DISSERTATION OVERVIEW

CHAPTER I

1.1 Introduction

There are various stakeholders that play a critical role in an organization’s success. Although

success is considered subjective and can be perceived in many ways, an enterprise’s main goal is

to be profitable and sustainable. Some important components of an enterprise organization’s

success are achieving and maintaining high quality and safety performance. These performance

measures are defined by the front-line workforce in a variety of industries. System safety

performance within an organization can help assist workers with comfortability and efficiency

and in-return result in positive organizational success.

There are certain business sectors in which there is high hazard work. In the construction and

manufacturing industries specifically, fatalities and serious injuries are affecting organizations

across the globe. Tasks that need to be completed are typically human-centered, which puts the

employee at risk of injury. Although federal safety standards such as OSHA and ANSI are

evolving, there still appears to be high incident rates among these sectors.

Wearable technology is an evolving trend among these business sectors. Wearable technology in

general is used to monitor human performance, identify potential precursors to injury, and can

assist enterprise organizations in finding the root cause of safety and quality-related concerns.

However, many of these wearable devices do not prevent the human from the risk of injury. One

wearable technology that has been used widely in these sectors is a wearable exoskeleton due to

its durability and its ability to assist with tasks that push the threshold of human physical

capabilities. When referring to NIOSH’s hierarchy of controls, engineering controls play a

critical factor in creating a barrier between the employee and their work environment. A

wearable exoskeleton can create a barrier that limits an employee’s ability to bend into awkward

postures and can prevent overexertion injuries from occurring. Therefore, this device has been

more effective in preventing injuries than examples such as wearable IMUs.

There are many types of wearable exoskeletons, with some examples being, but not limited to:

•Full body exoskeletons

•Passive back exoskeletons

•Upper limb exoskeletons

•Lower limb exoskeletons

•Knee assistive exoskeletons

These exoskeletons can be used in wide range of human-centered tasks that require overexertion,

or awkward postures. For example, back exoskeletons have been used to assist in manual

material handling and lifting tasks, and knee exoskeletons have assisted in kneeling tasks. While

these devices have been proven to be effective across these industries, there is often pushback

from various stakeholders of an enterprise organization. There are 3 critical stakeholders within

an enterprise organization:

•Front line workforce – user of the device and field management staff

• Human Resources (HR)

• Upper management

• Subject matter experts (SMEs)

To influence an organization, there must be a combination of buy-in and willingness from the

stakeholders listed above. The front-line workforce must accept the device because if they don’t,

it can negatively impact morale. There is also pushback from this stakeholder as humans in

general are used to routine tasks and do not like change. Especially within a business sector in

which there is long tenure, employees have been working the same way for a long time with

many of them never suffering an injury. However, by receiving feedback from this stakeholder

researchers can identify the main reasons why this stakeholder won’t accept the device even with

high incident rates and being exposed to serious injuries daily.

Human resources (HR) play a critical role in organizations. With high turnover rates within these

business sectors, human resources must develop trust with employees and put out strong

employee ethics practices to retain a strong quality workforce. Human resources also play a

critical role in employee wellness programs and other programs to increase employee morale.

Human resources must approve of adopting wearable technology in the workforce due to

negative employee implications such as privacy and ethics. Human resources are a stakeholder

that has not been studied often when it comes to wearable technology acceptance and identifying

gaps and disconnects between this stakeholder can help organizations push towards utilizing

these devices to its greatest potential.

Although upper management can be difficult to influence, they are the stakeholders that can

make big picture financial decisions which can lead to either positive or negative impacts.

However, with buy-in from multiple stakeholders and evidence of data that demonstrates that

wearable technology can assist in best practice implementation from a quality and safety

standpoint, there is an ability to influence this stakeholder.

