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Article

Assessing the Influence of Collaborative Technology Adoption—Mediating Role of Sociotechnical, Organizational, and Economic Factors

by
Svetlana Zemlyak
*,
Olga Gusarova
* and
Svetlana Sivakova
Department of Economics and Management, Financial University under the Government of the Russian Federation, Smolensk Branch, Smolensk 214018, Russia
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14271; https://doi.org/10.3390/su142114271
Submission received: 1 October 2022 / Revised: 26 October 2022 / Accepted: 27 October 2022 / Published: 1 November 2022
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Abstract

:
The study investigated the factors that influence the adoption of collaborative robots in the manufacturing sector in Russia from sociotechnical, organizational, and economic point of views. The study was driven by the increasing technological innovation in the manufacturing sector, especially in the use of robots and collaborative robot applications in daily manufacturing, flexibility, and operations activities. The study was a quantitative, descriptive survey that relied on primary data from respondents with varied experiences in the manufacturing sector in Russia. The study employed a total of 351 respondents selected for their insights into the application of robotics in the manufacturing process in Russia. The model adopted for the study was tested using confirmatory factor analysis (CFA), reliability, and validity analysis. The hypotheses of the study were evaluated using partial least-squares analysis. The results revealed that the adoption of collaborative robots was influenced by organizational factors and economic factors. Perceived performance improvement was significantly influenced by collaborative robot adoption and sociotechnical systems. The study recommended that the stakeholders in Russia’s manufacturing sector should improve their training, management support, perceived innovativeness, and prior experience to enhance the adoption of collaborative robots and flexibility in design.

1. Introduction

Over the decades, creating collaborative robotic devices in which a person may take over portions of the work that are too difficult for a robot has piqued the curiosity of scientists. Consequently, a collaborative robot, often abbreviated as a “cobot”, has been created to carry out numerous cooperative object manipulation tasks. The manufacturing sector is thought to be primarily driven by innovative technological advancements. Industrial collaborative robots are among these innovative technological advancements and significant growth enablers in the manufacturing sector. Industry 4.0 is related to the application of these innovative technological advancements to revolutionize business practices and enhance manufacturing efficiency. This involves placing a greater emphasis on the creation and application of cutting-edge technology, such as the use of robots, to enhance industrial performance. The forthcoming Industry 5.0 promotes innovation and the utilization of technology in alignment with societal values and with a focus on sustainability and the well-being of the workforce. Cobots are one example of how Industry 5.0 implies enhanced human–smart system collaboration [1,2,3].
Innovative computing developments have made collaborative robots more adaptable and simpler to construct than ever before. Installing them requires little to no code, lowering integration expenses. According to Yeamkuan et al. [4] small, low-power robots can now be aware of their environment and execute various activities safely in close contact with human workers because of improvements in digital technology, machine vision, cognitive computing, and touch technology. Sadangharn [5] pointed out that when a cobot works alongside an individual, it may learn tasks quickly through example and reinforcement learning. In the manufacturing industry, collaborative robots are seeing tremendous market expansion due to steadily falling prices. Improved versatility allows collaborative robots to execute various new functions and activities in industries [6]. Robotics groups and governmental and ethical agencies have offered numerous definitions of collaborative robots, and the distinctions between robots and cobots are becoming obscured. For this paper, cobots are defined as robots that perform tasks in collaboration with a human driver in a shared workspace setting.
In traditional industrial robot applications, robots and humans are physically isolated from one another, and functions are performed in a particular order to maintain safety. In comparison, collaborative robotic systems are built to communicate with human operators. To prevent crashes, lessen their effects, and innately ensure safety, built-in control mechanisms and sensors are used. Cobots are radically different from traditional industrial robots in terms of context and usage. Paniti et al. [7] quoted that in 2016 the ISO 15,066 technical specification presented specific recommended safety criteria for cobots with a heavy emphasis on ensuring safety separation, limiting kinetic energy in cobot applications with deliberate interaction, and establishing tolerance levels for various body parts. Despite these restrictions and standards for robotic devices, the safety of human–robot collaboration remains a significant concern in the manufacturing sector [8]. The regulation and safety of cobots are currently primarily concerned with the technological features of the cobot system for the protection and security of the user in industrial workplace settings. Successful implementation of an effective cobot system will have a positive economic impact on the economy. In Russia, manufacturing is a significant sector of economic contribution. As of 2020, it was valued at RUB78.8 trillion. For the same year, the sector contributed approximately 13% of the Russian GDP [9].
The Russian manufacturing industry has also improved business prospects since numerous organizations are progressively using cobots due to benefits, such as enhanced production and optimal personnel utilization. The government expects its manufacturing sector to recover in the coming months, primarily its electronics, steel, and automobile industries. Galin and Meshcheryakov [10] informed that collaborative robots will assist Russia’s manufacturing business the most since its operations include restricted repetitive labor organized locations where robots thrive. Robots can operate 24 h a day, seven days a week and generate consistent results under difficult operating circumstances. Russia is not generally considered a foremost adopter of cobot technology compared to other parts of Europe and Asia. However, there are positive indicators of high-level adoption and use in the manufacturing sector in the recent past and near future [11,12,13,14]. The Russian government has plans to accelerate the adoption and use of digital technologies, particularly robots [13]. There is also a booming demand for educational and manufacturing robots in the country. Further, the Russian robot manufacturers have set their sights on the robot’s service sector. With this rising interest and adoption of cobots in Russia, this study aims to investigate what influences the adoption of collaborative robots by evaluating the sociotechnical, organizational, and economic factors. This paper is structured as Section 1, introduction, followed by Section 2, literature review, Section 3, methodology, Section 4, results and discussion, and last is Section 5, conclusion.

