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Article

Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation

by
Kristen Haynes
*,
Gregory Harris
,
Mark C. Schall, Jr.
,
Jia Liu
and
Jerry Davis
Industrial & Systems Engineering, Auburn University, Auburn, AL 36849, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(12), 4969; https://doi.org/10.3390/su16124969
Submission received: 1 May 2024 / Revised: 1 June 2024 / Accepted: 7 June 2024 / Published: 11 June 2024
(This article belongs to the Section Social Ecology and Sustainability)
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Abstract

:
Recent technological advances are bringing about the digitalization of manufacturing, enabled by introducing and integrating new and improved technologies into existing processes and activities. Integrating advanced technologies into the workplace can have a positive effect on manufacturing efficiency and competitiveness, as well as sustainability and environmental impact. Employee acceptance of these new technologies is critical for manufacturing organizations to achieve these goals. Unfortunately, a notable deficiency of tools to assess the readiness of an employee work group or organization to accept a new technology exists. The objective of this study was to develop and validate a new tool for gauging employee technology acceptance in a pre-implementation decision context known as the Technology Acceptance in a Manufacturing Environment (TAME). Statistical validation measures were conducted on survey responses from 823 respondents across seven locations of one large organization. The results indicate that TAME is appropriate for assessing readiness for technology acceptance among manufacturing workers with little to no training or knowledge of the technology being considered for implementation (R2 = 86%). TAME can facilitate the organizational assessment of employee perception of new technologies before implementation, increasing the chances of a successful launch. This research results in the first known application of technology acceptance models in a pre-implementation context in a manufacturing environment.

1. Introduction

It is widely accepted that industry is undergoing a radical transformation, primarily driven by a “demand for faster delivery times, more efficient and automated processes, improved quality and customized products” [1]. In conjunction with process improvements, factors such as “legal requirements, national and international standards, and market and customer expectations” are leading organizations to pay more attention to their environmental footprint [2]. Of particular focus are “resource efficiency, greenhouse gas intensity, and … emissions behavior” [2].
Previous industrial revolutions were recognized and named after the implemented technological changes. There is currently research promoting the idea of both the fourth and fifth industrial revolutions. These are different from the first three in that they have been labeled as industrial revolutions before fully implementing the technologies and ideas associated with the significant changes. Germany coined the term “Industry 4.0” (I4.0) in 2011 and developed a government policy statement, “Industrie 4.0”, that codified the initiative in 2013. I4.0 focuses on connecting innovative “embedded system production technologies and smart production processes to pave the way to a new technological age which will radically transform industry and production value chains and business models” [3]. The German government, along with various universities and companies, presented the idea of “Smart Factories” integrated with Cyber-Physical Systems (CPSs) and the Internet of Things (IoT). CPSs use advanced computational capability to combine the physical and virtual worlds and make the IoT possible.
The IoT allows for the complete integration of manufacturing systems, creating a Smart Factory where equipment, warehouses, and production facilities communicate with logistics, marketing, and service to create flexibility and increased visibility of the entire manufacturing process. In addition to optimizing existing processes, I4.0 has the potential to globalize visibility into the entire manufacturing enterprise and facilitate increased communication with suppliers, customers, and employees [4]. These advancements can lead to revenue growth, increased employment, investment [5], and “creative solutions” [6] in the manufacturing industry. Implementing advanced technologies can in turn improve a company’s sustainability practices [7,8]. This is a dual win for a manufacturing organization in that it can both improve efficiency and achieve sustainability goals to reduce the environmental impact of its manufacturing operations.
Industry 5.0 (I5.0), introduced by the European Commission roughly ten years after the introduction of I4.0 [9], is characterized by a human-focused, sustainable integration of the I4.0 technologies into society [10,11]. Many of the drivers of I4.0 are the same for I5.0 [12], and the two can co-exist in the same space [13,14]. Although limited, human factors are already considered in some I4.0 research [15,16,17]. I5.0 seems to be a natural extension and adaptation of I4.0 as a step to the implementation of Smart Factories. For example, a recent literature review found that the aims of I4.0 and I5.0 papers were largely the same, just in different ratios [18]. Considering the subtle nuances between I4.0 versus I5.0, the authors will refer to the implementation of new technologies in the workplace and all considered factors as “smart manufacturing”.
Smart manufacturing is of significant interest to academics, researchers, and industry as it involves popular concepts such as the IoT, CPS, smart factories, and vertical and horizontal integration in the manufacturing enterprise. Although limited [6], research on I4.0 concepts details how these technologies are utilized and their necessity in developing organizational competitiveness. There is significant importance in human–machine interaction as these new technologies are developed and introduced to the manufacturing workforce [4,5,19,20]. The predictive nature of smart manufacturing presents organizations with an opportunity to prepare their workforce for the technologies being introduced and increase skill levels where necessary. Smart manufacturing technologies include systems to enhance a human’s physical capabilities (e.g., exoskeletons), cognition (e.g., mixed reality), and availability (e.g., autonomous systems). These applications are especially useful in eliminating safety and quality concerns and allocating human resources to more high-level, skilled tasks [8]. The ignorance of human factors in technology implementation can negatively affect both safety and performance outcomes [20].
Despite the potential benefits of smart manufacturing, it is acknowledged that the workforce might have “technology acceptance issues” [21]. Prior research posits that employees will resist taking on new responsibilities and accepting changes outside their current work [22,23]. Another possibility for the workforce’s lack of technology acceptance is a lack of trust in the new technology being introduced. Research shows that until humans accept new technology, its full benefit cannot be realized [24]. If new technologies are forced upon employees who are unable or unwilling to accept them, technology implementation may be delayed or fail, contributing to multiple adverse consequences for the organization. At the very least, a delay in implementation will cause additional resource investment associated with a lengthier implementation process. If implementation fails completely, the organization has invested resources for which they receive no benefit. A failed implementation may cause the organization to identify and attempt to implement an alternative, possibly inferior, technology. At this point, employees who have already experienced a failed implementation could be hesitant to accept any new technology, decreasing acceptance for the second attempt from the start. No matter the outcome, a delayed or failed implementation due to an unprepared workforce can harm the organization.
It would be beneficial for a manufacturing organization to determine the state of the workforce technology acceptance disposition before attempting to implement the technology [25]. Pre-implementation is important because employees tend to be more resistant to new technologies prior to implementation [26,27]. Gauging employee acceptance before making an implementation decision creates an opportunity to increase buy-in and avoid roadblocks before significant resource investments in a new technology are made. However, surveying employees in a pre-adoption technology stage requires gathering feedback from the workforce on the potential new technology without them possessing a working knowledge, having been trained, or using the technology in a “true” pre-implementation context. Therefore, “pre-implementation” may be considered a pre-decision context where participants have no working knowledge or training on the technology under consideration. Manufacturing organizations must have a tool validated in the pre-implementation context to have an accurate, reliable way to gauge employee technology acceptance before new technologies are implemented. The objective of this study was to develop and validate a new tool for gauging employee technology acceptance in a pre-implementation decision context known as the Technology Acceptance in a Manufacturing Environment (TAME) model. Specifically, TAME is intended to fill the research gap in the studies addressing technology acceptance in a pre-implementation, manufacturing context that includes both individual and organizational factors. The authors believe this approach will most closely align with the needs of manufacturing organizations who want to know employees’ level of acceptance before significant resource investment is made in a new technology.