Subject matter experts (SMEs) can play a critical role in the technical aspects of adopting the

wearable device, as they understand the current benefits and barriers of adoption. In addition,

this stakeholder understands the importance of collecting human factors related data in a variety

of different techniques. This data helps organizations determine the financial return on

investment (ROI) on the device. To incorporate these concepts, the dissertation is broken down

into 3 main studies:

Table 1.1 Overview of Research Studies

1. “A Comparison of Technology

Acceptance of a Wearable

Exoskeleton in the Construction and

Manufacturing Industries”

Compare technology acceptance between the

construction and manufacturing industries by

collecting questionnaire data on the front-line

workforce

2. “Understanding and Incorporating

Wearable Technology into an

Enterprise Organization: From a

Human Resources (HR) Perspective”

Identify current shortcomings and challenges

of wearable technology adoption from the

human resources (HR) perspective by

collecting questionnaire data within these

industry sectors

3. “Evaluating Subject Matter Expert’s

Perception of Wearable Exoskeleton

Adoption in an Industrial Setting”

Evaluate current benefits and limitations of

wearable exoskeleton adoption through the

lens of a subject matter expert (SME), and to

identify current best practices in data

collection among industry sectors for

participants that have attempted to, or have

deployed exoskeletons within industry sectors

that are human centered

1.2 Dissertation Research Aims

The goal of this dissertation is to evaluate the various stakeholders involved with the adoption

and acceptance of wearable technology such as exoskeletons within the construction and

manufacturing industries. The research objectives are to identify the key organizational

keyholders perception, motivation, and willingness to utilize devices such as wearable

exoskeletons in these industries. By understanding this information, practitioners and researchers

can consider these variables that are currently creating a barrier and develop interventions or

practices that incorporate these perceptions.

1.2.1 Study 1: “A Comparison of Technology Acceptance of a Wearable Exoskeleton in

the Construction and Manufacturing Industries”

There are two different potential stakeholders of wearable technology implementation and

acceptance, which are the front-line workforce and the organization to financially invest in these

products. The front-line worker must be bought into the process, as they are the individuals that

must wear the device and perform their typical job tasks. Management can provide the resources

necessary, however, the front-line worker is the stakeholder that must wear the device. It is also

important for organizations to develop a strong culture to receive buy-in from their front-line

workers. Without both critical components, there is a strong disconnect that will negatively

impact wearable technology acceptance.

Study 1 is geared towards the front-line workforce. A technology acceptance model

methodology will be used utilizing a questionnaire design method considering:

• Current organization – compare different sizes of organizations to see if there is a

correlation of responses based on this demographic

• Currently employed, part time/full time, or retired

• Job title

• Age and overall experience

• Tenure within the organization

• Experience with wearable technology

• Union or Non-Union

Additionally, the Perceived Usefulness (PU) and Perceived Ease-of-Use (PEU) from Davis, 1989

will be added along with subjective norm and behavioral intention-related questions such as

Lewis, 2019 and Buabeng-Andoh, 2017 as these are critical components of technology

acceptance models in previous studies.

The following are the main research questions for this study:

1. What are the most important factors for wearable exoskeleton adoption in the

construction and manufacturing industries from the front-line workforce’s perspective?

2. Is there statistical significance between construction and manufacturing participant

groups when it comes to wearable exoskeleton acceptance and adoption?

3. Is the front-line workforce generally in favor of wearable exoskeleton adoption within

their industry?

1.2.2 Study 2: “Understanding and Incorporating Wearable Technology into an

Enterprise Organization: From a Human Resources (HR) Perspective”

Another key stakeholder involved with technology acceptance is Human Resources (HR). This is

a stakeholder that must approve the devices considering potential negative implications such as

employee ethics and privacy. There have not been many studies that have focused on this

specific demographic. Like study 1, a questionnaire design has been developed based on these

parameters:

• Current industry

• Current tenure within the organization

• Has the organization adopted wearable technology

• Level of knowledge of wearable technology – i.e what wearables have they heard of

• What is seen as the main positives of wearables

• What is seen as critical roadblocks of wearables

The following are the main research questions of the study:

1. What does human resources (HR) see as the main benefits of wearable technology

adoption?

2. What are the critical roadblocks of wearable technology adoption from an HR

perspective?

3. Does it appear that HR within the construction and manufacturing industries would be

open to wearable technology adoption within their organization?