2. Literature Review

2.1. Organizational Factors

Organizational design evolves throughout time to capitalize on opportunities and adapt to a constantly changing environment. Failure to adapt to changes in regulations, such as trade agreements, client attitudes towards the brand, and worldwide competition, for example, will result in the business’ collapse. Various businesses have implemented more dynamic approaches to capitalize on this circumstance, creating chances that would otherwise be lost in a more conventionally defined system. Nevertheless, the complexities and instability of today’s organizations make this incredibly difficult [15]. Individuals’ readiness to embrace innovation is impacted by their opinions and business policies, strategies, and behaviors [16]. Organizations must develop in conducive conditions, such as the quantity and kind of help provided to individuals who influence the use of collaborative robot technology. According to the postulations of [17], enabling conditions are considered the accessibility of training and the provision of help. Administrative aspects include training, managerial support, and incentives. Since many sectors are characterized by near-constant change, many individuals get irritated and burned out due to adopting collaborative robot technology [18].
Fleischman et al. [19] pointed out that these development operations are designed to enhance an individual’s performance in their current work, teach new competencies for a future career or position, and promote overall growth for both people and organizations to satisfy their current and future goals. Organizations may start using incentives to support creativity and innovation behaviors by finding measurements that support these behaviors using collaborative robot technology [20]. Top management support is critical in influencing a company’s service innovation plans and choices. One of the most significant steps in collaborative robotic technology is adopting better ways to show support to the employees and workers through resource availability [21]. Organizational dynamics may inspire workers to adopt a technology.
Thürer et al. [22] inferred that competitive manufacturing skills do not appear to be planned or matched with competitive aims. Furthermore, Slack and Lewis [23] discussed the importance of operations management in achieving flexible production, mastering new process technologies, and lowering costs. They stated that companies do this by utilizing innovation and enhancement initiatives. Jain et al. [24] asserted that the traditional and most contemporary economic goal of innovation can assist operations management in fulfilling its targets and generating a competitive edge for a company through the application of process technology throughout production. Operations management achieves its goals in complementing business models by focusing on performance measures. Meanwhile, technological discoveries for the application of cobots in the sector with the aim of cost reduction and improving quality, scalability, and consistency are not yet a part of most firms’ corporate strategies, particularly Russian enterprises. Somehow this absence of long-term perspective is the consequence of crunch socioeconomic factors that make the investment environment for robotics more unpredictable.