2. Technology Acceptance Models

Several technology adoption models have been proposed and validated, primarily falling into two categories: individual or organizational acceptance.

2.1. Individual Technology Acceptance Models

In 1989, Davis introduced the Technology Acceptance Model (TAM (TAM is a specific technology acceptance model, so the abbreviation “TAM” references the singular model proposed by Davis. The complete phrase “technology acceptance model(s)” references the collective group of models proposed to study technology acceptance)) [28]. He proposed that perceived usefulness (i.e., the potential benefits one expects from using the proposed technology) and perceived ease of use (i.e., the anticipated user-friendliness of the proposed technology) were significant contributors to acceptance. Several derivative models have since been introduced and independently studied.
One of the larger contributions to the field of technology acceptance is the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Figure 1), in which the most significant aspects of various technology acceptance models were consolidated into one [29]. The consolidated models include the Theory of Reasoned Action, Technology Acceptance Model II, Motivational Model, Theory of Planned Behavior (TPB), Combined TAM/TPB, Model of PC Utilization, Innovation Diffusion Theory, and Social Cognitive Theory [30,31,32,33,34,35]. The UTAUT includes four primary constructs. The first is performance expectancy, or “the degree to which an individual believes that using the system will help him or her to attain gains in job performance”. The second is effort expectancy, “the degree of ease associated with the use of the system”. The third is social influence, “the degree to which an individual perceives that important persons believe he or she should use the new system”. The final primary construct in the UTAUT is facilitating conditions, which describes “the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system” [29].
Despite the evidence supporting the utility of gauging employee technology acceptance before implementation, the UTAUT was developed as a post-implementation tool where participants are already familiar with the proposed technology. Once participants are familiarized with a technology (e.g., through training or hands-on experience in a post-implementation scenario), acceptance or rejection has already occurred, and the decision-making window to improve technology adoption outcomes has passed [29]. The original UTAUT survey instrument is worded in a mixed hypothetical and post-implementation context, which is inappropriate for pre-implementation applications. Studies applying the UTAUT have been primarily conducted in current-use contexts or in situations where participants were familiarized with the system prior to the survey. While this approach is generally accepted in the technology adoption model literature, some authors have recognized the need to understand participant acceptance toward a specified technology before implementation, where only a brief introduction to the technology for basic awareness is given (i.e., [25]). In the small number of UTAUT studies with persons not using the technology, the participants are usually individual consumers who have already made a use decision or employees who have prior familiarity with the technology being studied [36,37,38], negating the benefits of gauging the level of acceptance pre-implementation.
In addition to almost exclusive post-implementation applications, not one of the current individual technology acceptance models includes an understanding of the organizational readiness to implement a new technology. The literature suggests that individuals who are a part of an organization can be influenced by the organization itself when considering using a new technology [39,40,41]. Due to the impact employees can have on the success of implementation efforts, it is important to gauge their level of acceptance ahead of time, including their perceptions of the organization’s readiness to implement new technologies. Therefore, a technology acceptance model intended for application in organizations should include a measurement to account for employees’ perception of the readiness of that organization to implement the new technology.

