A Socio-Technical Study of Industry 4.0 and SMEs: Recent Insights from the Upper Midwest

Abstract

The implementation of Industry 4.0 is becoming increasingly prevalent in the manufacturing industry since its inception. The purposeful joint optimization of social and technical factors of organizations is imperative to the successful adoption of these newer technologies. This paper shares the outcomes of a survey conducted among a group of small, medium, and large manufacturers in Minnesota and North Dakota. The survey posed questions based on a socio-technical theory framework, Industry 4.0, and productivity outcomes. Insights were provided into how regional manufacturers were utilizing the socio-technical design framework to both integrate Industry 4.0 into the organizational design and extract value, such as increased productivity. This research identifies potential challenges, as well as advantages in the current socio-economic landscape for manufacturers that may be both impeding and encouraging the development of a competitive and sustainable manufacturing business.

1. Introduction

The use of Industry 4.0 is becoming increasingly prevalent among industries, such as manufacturing. Industry 4.0 integration’s goals are to support more autonomous, decentralized, and responsible work teams to move toward customized production [1]. The Internet of Things, cloud computing, and big data and analytics create the opportunity to capture and leverage real-time data from products, processes, services, and people. These multi-faceted innovations will develop long-term solutions that create stronger sustainability, human resources, and supply chains [2]. Additionally, the vertical, horizontal, and end-to-end integration methodologies of these new enabling technologies will aid in the implementation of Industry 4.0 systems [3].

Industry 4.0 technologies are critical to enhancing innovation and the competitive advantage of manufacturers in local and global markets through a combination of new digital technologies, products, and services [4]. Socio-technical theory is an organizational theory that focuses on jointly optimizing the social and technical components of a work organization. Socio-technical theory was introduced by Trist and Bamford in 1951 as they studied the work-flow processes of coal miners. Socio-technical theory define employees as a resource to develop [5]. The theory details the social, technical, work organization, and design of internal and external factors affecting the work environment. According this theory, a business socially comprises employee engagement, employee development, knowledge, safety, personal interests, skills, and experience. The technical elements of businesses are described as operations, processes, tools, facilities, equipment, inventory, maintenance, and information flow. The work aspects involve policies, rules, procedures, instructions, information flows, teams, employee shifts, training, planning, and integration. The design principles of goals, cyber-physical connection, information, transparency, decentralized decision-making, and employee input contribute to the joint optimization of the social and technical elements of an organization. Market demands, production processes, employee condition improvement, financial/economic environment, regulatory environment, and customization represent environmental work elements [6].

Socio-technical system design supports sustainable development within organizations. Additionally, Industry 4.0 technologies are proving to support environmental sustainability goals, as manufacturing processes are completed with improved efficiency while relying on fewer resources for total production [7]. Industry 4.0 tools are enabling technologies to assist small, medium, and large businesses with the sharing of information and collaboration between individuals, accessibility and security of the flow of data, mobility of work times and locations, and improved productivity and working conditions for employees [8]. Industry 4.0 serves well an organizational design that requires agility to address continuous, complex, and non-linear change. Reasons for implementing Industry 4.0 are consumer demands for customization, supply chain complexity, and globally dispersed production, competition, and labor challenges [9,10]. In jointly optimizing Industry 4.0 with socio-technical work design, observations of increased performance with the technical system and Operator 4.0, job satisfaction, increased safety, a collaborative economy and improved economic outcomes have been reported [11,12]. Strong technology adoption rates are also indicated with the use of socio-technical theory [6].

In this paper, a socio-technical framework provided by Davis et al. is used to assess the interdependent nature of work systems that support both predictive work and design [13]. The socio-technical framework consists of three external factors and six internal factors. The external factors have been identified to influence the connections between technological and social aspects, including regulation, financial circumstances, and stakeholders. The internal factors of organizations can be categorized into three technological aspects: technology, infrastructure, process, and three social aspects, namely goals, people, and culture [14,15]. This analysis of technical, organizational, and employee-related factors is holistic and offers insights into the correlations among the five socio-technical constructs. These constructs are data gathering, analysis, and interpretation; summarization; testing; iterating; and amending with Industry 4.0 integration; and productivity outcomes among small, medium, and large manufacturers in North Dakota and Minnesota [13].

The existing literature gap encouraged our aims with this study to assess the socio-technical dimensions of manufacturers in North Dakota and Minnesota in the context of Industry 4.0 adoption. According to the National Association of Manufacturers, as of 2019, there were 624 manufacturing firms in North Dakota and 6387 manufacturing firms in Minnesota [16]. These manufacturing firms employ 6.32% of non-farm workers in North Dakota and 11.08% of non-farm workers in Minnesota as of 2021. Additionally, due to North Dakota and Minnesota’s low unemployment rates of 2% and 2.9%, respectively, as of June 2023, the assessment is also imperative of the integration of human factors through the implementation of the socio-technical theory framework to support sustainability plans [16]. The study is especially needed to assess regional manufacturing firms’ competitiveness in the Industry 4.0 context using the lens of socio-technical design.

This paper seeks to address the following research questions to assess the readiness of organizations in adopting Industry 4.0.

• Research Question 1: How applicable are socio-technical design principles in the Industry 4.0 context among North Dakota and Minnesota manufacturers?
• Research Question 2: Is there a positive correlation between Industry 4.0 and increased productivity among manufacturers in North Dakota and Minnesota?
• Research Question 3: Is there a positive correlation between socio-technical design principles and increased productivity?

In analyzing these research questions, the paper provides a literature review and a case study assessing small, medium, and large businesses in Minnesota and North Dakota.