2.2. Economic Factors

Intrinsic ideas have dominated arguments for why users act in certain ways regarding collaborative robot technology, showing a predisposition toward its industrial application. Regardless of the circumstances, subjective norms and images might impact the cognitive assumption of perceived usefulness. In other words, compelling social information may cause users’ perceptions of usefulness to increase. Min and Jeong [25] stated that people generate opinions about new technology by combining information from several sources and finding a common ground that identifies with their idiosyncrasies. Individuals with greater personal innovativeness are likely to generate more favorable ideas about collaborative robot technology when exposed to the same forms of media. Similarly, Al-Rahmi et al. [26] inferred that economic characteristics have the most direct impact on an individual’s cognitive interpretations of information technology. Singh and Srivastava (2018) defined personal innovativeness as the proclivity for taking risks in certain people but not in others. Empirical research on the link between past work experience and present job performance has been sparse [25]. As a result, how related experience is transferred across company borders has remained unanswered.
Prior work-related experience provides useful information and skills that may be applied to collaborative robot technology [27]. In most studies of performance and experience, experience is used as a surrogate for knowledge [11,28]. Previous work experience may include necessary knowledge and abilities, routines, and attitudes that are inconsistent with the present organizational architecture.

2.3. Sociotechnical Systems

Both social and technical systems are dependent on one another. People frequently form false attachments to the tools (such as robots and other devices) and technologies they use, making it difficult for them to picture living without them. Humans have evolved mechanical techniques or tools over time to execute jobs more quickly, more efficiently, and with less energy. The technological system consists of the products, procedures, instruments, approaches, and expertise utilized to reconfigure products and services for consumers. Organizational tasks are typically designed around enabling innovative technology since it is assumed that people and social systems will adapt to the demands of technology. At a corporate level, organizational structures and rules are generally strategically developed to disperse resources as needed [15].
Sometimes, new technology causes an organization’s structure to shift. The technical and social systems could be built while taking into account both human values and the significance of technology. The significance of making decisions where humans and technology can work together most effectively was emphasized. Technical infrastructure may impose limitations on communication between units, responsibilities, schedules, and locations, which could hinder actions, such as an impromptu resolution of differences of opinion or strategy. The deviations or variations from standard operating procedures can be used to describe the detrimental impact the technical system has on performance. The inspection function is the most prominent illustration. Inspections must be carried out concurrently with production according to the sociotechnical framework to prevent the reoccurrence of errors. Fundamental variations that are essential to the performance of the organization have been identified by analysis. The design teams can use this information to recommend adjustments to the technical or social systems that will fulfill environmental requirements as well as the requirements for the social and technological systems [15,17,21,29].
Social subsystems influence employees’ willingness to absorb new ideas. The acceptability of collaborative robot technology is likely to be impacted by other people’s innovations in the employees’ social subsystems [30]. The amount to which social group members affect one another’s adoption behavior is known as social influence. The firm’s efficiency increases the performance of its subsystems. Sony and Naik [31] opined that the system may readily separate itself efficiently with the correct flow of communication, workplace organization, and working atmosphere. Normative attitudes on the propriety of innovation adoption are a kind of influence [32]. In this viewpoint, employees may adopt an invention predicated on perceived social pressure rather than its utility. According to Lie et al. [32] the pressure might be regarded as sourced from those with clear views and attitudes, such as colleagues and others in social networks. There have been differing views on whether individuals employ innovation more often. Branny et al. [33] illustrated that technical subsystems have their own formal and informal norms and flow of communication and organization for optimum operations. They also involve individuals and procedures that may or may not be established. Because it comprises organizing work into the actions required to fulfill a specific task, work design substantially influences productivity and job satisfaction, ensuring that tasks, duties, and responsibilities turn into subtasks to achieve certain objectives and guaranteeing that employees are driven to complete them on time [34]. Due to overspecialization and automation, many activities have become boring and tiresome, and those who do them quickly grow tired.
The model of the study was developed from the review of the literature and the adopted model. The model comprised three independent variables (organizational factors, economic factors, and socio-technical factors) and two dependent variables (collaborative robot adoption and perceived performance improvement). From the relationship of the variables in the model as shown in Figure 1 and the following hypotheses were proposed.
H1. 
Organizational factors (at least two) have a positive influence on collaborative robot adoption.
H2. 
Economic factors (at least two) have a positive influence on collaborative robot adoption.
H3. 
Sociotechnical systems (at least two) have a positive influence on collaborative robot adoption.
H4. 
Sociotechnical systems (at least two) have a positive influence on perceived performance improvement.
H5. 
Collaborative robot adoption has a positive influence on perceived performance improvement.