2.2. Organizational Technology Acceptance Models

The Organizational Readiness for Implementing Change (ORIC; Figure 2) is designed to gauge employee acceptance in a pre-implementation context. It is intended to allow employee feedback on organizational readiness to implement new technology. This model is based on the theory of organizational readiness for change, which introduces two metrics: change commitment, which refers to “organizational members’ shared resolve” to actively make changes, and change efficacy, which refers to “organizational members’ shared beliefs in their collective capabilities” to make changes [42]. The level of change commitment and efficacy were determined to directly apply to the success or failure of a change across an organization [43]. However, while ORIC encompasses some important organizational considerations, it does not include measures that give insight into individual employee acceptance levels.

2.3. Summary of Technology Acceptance Models

Adopting new technologies is critical to the manufacturing industry due to potential increased revenue through efficiency gains and the ability to address important environmental and safety concerns [1,4,5]. Research on I4.0 technology implementation predicts increased employment [5], primarily consisting of skilled labor responsible for maintaining the automated systems that maintain quality and productivity [44]. However, little research has been conducted on I4.0 human–machine integration [21]. If employees do not trust or accept the new technology, it could lead to failed implementation and negatively impact the organization [45].
It is important to consider the employee opinions of new technology before implementation from both the organizational and individual standpoint. An organization can increase the chance of successful technology implementation by ensuring individual employees are ready to adopt the new technology. Pre-implementation feedback from employees also gives organizations a way to gauge the potential level of employee acceptance of a new technology before significant resource investment in a technology implementation is made.
The contribution of this paper is threefold: it presents both a new application of the UTAUT and a new, validated technology acceptance model, TAME, in a pre-implementation context. It then compares the two for applicability to a specific context. Therefore, this research study both extends an existing tool’s application context and adds a new tool to the technology acceptance model domain.
First, this study is an extension of the current UTAUT applications. It presents the UTAUT framework in a manufacturing context, as well as a pre-implementation context. This differs from past studies as the participants possessed no significant knowledge about the technology and no training had occurred, meaning the participants were still in the decision-making window. Pre-implementation employee feedback will help gauge the acceptance levels of the workforce and address any gaps in acceptance that could negatively impact implementation efforts.
This study also introduces, tests, and validates a technology acceptance model, capturing the needs of a manufacturing organization considering the adoption of a new technology. The new tool is not simply a derivative of existing tools. The newly developed TAME model combines two schools of thought in technology acceptance and allows for the study of both individual and organizational concepts in one tool.
The two models are then compared to determine which is best in a pre-implementation, manufacturing context. It was found that TAME is a more appropriate model in this context, which provides manufacturing organizations with a comprehensive technology acceptance tool to determine employees’ readiness to accept a new technology prior to investing significant resources into implementing a new technology. TAME is a tool that not only has not existed prior to this study, it is statistically the most capable technology acceptance model for manufacturing organizations to use when considering new technology implementations.

3. Development of the Technology Acceptance in a Manufacturing Environment Model

The Technology Acceptance in a Manufacturing Environment (TAME) model modifies the UTAUT to create a pre-implementation manufacturing instrument and adds an organizational readiness construct. The model and accompanying survey instrument gauge manufacturing employee readiness for adoption at an individual and organizational level before new technology implementation.

