Organizational Culture: The Key to Improving Service Management in Industry 4.0

Abstract

Industry 4.0 can enhance the operational efficiency of the supply chain, but the current research mainly focuses on analytics and smart things. Many companies integrate their organizations more closely with data by adopting Industry 4.0, but this study found that some companies have changed their leadership, organizational, and customer relationships through the adoption of CPS. Industry 4.0 is a socio-technical system that should be explored in terms of management practices, employee feedback, and the cause-and-effect relationship between them. This study proposes a modeling framework using the Su-field analysis of TRIZ method (Theory of Inventive Problem Solving) and applies the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to investigate the relationship between leadership, organizational culture, and service management in the Taiwan industry. The results show that the data analysis, CPS, IoT, and intelligent technologies of Industry 4.0 can facilitate connections within the value chain and increase agility in response to environmental changes. Companies must have a good organizational culture and provide the right incentives to gain the organizational commitment of their employees to implement Industry 4.0.

1. Research Motivations and Objectives

Industry 4.0 is a strategic initiative of Germany, aiming to create smart factories, and its core concepts involve Cyber-Physical Systems (CPS), cloud computing, Internet of Things (IoT), big data, lean production, and intelligent machinery, etc., to upgrade and reform manufacturing technology. Industry 4.0 focuses on integrating information and production to make production smarter and more flexible to respond to market dynamics. Industry 4.0 can increase the flexibility of the manufacturing industry as well as mass customization, better quality, and improved productivity. Thus, it enables companies to meet the challenge of producing more and more customized products with a shorter time to market and improved quality [1]. The key to technological progress is efficiency, innovation, and the digitization of the enterprise. The globalization of markets, mass customization of products, and more complex products need to be reflected in changes to the actual design approach and development of business processes and methods that must be data-driven, artificial intelligence-assisted, intelligent, and service-oriented [2]. In the era of Industry 4.0, an Intelligent Manufacturing System (IMS) uses service-oriented architecture (SOA) through the Internet to provide collaborative, customizable, flexible, and reconfigurable services to end-users to achieve a highly integrated human-machine manufacturing system [3]. For example, the adoption of the adaptive automation assembly system or collaborative assembly systems (CAS) [4,5], which reduces assembly cycles and improves productivity by decreasing the physical movement of the operator and also taking into account the flexibility of the human operator and the repeatability of the robot. Industry 4.0 combines an embedded production system technology with intelligent production processes, with new tools such as RFID to support process automation and improve system operation and management to pave the way for a new technological era, fundamentally changing industry value chains, production value chains, and business models [1,6]. This also poses management and service challenges, especially in terms of business collaboration and how companies create, deliver, and capture value in this context [7], including the impact of service management at the strategic level concerning the operating model. To ensure competitiveness and corporate sustainability, the quality of products, services, and processes is crucial [8]. From a strategic perspective, the introduction of Industry 4.0 can lead to a better understanding of the company’s strengths and weaknesses to optimize the business model, such as enhancing customization with key resources related to IT and software, increasing customer interaction, designing and proposing product enhancements based on the value of data, and strengthening collaboration with the supply chain. Laudien and Daxböck [9] argue that Industry 4.0 represents strategic leverage that can be used to improve the efficiency of supply chain operations within a company, as well as to leverage Industry 4.0 technologies to connect networks of partners to provide specific products and services to customers.

By Industry 4.0 technologies including IoT, additive manufacturing, big data, cloud manufacturing, digital twins, and blockchain, unique and complex requirements can be overcome. For example, Aheleroff, Mostashiri, Xu, and Zhong [10] developed personalized protection devices with MPaaS (Massive Personalization as a Service), a type of matching that includes not only the best solution for service providers and customers, but also service planning, scheduling, and execution. It is evident that, with the diversification of products and services, the cooperation model of Industry 4.0 has gradually transformed into a service network formed by various companies, with local production systems, clusters, or outsourcing around the supply chain of products or services. For sustainability, services in the era of Industry 4.0 will be highly flexible in the pursuit of output and customization, with extensive integration between customers, companies, and suppliers [11]. Benefiting from the concepts and technologies of Industry 4.0, companies are already using Industry 4.0 digital technologies to transform their supply chain operations [12] and increase the value of service management for their customers. The initial case in this study was a K company that implemented MPaas and found that the key to promoting CPS in the company was to transform the organizational culture. K Company changed from the original top-down leadership approach to using the CPS system, and through the same cyber model, employees at each level thought about the physical model at each stage to transform the production process, thus leading to the subsequent investigation of the impact factors of adopting Industry 4.0 in Taiwan.

K Company is a 38-year-old manufacturer of safeguarding armor. Because of the small volume and large variety of the Taiwanese industry, most of the orders are different. The company’s original manual design process took 480 min for lead time (L/T) and 3 days for production, from live mapping and design specifications to CAD design for 3D drawings. K Company logically integrates design rules and standards into the newly developed CPS system. The information processing module in CPS is linked to digital information such as digital manufacturing (e.g., CAD/CAM/CAE, etc.) and manufacturing execution system (MES) to obtain design and manufacturing information related to equipment, workpieces, products, processes, personnel, etc. In addition, it captures real-time data from the manufacturing system, the environment, and the workers, and then combine these two types of data to compute and process an optimized production process control program. As a result, the K Company’s CPS is adaptable, robust and fault-tolerant, and can generate decisions based on forecasts. In the smart manufacturing application, the same cyber model is used in each stage of the production process, the customer fills in the necessary values for his machine, and the CPS system automatically calculates and calibrates the design, producing 3D drawings, 3D exploded drawings, 2D engineering drawings, 2D expansion drawings, BOM sheets and other design drawings in only 8 min. In addition to the 8 min design calculation time, the input parameters and the finishing proofreading time are 20 min each, and the L/T required is only 48 min. After the adoption of CPS in production, the time from feeding to the production of the armor is less than 10 min. The total production process is shortened from 3 days to 4 h, and the working area is reduced from 1980 square meters to 990 square meters, but the production value is increased by 2 times. Instead of following the original production model, the employees in the entire enterprise organization focus on process transformation, abandoning the production unit’s use of the design unit’s uniform drawings, and instead, each production unit modifies the corresponding cyber model and then produces its drawings, and produces the customer’s single order in batches according to the demand date. In general, the inventory in the Taiwan industry often accounts for 15~45% of the annual revenue, but the cost of in-process and finished products in K Company only accounts for less than 1% of the total revenue. K Company believes that the key to the successful adoption of Industry 4.0 is to confirm the rules, processes, and goals, that is, to authorize the vice president, the general manager’s office, the manufacturing department, the technical department, the marketing department senior manager, and the management department manager to execute each business in decentralized management, so that employees can have space to perform, and at the same time, to encourage outstanding employees to start their own business, so that employees can have a sense of achievement and develop new customers.

