maturity.

H1b. Interactive use of a PMS is positively associated with Industry 4.0 maturity.

3.1.2. Mediating role of digital governance

Industry 4.0 is at the heart of when companies seek to gain a competitive advantage. Industry 4.0 covers new ways of operating and collaborating among suppliers, customers, supply chains, and other stakeholders both externally and internally (Dalenogare et al. 2018; Frank, Dalenogare, and Ayala 2019; Nasiri et al. 2020; Tortorella et al. 2020). Because the changes are remarkable, companies need to be ready to develop both technology-related and people-related abilities to successfully adopt Industry 4.0 (Büyüközkan and Göçer 2018; Sahi, Gupta, and Cheng 2020; Tortorella et al. 2020). As discussed earlier, PMSs can play a beneficial role in Industry 4.0 maturity (Frederico et al. 2020; Kamble et al. 2020). For example, Nudurupati et al. (2016) studied the resilient features of contemporary PMSs in digital economies, highlighting the benefits of new technological advancements and the integration of their maturity through strategy. They also highlighted the ability to use technologies such as big data and IoT to enhance decision making, as well as unlocking innovation through collaboration and cocreations to create a competitive advantage. The ability and skills of both the managers and knowledge workforce have also been highlighted in many studies that have considered the use of PMSs in digital transformation (Nasiri et al. 2020; Nudurupati, Tebboune, and Hardman 2016). Managers who are operationally responsible for digital strategy should have sufficient strategic capabilities and experience from transformational and technological projects (Matt, Hess, and Benlian 2015).

The features presented above are also the key elements of digital governance, which is a comprehensive assessment that involves digital leadership with the full support and initiatives about using digitalization and digital technology as a new way of working in digital solutions, digital networks, and digital platforms (e.g. El Sawy et al. 2016). As presented above, the use of PMSs can be beneficial in the maturity of Industry 4.0, and the elements of digital governance can facilitate this maturity. Based on the above evidence, we believe that digital governance can facilitate the relationship between both the diagnostic and interactive use of a PMS and Industry 4.0 maturity. Thus, the following hypotheses are set:

H2. Digital governance mediates the relationship between type of PMS use and Industry 4.0 maturity

H2a. Digital governance mediates the relationship between the diagnostic use of a PMS and Industry 4.0 maturity.

H2b. Digital governance mediates the relationship between the interactive use of a PMS and Industry 4.0 maturity.

3.2. Research model

Based on the literature discussed above, the research model is shown in Figure 1. The first hypothesis covers the direct effects, whereas the second hypothesis includes the mediating effects. These two major hypotheses are proposed to check if the type of PMS use can provide Industry 4.0 maturity or whether there is a need for a booster-like digital governance to mediate the relationship between the type of PMS use and Industry 4.0 maturity. Each major hypothesis encompasses two subhypotheses, which are shown in Figure 1. Regarding the first hypothesis, the effects of different types of PMS use (including diagnostic use and interactive use) on Industry 4.0 maturity will be checked. The second hypothesis examines the mediating effect of digital governance between different types of PMS use (including diagnostic use and interactive use) and Industry 4.0 maturity.

Figure 1. Research model and hypotheses.

4. Research method

4.1. Sample and data collection process

The initial sample was randomly selected from all SMEs in Finland. From the total number of 20,000 SMEs in Finland, 6,816 samples were randomly selected, representing more than 30% of the entire population. After removing invalid samples—986—the survey questionnaire was sent to the CEO of the 5,830 SMEs in Finland. After four reminders and data screening of invalid responses, 280 valid responses were achieved. Around 70% of the respondents were small companies with less than 49 employees and revenue of 2–10 million euros, while the rest were medium-sized companies with 50–249 employees and 10–50 million euros revenue. Approximately 42% of the respondents were operating in manufacturing companies, while 57% of the respondents were in service companies and about 1% without any responses.