3.1. Survey Development

TAME was developed by integrating parts of the UTAUT and ORIC instruments. Because UTAUT is a validated synthesis of various existing technology acceptance models, it was used as the basis of the individual acceptance portion of the survey instrument. ORIC is an organizational technology acceptance model emphasizing the employee’s opinions of his or her organization, which was deemed valuable in a technology acceptance model intended for the manufacturing environment. Both constructs tested in ORIC—change commitment and change efficacy—helped gauge employee acceptance of technology. They were, therefore, included in the present model as one combined “organizational readiness” construct. A graphical representation of the theoretical TAME model is depicted in Figure 3.
The questions regarding individual acceptance were converted from the UTAUT, and questions regarding organizational readiness were adapted from ORIC. Additionally, behavioral intention was measured as an antecedent of the five primary constructs. All the questions were restructured to address the pre-technology implementation context rather than a post-implementation or mixed-tense structure currently found in the UTAUT and ORIC instruments. For example, in the performance expectancy section, “Using the system increases my productivity” was changed to “Using this technology would increase my productivity”. In some cases, the questions were changed to a hypothetical context rather than a guaranteed-implementation context to account for initiatives where a decision on implementation has not been made. The resulting survey instrument consisted of six constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), organizational readiness (OR), facilitating conditions (FC), and behavioral intention (BI). All of the constructs and corresponding survey items are listed in Table 1.
The survey instrument consisted of twenty-four questions in six categories corresponding to six constructs. Four statements related to performance expectancy or whether participants feel the technology would help make their job easier. Four statements addressed effort expectancy, or how easy the participants feel the technology would be to use. Four statements related to social influence, or the pressure participants feel from management and/or peers to use the technology. Four statements were related to facilitating conditions or the participants’ belief that he or she has the actual ability to use the technology regardless of intention. Four statements are related to behavioral intention or the survey participant’s intention to use and learn the technology.
Lastly, four statements were related to organizational readiness or the level the survey participant believes the organization is prepared to implement the technology. The same pre-implementation structure was used to create this construct. This construct was not present in the original UTAUT model, as it was intended to measure individual-level acceptance. Statements for the organizational readiness construct were derived from ORIC, which intended to measure the technology acceptance of an organization and had a similar structure to UTAUT (statement format with a Likert scale intended to measure employee feedback in an organizational context). The four statements representing organizational readiness were a synthesis of the statements presented in ORIC with the most applicability to a pre-implementation manufacturing context.
A short, written paragraph introducing the technology was provided along with photos of sample applications to give participants a pre-implementation level of knowledge without providing a true “learning” experience. Due to the variety of the available smart manufacturing technologies and their potential applications, the participants need to answer questions with the same technology in mind. For example, one individual’s level of acceptance for 3D printing may be different from that same individual’s level of acceptance for autonomous robots. For this reason, the scope was narrowed to one specific augmented reality (AR) technology with direct applicability to manufacturing. AR is relevant to manufacturing because it combines physical and virtual domain capabilities to allow quicker, more informed decision making [6].

3.2. Survey Deployment

The sample for this study consisted of employees at a large-scale automotive manufacturing company who had the potential to be involved in smart manufacturing technology implementation. A link and QR code to the online survey were provided in a weekly company newsletter. Participant consent was obtained electronically. After reading an informational letter about the study, the participants were advised that by proceeding to the survey page they consented to participate. The participants were advised in this letter that they could answer all, some, or none of the questions and that all of the responses were anonymous and confidential.
Employees wishing to participate were briefly introduced to augmented reality via the method described in Section 3.1. The participants then rated each survey item on a seven-point Likert scale, ranging from one (strongly disagree) to seven (strongly agree). The questions were presented electronically, one at a time, and in a random order. A total of 823 responses were received. Of these, 458 responses included the organizational readiness component that creates TAME (UTAUT + OR). Full participant demographics are listed in Table 2.
The demographic makeup of responses was found to be representative of the population from which responses were derived (i.e., the ratio of male to female employees), so the analysis proceeded to validation.

4. Analysis & Results

Several analysis techniques were used to validate and compare the original UTAUT and proposed TAME models. Validating each model was necessary because the UTAUT had yet to be validated in a pre-implementation context. This same analysis was then applied to the TAME model, a novel survey instrument not yet tested in any context. The two were then compared to see which is a more appropriate fit for a pre-implementation manufacturing context.

4.1. UTAUT Results

Although the UTAUT model has been previously validated in various post-implementation applications, a confirmatory factor analysis was conducted to validate the model in a pre-implementation context in the manufacturing domain and for direct comparison with the TAME model. The model fit was assessed and determined to be satisfactory. The effect of each independent variable on behavioral intention was measured (Table 3). Performance expectancy and effort expectancy significantly affected behavioral intention, but social influence and facilitating conditions did not.
The squared multiple correlation (R2) for behavioral intention was 0.83, indicating that 83% of the variance in behavioral intention is accounted for by performance expectancy, effort expectancy, social influence, and facilitating conditions. The impact of performance expectancy and effort expectancy was both positive and significant, suggesting that increased performance expectancy and effort expectancy lead to an increase in behavioral intention in a pre-implementation manufacturing context. The impact of social influence was positive but insignificant. The impact of facilitating conditions was negative and insignificant. The insignificance of social influence and facilitating conditions indicates that neither construct predicts behavioral intention in a pre-implementation context.
The UTAUT results led to the model shown in Figure 4, representing a manufacturing, pre-implementation context. Although not predictive of behavioral intention, social influence and facilitating conditions were retained in the model as possible predictors of actual use behavior.