According to the concept of a socio-technical system, digital technology connects the components of people, technical systems, and organizations [13], thus enabling people to make autonomous and well-informed decisions in a networked environment. Although the process of promoting Industry 4.0 aims to reduce the human influence to achieve stable output quality, the leadership of business leaders, organizational culture, incentives, and organizational commitment of employees are still indispensable factors that cannot be quantified [14]. Employees’ identification with the organization, openness, and willingness to learn are crucial for the adoption of Industry 4.0 [15,16]. However, existing studies related to Industry 4.0 focus on analytics and smart things, but do not cover the impact of digital transformation into Industry 4.0 on people and service management models. Therefore, this study aims to answer two questions: (1) the factors that affect service management when companies promote Industry 4.0, and (2) the management level focusing on the direction, feedback from the organization’s employees, and the cause-and-effect relationship in the Industry 4.0 promotion process. To this end, this study plans to use an innovative TRIZ design method to deconstruct the correspondence of the influencing factors and validate them with PLS-SEM.

2. Literature Review and Research Hypotheses

2.1. Industry 4.0 Has Changed the Nature of Leadership

Leadership is about influencing others, creating a path for oneself and others, and inspiring others to take effective action to accomplish mutually agreed upon goals for the organization [17]. Leaders can help improve, shape, and retain the desired organizational culture, which may influence innovative work behaviors by generating new shared values [18]. The behaviors and competencies of managers or leaders also have positive and negative effects on employee engagement [19,20,21]. Leadership helps to inspire a culture of innovation in organizations [22,23] and can help to improve, shape, and retain the desired organizational culture, influencing innovative work behaviors by generating new shared values [18]. Leadership is mediated and moderated by organizational culture and organizational citizenship behavior (OCB), which has a substantial positive impact on employees’ work behavior [24].

The three main components of the Industrial Internet of Things (IIoT) in Industry 4.0 are smart devices, smart systems, and smart decisions [25]. The implementation of the Industry 4.0 concept, which concerns the future direction of the company, also requires the contribution of top executives to encourage comprehensive change management activities and processes and to align organizational and production structures with the need to create connected value [26].

In recent years, the push for Industry 4.0 has even been recognized as socio-technical systems for companies, as they involve complex interactions between people and technology in the workplace [27]. In contrast to the past when leaders could directly influence members of the organization to achieve their tasks. Schulze and Pinkow [28] argue that leaders should promote diversity within the organization to help it adapt and should actively participate in activities that force the organization to innovate and use the network structure of Industry 4.0 to expand innovation. In this way, the next generation of leaders has a maker instinct—the ability to build and develop things and to connect with others in the decision-making process [29]. As their roles and responsibilities increase and they take on additional obligations such as managing relationships, coordinating with stakeholders, and administration [30], they will also respond differently to organizational interactions with traditional businesses when faced with organizational culture conflicts.

Industrial digital transformation has mostly been studied from the perspective of cyber-physical system solutions as a driver of change. Until now, data processing activities in operations management have typically been organized according to existing business structures within and between companies. With the growing importance of Big Data in the context of digital transformation in Industry 4.0, business structures will evolve according to the new data processing solutions within them. Among them, the digital transformation of the implementation of Industry 4.0 shifts the attention of leaders from seeking efficiency and effectiveness from physical production processes to data management and influences the decision-making process [31]. Industry 4.0 requires an organizational design with flexible rules and policies where leaders can decentralize and delegate authority to employees due to the transformation of data structures, enabling teamwork and horizontal communication [32]. The comprehensive use of knowledge from historical data in the form of models and rules is another characteristic of Industry 4.0 [33,34], which will change the nature of leadership, so that leadership and management will no longer be concentrated in a few management levels but will be transformed into smaller organizational structures. As the quality and quantity of data processed change, operations management needs to modify the logic of decision making. The data provides direct insight into the actual status of resources and the progress of the value creation process in the industrial environment. Operations management can react to events in the plant and elsewhere without any significant delays. This allows for close feedback between decision-making and monitoring of consequences. Decisions can be based on integrated models developed from historical data and adapted to the entire system using established knowledge. In addition, the decision-making process can be distributed across different organizations without constant reference to a central control entity, and the evaluation during the decision-making process can better reflect the overall requirements of the entire organization [31].

2.2. Industry 4.0 Changes Organizations and Organizational Culture

Organizational culture can be thought of as a set of common norms, values, and world visions that develop within an organization as members interact with their environment. Organizational culture is the shared beliefs, principles, standards, and assumptions that shape behavior by establishing commitment, giving guidance, and generating integrated recognition. Organizational culture serves its intended function when it is aligned with the organization’s environment, resources, values, and goals [35]. Thus, organizational culture determines the quality of working life and professional performance, impacting organizational change and transformation [36]. According to Schein [37], organizational culture is influenced by the beliefs of the organization’s founders, the experiences of group members, and the new values of new leaders. Organizational culture has different aspects, such as bureaucratic culture, innovative culture, and supportive culture. The larger the organization, the less bureaucratic and supportive a culture the organization has. This is because bureaucratic and supportive cultures usually rely on interpersonal relationships, which depend heavily on mutual trust, encouragement, and cooperation, and this becomes more difficult to achieve as the organization grows larger; however, no significant differences are found between organizations of different sizes in terms of innovation culture, as smaller organizations mostly aim for innovation, while larger organizations are seeking innovative solutions [26]. With the promotion of Industry 4.0, the larger the organization, the greater the amount of information will increase, but the result-oriented and intelligent organization’s adaptation will also change the organizational culture from a supportive culture to an innovative culture.