1.2.3 Study 3:” “Evaluating Subject Matter Expert’s Perception of Wearable

Exoskeleton Adoption in an Industrial Setting”

Subject matter experts (SMEs) play a critical role in the acceptance of the device from multiple

stakeholders such as the front-line workforce, human resources (HR), and additional

management positions to influence change. These experts are human factors engineers, research

scientists, consultants, and more, who are hired to collect technical data on wearable exoskeleton

functionality. This is typically broken down into two main categories, being physical human

performance, and attitude and perception through psychosocial data collection.

By collecting this data, this helps the overall organization make the decision of adopting

wearable exoskeletons within industry settings. To better study these experts’ perception of their

experience deploying and working with the device, the following questions will be asked through

a structured interview process:

• Current industry

• Job title

• Years of experience using wearable exoskeletons

• Data currently collected using the device

• Data preference to obtain the best output

• Main benefits of wearable exoskeleton adoption

• Critical roadblocks of wearable exoskeleton adoption

The following are the research questions for this study:

1. What types of exoskeletons have subject matter experts attempted to deploy within

industry or work settings?

2. What types of data have subject matter experts collected from front-line employees, and

what is their preference for best output?

3. What are the main benefits of wearable exoskeleton adoption from a subject matter

experts’ perspective?

4. What are the current critical roadblocks and limitations of wearable exoskeleton adoption

from a subject matter experts’ perspective?

CHAPTER II

A COMPARISON OF TECHNOLOGY ACCEPTANCE OF A WEARABLE EXOSKELETON

IN THE CONSTRUCTION AND MANUFACTURING INDUSTRIES

2.1 Introduction

The construction industry is the largest industry in the United States, accounting for 10% of the

gross domestic product or GDP (Helander, 1980). While this industry has a tremendous amount

of growth and offers a wide range of job opportunities for individuals, the industry has been

negatively affected by workplace accidents (Haupt and Pillay, 2016). Root causes of these

instances occurring are in areas such as safety awareness, lack of corporate commitment to safety

lack of training, lack of strict safety rules, lack of personal protective equipment (PPE), lack of

skillset, and equipment-related issues (Ammad et al. 2020). The annual cost of accidents in this

industry is $12 billion (Helander, 1980). This statistic has remained consistent for many decades.

For example, the total cost of fatal and non-fatal injuries in the construction industry was $11.5

billion in 2002 (Waehrer et al. 2007).

In efforts to improve this statistic, Congress passed the Occupational Safety and Health Act

(OSHA) in 1970 (Hinze et al 2013). While OSHA has been passed, there is evidence that shows

that documentation such as report information cannot be collected readily following the time of

accidents (Hinze et al. 1998).

Besides the fatal four, many construction laborers suffer injuries such as musculoskeletal

disorders or soft tissue injuries. Some examples of these types of injuries is sprains, strains, and

low back pain (Buter et al. 1996). Data from Workers Compensation claims show that strains

and sprains are the leading compensable injury for the construction industry (Welch et al. 1999).

Evidence shows that construction workers’ risk of developing these injuries is much higher than

those with less intensive labor work, which is about 50% higher than those workers (Schneider,

2001). If employees do suffer these types of injuries, there have been attempts for modified work

programs, which are meant to ease employees back to work following a work-related injury

(Brooker et al. 2001).

The industry has attempted to promote safe workplaces. A safe workplace is when safety

indicators are inspected and evaluated and that have positive outcomes (Mikkelsen et al. 2010).

The size of the organization can play a major factor on injuries in the workplace. For example,

large construction firms have consistently lower frequency of lost time injuries compared to

smaller firms (McVittie et al. 1997).

The purpose of this study is to identify the barriers of acceptance of wearable technology such as

exoskeletons, as this technology has been proven to work in a variety of studies involving human

participants.