3. Methdology

Research Population

This research is geared toward evaluating the factors that influence collaborative robot adoption by comparing social-technical, organizational, and economic aspects. The study was conducted by considering the manufacturing sector in Russia. Therefore, the population of the study is the staff working in the companies operating in the Russian manufacturing sector. However, since the population is quite large, the sample size was selected as a representative sample of the population. The data was collected using a structured questionnaire. The questionnaire was adopted from previous research, as summarized in Table 1. The data was collected from the staff holding management positions in these companies, as they had a clear understanding of the subject of collaborative robots in their companies. The data was collected in Moscow, Russia, between 1 December 2021 and 28 February 2022.
The study used a simple random sampling technique to distribute copies of the questionnaire. The first stage was to identify companies utilizing collaborative robot technology in Moscow using the database from McFarlane [35] and Tracxn [36]. The database revealed 100 companies from which ten were randomly selected, and 500 copies of the questionnaire were distributed using a stratified sampling technique. The first inclusion condition for participation in the study was that respondents work in a manufacturing organization. Secondly, the concerned manufacturing company should have adopted collaborative robot technology in its operations. Thirdly, the respondent was required to have an understanding of the subject and agree to voluntarily participate in the study through an informed consent agreement. The questionnaire was developed using a 5-point Likert scale. Out of the 500 copies of the questionnaire distributed, 437 were returned, while 351 were considered suitable to be used for analysis. Data analysis was conducted first to determine the demographic characteristics of the respondents, such as age, gender, and years of experience. The next data analysis was conducted to evaluate the appropriateness of the model adopted. The model fitness was evaluated by running a confirmatory factor analysis (CFA) where various fitness indices were assessed. The reliability of the constructs was evaluated using two techniques—Cronbach’s alpha and convergent reliability. The validity of the constructs was tested using average variance extracted (AVE) and the standardized factor loadings of the paths between the latent variables. Finally, structural equation modeling (SEM) was used to evaluate the proposed hypotheses of the study. Figure 2 presents a flow diagram of the methodology steps taken.
Table
Table 1. Study variables, items, and sources.
Table 1. Study variables, items, and sources.
VariablesObserved VariablesSources
TrainingReceiving training would help me understand about collaborative robot adoption.
Training is necessary for me to use collaborative robots.
If I receive training, it would be easy for me to use collabo-rative robots.		[37,38]
IncentivesAdoption of collaborative robots is expensive and sponsorship may be required [21]
I need incentives to start using collaborative robots
In our organizations, incentives helped us in adopting collaborative robots
Collaborative Robots AdoptionI would encourage my company to start using collaborative robots
I would encourage my company to start using collaborative robots
I think my company should use collaborative robots in the future
I think it is the right decision to start using collaborative robots		[39]
Managerial support I think my company should use collaborative robots in the future [40]
I think it is the right decision to start using collaborative robots
I think it is the right decision to start using collaborative robots
Perceived Usefulness I think that collaborative robots are useful for my life[41,42]
Using collaborative robots increases my productivity
Using collaborative robots helps me conveniently perform many tasks
collaborative robots provide very useful service and information to me
Work DesignCollaborative robots would help ensure employees job description will achieve its mission
Employees job duties would be achieved easily with collaborative robots
Employees performance would be enhanced by using collaborative robots
Work systems would be effectively defined through collaborative robots		[43]
Personal Innovativeness Generally, I am interested in trying out collaborative robots [44,45]
Whenever I hear about collaborative robots, I am always eager to try it out
Prior Experience I have prior experience in using collaborative robots[46,47]
Prior experience is necessary to effectively use collaborative robots
If I have prior experience in using collaborative robots it would be easy for me to adopt
Prior experience would increase the effectiveness of using collaborative robots
Technical SubsystemsCollaborative robots would help people work together on interrelated activities[48,49]
Collaborative robots using would help effective use of knowledge, techniques, equipment and facilities
Employees and their social relationships would be improved by collaborative robots
Social Subsystems Supervisory relationship with the employees would enhance use of collaborative robots[50,51]
Peer group interaction is important for collaborative robots
Proper governance is resources is important for adopting collaborative robots
Collaborative robots require effective processing and marketing aspects
Perceived Per-formance Im-provement Performance would be better if collaborative robot tech-nology is used.
Performance for those using collaborative robots is better than for those not using.
Companies using collaborative robots are more success-ful.
Employees would perform better is they are allowed to use collaborative robots.		[52]