Chonsawat and Sopadang [38] consider that business readiness is crucial for the implementation of Industry 4.0. An open mind and a flexible culture are expected to support the implementation of Industry 4.0 [15]. Adequate resources, skilled and competent employees, and a good organizational culture are considered necessary to implement the concept of intelligence [39]. Hahn [40] uses the concept of socio-technical systems to link digital technology to three constructs such as people, technical systems, and organizations. Digital technologies enable people, technologies (“things” such as machines and products), and organizations to become “smart” [41], that is, capable of making autonomous, well-informed decisions in a networked environment. As a result, intelligent organizations rely on similar digital technologies to facilitate horizontal and vertical integration [42], connecting autonomous entities to extended value networks [43]. Thus, Industry 4.0 defines the organization of the enterprise, while the production process is rooted in interactive technologies and equipment, in other words, a “smart” factory where physical processes are controlled by computer-driven systems and decentralized decisions that would rely heavily on self-control mechanisms of the organization’s members [44].

Therefore, Kiel, Müller, Arnold, and Voigt [15] concluded that the changes induced by Industry 4.0 cannot ignore the model of organizational change, and they believe that organizational innovation is necessary to enable the smooth operation of the Industrial Internet of Things. Industry 4.0 as “socio-technical systems” (socio-technical systems) in which people, machines, and surroundings, as well as all organizational levels, need to be re-evaluated and redesigned to develop new technologies [45]. The fundamental principle of Industry 4.0 is the Internet of Things and Smart Manufacturing, through which products, components, and production machines at work will collect and share data in real-time, which leads to a shift from centralized factory control systems to decentralized intelligence [46]. Under this, the organization operates networked on a real or virtual platform, as it operates through online platforms and tools or structures that share and participate in services provided by other enterprises, professionals, and productive communities [47]. Industry 4.0 will be the integration of advanced technologies and workers with systems, and organizational governance is the key to driving companies towards Industry 4.0 [38]. For this reason, cultural barriers must be considered when redesigning company organizations, and culture must support the adoption of Industry 4.0 [48,49]. In reality, it will be encountered that the company organization will be resistant, reluctant to change, and emotionally reactive, which may seriously affect the adaptation of smart factory technologies [50]. Therefore, organizational goals must include digital strategy, organizational culture, employee perceptions, and leadership to respond to the transformation of Industry 4.0 [51].

2.3. Impact of Incentives on Organizational Culture

Organizational culture is a pattern of values, norms, beliefs, attitudes, and assumptions that determine how people in an organization behave and how things get done, and it can be shaped through organizational management. Team support has a positive direct impact on organizations. Workers often need information exchange with other workers and team support to complete their tasks. Conversely, if mid-level managers’ departments are cost-cut or disempowered, they are said to be an obstacle to successful organizational transformation. Therefore, successful organizational transformation is very important and needs to be carried out thoroughly, but also taking into account the personal interests of several stakeholders in the existing organizational structure [52]. In addition to team support, employees also need guidance and incentives from their leaders, who then play the role of a guide and provide contextual support to employees in the pursuit of various and diversified goals. Sufficient communication needs to be established within the company, especially from management and leadership, and the need for change will only be accepted by employees if the requirements and clear strategies can be presented. Otherwise, internal resistance will be the result of unclear or dishonest communication between management and employees [52].

Incentives refer to the methods adopted by leaders to address the needs and goals of employees and are used to create a suitable working environment to induce and motivate employees to work effectively so that employees can spontaneously develop their potential and dedicate themselves to the organization while facilitating the achievement of the organization’s goals. According to social exchange theory, leaders and followers have a two-way relationship, where one receives something valuable from the other and in return, the other is obliged to respond in the same way [53], which means that when employees feel that the organization is giving a lot of support, the more likely they are to perform well. Trust, communication, reward systems, and organizational structure can positively influence organizational culture [54]. From a corporate perspective, incentives are the various methods that a company uses to achieve its organizational goals by increasing employee productivity. Organizational culture either impedes or facilitates the effectiveness of the network of relationships and interaction dynamics available within the organization, and thereby has the potential to improve employee performance through increased productivity. Organizational culture should be consistent, adaptable, engaging, and mission-based. In other words, to increase productivity, a company must be participatory and consistent. An organization’s reward system not only affects organizational culture but also has a great influence on employee behavior. The risk of an organization being paralyzed and missing important developments due to a lack of openness and courage to do new things is crucial. If new systems and processes of Industry 4.0 should be accepted and used, companies should arrange all necessary preparations such as training and incentives [52]. Once a new technology is accepted, it can only confer the desired effect with the support of the leaders. To do this, senior management support must facilitate collaboration and technological learning among organizational members [55].

2.4. The Impact of Organizational Commitment on Organizational Culture

Organizational commitment is defined as “the relative strength of an individual’s identification with and involvement in a particular organization” [56]. Schein [37] considers that organizational culture has the ability to shape and lead members to perform the attitudes and behaviors expected by the organization, which leads members to develop commitments to the values of the organization. Organizational commitment is a state in which employees identify with a particular organization and want to maintain their identity as its members. Because employees accept and believe in the goals and values of the organization, they work hard and expect to receive the benefits offered by the organization. Committed employees are likely to perform more effectively because they put more effort into working towards achieving the organization’s goals and mission. Employee performance can be expressed as a commitment to persevere in completing tasks and achieving organizational goals, maintaining service quality, accepting changes, and taking on additional work assignments. Therefore, it is also known as the degree of employee involvement and identification with the organization [57]. According to Emma et al. [58], organizational commitment is important for the connections that arise between individuals and organizations. These connections encourage the desire for optimal organizational performance. Many researchers have discussed the positive meaning of organizational commitment and its impact on productivity, motivation, turnover intentions, and absenteeism, and have identified it as a powerful tool for employees and organizations to increase productivity and efficiency [59].