2. Research Context

2.1. Socio-Technical Design

The structure of work, technology, and design practices are ever changing, informing the use of socio-technical principles and applications. The work organization operates with goals and metrics and the aid of people of varying skills and attitudes, using a range of technologies and tools, leveraging infrastructures, holding specific cultural orientations, and following a set of processes. As mentioned previously, a work organization is a system that operates within the external environment and is impacted by regulatory frameworks, stakeholders, and the economic and financial environment. The socio-technical analysis offered by Davis et al. outlined that the socio-technical framework includes the constructs of data gathering, analysis and interpretation, summarization, testing, and iterating and amending. This socio-technical framework assists with predictive work and is significant in designing and overseeing project implementation. The themes of end-user engagement and team-based approaches promote sustainability. Socio-technical systems design includes incompletion, in which continuous improvement is fostered [13].

The framework for applying socio-technical design may differ based on variables such as the size of the business. Participative design is a democratic process of integrating the norms and values within the organizational culture into a system’s design. The employee is the participant and provides direct input with regard to design, length of participation in the design process, the significance of the input, and the level of decision-making [17]. The three principles guiding socio-technical design per Bastidas et al. are trustworthiness and trust among stakeholders, technical design correlations with organizational context, and access to resources and solutions that directly align with organizational culture. Designing socio-technical work organizations also considers the assessment of competencies, developing cross-disciplinary leaders and defining the tasks of the Industry 4.0 technologies [18]. Furthermore, the socio-technical skill of team fluidity is essential for successfully implementing Industry 4.0 [19]. Additionally, a study conducted by Marcon et al. found that joint optimization of the social and technical aspects will lead to greater technology adoption rates [6].

Socio-technical attributes encourage team responsibility [20]. Organizational learning is achieved through the empowerment of employees, creating responsible autonomy observed at both the individual and team levels. Responsibility is the foundation for control, which is found in skill discretion and task authority [21]. Sharing autonomy is an operational concept that sustains the dynamic relationship between individual autonomy and collective behavior [22].

2.1.1. Industry 4.0 Adoption Differences among Small, Medium, and Large Manufacturers

The key design principles of Industry 4.0 are interoperability and predictability [19]. The cyber-physical information flow processes are information acquisition, information analysis, decision selection, decision implementation, and innovation [22]. Studies have shown that small and medium-sized enterprises resist sharing data within the supply chain due to data security and negotiation power [19]. Small, medium, and large businesses choose to adopt Industry 4.0 for a variety of reasons. These reasons include increased productivity, customized software, increased customer care, and reduced labor requirements. McDermott et al. conducted a study of small, medium, and micro enterprises in the west of Ireland in 2022. The popular tools indicated to be helpful in this study by survey respondents were automation, smart processes, automated inspection, and cloud computing. The aims for adopting Industry 4.0 within small, medium, and micro enterprises were improved customer experience, reduced costs, improved long-term outlook for the business, improved quality, increased profits, and improved capacity [23]. The study also found that having the right equipment or software solution, having knowledgeable employees, having consultancy support, and having an adequate budget were critical success factors. A factor that the study considered as indicating a lack of Industry 4.0 adoption was the absence of leadership vision tethered to innovation through the use of new technologies [23].

Obstacles exist to adopting Industry 4.0, such as increased unemployment generation, data vulnerability, and challenges to device interconnection. Short-term risks to adopting new technologies include lack of employee expertise and short-term strategy [24]. Moeuf et al. reported that small and medium businesses have the potentially downside characteristics of local management, short-term strategy, lack of expertise, non-functional organization, limited resources, short-hierarchical lines, and lack of methods and procedures, which are challenges to adopting Industry 4.0. The study suggested simplifying a tool’s use, such as a cloud computing platform for big data analysis to “offset a lack of technical competency, which is characteristic of the small and medium enterprise context”. Another characteristic highlighted in the report by Moeuf et al. is that small and medium-sized businesses are innovative, entrepreneurial, and studious. Relaying the importance of data and orienting the small and medium-sized manufacturing operator toward leveraging data for regular operational use are catalysts for Industry 4.0 adoption and require the application of socio-technical theory design principles, as they imply continuous improvement processes. Implementing Industry 4.0 for these business sizes requires a leader specifically assigned to the Industry 4.0 project, who will engage in the necessary communication, have the necessary skills, factor in training versus consulting, and ensure the simplification of Industry 4.0 tools [25].

Managing the Industry 4.0 transformation in small and medium-sized enterprises (SMEs) differs compared to large enterprises. As mentioned above, small and medium-sized enterprises tend to focus on short-term strategies and objectives. Flexibility is used when assessing new opportunities and challenges. Processes may not be in place compared to more organizationally mature large enterprises. Additionally, in comparison, large companies often strategize digital transformation throughout a larger architectural footprint. Systems are implemented to acquire vast amounts of data and initiate data valorization projects to leverage insights from the data acquired [26]. Success factors for small and medium-sized businesses that were highlighted by Brodeur et al. are aligning Industry 4.0 with business strategy, leadership, aligning along a hierarchical line, conducting a study prior to Industry 4.0 projects, managing communication, teamwork and team composition, employee training and knowledge management, organizational culture and change management, project management, and continuous improvement strategies. Continuous improvement strategies foster the development of employees’ agility to learn new tools and processes [26].