4. Results and Discussion

The descriptive statistics of the respondents were the first to be conducted to determine the characteristics of various aspects, such as their age, gender, etc. The results revealed that most of the respondents were aged between 30 and 40 years (44%), followed by those in the age range of 40 to 50 years (28%), and those aged between 20 and 30 years (22%). Considering the gender variable, males were the majority, constituting 56% of the total population, while females made up the remaining 44% of the total population. The study also evaluated the working experience of the respondents in the industry. The majority of respondents indicated that they have worked in their companies for 1–5 years (37%), followed by those who have worked for 5–10 years (34%). Those with less than a year were 19%, while those with more than 10 years were 10%. The research also evaluated how long the respondents’ companies have been using collaborative robot technology. The majority indicated 1–5 years (36%), followed by 1–5 years (31%), then less than one year (19%), and lastly more than 10 years (14%). Table 2 indicated the reliability and validity of the construct.
The model of the study and the research constructs were tested for their reliability, validity, and fitness (see Table 3 and Figure 3). Confirmatory factor analysis (CFA) was conducted to test for composite reliability, average variance extracted (AVE), factor loadings, and Cronbach’s alpha. All the measurement factors showed adequate and convergent reliability. The standardized factor loadings ranged from 0.68 to 0.87, the average variance extracted (AVE) ranged from 0.57 to 0.73 (all were >0.50), and the composite reliability values were greater than 0.7. In addition, the model fitness was evaluated using chi-square fitness indices. From the results, CFI was 0.93, TLI was 0.953, RMSEA was 0.0627, x2/df was 0.318, and NFI was 0.910.
According to Jöreskog and Sörbom [53], the recommended RMSEA should be between 0.05 and 0.08; however, [39] recommended that TLI, CFI, and NFI should be above 0.90. These thresholds were met; hence, the proposed model fitness was acceptable.
The hypotheses were evaluated by partial least square (PLS) analysis to determine the relationship between the study variables. The research results revealed that two of the organizational factors had a significant and positive effect on collaborative robot adoption. These were training (β = 0.396, p < 0.01), and management support (β = 0.126, p < 0.05), which led to the confirmation of hypothesis 1 (H1) that organizational factors (at least two) have a positive influence on collaborative robot adoption. The results also indicated two economic factors have a significant and positive effect on collaborative robot adoption. The statistics were personal innovativeness (β = 0.236, p < 0.01) and prior experience (β = 0.180, p < 0.01), while perceived usefulness had a negative but significant effect (β = −0.126, p < 0.05). As a result, hypothesis 2 (H2) was supported that economic factors (at least two) have a positive influence on collaborative robot adoption. Only one variable in sociotechnical systems (technical subsystems) was found to have a significant and positive effect (β = −0.205, p < 0.01) on collaborative robot adoption. Since the other two variables (work design and social subsystems) were not significant, hypothesis 3 (H3) was not supported. The results also indicated that collaborative robot adoption has a positive and significant effect on perceived performance improvement (β = −0.203, p < 0.01), confirming hypothesis 5 (H5). Table 2 indicated the reliability and validity of the construct.