Organizational commitment is the relative strength of an employee’s emotional connection and involvement with the organization. It also means that employees who adopt the organization’s goals and values have a high level of confidence and a strong desire to stay with the organization. The actions of employees also affect organizational culture, and organizations strive to improve their employees’ organizational commitment. Thus, the organization can be described using the concept of a cluster, such as a country or a family, forming a culture where the whole organization works together [60].

Brooks et al. [61] considered that Industry 4.0 requires changes in business processes, technology infrastructure, organizational operations and culture, and employee skills if it is to be successful in business, where the factors are governance, IT, business and partner relationships, data-based communication and analysis, and business and IT data quality. However, with the adoption of Industry 4.0, the broader the dimensions involved, the less supportive the organizational culture becomes. Because a supportive culture often relies on interpersonal relationships, it is deeply dependent on mutual trust, encouragement, and cooperation, and as organizations grow larger, this supportive culture becomes more difficult to possess [26]. The success factors of a business are cooperation, exploration, and entrepreneurial mindset, which are essential to building among the company’s employees, who are considered to be the most important resource. Managers must convince employees of the beneficial aspects of Industry 4.0 and proactively address their concerns. Considering this fact, employee training and development should focus on Industry 4.0 specific abilities and skills, such as data analysis, IT, software, and human-computer interaction technologies, which help employees to strengthen their cooperation and trust in the company [62].

2.5. Transforming Organizations with Industry 4.0 to Build Service Management Models

To share product expectations and expertise, there must be closer relationships between company employees, customers, and suppliers. Digitization allows companies to share core information openly with suppliers and vice versa [63]. Knowing, understanding, and listening to customers has become critical [30]. High competition and declining customer loyalty have led to the emergence of business models that focus on cultivating customer relationships. When services are customer-centric, customers are happier; factors such as fair pricing, quality service, and customization indicate that companies value the relationship with customers over profit or marginal revenue. Companies should not only strive for the best service management but also cultivate a strong relationship with consumers. However, we must also take into account the changing needs of customers and their lack of acceptance of Industry 4.0-related technologies. Customers may be increasingly concerned about the new capabilities of products and services but may not be willing to pay for new technologies, causing the entire supply chain to miss this trend. Now, many companies are still focused on marketing their products. In the future, the focus will be on providing solutions and solving customer problems. Therefore, the risk for companies is that the service management model will not be able to adapt quickly to these future requirements. At the same time, to implement the new model, the company’s organization will have to change radically, which in itself poses a serious challenge to the company [52].

Therefore, Industry 4.0 could combine the potential of production technologies with various process relationships, coupled with e-commerce services and logistical links to deliver products via the Internet, and the new business model “Design to Consumer” (D2C) is consolidated [64]. In Industry 4.0, the combination of jobs and careers is very flexible, involving a non-linear, dynamic process where the line between client and end-user becomes blurred, and collaboration between businesses is similar to the relationship between companies and clients. Salespeople who focus on service management can more accurately identify customer needs and provide better services. A company’s service management strategy should include listening, providing services, offering promises, and satisfying customers, and aiming to improve customer satisfaction. The innovation networks constructed by Industry 4.0 can generate innovative service value by integrating suppliers and customers to form horizontal alliances [65]. Companies use this advantage to organize their networks precisely with other companies and coordinate with each other to ensure compliance with the principles of service management [66].

Lak and Rezaeenour [67] argue that Industry 4.0 can use CPS to enable humans and smart factories to connect and communicate with each other through virtualized, decentralized production and the ability to collect and analyze data, making it possible for smart factories to offer service-oriented products. Benefiting from the conceptual and technological advances that are part of Industry 4.0, companies are already using digital technologies to transform their service management models to provide value to their customers [12,68,69]. For example, Laudien and Daxböck [9] studied the case of 11 manufacturing companies and concluded that Industry 4.0 technologies represent strategic leverage that can be used to improve the efficiency of internal service management operations and that companies can use a network of partners connected by Industry 4.0 technologies to offer bundled specific products to their customers. In an Industry 4.0 modular smart factory, CPS processes can be monitored through virtual mapping and decentralized real process decisions. Real-time communication and collaboration can be between humans, between machines, or between humans and machines. With the support of the IoT (Internet of Things) domain, internal and external organizations can provide services from within or outside the enterprise [70]. The smart technologies employed in Industry 4.0 enable the further presentation of information between providers and customers [71], which can facilitate connections within the value chain and increase agility in response to environmental changes [72]. To take advantage of this, companies must organize their networks precisely with other companies and coordinate with each other to ensure that the right information is available to the target users [66]. To solve the interoperability problem of large-scale automated systems, service-oriented architectures (SOA) have been introduced, where information exchange is embodied by the system providing and consuming services [72].

2.6. Research Hypothesis Modeling by TRIZ Method

The Theory of Inventive Problem Solving (TRIZ) is a tool for linking a general problem to a specific problem to create an appropriate specific solution. This tool was created by the Russian scholar G. Altshuller in 1946 to obtain ideas for solving general problems [73]. TRIZ applications include product and process improvement, technology forecasting, new product development, patent avoidance, and other technical issues. Su-field Analysis is a modeling technique that presents the change of technical system with graphical symbols for problems related to an existing system. The function of each system is the output of one object or substance (S1), which is transmitted to another substance (S2) by some type of energy (F), and the entire action is called a field. F can represent the actual force in the technical area, but in the non-technical area, it can represent the influence, meaning the effect between both sides. F can be a desired effect or a harmful effect. Su-field analysis provides a quick and simple model for considering different ideas from the knowledge base.