Throughout the process, self-evaluation translates into the capacity to execute and manage Industry 4.0 transformation, assessment of financial abilities, employees’ expertise and experience in Industry 4.0 technologies and projects, internal project management and continuous improvement processes, employees’ and managers’ resistance to change, and external resource availability. The company’s management and key employees should be actively engaged in the evaluation process and communicate openly the results with the SME management [26]. Industry 4.0 technological solutions will differ among small, medium, and large businesses. Implementation standards among groups will vary. Research has also shown that small and medium-sized businesses are often suppliers for larger businesses that are operating on Industry 4.0 principles. Digital transformation applied to logistics will realize efficiency gains along the entire supply chain [27].

Furthermore, the differences impacting Industry 4.0 implementation outlined between SMEs and large businesses are as follows. SMEs have barriers to accessing financial resources, advanced manufacturing technologies, research and development, standards, and strategic partnerships with universities and research institutions. Moreover, SMEs utilize software that is tailored and therefore not standardized. Additionally, there are few knowledge carriers, and the leader is responsible for decision making. The SME organization is informal and simpler. The SMEs’ human resources operate in a variety of domains. The SMEs’ industry knowledge and experience are very specific. They are dependent on collaborative networks. Large businesses make decisions through boards of advisors and internal and external consultants. Large businesses exhibit contrary characteristics to SME traits [27].

2.1.2. The Nature of Small, Medium, and Large Manufacturers—The Changing Environment and Current Skills Gaps

The constructs of management, operations, and technology readiness directly correlate with the readiness of an organization to implement Industry 4.0 technologies [28]. Employees will be required to develop new skillsets to manage the utilization of cyber-physical systems, the Internet of Things, cloud computing, enterprise resource planning, radio frequency identification, and social product development as key technologies in the manufacturing setting.

3. Research Methodology

As part of this study, an online qualitative survey was utilized, which captured the individual responses of small, medium, and large enterprises in North Dakota and Minnesota. A qualitative survey was used to ascertain the level of socio-technical organizational design utilized and the implementation of Industry 4.0 among business sizes. Additionally, an outcome of productivity increases due to Industry 4.0, socio-technical design, and/or business size implementation was assessed. This study included participants from 24 small, medium, and large manufacturers in North Dakota and Minnesota. The unit of analysis used in this research was the employees’ opinions about the organizational changes observed using socio-technical theory. Random sampling from each business category was conducted in this anonymous study. Respondents represented small, medium, and large manufacturers. Thus, a variety of socio-economic contexts was sampled.

Participant enterprise details were found from the authors’ LinkedIn networks, UsBizData.com, Impact Dakota, and the Minn-Dak Manufacturers Association. The online survey consisted of 20 questions, and one format of the survey was provided to all respondents to ensure the consistency and comparability of the qualitative study. The survey participants were anonymous. The geolocation of respondents was identified through using Qualtrics Survey software analytics. A five-level Likert Scale was used with the following five response options: (1) Extremely unlikely, (2) Somewhat unlikely, (3) Neither likely nor unlikely, (4) Somewhat likely, and (5) Extremely likely.

The online survey process was selected to streamline and expedite the information-gathering stage. The online survey link was sent through Qualtrics to approximately 750 small, medium, and large manufacturers. A total of 24 responses were received providing viable information over a period of approximately one month, yielding a response rate of 3%. Of the 24 respondent enterprises from North Dakota and Minnesota, there were 10 small, six medium-sized, and eight large manufacturers that responded from North Dakota and Minnesota. Small businesses were considered to have fewer than 50 employees; medium-sized businesses were considered to have between 50 and 250 employees; and large businesses were considered to have more than 250 employees.

4. Results

It was stated upfront in the message sent to respondents that anonymity and confidentiality would be ensured. Pearson’s correlation coefficient was used to check the pairwise linear relationships. The relationships tested were among the socio-technical constructs, Industry 4.0 integration, productivity increases, and business sizes among manufacturers surveyed in North Dakota and Minnesota.

The manufacturing businesses were assessed for the current state of socio-technical design in manufacturing setting and Industry 4.0 integration.

The manufacturing industry was assessed for the Minnesota and North Dakota markets. In Minnesota there were four (4) medium-sized businesses and two (2) large businesses that responded. In North Dakota, there were 18 manufacturing businesses with ten (10) representing small businesses, two (2) representing medium-sized businesses, and six (6) representing large businesses.

The following questions were posed and pertain to the first socio-technical construct of data gathering.

How likely is your organization to gather relevant data from appropriate sources to assist in predicting solutions for integrating digital technology? (This was question 3: Q3.)

How likely is your organization to systematically consider the relationships between internal and external factors to identify the contingencies and direction of relationships? (Q7)

How likely is your organization to consider that a given end state or result may be reached by many potential means with each of an organization’s six dimensions of goals, people, buildings/infrastructure, technology, culture, and process/procedures? (Q14)

The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota businesses in the manufacturing industry for the first question were 83.3% and 77.77%, respectively. Similar Likert responses for the second question on the first socio-technical construct of Data Gathering for Minnesota and North Dakota were 66.66% and 72.22%, respectively. Similar Likert responses for the third question on the first socio-technical construct of Data Gathering for Minnesota and North Dakota were 83.33% and 8.33%, respectively.

The following questions were posed and pertain to the second socio-technical construct of Analysis and Interpretation.

How likely is your organization to analyze and classify data collected in your organization to support organizational design? (Q4)

How likely is your organization to consider the implication of the external environment as it relates to the organizational design? (Q6)

How likely is your organization to engage in self-inspection to identify the origin of variance? (Q14)

The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota businesses in the manufacturing industry for the first question were 66.67% and 72.22%, respectively. Similar Likert responses for the second question on the second socio-technical construct of Analysis and Interpretation for Minnesota and North Dakota were 50% and 72.22%, respectively. Similar Likert responses for the third question on the second socio-technical construct of Analysis and Interpretation for Minnesota and North Dakota were 66.67% and 77.78%, respectively.