Discussion

The purpose of this study was to determine what factors influence the adoption of collaborative robots in the manufacturing sector of Russia. The study compared three aspects: economic factors, sociotechnical systems, and organizational factors. The first important observation is that the adoption of collaborative robots is influenced by organizational factors. The organizational factors that have significant influence are training and management support. This implies that if the workers in the manufacturing sector received training, it would be easy to understand, collaborate, use, and encourage the adoption of collaborative robots. Similarly, the presence of managerial support in the manufacturing sector would increase the confidence and efforts towards the adoption of collaborative robots.
These findings suggest that cobots can help a business’ competitive advantage. One of the identified ways that cobots can provide a competitive advantage is the ability to lead to cost reductions. This establishes an essential competitive priority, which is followed by enhancements of other competitive advantages that lead to the optimum production of quality in high volumes, improved flexibility requirements, and innovativeness. These findings are comparable to those made by Thürer et al. [22] who claimed that competitive production competencies do not seem to be entirely intentional or in line with performance measures. However, the findings of this study regarding the use of cobots in a collaborative workplace support Slack and Lewis’ [23] findings regarding the importance of operations management in achieving flexible production, mastering new process technologies, and lowering costs through the use of improvement and enhancement strategies. According to process technologies used during production and innovation, a traditional and current competitive priority can assist operations management in achieving its goals and in giving a company a competitive advantage [24]. Operations management achieves its goals in supporting business strategies by focusing on competitive priorities [54]. The results imply that most companies and Russian robotic firms, in particular, do not yet apply new developments for the use of collaborative robots in the industry with a view of reducing costs and enhancing quality, versatility, and stability in the production and running of manufacturing operations. This lack of strategic vision may be due to the current economic conditions, which make the environment for robotics investments more unstable.
The scenarios of productivity and flexibility have the most variations. Naturally, if a corporation wants to increase the flexibility of its production, the cobot’s versatility, suppleness, and orientation concerning materials and structures in the workspace are much more crucial to production capacity. Rapid equipment modifications and quick transition times enable the system’s required adaptability, leading to a high degree of production tractability [23]. The systematic management of faults while introducing the cobot is extremely relevant given that cobots are suitable for flexible production environments and that there is a growing desire for increased product diversification and personalization of the design [55,56]. Therefore, it is believed that developing well-established conventional techniques for diagnosing and analyzing errors is more crucial for enhancing flexibility than for improving efficiency. The cobot could malfunction if human interface collaborators deviate from the prescribed job procedure. Without machine learning for pattern identification and autonomous scheduling of itineraries and work procedures, for instance, if an operator does not place a particular component in the designated joint hand-over region with the cobot, the cobot may not proceed. Unintended cobot actions, flaws, or downtimes are avoided by well-defined, comprehensive documentation on how to carry out the manual task. Employees should be able to resolve issues to restart the working process quickly in the event of a fault. It is becoming increasingly important to have these instructions produced for each step in a flexible manufacturing environment where the robot must continually adapt to changing production processes.
Economic factors were also found to have a significant influence on the adoption of collaborative robots. The specific economic factors that are worth considering include personal innovativeness and perceived usefulness. If the targeted manufacturing users consider the collaborative robots to be useful in their lives, increase their productivity and help them work conveniently, then their adoption could be enhanced. Similarly, if the eagerness of people, interests, and readiness to use were high, then collaborative robot adoption would be enhanced. Though sociotechnical systems were found not to influence collaborative robot adoption, one of its factors, technical subsystems, were considered to have a significant influence [57]. The aspects of technical subsystems include the ability to work together on interrelated activities, effective use of knowledge, techniques, equipment, and facilities, and improvement of social relationships [58]. However, all sociotechnical systems, including work design, social subsystems, and technical subsystems, were found to significantly influence perceived performance improvement [59]. This study further laid path for the technology (robot) utilization in cooperative social responsibility (CSR); Turoń et al. [60] postulated CSR-oriented strategy contributes significantly to the performance of the organizations. Moreover, CSR influences the competitiveness of technology companies and particularly their sustainability. It implies that the inclusion of aspects of resources, processing of marketing aspects, effective interrelated activities, and abilities of robots to achieve the job descriptions would improve the overall performance. More importantly, the adoption of collaborative robots positively increases perceived performance improvement. This is achieved through the success and improved employee performance associated with collaborative robots.