In addition to solving technical problems, TRIZ can also address non-technical problems. For example, Rupani et al. [74] applied TRIZ to service improvement; Lee, Chen, and Trappey [75] focused on enhancing customer satisfaction; Maia et al. [76] applied TRIZ to lean production. Swee [77] uses the TRIZ toolset to improve quality in the food industry. In addition, TRIZ can be used with other methods, such as Brad and Brad [78] combining TRIZ with SWOT analysis; Karnjanasomwong and Thawesaengskulthai [79] combining TRIZ with Pugh matrix; Lee, Leu, and Huang [80] by using FMEA, lean production, TRIZ, and 6 sigma to improve services. Shealy et al. [81] combined brainstorming, Morphological Analysis, and TRIZ to generate better ideas.

Piccarozzi, Aquilani, and Gatti [82] argue that Industry 4.0 will affect changes in the management field, for example for new business models [83] or corporate strategies [84]. As previous studies have rarely adopted a more interdisciplinary approach, it is necessary to analyze firms from the perspective of being able to focus on the intersection of these domains, such as the appropriate firm organization model, the systematic way of doing business, and the relationship between factors within the firm that influence each other. In this study, the Su-Field analysis of the TRIZ method can be inferred that leadership has the desired effect on service management. However, with the adoption of Industry 4.0, with the increase in data and the change of the processing model, the original organization and leaders will not be able to take care of more and more customer management issues, and at this time, if we rely only on leadership, it will become the harmful effect of service management, as shown in Figure 1.

Based on the recommendations of the 76 Standard Solutions of TRIZ, this study adds organizational culture as a new substance to block the harmful effect. Meanwhile, leadership affects organizational culture, and organizational culture has a positive effect on service management (desired effect). Based on the literature and empirical findings of previous scholars, this study proposes a model framework using Su-field analysis (Figure 2) and establishes the following research hypotheses for the above concept:

Hypothesis 1 (H1).

From the F1 force field, leadership has a significant positive relationship with service management.

Hypothesis 2a (H2a).

From the F2 force field, leadership has a significant positive relationship with organizational culture.

Hypothesis 2b (H2b).

From the F4 force field, incentives have a significant positive relationship with organizational culture.

Hypothesis 2c (H2c).

From the F5 force field, organizational commitment has a significant positive relationship with organizational culture.

Hypothesis 3 (H3).

From the F3 force field, organizational culture has a significant positive relationship with service management.

3. Methodology

3.1. Research Structures and Methods

Partial least squares structural equation modeling (PLS-SEM) is a variance-based structural equation modeling technique that is used to model potential variables, especially combinatorial variables, and the relationships between them [85]. PLS-SEM supports prediction-oriented objectives (i.e., interpreting and predicting the target constructs in structural models). Its flexibility and its relatively high statistical power make PLS methods particularly suitable for SEM applications, aiming at prediction or theory construction [86,87,88]. Therefore, it is a useful tool for testing hypotheses and answering research questions, such as investigating mediation. Mediation refers to the existence of an intermediate variable or mechanism that transmits the effects of antecedent variables to the potential effects of consequent variables [89]. This study proposes a model using Su-field analysis, aiming to investigate the mediating relationship of organizational culture, and establishes the research framework as shown in Figure 3. The design of the questionnaire was modified regarding relevant theoretical literature. The first draft of the questionnaire was revised by 25 mechanical engineers after pre-testing by three leaders from different manufacturing companies who provided comments on the questionnaire items and semantics. In this study, two models were analyzed using the PLS method.

Our procedure can be summarized as (i) analyzing the hypotheses using partial least squares structural equation modeling (PLS-SEM), and (ii) validating the hypotheses by bootstrapping.

3.2. Measurement of Research Variables

Although this study targeted the machinery industry, there are differences in the environments and technologies of different machinery sub-industries, and it is, therefore, difficult to use an objective approach to measure it. Therefore, this study used subjective self-assessment to analyze the machinery industry. The reference sources for the constructs in this questionnaire related to leadership [90,91], organizational culture [92,93,94], incentives [95,96], organizational commitment [97,98], and service management [99,100]. All questions were scored on a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”).

3.3. Survey Subject

In this study, a questionnaire survey was conducted on the personnel of Taiwan’s industry, and the sampling subjects were the R&D, sales, and business personnel of Taiwan’s industry manufacturers with global supply capability, and their personnel are located all over the world with a representative. The respondents were selected to be the front-line personnel who would communicate with customers so that the company could have the most direct experience to grasp the customer-oriented behavior.

4. Results

4.1. Sample Analysis

The study will be preceded by an interview to explain that this study focuses on the influence of companies’ leadership style, organizational culture, and service management after the adoption of Industry 4.0-related technologies. The purpose of the study was explained through face-to-face interviews before the formal survey. The questionnaires were distributed and collected by the researchers, and the survey was conducted from 13 December 2018 to 22 February 2019. A total of 370 questionnaires were distributed, and 271 were collected. Twenty-one invalid samples with too many omitted questions were deleted, and finally, 252 valid questionnaires were obtained. The effective recovery rate was 92.99%, and the valid sample comprised 68.11%. Among the valid sample, male and female respondents accounted for 79.8% (201) and 20.2% (51) of the total respondents, respectively, which is representative of the fact the industry is male-dominated in Taiwan. The age structure of the respondents was normally distributed. The education level was mainly college/university.

SmartPLS (SmartPLS GmbH, 3.0, Bönningstedt, Germany) is a software that allows variance-based structural equation modeling (SEM) using partial least squares (PLS) path modeling methods [101]. The software can estimate path models with potential variables using PLS-SEM and also calculate standard outcome evaluation criteria [102]. In this study, the SmartPLS statistical software was used to conduct data analysis and examine the relationship between the constructs based on the recovered samples. The Lilliefors test (a modified version of the Kolmogorov–Smirnov test with a p-value > 0.2) was used to measure the normality of different measurement variables. The items that passed the Lilliefors test are listed in

Table 1. Items that passed the Lilliefors test.