The following questions were posed and pertain to the third socio-technical construct of Summarizing the Findings.

How likely is your organization to identify and group key system factors using visual aids, such as infographics? (Q5)

How likely is your organization to generate key inferences regarding the system and how it works to support predictive work? (Q11)

The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota businesses in the manufacturing industry for the first question on the third socio-technical construct of Summarizing the Findings were 83.33% and 55.56%, respectively. Similar Likert responses to the second question on the third socio-technical construct for MN and ND, were 83.33% and 66.66%, respectively.

The following questions were posed and pertain to the fourth socio-technical construct of Testing the Results with Stakeholders.

How likely is your organization to visually consider internal and external dimensions of the work organization to assess underexplored or related areas? (Q7)

How likely is your organization to include feedback or test analysis from key stakeholders for accuracy, omissions, and interpretations in the organizational design process? (Q9)

How likely is your organization to diversify the resources utilized among various dimensions by supervisors, technicians, and managers? (Q16)

How likely is your organization to allow for the employee growth through organizational design without peer pressure to support high-quality work? (Q18)

The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota businesses in the manufacturing industry for the first question on the fourth socio-technical construct of Testing the Results with Stakeholders were 83.33% and 61.11%, respectively. Similarly, Likert responses to the second question on the fourth socio-technical construct for Minnesota and North Dakota were 50% and 55.55%, respectively. Similar Likert responses to the third question on the fourth socio-technical construct for Minnesota and North Dakota were 83.33% and 66.66%, respectively. Similar Likert responses to the fourth question on the fourth socio-technical construct for Minnesota and North Dakota were 66.66% and 61.11%, respectively.

The following questions pertain to the fifth socio-technical construct of Iterating and Amending as Necessary.

How likely is your organization to modify the organizational design process after discussion? (Q10)

How likely is your organization to design information systems to provide information in the first place when action is needed? (Q17)

How likely is your organization to task multidisciplinary teams to continuously evaluate and review the work system design process? (Q19)

How likely is your organization to add any relevant factors to the organizational design that emerge from the data during analysis or following previous steps? (Q20)

The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota businesses in the manufacturing industry for the first question on the fifth socio-technical construct of Iterating and Amending as Necessary were 66.67% and 83.33%, respectively. The similar Likert responses to the second question on the fifth socio-technical construct for Minnesota and North Dakota were 100% and 72.22%, respectively. Similar Likert responses to the third question on the fifth socio-technical construct for Minnesota and North Dakota were 83.33% and 66.66%, respectively. Similar Likert responses to the fourth question on the fifth socio-technical construct for Minnesota and North Dakota were 66.67% and 72.22%, respectively.

Question 12 addressed Industry 4.0 specifically. It read, “How likely is your organization to align the organizational design with Industry 4.0 integration?”. The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota were 16.67% and 38.89%, respectively.

Question 21 addressed the variable of productivity. It read, “How likely is your organization to observe increased productivity per employee due to the implementation of organizational design?” The combined Likert scale responses of Somewhat Likely and Extremely Likely from Minnesota and North Dakota were 50% and 33.33%, respectively.

The tables are organized with the top numbers being the Pearson’s correlation coefficient and the lower numbers the p-values. The null hypothesis between the variables was zero. The survey questions are included in Appendix A.

Table 1. All Minnesota and North Dakota Manufacturers Surveyed.

Pearson Correlation Coefficients, N = 24 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21	Q1
C1 Data Gathering	1.00000	0.78702	0.66922	0.59699	0.60184	0.68726	−0.17301	0.03850
		<0.0001	0.0003	0.0021	0.0019	0.0002	0.4188	0.8582
C2 Analysis and Interpretation	0.78702	1.0000	0.66179	0.63047	0.60041	0.62221	−0.35932	0.00501
	<0.0001		0.0004	0.0010	0.0019	0.0012	0.0846	0.9815
C3 Summarization	0.66922	0.66179	1.00000	0.63335	0.70082	0.66714	0.01948	−0.19964
	0.0003	0.0004		0.0009	0.0001	0.0004	0.9280	0.3496
C4 Testing	0.59699	0.63047	0.63335	1.00000	0.80213	0.65348	−0.25893	−0.31335
	0.0021	0.0010	0.0009		<0.0001	0.0005	0.2218	0.1360
C5 Iterate and Amend	0.60184	0.60041	0.70082	0.80213	1.00000	0.67129	−0.21569	−0.36567
	0.0019	0.0019	0.0001	<0.0001		0.0003	0.3114	0.0789
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.68726	0.62221	0.66714	0.65348	0.67129	1.00000	−0.18761	−0.06654
	0.0002	0.0012	0.0004	0.0005	0.0003		0.3800	0.7574
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	−0.17301	−0.35932	0.01948	−0.25893	−0.21569	−0.18761	1.00000	0.23030
	0.4188	0.0846	0.9280	0.2218	0.3114	0.3800		0.2790
Q1 How many employees are in your work organization?	0.03850	0.00501	−0.19964	−0.31335	−0.36567	−0.06654	0.23030	1.00000
	0.8582	0.9815	0.3496	0.1360	0.0789	0.7574	0.2790