5. Conclusions

The study explored the sociotechnical, organizational, and economic aspects that determine the adoption of collaborative robots in Russia’s manufacturing industry. From this study, several conclusions could be drawn. First, collaborative robots are an important technology that could revolutionize the manufacturing sector in Russia if adopted and used effectively. The adoption of collaborative robots in Russia’s manufacturing sector is influenced by organizational factors, including training and management support, and economic factors, including personal innovativeness and perceived usefulness. The social structural systems and the adoption of collaborative robots were considered to significantly influence performance improvement. The research recommended that to improve the adoption of collaborative robots in the manufacturing sector, stakeholders should consider training workers and management support. Additionally, to enhance the adoption of collaborative robots, personal innovativeness and perceived usefulness should be enhanced, including usefulness in users’ lives, increasing their productivity, and helping them work conveniently. To enhance and improve performance, the aspects of social subsystems, technical subsystems, and work design should be enhanced. Due to the presence of two very diverse resources in the same workstation as well as their interaction, collaborative applications are more complex than standard robotic systems. This interaction results in both advantages and disadvantages. In essence, the entire effectiveness of such an industrial application is influenced by the operator’s perception of the deployed work package.
The findings of this study can be used to investigate and comprehend how humans and robots might work together in the manufacturing industry. However, additional confirmation will be required before using the mechanisms demonstrated in this paper to enhance the employee’s flexibility to change and collaborate with robotic technologies. Another limitation is that the quantitative data is based on professional ratings, which are inherently interpretative by essence. The authors are incapable of determining whether the performance variables are indeed significant. To objectify the subjective judgments, sizable datasets on the evolution of firms’ performance, the sociotechnical subsystem, design, and earlier and future cobot deployments would be needed. The practical contribution of the research consists in developing a managerial instrument to help researchers and organizations working in cobot environments understand collaborative robot technology and predict aspects that could determine employees to act toward the company’s strategic direction. Based on observations from managers with practical knowledge of the creation, implementation, and management of the cobot system in manufacturing, the instrument was verified in the cobot manufacturing sector in Russia. The research findings are promising and bring to attention the importance of the coming industrial revolution with companies deploying collaborative robot technology.

Author Contributions

Conceptualization, S.Z.; methodology, O.G.; investigation, S.S. All authors have read and agreed to the published version of the manuscript.

Funding

The article has been prepared based on the results of studies supported by budgetary funds in accordance with the state order for the Financial University under the government of the Russian Federation on the topic of “Development of mechanisms for organizing innovative interaction and forms of exchange of intangible assets as factors of economic growth in the context of economic transformation.”

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Acknowledgments

The authors wish to thank the Economies Journal editors and the reviewers for their valuable time and effort in improving the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 14271 g001 550
Figure 1. Conceptual Framework of the Study.
Figure 1. Conceptual Framework of the Study.
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Figure 2. Flow diagram of the methodology steps.
Figure 2. Flow diagram of the methodology steps.
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Figure 3. Empirical data.
Figure 3. Empirical data.
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Table
Table 2. Validity of the instrument/constructs.
Table 2. Validity of the instrument/constructs.
No of Items Cronbach Alpha
110.847
Table
Table 3. Path relationship of the result.
Table 3. Path relationship of the result.
HypothesisPathsEstimateS.E.C.R. p
H1Organizational Factors
TR→CR0.3960.0606.613***
MS→CR0.1260.0502.5410.011
IC→CR−0.0340.048−0.7060.480
H2Economic Factors
PU→CR−0.1260.049−2.5690.010
PI→CR0.2360.0435.446***
PE→CR0.1800.0503.593***
H3Sociotechnical Systems
WD→CR0.0560.0351.5880.112
TS→CR0.2050.0464.500***
SS→CR0.0100.0400.2420.809
H4Sociotechnical systems
WD→PPI0.6520.0748.851***
TS→PPI0.2190.0524.234***
SS→PPI0.1950.0464.257***
H5CR→PPI0.2030.0762.6800.007
*** = significant at 99% confidence, *** = significant at 95% confidence, TR = training, MS = managerial support, IC = incentives, PU = perceived usefulness, PI = personal innovativeness, CR = collaborative robot adoption, PE = prior experience, SS = social subsystems, TS = technical subsystems, WD = work design, PPI = perceived performance improvement.
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Zemlyak, S.; Gusarova, O.; Sivakova, S. Assessing the Influence of Collaborative Technology Adoption—Mediating Role of Sociotechnical, Organizational, and Economic Factors. Sustainability 2022, 14, 14271. https://doi.org/10.3390/su142114271

AMA Style

Zemlyak S, Gusarova O, Sivakova S. Assessing the Influence of Collaborative Technology Adoption—Mediating Role of Sociotechnical, Organizational, and Economic Factors. Sustainability. 2022; 14(21):14271. https://doi.org/10.3390/su142114271

Chicago/Turabian Style

Zemlyak, Svetlana, Olga Gusarova, and Svetlana Sivakova. 2022. "Assessing the Influence of Collaborative Technology Adoption—Mediating Role of Sociotechnical, Organizational, and Economic Factors" Sustainability 14, no. 21: 14271. https://doi.org/10.3390/su142114271

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