Construct	Items	p-Value	Lilliefors Test
Leadership	B02. The management style of the leader can gain my respect.	0.877	Passed
	B04. The leader motivates me and the group to work together to achieve our work goals.	0.869	Passed
	B05. Leaders often encourage colleagues to self-growth.	0.833	Passed
	B07. When I feel neglected, the leader can give care in time.	0.866	Passed
	B08. When I complete a task successfully, the leader will express his appreciation in time.	0.845	Passed
	B09. I have clear methods and steps to improve my work quality.	0.819	Passed
	B15. My leader always accepts my new approach when I encounter a bottleneck at work.	0.748	Passed
Organizational Culture	C01. The company is like a big family, sharing work and life together.	0.823	Passed
	C02. The leader of the company has a mentor or parental figure.	0.865	Passed
	C03. The company will emphasize the importance of unity and cooperation.	0.828	Passed
	C04. Co-workers are innovative and willing to take risks	0.790	Passed
	C05. The company is committed to following the rules and regulations.	0.682	Passed
	C06. The leadership of a company is like a group of commanders or managers.	0.683	Passed
	C07. The company’s formal rules and regulations are an important force for employees to operate smoothly.	0.796	Passed
Incentives	D01. The company has a fair assessment system.	0.858	Passed
	D02. The development direction of the company is consistent with the efforts of employees.	0.842	Passed
	D04. No rigid bureaucracy in the company, allowing flexibility in the system	0.787	Passed
	D05. The company has an unobstructed promotion pathway.	0.856	Passed
	D06. The company has established a clear evaluation mechanism for reward and punishment.	0.850	Passed
	D08. The Company has specific incentives to encourage those who are brave enough to express different views or opinions.	0.828	Passed
	D09. The company has additional incentives for research on new things.	0.817	Passed
Organizational Commitment	G01. The company purchases or introduces patents of inventions from other companies to enhance its operations.	0.611	Passed
	G05. I am well trained in project execution.	0.881	Passed
	G07. I have clear instructions for the current work rules.	0.897	Passed
	G08. I have strict actions to control the progress of the project (PDM, ERP…).	0.793	Passed
	G09. I have clear methods and steps to improve my work quality.	0.880	Passed
Service Management	F01. Our customers are very satisfied with our company.	0.876	Passed
	F02. The company emphasizes the quality of service to customers.	0.739	Passed
	F05. All staff can quickly grasp the needs of customers.	0.874	Passed
	F06. Our staff can always actively interact with people.	0.848	Passed
	F07. The company will arrange courses and activities to help train colleagues’ communication skills.	0.684	Passed
	F08. Our colleagues are good at building long-term relationships with our customers.	0.775	Passed

.

4.2. Data Analysis

This study used PLS-SEM to examine and verify the logical relationships between hypotheses testing, the measurement model, and the structural model. SEM was conducted in two stages, following Anderson and Gerbing [103]. In the first stage, the measurement model was evaluated to understand the reliability and validity of the constructs, and PLS path analysis was used to assess the reasonableness of the hypotheses; for the sake of rigor, the structural model was evaluated by the bootstrap method in the second stage to verify the hypotheses about the causal relationships among the constructs and to check whether the paths of the model are definitely significant.

4.2.1. Evaluation of Measurement Models

The reliability analysis of the constructs in this study was based on the recommendation of Bagozzi and Yi [104] to use at least three of the most commonly used indicators. We selected Cronbach’s alpha, composite reliability (CR), average variance extracted (AVE), cross-loading, heterotrait–monotrait ratio (HTMT), and the Fornell–Larcker criterion to evaluate the measurement model.

In our measurement model, the factor loading of organizational culture on service management (Hypothesis H3) was greater than 0.50, a statistically significant level, but the factor loading of the other constructs was less than 0.50, which is generally not considered statistically significant [105]. Additionally, the factor loadings between the constructs were all greater than 0.50, which was statistically significant (see Figure 4). To verify the significance of the above assumptions, bootstrapping (5000 samples) was conducted.

Analysis of individual item reliability was conducted as follows. The statistical significance of the loading of each variable was measured by assessing the loading of potential variables (outer loading). The loadings of all variables were higher than 0.6 and significant, and the sample loadings’ coefficients ranged from 0.611 to 0.897. The variance inflation factor (VIF) for each indicator was less than five, indicating that there is no collinearity in the variables, following Hair, Ringle, and Sarstedt [106]. In terms of Cronbach’s alpha, Hair, Black, Babin, and Anderson [107] stated that a Cronbach’s alpha of 0.8 or higher and 0.7 or higher for the total scale and subscales, respectively, indicates good reliability. In this study, the Cronbach’s alpha of the questionnaire and the subscales of each component were above 0.8, indicating good reliability (see

Table 2. Composite reliability (CR) and average variance extracted (AVE) of the items.

Construct	Variables	Outer Loading	Variance Inflation Factor (VIF)	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)
Leadership	B02	0.877	4.386	0.929	0.943	0.702
	B04	0.869	4.212
	B05	0.833	2.575
	B07	0.866	3.304
	B08	0.845	2.992
	B09	0.819	2.472
	B15	0.748	1.812
Organizational Culture	C01	0.823	2.940	0.879	0.906	0.555
	C02	0.865	3.336
	C03	0.828	2.574
	C04	0.790	2.218
	C05	0.682	1.998
	C06	0.683	1.944
	C07	0.796	2.224
Incentives	D01	0.858	3.030	0.927	0.941	0.696
	D02	0.842	2.703
	D04	0.787	2.334
	D05	0.856	3.225
	D06	0.850	2.866
	D08	0.828	2.834
	D09	0.817	2.971
Organizational Commitment	G01	0.611	1.334	0.873	0.910	0.672
	G05	0.881	2.742
	G07	0.897	3.211
	G08	0.793	2.047
	G09	0.880	2.890
Service Management	F01	0.875	3.268	0.888	0.916	0.646
	F02	0.738	2.062
	F05	0.873	3.055
	F06	0.853	2.743
	F07	0.689	1.605
	F08	0.777	1.867

).