displays the outcomes of all 24 manufacturers surveyed in Minnesota and North Dakota. The table measures socio-technical constructs, which are noted as C1 through C5, Industry 4.0 integration (Q12), productivity increases (Q21), and small, medium, and large business sizes (Q1). A strong and positive Pearson’s correlation coefficient is observed between the five socio-technical constructs (C1–C5) and Industry 4.0 integration (Q12). A negative relationship was observed among Data Gathering (C1), Analysis and Interpretation (C2), Testing (C4), Iterating and Amending as Necessary (C5), and Increased Productivity (Q21). A weak, positive relationship was observed between Summarization (C3) and Increased Productivity (Q21). A positive relationship was observed between (C4) Testing and (C5) Iterate and Amend. A weak, positive relationship was observed among (C1) Data Gathering, (C2) Analysis and Interpretation, and (Q1) business size. A strong positive, correlation exists among the five socio-technical constructs C1 through C5. Aggregately, the business size (Q1) had a weak, positive or negative correlation to socio-technical constructs (C1–C5). One small manufacturer from North Dakota responded affirmatively on the Likert scale to Industry 4.0 integration (Q12) and Increased Productivity (Q21). Additionally, Testing the Results with Stakeholders (C4) appeared to have a less robust positive correlation with Data Gathering (C1), Analysis and Interpretation (C2), and Summarization (C3). Moreover, Iterating as Necessary had a less robust positive correlation with Data Gathering (C1) and Analysis and Interpretation (C2). Further, a negative correlation between Industry 4.0 integration (Q12) and increased productivity (Q21) existed. Aggregately, the business size had a weak, positive or negative correlation to socio-technical constructs. One small manufacturer from North Dakota responded affirmatively on the Likert scale to both questions.

Table 2. Small Manufacturers in Minnesota and North Dakota Integrating Industry 4.0 (Q12).

Pearson Correlation Coefficients, N = 10 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21
C1 Data Gathering	1.00000	0.73155	0.73828	0.65554	0.90057	0.77874	−0.17848
		0.0162	0.0148	0.0396	0.0004	0.0080	0.6218
C2 Analysis and Interpretation	0.73155	1.00000	0.68153	0.84028	0.82563	0.52700	−0.39377
	0.0162		0.0300	0.0023	0.0033	0.1175	0.2602
C3 Summarization	0.73828	0.68153	1.00000	0.71746	0.72604	0.85856	0.11806
	0.0148	0.0300		0.0195	0.0174	0.0015	0.7453
C4 Testing	0.65554	0.84028	0.71746	1.00000	0.79590	0.58580	−0.33696
	0.0396	0.0023	0.0195		0.0059	0.0752	0.3410
C5 Iterate and Amend	0.90057	0.82563	0.72604	0.79590	1.00000	0.61916	−0.23724
	0.0004	0.0033	0.0174	0.0059		0.0563	0.5093
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.77874	0.52700	0.85856	0.58580	0.61916	1.00000	0.21921
	0.0080	0.1175	0.0015	0.0752	0.0563		0.5429
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	−0.17848	−0.39377	0.11806	−0.33696	−0.23724	0.21921	1.00000
	0.6218	0.2602	0.7453	0.3410	0.5093	0.5429

displays the 10 responses to integrating Industry 4.0 by small manufacturing businesses in North Dakota and Minnesota. A weak, positive relationship between Industry 4.0 integration (Q12) and Increased Productivity (Q21) was observed. A strong, positive correlation between the socio-technical constructs of C1 through C5 and Industry 4.0 integration (Q12) was observed. Of the five socio-technical constructs, Analysis and Interpretation (C2) and Testing (C4) correlated the least positively with Industry 4.0 integration (Q12). A strong, positive correlation among the socio-technical constructs of C1 through C5 was observed. A weak, positive correlation between Summarization (C3) and Increased Productivity (Q21) was observed. One small manufacturer from North Dakota responded affirmatively to both Industry 4.0 integration (Q12) and Increased Productivity (Q21) survey questions.

Table 3. Medium-sized Manufacturers in Minnesota and North Dakota Integrating Industry 4.0 (Q12).

Pearson Correlation Coefficients, N = 6 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21
C1 Data Gathering	1.00000	0.84830	0.41690	0.89285	0.93517	0.84830	−0.44661
		0.0328	0.4109	0.0166	0.0062	0.0328	0.3746
C2 Analysis and Interpretation	0.84830	1.00000	0.58199	0.62855	0.89767	0.89286	−0.52378
	0.0328		0.2256	0.1813	0.0152	0.0166	0.2862
C3 Summarization	0.41690	0.58199	1.00000	0.06455	0.54993	0.24942	0.09380
	0.4109	0.2256		0.9033	0.2583	0.6336	0.8597
C4 Testing	0.89285	0.62855	0.06455	1.00000	0.68476	0.68401	−0.27113
	0.0166	0.1813	0.9033		0.1334	0.1340	0.6033
C5 Iterate and Amend	0.93517	0.89767	0.54993	0.68476	1.00000	0.89767	−0.61295
	0.0062	0.0152	0.2583	0.1334		0.0152	0.1957
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.84830	0.89286	0.24942	0.68401	0.89767	1.00000	−0.76553
	0.0328	0.0166	0.6336	0.1340	0.0152		0.0760
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	−0.44661	−0.52378	0.09380	−0.27113	−0.61295	−0.76553	1.00000
	0.3746	0.2862	0.8597	0.6033	0.1957	0.0760

displays the six responses to integrating Industry 4.0 by medium-sized manufacturing businesses in North Dakota and Minnesota. A negative relationship was observed between Industry 4.0 integration (Q12) and increased productivity (Q21). A strong, positive correlation among Data Gathering (C1), Analysis and Interpretation (C2), Testing (C4), Iterating and Amending as necessary (C5), and Industry 4.0 integration (Q12) existed. A weak, positive correlation between Summarization (C3) and Increased Productivity (Q21) existed. A strong positive correlation among all socio-technical constructs with the exception of a moderate positive correlation between Data Gathering (C1) and Summarization (C3) was observed. A negative relationship among C1, C2, C4, and C5 constructs and Increased Productivity (Q21) existed. A weak, positive relationship with Summarization (C3) and Increased Productivity (Q21) existed.