Different statistical and nonstatistical tests were conducted to minimize the potential bias of the research. Potential bias was detected beforehand by defining the target population and sampling frame carefully and by matching these two. The survey was made as short and accessible as possible to increase the possibilities of gaining responses. We also sent several reminders to nonrespondents to avoid the possibility of self-selection bias. In terms of statistical tests, an analysis of variance (ANOVA) test was implemented to check if there were any significant differences between the respondents who answered the survey before the first reminder and those who answered the survey after the last reminder. The results of the ANOVA tests confirmed that there was no nonresponse bias because the differences between two groups were insignificant (Armstrong and Overton 1977). Furthermore, Harman’s single factor test was applied to check whether the research suffered from common method bias (Podsakoff et al. 2003). The results of exploratory factor analysis of all the utilized items confirmed that there was no issue regarding common method bias because factor analysis of all applied measures loaded onto more than one factor, and the total explained variance of one single factor was less than 40%. In terms of nonstatistical tests, some solutions, such as creating anonymous surveys within direct and clear questions, can reduce common method bias (Podsakoff et al. 2003). Because the conducted survey was anonymous and different iterative sessions with experienced researchers were organized to create direct and understandable questions, the issue of common method bias was not a major issue.

4.2. Measurement of construct

The construct of the measures was built through a combination of literature review in strategic management, Industry 4.0, and PMS research. All the measures were assessed on scales ranging from 1 to 7, where 1 represented strongly agree and 7 strongly disagree. Four different types of variables (e.g. dependent, mediating, independent, and control variables) were developed, each of which was measured by multiple items. Referring to the dependent variable, ‘Industry 4.0 maturity’ was applied as a single dependent variable, which was assessed using four items. The mediating variable ‘Digital governance’ was assessed with six items. In terms of independent variables, ‘Diagnostic use of a PMS’ and ‘Interactive use of a PMS’ were two independent variables, with the former assessed by six items and the latter by three items. Two control variables – company size and industry – were used to control the confounding outcomes. Company size was represented by number of employees and controlled by dummy variables split up into small- and medium-sized companies. Likewise, industry was controlled by a dummy variable asking the respondents whether they operated in the service industry or manufacturing. Table 2 provides the details of each measurement item and the references.

5. Results and analysis

5.1. Results of validity and reliability

The validity and reliability of the construct are important factors for testing the hypotheses. The combination of different criteria was used to confirm that the present study did not suffer from a lack of validity and reliability. These criteria were Cronbach’s α and composite reliability (CR) for the reliability of the construct, average variance extracted (AVE) and standardized loadings for convergent validity, and Fornell – Larcker criterion and Heterotrait – monotrait ratio (HTMT) for discriminant validity. As asserted by Fornell and Larcker (1981), if the square root of AVE is more than the correlation between the respective construct and the remaining constructs of the model, discriminant validity is confirmed. Table 1 demonstrates the results of discriminant validity, confirming the discriminant validity of the construct because each value of the construct correlation is less than the diagonals. HTMT is another criterion that checks discriminant validity: here, the discriminant validity is accepted if the value of HTMT is less than 0.9 (Henseler, Ringle, and Sarstedt 2015). Because the accounted value of HTMT is less than 0.9, discriminant validity is confirmed.

Table 1. Results of discriminant validity: the Fornell – Larcker criterion.

	Industry 4.0 maturity	Digital governance	Diagnostic use of a PMS	Interactive use of a PMS
Industry 4.0 maturity	0.760^a
Digital governance	0.479	0.784^a
Diagnostic use of a PMS	0.311	0.260	0.825^a
Interactive use of a PMS	0.414	0.488	.751	0.837^a

Notes: ^a Square root of AVE.

Reliability of the construct was checked by Cronbach’s α and CR. The internal consistency and reliability of the construct were confirmed because both the value of Cronbach’s alpha and CR of all constructs were more than 0.7 (Table 2) (Fornell and Larcker 1981; Hair et al. 2009). The convergent validity of the construct was checked by AVE. Because the value of AVE for all constructs was more than the threshold of 0.5 (Table 2), the convergent validity of the construct was confirmed (Fornell and Larcker 1981). Thus, the reliability and validity of the construct was confirmed based on the satisfied criteria presented in Tables 1 and 2.

Table 2. Results of the validity and reliability tests.