Overall health and safety within industries that are human centered as vitally important. There

are many components that can cause injury or death within the manufacturing industry. Over the

past few years, the manufacturing industry has suffered the highest injury and illness rates of any

other industry (Grindle et al. 2000). Caught in-between injuries have served as the leading cause

of accidents in the manufacturing industry (Jeong, 1999). Injuries that have occurred during

night shifts were also a leading cause of injury and illness (Jeong, 1999). This is prevalent due to

many manufacturing sectors working during mornings, afternoons, and nights to meet business

productivity needs.

When looking at injuries in depth, there are many variables that effect the overall statistics. For

example, results have shown that younger workers tend to encounter more non-fatal injuries and

death has occurred more in the older workforce demographic in this industry (Jeong, 1999). In

another study conducted by Chi et. al 2004, a high-risk group for the manufacturing industry was

workers who had 1-15 years of experience and were working for larger manufacturing

organizations in Taiwan. A study conducted by Jeong, 1997 found that more than half of

manufacturing injuries occurred with employees that had a tenure of less than a year in South

Korea.

The manufacturing industry is constantly adapting, especially in areas such as industry 4.0, there

is a need to make safety advancements as part of this overall process (Reniers, 2017). To

improve these statistics, there are many components of an overall safety program that should be

implemented in the manufacturing industry. Like many industries that have intensive labor and

human centered tasks, the quality of training, employee turnover rates, experience, current

jobsite conditions, gender, and parameters play critical roles in mitigating hazards and risks

(Subramanian et al. 2006).

The research team has identified the following research questions:

1. What are the most important factors for wearable exoskeleton adoption in the

construction and manufacturing industries from the front-line workforce’s perspective?

2. Is there statistical significance between construction and manufacturing participant

groups when it comes to wearable exoskeleton acceptance and adoption?

3. Is the front-line workforce generally in favor of wearable exoskeleton adoption within

their industry?

2.2 Overview of Wearable Technology in Industry Applications

Wearable technology is electronics and computers that are integrated into accessories or clothing

that can be worn comfortably on the body (Wright and Keith, 2014). Wearable devices can be

placed on glasses, watches, hardhats or headbands, or jewelry (Wright and Keith, 2014). These

sensors can track, analyze, and guide the user’s behavior (Schull, 2016). Due to this reason, there

is much room for growth and innovation for businesses in many different sectors (Raj and Ha-

Brookshire, 2016).

Wearable technology has been used in a wide range of market sectors. Some examples of

industry sectors where wearable technology has been seen is within the healthcare, construction,

manufacturing, automotive, mining, and more. Essentially any industry that has tasks that are

human-centered can utilize this technology. Wearable technology is even being used for human-

centered intelligent robots (He et al. 2017).

2.2.1 Wearable Devices in the Construction Industry

To promote safety in the construction industry in particular, wearable technology applications

have been adopted. These technologies typically break down into two main categories which are

motion sensors and physiological sensors (Ahn et al. 2019). Examples of motion sensors that

have been used are wearable inertial measurement units (IMUs), EMGs and 3D motion capture.

Physiological sensors that have been used in the industry have been heart-rate sensors,

electrodermal-activity sensors, skin temperature sensors, eye trackers, and brainwave monitors

(Ahn et al. 2019). The goal of these technologies is to continuously monitor the workers’ health

on the jobsite and to promote jobsite safety (Ahn et al. 2019).

Wearable devices can be used as a solution for enhancing worker efficiency, improving worker

health, and creating positive interaction between the user and their work environment (Stefana et

al. 2021). Wearable technologies in general have been proven to be effective across many

different industry sectors. For example, wearable IMUs can be used to gather quantitative data

during a task such as material handling (Kim and Nussbaum, 2014). Wearable IMUs have also

been used to analyze the biomechanics of construction workers during brick laying activities

(Valero et al. 2017). These sensors are being used more widely in industries that are human-

centered such as construction because of its ability to capture data without effecting day-to-day

operations (Yan et al. 2017).

Wearable IMUs can be used effectively in the workplace for risk assessment of work-related

musculoskeletal disorders or MSD’s (Huang et al. 2020). In these types of studies, these types of

wearable devices can be used to categorize tasks into low and high-risk categories (Ranavolo et

al. 2018). Wearable IMUs have been popular in these types of studies as these applications can

be low cost and can accurately measure workplace demands (Cao et al. 2019).