Table 4. Large Manufacturers in Minnesota and North Dakota Integrating Industry 4.0 (Q12).

Pearson Correlation Coefficients, N = 8 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21
C1 Data Gathering	1.00000	0.87370	0.86614	0.55183	0.38960	0.46351	0.05143
		0.0046	0.0054	0.1562	0.3401	0.2474	0.9037
C2 Analysis and Interpretation	0.87370	1.00000	0.93611	0.66031	0.61498	0.64409	−0.12309
	0.0046		0.0006	0.0747	0.1047	0.0848	0.7715
C3 Summarization	0.86614	0.93611	1.00000	0.79146	0.75388	0.78079	−0.03018
	0.0054	0.0006		0.0193	0.0307	0.0222	0.9434
C4 Testing	0.55183	0.66031	0.79146	1.00000	0.80050	0.77009	−0.09934
	0.1562	0.0747	0.0193		0.0170	0.0254	0.8150
C5 Iterate and Amend	0.38960	0.61498	0.75388	0.80050	1.00000	0.78865	0.00000
	0.3401	0.1047	0.0307	0.0170		0.0200	1.0000
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.46351	0.64409	0.78079	0.77009	0.78865	1.00000	−0.14535
	0.2474	0.0848	0.0222	0.0254	0.0200		0.7313
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	0.05143	−0.12309	−0.03018	−0.09934	0.00000	−0.14535	1.00000
	0.9037	0.7715	0.9434	0.8150	1.0000	0.7313

displays the eight responses to integrating Industry 4.0 by large manufacturing businesses in North Dakota and Minnesota. A strong negative correlation between Industry 4.0 integration (Q12) and Increased Productivity (Q21) existed. A neutral relationship between Iterating and Amending as necessary (C5) and Increased Productivity (Q21) existed. A negative relationship among C2, C3, C4, and Increased Productivity (Q21) existed. A weak, positive relationship between Data Gathering (C1) and Increased Productivity (Q21) existed. A moderate, positive relationship between Data Gathering (C1) and Industry 4.0 integration (Q12) existed. A strong, positive correlation among all socio-technical constructs with the exception of a moderate, positive relationship between Data Gathering (C1) and Iterating and Amending as necessary (C5) existed. A strong, positive correlation between C2 through C5 and Industry 4.0 integration (Q12) existed. A moderate, positive correlation between Data Gathering (C1) and Industry 4.0 integration (Q12) existed.

Table 5. Minnesota Manufacturers.

Pearson Correlation Coefficients, N = 6 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21
C1 Data Gathering	1.00000	0.92376	0.51640	0.90439	0.73581	0.77460	−0.24194
		0.0085	0.2943	0.0133	0.0955	0.0705	0.6442
C2 Analysis and Interpretation	0.92376	1.00000	0.59628	0.73715	0.73245	0.74536	−0.34922
	0.0085		0.2116	0.0946	0.0978	0.0890	0.4975
C3 Summarization	0.51640	0.59628	1.00000	0.34340	0.91717	0.33333	−0.15617
	0.2943	0.2116		0.5051	0.0100	0.5185	0.7676
C4 Testing	0.90439	0.73715	0.34340	1.00000	0.60292	0.68680	−0.10726
	0.0133	0.0946	0.5051		0.2052	0.1318	0.8397
C5 Iterate and Amend	0.73581	0.73245	0.91717	0.60292	1.00000	0.65512	−0.37856
	0.0955	0.0978	0.0100	0.2052		0.1579	0.4593
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.77460	0.74536	0.33333	0.68680	0.65512	1.00000	−0.78087
	0.0705	0.0890	0.5185	0.1318	0.1579		0.0668
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	−0.24194	−0.34922	−0.15617	−0.10726	−0.37856	−0.78087	1.00000
	0.6442	0.4975	0.7676	0.8397	0.4593	0.0668

displays the responses of the six manufacturers in Minnesota with regard to Industry 4.0 integration, increased productivity, and socio-technical constructs. A strong, negative correlation between Industry 4.0 integration (Q12) and Increased Productivity (Q21) existed. A strong, positive correlation was observed among all socio-technical constructs with the exception of the linear relationship between Summarization (C3) and Testing (C4), in which a moderate positive correlation was observed. A strong, positive correlation between C1, C2, C4, and C5 and Industry 4.0 integration (Q12) existed. A moderate, positive correlation between Summarization (C3) and Industry 4.0 integration (Q12) existed. A negative correlation between C1 through C5 and Q21 increased productivity existed.

Table 6. North Dakota Manufacturers.