Construct	Items	St.weight	α	CR	AVE
Industry 4.0 maturity (Dalenogare et al. 2018; Frank, Dalenogare, and Ayala 2019; Tortorella et al. 2020)	In our company, digitalization is utilized as a new way of considering customer connectivity	0.588	0.851	0.843	0.577
	In our company, digitalization is utilized as a new way of considering stakeholder connectivity	0.771
	In our company, digitalization is utilized as a new way of considering employee connectivity	0.769
	In our company, we utilize digitalization as technologies that facilitate a better user experience	0.883
Digital governance (El Sawy et al. 2016; O’Mahony and Bechky 2008; Sama, Stefanidis, and Casselman 2021)	In our company we evaluate the managers’ ability to support digitalization	0.725	0.906	0.904	0.614
	In our company we evaluate the digital skills/abilities of employees	0.684
	In our company we evaluate the ability to utilize new technologies	0.716
	In our company we assess the readiness to utilize digitality in business	0.749
	In our company we evaluate the ability to produce new digital solutions	0.858
	In our company we evaluate networking capabilities in a digitalizing operating environment	0.939
Diagnostic use of a PMS (Henri 2006b)	In our company we use performance measures to track the progress of the company toward goals	0.753	0.930	0.926	0.680
	In our company we use performance measures to monitor results	0.630
	In our company we use performance measures to enable the organization to focus on issues	0.871
	In our company we use performance measures for making strategic decisions	0.886
	In our company we use performance measures to justify decisions	0.909
	In our company we use performance measures to support actions	0.865
Interactive use of a PMS (Koufteros, Verghese, and Lucianetti 2014)	In our company we use performance measures to make decisions when it is difficult to differentiate among plausible solutions to a problem	0.755	0.871	0.874	0.700
	In our company we use performance measures to develop learning	0.908
	In our company we use performance measures to enhance communication	0.840

5.2. Results of the structural equation model

Structural equation model was utilized with Smart-PLS to check the relationship between the constructs included in the proposed research model. The results of the structural equation model are presented in Table 3. The path from company size to Industry 4.0 maturity and industry to Industry 4.0 maturity tested the effect of the control variables on the dependent variable. As shown in Table 3, the company size did not significantly affect Industry 4.0 maturity (β = 0.779, P-value = 0.436, insignificant). Likewise, the industry did not significantly affect the Industry 4.0 maturity (β = 1.935, P-value = 0.054, insignificant). Thus, none of the control variables significantly affected Industry 4.0 maturity. The path from diagnostic use of a PMS to Industry 4.0 maturity and interactive use of a PMS to Industry 4.0 maturity tested the effects of independent variables on the dependent variable (H1a and H1b). As shown in Table 3, the diagnostic use of a PMS did not significantly affect Industry 4.0 maturity (β = 0.708, P-value = 0.479, insignificant). Thus, H1a was rejected. Likewise, the interactive use of a PMS did not significantly affect Industry 4.0 maturity (β = 1.034, P-value = 0.302, insignificant). Therefore, H1b was also rejected. Consequently, none of the dependent variables significantly affected Industry 4.0 maturity, and both parts of the first hypothesis (H1a and H1b) were rejected.

Table 3. Results of structural equation model.

	Path Coefficients	T Statistics	P-Values
Company size -> Industry 4.0 maturity	0.048	0.779	0.436
Industry -> Industry 4.0 maturity	0.121	1.935	0.054
Diagnostic use of a PMS-> Industry 4.0 maturity	0.095	0.708	0.479
Interactive use of a PMS-> Industry 4.0 maturity	0.168	1.034	0.302
Diagnostic use of a PMS-> Digital governance	−0.244	2.010	0.045*
Interactive use of a PMS -> Digital governance	0.671	5.649	0.000***
Digital governance -> Industry 4.0 maturity	0.355	3.823	0.000***

***Significance level 0.001.

**Significance level 0.01.

*Significance level 0.05.