4.2. TAME Results

Model fit was achieved by adding the organizational readiness factor and deleting the facilitating conditions factor.
Relationships between each independent variable and behavioral intention were calculated to establish whether significant directional relationships exist where predicted. The values are reported in Table 4. Performance expectancy, effort expectancy, and organizational readiness were found to have positive and significant relationships with behavioral intention, confirming that each construct can predict behavioral intention in a pre-implementation context. Social influence was not found to significantly affect the TAME model, meaning it does not predict behavioral intention in a pre-implementation context.
The R2 value for the TAME model was 0.86, indicating that the constructs explain 86% of the variance in behavioral intention in TAME. This was an improvement relative to the UTAUT model, which had an R2 of 83%.
The resulting TAME model is depicted in Figure 5. As with the UTAUT, social influence was left in the model as a possible predictor of use behavior despite not having a significant effect on behavioral intention.

Moderating Effects

In addition to testing the relationship between the independent variables and behavioral intention, the moderating variables of age, gender, and experience on the job were tested. The moderating variables affect the power or directionality of a link between an independent and dependent variable [46,47,48]. Age and experience were found to be the moderators of performance expectancy and organizational readiness for TAME at the p < 0.05 level. Experience, but not age, was found to moderate effort expectancy for TAME. Gender was not found to moderate any independent variable at the p < 0.05 level. Figure 6 depicts TAME with moderating variable effects.
Group comparisons were also made to explore any significant differences in questionnaire responses by various respondents’ demographics. Data were analyzed by country and facility location (Figure 7, Figure 8 and Figure 9), department (Figure 10), position within the organization (Figure 11), age, gender, and level of experience in manufacturing. Differences were found for all of the above categories except gender, which supports the same finding for gender in the moderating variable analysis.

5. Discussion

5.1. UTAUT

A thorough validation was conducted on the UTAUT instrument in a manufacturing, pre-implementation context, extending the usability of the modified UTAUT to a new application. All statistical testing results support using the UTAUT as a good model fit. However, one major difference was found between the results of the original UTAUT study [29] and this study, which was the insignificance of social influence in predicting behavioral intention. The facilitating conditions result was similar to that of the original study, which reported that facilitating conditions were not significant in predicting behavioral intention but helpful in predicting actual use.
Although the result for facilitating conditions was not surprising, as the original UTAUT study found facilitating conditions to be insignificant in predicting behavioral intention, the result for social influence requires interpretation. The survey items for social influence intend to capture the amount of influence an individual perceives others have on his or her use of the technology. If social influence does not significantly predict behavioral intention, this suggests that outside influences on the individual do not affect that person’s level of technology acceptance when they are unfamiliar with the proposed technology. Whether a peer, authority figure, or the organization would want the individual to accept the technology does not appear to have a significant bearing on that person’s intention to use it.
In the manufacturing industry, employees do not have final decision-making power on technology implementation. Instead, depending on a person’s role within the company, he or she may provide a business case to executive decision-makers for a certain technology, lead or assist with implementation, or be an end user. Whether the employee feels their peer group or manager would want to implement the technology may not necessarily be relevant to acceptance. Therefore, the social influence construct is likely more relevant to an individual consumer than an organizational context.
Another explanation may be that the pre-implementation phase is too early to adequately examine the relationship between social influence and behavioral intention. Suppose an employee has no experience with a new technology. In that case, as in a pre-implementation context, that individual may not have enough information to know whether others would want him or her to use the technology. Once an employee work group is familiar with or trained on the technology, social influence could be a significant factor in their eventual behavioral intention. The result may explain why there was a significant relationship in the original UTAUT study between social influence and behavioral intention in a post-implementation context [29].