These sensors can collect data 24/7, without the need for intervention or maintenance

(Williamson et al. 2015). Researchers have taken advantage of this enhancement. For example, a

research team monitored physical activity using wearable accelerometers for construction

workers (Arias et al. 2015). The participants wore these devices for 7 days straight, and the

researchers found that over 2/3 of physical activity was from their job during the typical week

(Arias et al 2015). Using these devices, the data from the accelerometers helped the research

team categorize rigorous versus moderate exercise (Arias et al. 2015).

There are opportunities to combine multiple wearable devices for industry applications. For

example, combining motion capture with 2-D video can track multiple people simultaneously

(Malleson et al. 2019). This method has been used widely in athlete applications, such as using

these devices to analyze biomechanics in pitching athletes in baseball (DeFroda at al. 2016).

Wearable technology can be used to collect and deliver data for inspection tasks in a wide range

of different industries in real time (Pray and McSweeney, 2018).

There is also the potential of utilizing wearable technology for accident re-construction and

investigation. Wearable technology has been used in crime scene investigation (Baber et al.

2005). Radio frequency identification tags were used for evidence bags in this investigation to

capture images of scenes and objects (Baber et al. 2005). The wearable technology was able to

identify the time and location of the recovery utilizing a GPS system (Baber et al. 2005).

Table 2.1 Overview of Motion and Physiological Sensors

Motion Sensors

• Inertial Measurement Units (IMUs)

• Surface Electromyography (EMGs)

• 3D Motion Capture Physiological Sensors

• Heart-rate sensors

• Electrodermal-activity sensors

• Skin temperature sensors

• Eye tracking

• Brainwave monitors

While there is room for this growth and potential, there are many barriers that organizations

encounter that negatively impacts their ability to adopt the technology (Page, 2015). There is a

constant challenge to meet the demands of people who want to access the technology, and it is

unknown to this point how developers can collaborate with external stakeholders in this fast-

changing industry (Raj and Ha-Brookshire, 2015).

2.2.2 Overview of Wearable Acceptance

Table 2.2 Human Factors to Acceptability of Wearable Devices: “Six human factors to

acceptability of wearable computers”

Behavior Factors

Physical Factors

Demographic

Characteristics

Cognitive Attitude

Perceived Usefulness

Perceived Ease of Use

Social Aspects

Personal Privacy

Physical Comfort and Safety

Aesthetic and Appearance

Mobility

Age and Gender

Technical Expertise

International Journal of Multimedia and Ubiquitous Engineering, 8(3), 103-144 Gimhae, 2013.

When it comes to wearable acceptance models, there is a lack of questionnaires that identify and

measure the user’s acceptance of solutions or interventions (Fensli et al. 2008). The evaluation of

the use of wearable sensors is important for consumer buy-in (Fensli et al. 2008). One of the

main issues associated with wearable devices in general is creating buy-in from the user.

Acceptance of the wearable device itself is the main barrier of many technology applications in

the industry (De Looze et al. 2017). Organizations must promote a company culture that can

motivate their employees in this sense. Examples of this include their motivation to enhance

workplace safety, drive a stronger safety culture, display evidence on how the wearable can help

the user, and involving the employees from start to finish in the process of choosing wearables

that would fit them (Jacobs et al. 2019).

2.2.3 About Exoskeletons

Exoskeletons are a structural device that links to certain areas of the human body (Perry et a.

2007). Exoskeletons are designed to enhance human strength and performance measures such as

endurance and speed while also being comfortable (Pratt et al. 2004). This can be completed by

integrating sensing, control, and technology to evaluate the user’s task (Shi et al. 2019). For the

exoskeleton to be valuable, the device must apply forces when it is appropriate to the user’s task

(Pratt et al. 2004). The device can assist in enhancing human body motion (Kong and Jeon,

2006). The design process of the exoskeleton is critical as there must be physical connections

between the device and specific human limb (Jarrasse and Morel, 2011). Along with this, the

wearable exoskeleton should be lightweight and easy to use from a usability standpoint while

also being compact (In et al. 2011).