The path from the diagnostic use of PMSs to digital governance, interactive use of PMSs to digital governance, and digital governance to Industry 4.0 maturity tested the effect of the independent variable on the mediating variable and the effect of the mediating variable on the dependent variable. According to Baron and Kenny (1986), the mediating effect was confirmed if the direct effect of the independent variable on the dependent variable was not statistically significant, but both the direct effect of the mediating variable on the dependent variable and direct effect of the independent variable on the mediating variable were statistically significant. As demonstrated in Table 3, the effect of diagnostic use of a PMS on digital governance was negatively significant (β = 2.010, P-value = 0.045, significant at the 0.05 level), and the effect of digital governance on Industry 4.0 maturity was significant (β = 3.823, P-value = 0.000, significant at the 0.001 level). Thus, H2a was accepted, and digital governance negatively mediated the relationship between the diagnostic use of a PMS and Industry 4.0 maturity. In addition, the effect of interactive use of a PMS on digital governance was positively significant (β = 5.649, P-value = 0.000, significant at the 0.001 level), and the effect of digital governance on Industry 4.0 maturity was significant (β = 3.823, P-value = 0.000, significant at the 0.001 level). Thus, H2b was accepted, and digital governance positively mediated the relationship between the interactive use of a PMS and Industry 4.0 maturity. Consequently, both parts of the second hypothesis (H2a and H2b) were accepted.

6. Discussion

The aim of the present study was to investigate the effect of digital governance on Industry 4.0 maturity, along with its mediating role on the relationship between the use of PMSs (diagnostic and interactive) and Industry 4.0 maturity. Regarding the direct effects of diagnostic and interactive use of a PMS on Industry 4.0 maturity, no significant effects were found. This result is, to some extent, contradictory with prior studies, which show that PMSs are beneficial in Industry 4.0 maturity (Frederico et al. 2020; Horváth and Szabó 2019; Kamble et al. 2020). However, these studies have specific features that allow for positive effects. For example, Horváth and Szabó (2019) highlight the increased managerial emphasis on PMSs, increased control, and permitting real-time performance measurement as the drivers for the maturity of Industry 4.0. Frederico et al. (2020) also highlight the managerial viewpoint of a PMS in the maturity of Industry 4.0 in supply chains, where the monitoring purpose of diagnostic use helps managers track the progress of the company toward goals and monitor the results (cf. Henri 2006b). Consequently, the contrasting results of this study may be because in previous studies (e.g. Frederico et al. 2020; Horváth and Szabó 2019), PMSs have already included the elements of digital governance, such as digital leadership, support from managers and employees, eagerness to learn digital skills and initiatives to use digital technologies as new ways of working (Chen 2017; El Sawy et al. 2016; Sia, Soh, and Weill 2016). Regarding the interactive use of a PMS, Klovienė and Uosytė (2019) present that transformations in technology, the business environment, and organizational processes force the development of PMSs in a way so that a PMS can provide a rapid reactive ability, which is crucial in the maturity of Industry 4.0. However, based on the current research, the diagnostic or interactive use of PMSs alone does not seem to facilitate Industry 4.0 maturity. This may indicate that, if the management engagement and real-time performance measurement of PMSs are not at an adequate level, it will not enable rapid reactions and successful Industry 4.0 maturity. This may also indicate that both the diagnostic and interactive use of a PMS are still based on measures calculated from historical data and, thus, do not promote the maturity of such a diverse and extensive entity as Industry 4.0. Thus, the result highlights the need for digital governance, where the digitization of manufacturing has opened several opportunities to monitor, control, optimize and manage various information-intensive processes to improve real-time decision-making support and problem solving, as Cheng and Bateman (2008) and Katchasuwanmanee et al. (2016) have suggested.

As predicted, digital governance had a direct effect on Industry 4.0 maturity. The evidence here suggests that the concept of digital governance has an important role to play in managing Industry 4.0. The findings provide evidence that digital governance allows digital leadership and access to top managers with the full support and initiatives about using digitalization and digital technology as a new way of working, such as digital solutions, digital networks, and digital platforms (El Sawy et al. 2016), in turn leading to enhanced Industry 4.0 maturity. The study also investigates whether digital governance facilitates the relationship between the diagnostic and interactive use of PMSs and Industry 4.0 maturity; the results indicate that the interactive use of PMSs positively affects Industry 4.0 maturity via digital governance. When the interactive use of a PMS encompasses learning, communication, and interaction to enable the room to generate new ideas and initiatives (Franco‐Santos et al. 2007; Guenther and Heinicke 2019; Henri 2006b), it also seems to allow for room for digital governance, which drives Industry 4.0 maturity. The maturity of Industry 4.0 requires agility, various skills, and quick reactions to changes that can be addressed by digital governance, such as the ability and engagement of managers and employees, the ability and readiness to utilize new technologies, and the ability to collaborate in digital platforms (Chen 2017; El Sawy et al. 2016; Sia, Soh, and Weill 2016). The current study has also indicated that the diagnostic use of a PMS negatively affects digital governance. Because the diagnostic use of a PMS refers to a group of formal processes, including monitoring systems, reporting tools, and performance outcomes (Henri 2006a; Koufteros, Verghese, and Lucianetti 2014), moving from rigorous metrics to evaluation and assessment (i.e. digital governance) may lead to resistance to change. This is also supported by Nudurupati et al. (2016), who study performance measurement and management in digital economies, highlighting organizations’ need to move from measurement to evaluation, especially when collaborating on global networks. Furthermore, the diagnostic use of a PMS may lack the proactive nature of measurement needed in digital governance. The updates in Industry 4.0 are so fast that diagnostic use is not useful because the updates in the criteria of the diagnostic PMS take time and need to be changed regularly.