5.2. TAME

As with the validation of the UTAUT, statistical testing revealed that TAME is also a valid model for gauging the technology acceptance of employees in a pre-implementation manufacturing context. However, our data suggest that TAME is the stronger of the two models.
Several possible explanations exist for the low contribution level of social influence and facilitating conditions to the TAME model. One possible explanation is facilitating conditions being too close to another latent variable, decreasing its contributory power to the model. The construct closest to facilitating conditions in TAME is organizational readiness. Facilitating conditions describe the actual resource availability and compatibility of the new technology with existing technologies. In contrast, organizational readiness describes the organization’s willingness and ability to devote resources and support the implementation of new technology. The two constructs could be related enough to challenge one another statistically. Interestingly, the only difference between the UTAUT model containing facilitating conditions and the TAME model, where facilitating conditions were rejected, is the addition of organizational readiness. However, while facilitating conditions were found to be insignificant for the UTAUT, organizational readiness was found to be significant for TAME. This finding suggests that the organizational readiness construct captures employee technology acceptance more adequately in a pre-implementation context than the facilitating conditions.
Facilitating conditions and social influence were not significant factors in UTAUT or TAME for the pre-implementation context. However, including them in future studies incorporating actual use data may be useful. It is possible that either facilitating conditions or social influence could be statistically significant in predicting actual use in a longitudinal or post-implementation study. Also, while these constructs do not add to the statistical power of the model in the pre-implementation phase, the answers to the questions on the survey instrument itself may still provide insight for organizations. For example, it may be useful to an organization to know whether an employee believes his or her peers would want him or her to use a new technology. The information can help pinpoint how different workgroups perceive new technologies, allowing organizations to implement new technologies with the more adaptable workgroups before implementation. However, as noted previously, the pre-implementation timing may be too early for an employee to assess whether his or her peers support using the newly proposed technology. This possibility and the original UTAUT findings of significance between social influence and behavioral intention [29] point to a relationship between the two variables that should be considered during the implementation process.
Comparing the differences in group responses can provide valuable insight for the organization. Moderating variable analysis showed that increasing experience in manufacturing has a negative effect on performance expectancy, effort expectancy, and organizational readiness in predicting the behavioral intention of employees to use new technology. Increasing age also leads to a less salient effect on performance expectancy and organizational readiness on behavioral intention. One theoretical interpretation of these results is that as employees become more familiar with a particular job or process, they are less likely to be open to changes in that job or process that technology may bring. This theory is supported by findings of prior research studies on employee resistance to change in a work environment [22,23].
The same theory can be applied to age. Employees may be more resistant to adopting new technologies in their workplace as they age. It is important to note that while these moderating variables weaken the positive relationship between each independent construct and behavioral intention, they do not reverse or negate it. The overall responses received for the survey indicate that employees at all levels of experience and all ages are open to accepting new technologies, just less strongly as experience and age increase. Organizations need to recognize the possible effect of reduced behavioral intention, begin introducing the idea of new technology, and seek feedback earlier in work groups that are more experienced and/or aged.
The exploratory analysis of multiple group demographics also revealed important findings for the organization. The first key finding is that there was a difference in performance expectancy, effort expectancy, and behavioral intention across the two countries studied, as well as facilities at multiple locations within those countries. Of note, a significant difference (p = 0.04) in performance expectancy was found between the Engineering and Front-Line positions. A significant difference in performance expectancy was also found between departments (p = 0.01).
Various factors can contribute to location differences, including affluency, the strength of the economy, existing technology, governments, and societal norms [49]. These factors could help an organization make informed decisions on which technologies to implement in a certain location, or whether to introduce a technology in one particular location before cross-deploying to others. For example, a group with lower overall scores might be willing to provide insight into why a technology may not fit that workgroup or application. In comparison, a group with higher overall scores may have a higher implementation success rate and fewer roadblocks in the initial stages of adoption. The group with which an organization chooses to implement a new technology is situation-dependent.
Organizational demographics can also play a role in differences between groups. For example, both department and position groups had different levels of performance expectancy when compared to one another. Again, an organization should determine which employee groups are more prone to acceptance before implementation. The organization can then choose to implement a particular technology in a specific area within the facility based on a higher level of acceptance, or it can identify gaps in acceptance and attempt to address them with those groups prior to attempted implementation. When testing for one particular technology, comparisons between departments and roles within an organization can help identify which groups this technology may be useful for and which groups may need a different technology more suited to their job needs. However, as visualized by the box plots, the mean response for each group fell between a score of 5 (somewhat agree) and 6 (agree), suggesting that the differences may not be practically meaningful.
A final finding of note is that low-lying outliers were discovered for many groups and demographics. While this may be discouraging to the organization, it should be seen as an opportunity to learn what is negatively impacting employee buy-in. An advantage of TAME is that it provides responses at the individual level, so supplemental interviews or focus groups can be held to help pinpoint any causes of concern among employees. Additionally, these outliers were found at the pre-implementation stage, allowing the organization to mitigate negativity surrounding implementing new technology. However, it is also important to recall the finding of a significant interaction between social influence and behavioral intention after training employees on the technology [29]. These low-lying outliers may be positively affected by the rest of the workgroup with time, or vice versa. The organization should monitor any possible effects throughout the implementation process.