Pearson Correlation Coefficients, N = 18 Prob > |r| under H0: Rho = 0
	C1	C2	C3	C4	C5	Q12	Q21
C1 Data Gathering	1.00000	0.75822	0.74342	0.51797	0.58387	0.67158	−0.17984
		0.0003	0.0004	0.0277	0.0110	0.0023	0.4752
C2 Analysis and Interpretation	0.75822	1.00000	0.74224	0.62763	0.60058	0.57846	−0.32501
	0.0003		0.0004	0.0053	0.0084	0.0119	0.1882
C3 Summarization	0.74342	0.74224	1.00000	0.67800	0.68353	0.77803	−0.04848
	0.0004	0.0004		0.0020	0.0018	0.0001	0.8485
C4 Testing	0.51797	0.62763	0.67800	1.00000	0.83735	0.66499	−0.37122
	0.0277	0.0053	0.0020		<0.0001	0.0026	0.1293
C5 Iterate and Amend	0.58387	0.60058	0.68353	0.83735	1.00000	0.69970	−0.25241
	0.0110	0.0084	0.0018	<0.0001		0.0012	0.3123
Q12 How likely is your organization to align the organizational design with Industry 4.0 integration?	0.67158	0.57846	0.77803	0.66499	0.69970	1.00000	0.04767
	0.0023	0.0119	0.0001	0.0026	0.0012		0.8510
Q21 How likely is your organization to observe increased productivity per employee due to the implementation organizational design?	−0.17984	−0.32501	−0.04848	−0.37122	−0.25241	0.04767	1.00000
	0.4752	0.1882	0.8485	0.1293	0.3123	0.8510

displays the relationship among socio-technical constructs, Industry 4.0 integration, and increased productivity among 18 manufacturers in North Dakota. A strong, positive correlation among all socio-technical constructs, C1 through C5, and Industry 4.0 integration (Q12) existed. The least positive socio-technical construct correlation was Industry 4.0 integration (Q12) with Analysis and Interpretation (C2). A strong, positive correlation among all socio-technical constructs existed. A negative correlation between C1 through C5 socio-technical constructs and increased productivity (Q21) existed. A weak, positive correlation between Industry 4.0 integration (Q12) and increased productivity (Q21) existed.

Individual scatter plots of the socio-technical constructs of Data Gathering (C1), Analysis and Interpretation (C2), Summarization (C3), Testing (C4), and Iterate and Amend (C5) on the x-axis and Industry 4.0 (Q12) on the y-axis were created for the manufacturing firms surveyed. These are illustrated in Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5 below. The three lines are the simple linear regressions for small, medium, and large companies. The linear regression highlights positive slopes for all three business sizes, indicating that, as Industry 4.0 integration increases, the socio-technical constructs are increasingly utilized within the work organization. For example, in Figure 1, medium-sized companies integrating Industry 4.0 are more aligned with the socio-technical construct of Data Gathering than small and large companies.

5. Discussion

The digital transformation of Industry 4.0, which impacts both social and technical aspects of work organizations, is increasing the interfaces between human labor and computer-controlled processes. The replacement of low-skilled work tasks by highly skilled, non-routine work is creating a more human work design. Digitalization is influencing organizational learning by creating competencies for the use of innovative Industry 4.0 technologies [29]. The socio-technical constructs used in this survey were based on a socio-technical framework of identifying cross-system relationships between social and technical system components, such as people and processes, to develop system-level advice and to lead organizational change [13].

In reference to Table 1, SMEs and large manufacturers surveyed in Minnesota and North Dakota need to invest more time in the socio-technical tasks involved with testing results with stakeholders and utilizing data to make meaningful contributions to increase productivity. The Testing the Results with Stakeholders construct requires resources and a long-term strategy. As the socio-technical construct questions suggest, this process requires an investment in organizational learning for employees, resources for employees at all levels, inclusion of external stakeholders in the data-capturing process for decision-making at the organization design level, and surveying of all internal and external stakeholders to inform socio-technical organizational design. Iterating and amending the organizational design process requires a multidisciplinary task to continuously evaluate, extract, and use data meaningfully. The socio-technical principle of incompletion supports this focus of identifying the solutions that must first be implemented. The socio-technical design process is continuous improvement and must constantly be monitored.

The survey illustrated that, among all Minnesota and North Dakota manufacturers, a strong, positive correlation between the socio-technical constructs and Industry 4.0 integration exists, yet increased productivity is not pervasive. Additionally, there may be a lack of diffusion of knowledge regarding the utility of innovative technologies, hindering work teams from practicing responsible autonomy. This finding may indicate gaps in organizational learning, leading to a deficit in the technical knowledge of leadership and employees among SMEs and large manufacturers in the region to extract data from newer autonomous and communication technologies. Moreover, this outcome may indicate that the organizations employing these technological innovations are operating on short-term strategic plans, rather than long-term strategies. This finding answers the first research question of how applicable socio-technical design principles are to the Industry 4.0 context among North Dakota and Minnesota manufacturers.

The second research question focused on whether a positive correlation between Industry 4.0 and increased productivity among manufacturers in North Dakota and Minnesota would be observed. A negative correlation between Industry 4.0 integration and productivity among all manufacturers surveyed was identified. The root cause of this outcome may be related to inequalities in how the socio-technical design constructs are applied with each work organization. Additionally, the output of data from the innovative technologies may also be under-utilized or not leveraged toward furthering organizational design.

There is an aggregately negative correlation between socio-technical constructs and increased productivity regardless of small, medium, or large business size in Minnesota and North Dakota. This outcome is also reflected in a poor correlation between Industry 4.0 and increased productivity. Although the organizations surveyed are utilizing socio-technical design methods, the social and technical aspects are not jointly optimized for extracting maximum value from Industry 4.0. There are inequalities among how the socio-technical constructs are being leveraged, which is contrary to the socio-technical design principle of incompletion, which ensures continuous improvement. This design principle is integral to effectively implementing both Industry 4.0 and socio-technical theory frameworks in organizational design. This outcome addresses the third research question with regard to whether a positive correlation between socio-technical design principles and increased productivity would be observed.