7. Conclusion

The present study has explored the impact of different types of PMS use (diagnostic use and interactive use) on Industry 4.0 maturity, investigating whether digital governance mediates the effect of PMS use on Industry 4.0 maturity. The results provide new insights for both academics and practitioners. From the viewpoint of academics, the current research has revealed how different types of PMS use can lead to Industry 4.0 maturity. In addition, different types of PMS use (diagnostic use and interactive use) are more prone to significantly impact Industry 4.0 maturity through digital governance. However, the diagnostic use of a PMS significantly hinders digital governance, while the interactive use of a PMS significantly drives digital governance. Furthermore, digital governance facilitates Industry 4.0 maturity. As the greatest novelty, the study contributes to manufacturing research streams, especially to Industry 4.0 and smart manufacturing, by showing how the appropriate use of PMSs together with digital governance can achieve the maturity of industry 4.0. The novelty and importance of the study are emphasized, especially, when commonly known that PMSs are the most important management tools of companies and that the existence of emerging technologies at the core of Industry 4.0 increases the complexity of this phenomenon, highlighting the need for a specific approach and method to adopting Industry 4.0. The study also contributes to the literature on performance measurement systems, especially in a manufacturing environment.

From the viewpoint of practitioners, solely applying a PMS within different types of use will not provide Industry 4.0 maturity; however, there is a need for digital governance to synchronize the relationship between the type of PMS use and Industry 4.0 maturity. Thus, practitioners who want to utilize PMSs within different types of use should add digital governance criteria in their business to enhance Industry 4.0 maturity. Additionally, the mediating effects of digital governance between different types of PMS use (diagnostic use and interactive use) and Industry 4.0 maturity are different; therefore, practitioners should be careful when using digital governance in different types of PMS use to adopt Industry 4.0. When it comes to using PMS as a diagnostic purpose, it will affect digital governance negatively, but when it comes to using a PMS as an interactive purpose, it will affect digital governance positively. SMEs should utilize PMS interactively to facilitate digital governance and further Industry 4.0 maturity. This means utilizing performance measures for learning and communication purposes. Utilizing performance measures just for tracking the progress toward goals or monitoring and justifying decisions will have negative influence on digital governance. Thus, SMEs that want to benefit from digital governance in Industry 4.0 maturity should consider the role of PMSs as both a booster and hinderance.

However, like any other research, the present study has some limitations that can be used as opportunities for expanding knowledge on this topic. First, the results of the present study have been achieved within data collected over a short time frame. However, this might mitigate a deep understanding of the processes that occur over time; hence, there is the potential for more research using longitude data. Second, the data were gathered in a single country (Finland), which might pose the possibility for a lack of generalizability. However, this has also provided an opportunity to implement the present research in different countries with different cultures and to understand if the same results will be attained. Third, because the results of the current study are based on the perspectives of the CEO of the SMEs, there is the possibility for bias, which is not the case in the current study because of all the conducted statistical and nonstatistical remedies. This limitation can also become an opportunity for more research within different staff members of the company to better understand their viewpoints about different types of PMS use in Industry 4.0 maturity.

Availability of data and materials

The authors do not have permission to share data.

Consent for publication

approved

Ethics approval and consent to participate

Accepted principles of ethical and professional conduct have been followed.

Disclosure statement

No potential conflict of interest was reported by the author(s).