6. Conclusions

The advent of smart manufacturing drives the need for a technology acceptance model for the manufacturing sector to use when determining employee readiness to adopt new technologies. Several models previously existed, but none were usable in a pre-implementation context in their current form. In choosing which technologies to implement, the human component must be considered. Using TAME will help employers gauge employees’ readiness to accept a new technology before making an implementation decision. Pre-implementation surveying allows employers to identify gaps in the readiness to accept new technologies before launch and mitigate employee concerns so that they are bought into the idea of a new technology before it reaches them, thereby reducing risks in the technology implementation initiative. Risk mitigation will increase the chances of a successful technology launch and may increase employee openness toward new technologies. Increased employee acceptance will decrease the amount of time and resources needed for implementation. Meeting project deadlines will both increase organizational competitiveness and efficiency while ensuring sustainability goals are met sooner. This will decrease environmental impact and improve the sustainability of the organization.
Existing technology acceptance models were created with the individual or organization in mind. Individual technology acceptance models do not comprehensively consider a context where the individual surveyed may be a part of a company and, therefore, not the end decision-maker. Additionally, individual employees may not have control over the implementation process, and a lack of confidence in the organization can lead to failed implementation. Employees’ reliance on the organization for implementation leads to a need for organizations to know whether employees feel that it is ready to implement new technologies. On the other hand, existing organizational technology acceptance models are too narrow in scope to be applied to individual employees. Many organizational technology acceptance models focus on metrics relevant to decision-makers but not the working-level employees interacting with the technology.
Available technology acceptance models have been created for a current or post-use implementation context and have not been applied to a pre-implementation context. Research in manufacturing contexts is also limited. Manufacturing organizations must have an appropriate measure for pre-implementation, as this is a critical stage in integrating smart manufacturing and the manufacturing workforce. Attempting implementation before buy-in from employees can lead to a delay in implementation or even failure to implement the new technology, which wastes organizational resources and negatively affects employee perception of new technologies. Implementation delays can not only cause issues with meeting internal company sustainability goals, but it can also mean missed compliance deadlines and regulatory fines.
An initial study of TAME, a new technology acceptance model considering organizational and individual constructs, suggests that the new instrument is more appropriate for a pre-implementation manufacturing context than previously developed technology acceptance models. The major finding was that pairing the organizational readiness component derived from ORIC with the performance expectancy, effort expectancy, and social influence constructs in the UTAUT model resulted in a more comprehensive model for measuring employee acceptance of new technology in the pre-implementation stage. The significance of TAME indicates that individual and organizational constructs should be used to measure technology acceptance. Overall, human factors are an essential consideration in technology implementation efforts, and gauging employees’ level of technology acceptance before a new technology launch can provide insight for organizations that may increase the chances of implementation success and meeting long-term efficiency and sustainability goals.

Limitations & Future Work

Limitations of this study are as follows:
  • Participation was via self-selection, as the goal was to get as many responses as possible to ensure an adequate sample size for validity testing and to retain participant anonymity.
  • The possible relationships between social influence, facilitating conditions, and actual use could not be studied due to the nature of this study being a pre-implementation context.
  • The full-scale study was conducted with only one manufacturing organization, albeit at seven locations; evaluating TAME in other manufacturing organizations will help extend the generalizability of this model.
  • The study was conducted in only two countries, selected by convenience as the organization had existing locations in each of these countries. Further study in other countries and regions will extend the generalizability of this model and context.
  • The full-scale study was conducted with only one type of technology, augmented reality. Testing other technologies may produce different results.
  • The majority of respondents to the survey questionnaire were male. While this is reflective of typical demographics in a manufacturing environment, further studies should be done to explore the relationship between gender and TAME variables.
  • The same limitation applies to age, level of experience, and other demographics such as level of management and department. There may be a different distribution in other organizations or in other manufacturing applications that should be studied.
While this study suggests the viability of TAME in a pre-implementation manufacturing context, there are opportunities for future research related to this study. First, deploying this survey instrument with other population groups will continue to provide insight into the viability of the proposed model. TAME should be tested in other organizations within the manufacturing industry and with additional types of technology.
Measuring actual use will also allow for studying possible relationships between social influence and actual use and facilitating conditions and actual use. Due to the intention to study a pre-implementation context, measuring actual use was outside the scope of this study. Therefore, the relationship between facilitating conditions and actual use could not be derived from the current data. An analysis of facilitating conditions with actual use data may add facilitating conditions back to TAME as an antecedent of actual use but not behavioral intention (similar to the UTAUT model) or remove social influence from the model if it has no relationship with actual use. Increasing facilitating conditions may increase actual use after the technology is implemented but this cannot be confirmed without further data collection.