Exoskeletons can provide benefits to both healthy and disabled populations (Rosen and Perry,

2007). Exoskeletons have been mainly used in rehabilitation and virtual reality simulation

applications (Perry et al. 2007). Exoskeletons can be used in virtual environments by recording

the movements of human body parts (Bergamasco et al. 1994). These devices have been used

widely in the healthcare and rehabilitation industry. For example, exoskeletons have been used in

enhancing paraplegics ability to stand and walk (Wang et al. 2014). These devices that assist in

these tasks are typically robotic ankle exoskeletons or a lower limb exoskeleton (Gordon and

Ferris, 2007). This can be done by minimizing joint activity during walking activities (Van Dijk

et al. 2011). These devices have helped the elderly, as this demographic tends to have reduced

physical capabilities and requires a lot of effort to walk (Galle et al. 2017).

The healthcare industry in general Is struggling with providing rehabilitation to patients who

have suffered a stroke (Lo et al. 2012). Exoskeletons have been used in arm therapy for stroke

patients (Nef et al. 2007). Stroke survivors can be left with poor function in their hand, in which

a exoskeleton has been used in rehabilitation therapy to gain functionality back (Schabowsky et

al. 2010). The technology has also shown benefits for wheelchair users overall health and

mobility (Wolff et al. 2014).

Exoskeletons have been used in human-centered tasks that require material transporting (Chu et

al. 2005). These tasks are done mostly through manual labor which causes a burden to the

individual (Chu et al. 2005). For example, exoskeletons have been an innovative technology in

the manufacturing industry for risk and quality assessment (Spada et al. 2017).

2.2.4 Exoskeletons in the Construction Industry

There is a demand for the construction industry in general to improve its efficiency (Bogue,

2018). Exoskeletons can be used in the workplace in construction, as there are environments in

which unstructured and complex work tasks occur (Bock and Ikeda, 2012). Exoskeleton

applications have been proven to be useful in the workplace to mitigate the risk of injury. This is

possible because the exoskeleton itself physically prevents the worker from having unsafe

posture during high risk working tasks (Cho et al. 2018). There is also different sets of data that

can be collected using an exoskeleton. For example, researchers can focus on the posture of the

user, the mass of the object that is being lifted or manipulated, or a combination of the two

(Toxiri et al. 2018).

However, there have been few studies that use wearable exoskeletons with a high number of

human participants (Howard et al. 2020). At this point in time, the use of exoskeletons in the

construction industry is rare (Linner at al. 2018). In studies involving human participants, muscle

activity reductions have been as high as 80% utilizing this wearable device (De Looze et al.

2016). These devices have also been used to lower back muscle activity by 35-38% during

assembly work, with the workers endurance being 3x higher while utilizing the device (Bosch et

al. 2016). One potential limitation in this study was reports of back discomfort, and chest

discomfort (Bosch et al. 2016).

Exoskeletons have been used in manual handling tasks specifically. Wearing exoskeletons can

reduce physical fatigue and muscle activity in the workplace during manual handling tasks (Zhu

et al. 2021). During a study with human participants, results indicated that discomfort was

lowered significantly, and task completion time increased by 20% (Ogunseiju et al. 2021). In a

similar study, the researchers used an exoskeleton system as an ergonomic intervention tool for

evaluating spine biomechanics (Antwi-Afari et al. 2021). Some of the results of this study

showed that the exoskeleton had acceptable usability, reduced discomfort, and a significant

reduction of muscle tension in the shoulder, lower back, and leg body parts (Antwi-Afari et al.

2021). The exoskeleton can be used to reduce muscle and spine force, along with extensor

movements in construction workers (Antwi-Afari et al. 2021).