In this study, the CR and AVE of the potential variables were used for convergent validity analysis, as suggested by Fornell and Larcker [108]. Table 2 shows that the CR values ranged from 0.906 to 0.943, higher than the threshold of 0.8 suggested by Hair, Ringle, and Sarstedt [109], indicating good internal consistency of the study’s model. The AVE of potential variables was used to calculate the explanatory power of each measured variable on the potential variables. Table 2 shows that the AVE values of the potential variables ranged from 0.555 to 0.702, higher than the standard value of 0.50 suggested by Bagozzi and Yi [104], indicating that the model has good discriminant validity and convergent validity.

This study analyzed the degree of correlation between the factors and variables in terms of cross-loading. In this study, the factor loadings of the individual factors between the constructs were greater than 0.50, which was statistically significant. In the structural model, the standardized path coefficients were statistically significant, and the self-loadings of each component were larger than the cross-loadings, indicating good convergent validity and discriminant validity of this measure, as shown in

Table 3. Cross loading of each factor.

Construct	Items	Incentives	Organizational Commitment	Organizational Culture	Leadership	Service Management
Leadership	B02.	0.623	0.505	0.660	0.877	0.538
	B04.	0.597	0.535	0.682	0.869	0.538
	B05.	0.535	0.479	0.647	0.833	0.472
	B07.	0.620	0.555	0.633	0.866	0.553
	B08.	0.525	0.496	0.618	0.845	0.510
	B09.	0.497	0.413	0.542	0.819	0.469
	B15.	0.577	0.497	0.579	0.748	0.538
Organizational Culture	C01.	0.661	0.615	0.823	0.694	0.660
	C02.	0.686	0.611	0.865	0.724	0.656
	C03.	0.573	0.544	0.828	0.618	0.609
	C04.	0.679	0.635	0.790	0.669	0.653
	C05.	0.466	0.495	0.682	0.381	0.467
	C06.	0.438	0.427	0.683	0.376	0.437
	C07.	0.632	0.615	0.796	0.567	0.651
Incentives	D01.	0.858	0.666	0.691	0.612	0.722
	D02.	0.842	0.641	0.740	0.624	0.703
	D04.	0.787	0.523	0.571	0.551	0.630
	D05.	0.856	0.617	0.590	0.554	0.633
	D06.	0.850	0.663	0.654	0.568	0.612
	D08.	0.828	0.662	0.653	0.570	0.662
	D09.	0.817	0.656	0.566	0.469	0.593
Organizational Commitment	G01.	0.490	0.611	0.441	0.331	0.524
	G05.	0.701	0.881	0.678	0.560	0.696
	G07.	0.688	0.897	0.672	0.522	0.720
	G08.	0.533	0.793	0.501	0.425	0.565
	G09.	0.667	0.880	0.634	0.561	0.732
Service Management	F01.	0.705	0.651	0.632	0.512	0.875
	F02.	0.481	0.541	0.556	0.407	0.738
	F05.	0.686	0.677	0.655	0.574	0.873
	F06.	0.651	0.694	0.632	0.511	0.853
	F07.	0.643	0.697	0.590	0.462	0.689
	F08.	0.593	0.574	0.601	0.500	0.777

.

Although cross-loadings have been widely used to assess discriminant validity in PLS-SEM, Henseler, Ringle, and Sarstedt [110] suggested that the HTMT should be used to further assess the discriminant validity between the constructs to enhance sensitivity. As shown in

Table 4. HTMT and Fornell–Larker Criterion.

HTMT	Incentives	Organizational Commitment	Organizational Culture	Leadership	Service Management
Incentives	-	-	-	-	-
Organizational Commitment	0.838	-	-	-	-
Organizational Culture	0.834	0.807	-	-	-
Leadership	0.727	0.652	0.788	-	-
Service Management	0.858	0.893	0.844	0.679	-
Fornell–Larcker Criterion	Incentives	Organizational Commitment	Organizational Culture	Leadership	Service Management
Incentives	0.834	-	-	-	-
Organizational Commitment	0.760	0.820	-	-	-
Organizational Culture	0.770	0.725	0.745	-	-
Leadership	0.680	0.596	0.746	0.838	-
Service Management	0.783	0.797	0.762	0.618	0.804

, the HTMT value for each variable is below 0.9, representing a fair level of discriminant validity across the constructs [111]. The Fornell–Larcker criterion was higher than the recommended value of 0.6, indicating high internal consistency of all measured variables [108].

4.2.2. Evaluating Structural Models

In this study, the coefficient of determination (R²) and Cohen’s f² were used to evaluate the explanatory power and fitness of the overall study model [112]. The higher the R² value, the higher the explanatory power of the model [107]. In this study, the R² of leadership, incentives, and organizational commitment on organizational culture was 0.713, and the R² of leadership and organizational culture on service management was 0.587, indicating that the explanatory power of the latent variables was above 0.5 (see Figure 3), revealing its robustness and stability. Cohen’s f² can be used to assess whether the exogenous variables have significant explanatory power for the endogenous variables. In this study, the effect of organizational culture on service management (0.5) and leadership on organizational culture (0.3) is consistent with Cohen’s [113] classification of f² > 0.5 as a large effect and f² > 0.3 as a medium effect.

For the model fitness analysis, standardized root mean square residual (SRMR) and normed-fit index (NFI) were used as judgment criteria. The SRMR of this study was 0.067, which is less than the threshold of 0.08 that is deemed an acceptable fit [114]. However, the NFI was 0.805, higher than 0.8 which was suggested as an acceptable standard by Hair et al. [115]. From the above indicators, we can conclude that the model generally fits well and meets the requirements for overall fit established in the literature.