Author Contributions

Conceptualization, K.H., G.H. and M.C.S.J.; formal analysis, K.H.; investigation, K.H.; methodology, K.H., G.H., M.C.S.J., J.L. and J.D.; supervision, G.H.; validation, K.H.; writing—original draft, K.H.; writing—review and editing, G.H., M.C.S.J., J.L. and J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was approved by Auburn University IRB, protocol #21-528 EX 2111.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets presented in this article are not readily available due to privacy restrictions of the company where the research was conducted. Requests to access the datasets should be directed to [email protected].

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. UTAUT [29].
Figure 1. UTAUT [29].
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Figure 2. Organizational Readiness to Implement Change [43].
Figure 2. Organizational Readiness to Implement Change [43].
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Figure 3. Theoretical framework for TAME.
Figure 3. Theoretical framework for TAME.
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Figure 4. UTAUT for pre-implementation + manufacturing context.
Figure 4. UTAUT for pre-implementation + manufacturing context.
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Figure 5. TAME.
Figure 5. TAME.
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Figure 6. TAME with moderating effects.
Figure 6. TAME with moderating effects.
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Figure 7. Performance expectancy response by location.
Figure 7. Performance expectancy response by location.
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Figure 8. Effort expectancy response by location.
Figure 8. Effort expectancy response by location.
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Figure 9. Behavioral intention response by location.
Figure 9. Behavioral intention response by location.
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Figure 10. Performance expectancy by department.
Figure 10. Performance expectancy by department.
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Figure 11. Performance expectancy by position.
Figure 11. Performance expectancy by position.
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Table 1. TAME survey constructs and items.
Table 1. TAME survey constructs and items.
ConstructSurvey Item
Performance ExpectancyI would find this technology useful in my job.
Using this technology would help me accomplish tasks more quickly.
Using this technology would increase my productivity.
Using this technology would increase my chances of getting a raise, promotion, or other positive recognition.
Effort ExpectancyMy interactions with this technology would be clear and understandable.
It would be easy for me to become skillful at using this technology.
This technology would be easy to use.
Learning to use this technology would be easy for me.
Social InfluenceMy direct supervisor would want me to use this technology.
My peers would want me to use this technology.
My senior manager or director would want me to use this technology.
The organization as a whole would want me to use this technology.
Organizational ReadinessMy organization is committed to implementing new technologies.
My organization has the resources to support new technology implementation.
My organization has a history of supporting new technology implementation.
My organization would benefit from implementing new technologies.
Facilitating Conditions
(Note: not included in the final TAME model after statistical analysis)		I have the resources necessary to use this technology.
I have the knowledge/skills/abilities to use this technology.
This technology would be compatible with my other job tasks, equipment, and technologies I currently use.
There is a specific person (or group) that could assist me with difficulties in using this technology.
Behavioral IntentionI am interested in using this technology.
I would actually use this technology in my current role.
I am interested in learning how to use this technology.
I would try this technology even if it were challenging to learn at first.
Table 2. Survey demographics.
Table 2. Survey demographics.
Demographic Information (n = 823)%
RegionUnited States44%
Mexico56%
RoleEngineer27%
Front Line31%
Manager42%
GenderMale88%
Female11%
Non-Binary1%
Age (years)18–24 4%
25–34 30%
35–44 29%
45–54 27%
55+ 10%
Experience (years)Mean: 16.2
Range: <1–50
Standard Deviation: 10.3
Table 3. Relationships between independent variables and behavioral intention.
Table 3. Relationships between independent variables and behavioral intention.
ConstructEstimatep-ValueSignificant?
PE0.526<0.001Yes
EE0.628<0.001Yes
SI0.1160.113No
FC−0.3180.074No
Table 4. Relationship between independent variables and behavioral intention for TAME.
Table 4. Relationship between independent variables and behavioral intention for TAME.
ConstructEstimatep-ValueSignificant?
EE0.463<0.001Yes
PE0.480<0.001Yes
SI−0.0460.424No
OR0.080<0.001Yes
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Haynes, K.; Harris, G.; Schall, M.C., Jr.; Liu, J.; Davis, J. Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation. Sustainability 2024, 16, 4969. https://doi.org/10.3390/su16124969

AMA Style

Haynes K, Harris G, Schall MC Jr., Liu J, Davis J. Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation. Sustainability. 2024; 16(12):4969. https://doi.org/10.3390/su16124969

Chicago/Turabian Style

Haynes, Kristen, Gregory Harris, Mark C. Schall, Jr., Jia Liu, and Jerry Davis. 2024. "Gauging the Technology Acceptance of Manufacturing Employees: A New Measure for Pre-Implementation" Sustainability 16, no. 12: 4969. https://doi.org/10.3390/su16124969

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