Passive back exoskeletons can be used to determine muscle activity, energy expenditure and risk

assessment during human-centered tasks such as repetitive lifting (Alemi et al. 2020). In many of

these studies, endurance during lifting and static bending improved utilizing the device

(Kermavnar et al. 2021). Researchers have used passive back-support exoskeletons to evaluate

the risk factors associated with flooring work (Ogunseiju et al. 2021). Along with this, back-

support exoskeletons have been used to relieve stress on the lower back during the physical

demands of rebar work (Gonsalves et al. 2021).

Arm-support exoskeletons have also been used in the construction industry, which has been used

mainly in studies involving single postures or movements such as overhead work (de Vries et al.

2021). In these situations, muscle activity is reduced, upper arm velocity increases, and task

effort is reduced (Otten et al. 2018). The effectiveness of an arm support skeleton was tested

during plastering activities, which requires multiple arm movements (de Vries et al. 2021).

Results of the study showed that muscle activity and perceived exertion was reduced using this

technology (de Vries et al. 2021).

In a similar study, an adjustable shoulder support exoskeleton was used for static and dynamic

overhead tasks (Van Engelhoven et al. 2018). The wearable technology reduced muscle activity

in the shoulder, along with creating a more balanced pattern of shoulder activation which can be

considered potential precursors to injury (Van Engelhoven et al. 2018). These types of studies

can be used more frequently, as overhead tasks are a continuous exposure to injury.

Along with this, upper limb exoskeletons have been used to assist refractory construction

workers in furnace operations (Yu et al. 2018). This device was able to withstand 50 kg worth of

force, which is very useful in this industry (Yu et al. 2018). There are many scenarios in which

construction workers would have to handle heavy loads, so this type of technology can be used

in future studies. There are also lightweight and less expensive exoskeletons that can assist

construction workers during load lifting and carrying tasks (Jain et al. 2021). This type of device

supports the elbow and wrist joints and can support 8 kg worth of weight (Jain et al. 2021). This

type of device may be more useful for contractors that do not have to manually handle as much

material.

Lower limb exoskeletons are wearable devices that are used to support the action and movement

of human lower limbs (Ren et al. 2021). Lower limb exoskeletons have been used in

rehabilitation of disabled people that require the motion of lower limbs to heal and improve

(Stopforth, 2012). Lower leg support exoskeletons have been used for below hip height panel

work in the construction industry (Pillai et al. 2020). In this study significant reduction of the

rectus femoris was observed with the wearable device which can reduce pain and discomfort

associated with tasks that require squatting (Pillai et al. 2020). Lower limb exoskeletons have

also been used in this industry to assist workers in shotcrete projection (Capitani et al. 2021).

Knee assistive exoskeletons have been used to evaluate construction workers knee flexors,

muscle activity, and stiffness by combining an exoskeleton with wearable sensors (Yu et al.

2008). These types of wearable devices have also been used for researchers to analyze kneeling

tasks in the construction industry (Chen et al. 2018). This device can be used to analyze tasks

such as awkward kneeling postures, kneeling, and crawling for long periods which can result in

musculoskeletal disorders or MSDs over time (Chen et al. 2018). Future studies can also utilize

this type of technology to prevent hazards while walking, along with slip and fall incidents in

this industry.

Determining whether exoskeletons can be a type of personal protective equipment has been a gap

in the health and safety industry (Howard et al. 2020). Exoskeletons have been used as personal

protective equipment (PPE) on construction projects by utilizing a passive exoskeleton safety

jacket (Rosman and Mahmud, 2021). Some components of this jacket were the exoskeleton

device, heart beating sensors and global positioning systems (GPS) to analyze potential risk

factors in the workplace (Rosman and Mahmud, 2021).

Exoskeletons have the potential to be used in smart textiles in a wide range of industries. For

example, soft exoskeletons have been used for knee-sensing in the healthcare industry

(Bottenberg at al. 2018). The XOSoft lower limb exoskeleton has assisted people with mobility

issues (Ortiz et al. 2018). Soft arm exoskeletons have aided in elbow joint motions (Cappello et

al. 2015), which can be used in future studies as this is a prevalent risk factor in the construction

industry.