4.3. Research Hypotheses Validation and Discussion

To ensure rigor, this study used the bootstrap method (bootstrapping) to test the research hypotheses. Bootstrapping uses the observed sample to estimate the parent and then re-samples the estimated parent to implement statistical inference. In most cases, the approximation provided by bootstrapping is more precise than the commonly used limit approximation and works well for small data sets [116]. Its advantage lies in its ability to correct for the limitation that the study model does not conform to the normal distribution, to obtain more precise estimates [117]. As suggested by Hair, Ringle, and Sarstedt [106], 5000 samples should be used to obtain more stable results; i.e., 5000 repetitions of sampling can verify the original PLS model more precisely. The t-value for all questions in this study was greater than 2.57, indicating that each question reached a significance level of 1% or more (p < 0.01), as shown in Figure 5.

The PLS model used to investigate the factor loading of organizational culture on service management (Hypothesis H3) was greater than 0.50, a statistically significant level, but the factor loading of the other constructs was less than 0.50, which is generally not considered to be statistically significant (see Figure 2). However, after 5000 bootstrapping samples, the t-value of leadership to service management reached 1.656, a significance level of 10% (p < 0.1); the other constructs were significant at the 1% level (p < 0.01) (see Figure 3). The path relationship between the constructs was then determined by bootstrapping, and the path values are listed in

Table 5. Results of hypotheses validation.

Hypothesis	Path Relations	Path Value	Result
H1	Leadership → Service Management	1.656 *	Valid
H2a	Leadership → Organizational Culture	6.650 **	Valid
H2b	Incentives → Organizational Culture	4.335 **	Valid
H2c	Organizational Commitment → Organizational Culture	3.869 **	Valid
H3	Organizational Culture → Service Management	10.712 **	Valid

**: p < 0.01, *: p < 0.1.

. Four of the five hypotheses (H2a, H2b, H2c, and H3) were found to be significant at the 1% level (p < 0.01), and one hypothesis (H1) was found to be significant at 10% (p < 0.1); i.e., hypotheses H1, H2a, H2b, H2c, and H3 were supported using bootstrapping. Therefore, the empirical results show that service management is more affected by organizational culture, and the factors that affect organizational culture are leadership, incentives, and organizational commitment (Table 5).

The results of this study show that organizational culture is the most important factor affecting service management, followed by leadership. In the past, service management was handled by leaders or service supervisors, so leadership would influence the success or failure of service management; however, for the cooperative type of Industry 4.0 networking, the decision-making process would be dispersed between people and machines through the data and information circulating among the enterprise modules, and the influence of organizational culture would be highlighted instead. Therefore, how to motivate employees and how they treat the management level of the system will still be factors that affect service management.

5. Conclusions

Many companies tend to focus on the application of new technologies when adopting Industry 4.0 but neglect the impact of non-technical factors. The adoption of Industry 4.0 involves leadership, organizational culture, and employee response to new technologies, all of which will change the way companies manage their customers and have an impact on their sustainability. Many companies are moving into Industry 4.0 and trying to integrate their organizations more closely with data, but many fail because of the relationship between leaders and the organization, which in many cases is blamed on customer demands, which is a matter of service management for the company’s customers. However, previous research has not examined the interactions between the factors that contribute to the implementation of Industry 4.0. In this study, the key to adopting Industry 4.0 was found to be the transformation of the organizational culture in the initial case study. The original top-down leadership of K Company was transformed through the process of introducing Industry 4.0 into a transformed production process reflected by employees at each level, which led to the subsequent investigation of the factors influencing the adoption of Industry 4.0 in Taiwan. This study proposes a research model that covers these structures. The results of this study confirm the important role of organizational culture in adopting Industry 4.0. In addition, employees are resistant and the manager’s leadership with incentives will enhance the cooperation of employees, which can increase the value of service management through the combination of Industry 4.0 technologies.

With the promotion of Industry 4.0, the larger the enterprise is, the greater the amount of data will increase, but the result-oriented and intelligent organizational adjustment will also change the organizational culture. This study found the importance of organizational culture in influencing service management. Therefore, this study expanded the scope of previous studies that only investigated leadership or organizational culture and combined the TRIZ and PLS-SEM methods to derive and validate a reasonable service management model. The empirical results show that organizational culture has a positive mediating effect on leadership and service management when companies adopt Industry 4.0. When employees are confronted with a large amount of data and information in Industry 4.0, they are mostly resistant to the collaborative relationship between humans and machines, which leads to changes in organizational culture. Leadership certainly affects organizational culture, but good incentives for employees will positively affect the service management results of the organization. Conversely, employee self-commitment to the organization also affects the organizational culture, meaning that employees with a high degree of self-discipline and commitment to stay on task and achieve organizational goals will change the organization and thus affect service management. Although the development of a company depends on its leaders, who are responsible for shaping an open organizational culture, companies should strengthen the cooperation between people and machines based on the data of Industry 4.0. Compared to the past, when companies often relied on the charisma of their leaders, the introduction of Industry 4.0 has highlighted the importance of corporate organizations in decision making, which has led to the development of a decentralized concept of organizations, naturally reducing the occurrence of decision errors.

This study can help understand how companies can increase their service management effectiveness even if in the form of outsourcing. However, it is still important to establish an organizational culture. The implications of this study for business management are that operators can actively promote human-machine collaboration through Industry 4.0 technologies but must continue to be oriented towards service management. Companies must accurately organize their networks with other units and coordinate with each other to ensure that the right information is available to target users. The data analysis and CPS of Industry 4.0 can implement decision making between humans, between machines, or between humans and machines, and facilitate the connection within the value chain and improve agility in response to environmental changes with the support of IoT and intelligent technologies and realize the service-oriented architecture (SOA) through the information exchange provided by the system. Of course, enterprises are composed of people. Although Industry 4.0 has enabled closer cooperation between humans and machines, improved work efficiency, and improved services to customers, employees are facing the adoption of new technologies, and their work will be split up and they will have resistance. Therefore, companies must establish good incentives to obtain a good organizational commitment from employees so that they can continue to implement Industry 4.0 sustainably.