However, assessing only the small manufacturers in Minnesota and North Dakota, a weak, positive relationship between Industry 4.0 integration and increased productivity was observed. This feedback supports the second research question. Possible explanations for this outcome are being unaware of how to capture data fully and not having the technically trained staff to assist with the Industry 4.0 implementation process. The small manufacturers indicated a strong, positive correlation between socio-technical constructs and Industry 4.0 integration, which may indicate a trajectory toward, but not complete realization of, joint optimization of both social and technical factors. This finding may indicate a prematurity of implementing Industry 4.0 prior to socio-technical design readiness. This insight supports the first research question. The full socio-technical framework is applied to support organizational design efforts in small manufacturers surveyed from Minnesota and North Dakota. However, the organizational design may not employ socio-technical theory design continuously; therefore, the outcome of minimal productivity increases.

Medium-sized manufacturers in Minnesota and North Dakota are not gleaning value from Industry 4.0 to increase productivity. The socio-technical design is directly related to Industry 4.0 integration, as it reflects the organization’s preparedness to adopt new operational strategies. The socio-technical design is partially implemented, providing an environment for new technology adoption. A poor correlation between socio-technical constructs and productivity reveals that possible knowledge barriers exist to the operational practices of the manufacturer to extract value from the organizational design and new technologies, thus meaning that the full socio-technical framework is not jointly optimized.

Large manufacturers in Minnesota and North Dakota experienced a negative correlation between Industry 4.0 (Q12) integration and increased productivity (Q21), which may indicate the lack of leadership’s technical knowledge about how to extract and use data from new technologies. A positive correlation existed between all socio-technical constructs and Industry 4.0 integration, which may indicate that this framework is conducive to new and increased technology adoption rates.

Considering specifically Minnesota manufacturers, a negative correlation between Industry 4.0 and increased productivity was observed, which may indicate a lack of technical expertise to capture value from new technologies. A negative correlation between socio-technical constructs and increased productivity existed, indicating that the framework is not fully employed to capture operational efficiencies.

The North Dakota manufacturers surveyed displayed a strong, positive correlation between socio-technical constructs and Industry 4.0 integration, indicating that socio-technical design facilitates the adoption of newer technologies. A positive correlation existed among all socio-technical constructs, indicating that manufacturers are actively engaged in socio-technical design frameworks. The weak correlation between Industry 4.0 integration and increased productivity indicated a potential lack of technical knowledge to address the full adoption of new technology to realize all of its benefits. Additionally, it indicates that the continuous nature of socio-technical design is not utilized at its full capacity.

Industry 4.0 allows for product-centric organizations to move toward servitization, creating a closer relationship with the consumer market. A greater volume of data and analytics resulting from the implementation of Industry 4.0 will create bargaining power for buyers. There are pros to implementing Industry 4.0, which include competitive advantage, operational efficiency, improved ergonomics, and long-term sustainability. The cons include the negative impact of financial investment in all operationally necessary Industry 4.0 technologies, lack of data confidentiality, and the necessary technical skillsets among leadership and employees of the socio-technical organization [30]. The last previous disadvantage noted interferes with the sharing of innovation throughout an organization [31]. Industry 4.0 implementation must be welcomed by an organization that has jointly optimized its social and technical aspects to receive the full benefits of productivity from these innovations.

The survey was based on the socio-technical framework developed by Davis et al. Their proposed socio-technical framework assesses the six interrelated components of goals, people, building/infrastructure, technology, culture, processes/procedures within the external environment of financial/economic, stakeholders, and regulation. This socio-technical framework supports both predictive and design work. The framework was explained as major steps involved in analyzing and understanding an existing socio-technical system [13]. This study is a unique approach to analyzing the organizational joint consideration of Industry 4.0, socio-technical factors, and predictive work, as the socio-technical assessment steps were transformed into questions and posed to employees of small, medium, and large manufacturing firms. These survey questions provide a useful tool for socio-technical competence management within an organization when jointly considering human factors while implementing new technologies. Additionally, the survey may serve the purpose of checking the sustainability status of small, medium, and large manufacturing firms by operators from upper management to ensure that new investments in Industry 4.0 technologies and generated data usage therefrom are leveraged adequately to support productivity targets. Small and medium-sized manufacturing firms may be unfamiliar with utilizing this type of analysis to improve an organization. This survey illustrates an emerging management approach with SMEs in supporting continuous improvement efforts.

6. Conclusions

Researchers are focusing on gleaning further insights with regard to how Industry 4.0 relates to value chains and supply networks, clusters and industrial districts, readiness and adaptation of regional industries, innovation development and ecosystems, and labor markets [32]. The North Dakota and Minnesota manufacturers surveyed illustrate the changing environment that is shifting toward the adoption of the new technologies of Industry 4.0. The limitations of this study include the small sample size of regional manufacturers that was assessed. Additionally, companies may not be utilizing Industry 4.0 and therefore are not benefiting from its advantages. More education and training for managers and employees on the benefits of socio-technical awareness in the context of Industry 4.0 are needed to develop a future cohort of enhanced manufacturers in the region. Additional studies should be conducted to determine the socio-technical readiness of manufacturers in the region, as doing so would lead to an Industry 4.0 integration readiness. Moreover, future studies should be conducted to assess how manufacturers in North Dakota and Minnesota are utilizing Industry 4.0 technologies to determine opportunities for and challenges to realizing increased productivity levels from these socio-technical organizations and to inform various industrial sustainability policies for regional economic development authorities.