There has also been an interest in developing smart anklets which can serve as a smart

exoskeleton for patients with limb deficiencies (Petrella, 2018). These types of devices have the

potential to give feedback around human performance data with tasks such as abnormal walking

or postures (Petrella, 2018). Future studies can integrate smart exoskeletons in any industry that

requires human-centered tasks. For example, there is a push to incorporate this in the mining

industry, as there are emerging ergonomic issues and opportunities (Dempsey et al. 2018).

Overall, there is an opportunity to incorporate soft exoskeletons and smart textiles involving this

technology in the construction industry, however there is very few studies that demonstrate these

practices. There is evidence in the healthcare and mining industry and should be quickly adopted

into the construction industry as research shows it can be effective in preventing soft tissues

injuries.

2.2.5 Exoskeletons in the Manufacturing Industry

The main goal of utilizing an exoskeleton in the manufacturing industry is to eliminate risk of

injury through manual material handling tasks, and to strive to keep employee turnover low

(Constantinescu et al. 2016). This can be done through reinforcing posture of the individual

using the device for support mainly in the lower back and upper extremity during these tasks

(Howard et al. 2019). Looking at individual worker demographic, workers from larger

organizations had more of an interest to using an exoskeleton versus smaller organizations

(Schwerha et al. 2021).

Organizations will need to see the positive effects of occupational exoskeletons through field

studies which can be conducted by field management personnel such as field supervisors, safety

professionals, and ergonomists (Crea et al. 2021). Using exoskeletons can lead to in-depth

conversations between stakeholders such as project management, research and development,

ergonomics practitioners, and workers (Toxiri et al. 2019).

In the automotive manufacturing industry, arm-support exoskeletons resulted in a major decrease

in self-reported discomfort in the neck and shoulders (Smets, 2019). Passive back exoskeletons

have assisted in physical workload in measurements such as posture analysis, usability testing,

and cardiac cost for workers completing arm-elevated finishing tasks (Moyon et al. 2019).

There is an opportunity to combine wearable exoskeletons with other wearable technology

devices. For example, an ergonomic risk assessment of an upper body passive exoskeleton was

used on-site to determine muscle fatigue during tasks such as cab and hydraulic assembly,

painting, hanging, and welding tasks with and without an exoskeleton (Gilette, 2019). The

researchers found that the EMG device helped measure threshold limit values for overhead job

tasks (Gilette, 2019). Through this study, the researchers received feedback from the participants

mentioning that an exoskeleton would be difficult to use in confined spaces and during bending

tasks which can serve as a limitation. In a similar study, the research team used three passive

back exoskeletons in a mock overhead drilling task and found that even with increased muscle

activation, there was deteriorated quality using the devices (Alabdulkarim et al. 2019).

Passive back and passive-shoulder assist exoskeletons were used in the wholesale and retail

environment to prevent risk of injury (Marino, 2019). 14 participants were involved in the study,

and overall found these exoskeleton devices useful. However, as a limitation, workers

complained about movement, task performance, and comfort issues (Marino, 2019).

While there are limitations as far as design and usage, The National Institute of Standards and

Technology (NIST) has been forming standards and committees based on exoskeleton research

in hopes to further develop standards (Bostelman et al. 2019). Many of these experiments consist

of trying to alleviate discomfort by considering variables such as overall fit, anthropometry, and

gender (Bostelman et al. 2019).

2.2.6 Limitations of Exoskeletons and Other Wearable Technologies in Industry

Perceived risk and compatibility appear to be some of the issues associated with wearable

devices (Nasir and Yurder, 2015). Interviews have been conducted with workplace employees

based on perceived privacy concerns, acceptance of wearables, and protection motivation

(Sergueeva and Shaw, 2016). If organizations can address these issues first-hand, this can result

in the user being more comfortable using the device (Sergueeva and Shaw, 2016).

If organizations can have these discussions with employees, this can result in the increase of

productivity and culture. Organizations can use themes to determine the potential gaps of

wearable technologies, since there is currently push-back in a variety of different industries.

Some of these are comfort level of their employees, goal setting, purpose of the data tracking,

and the future of wearable technology being utilized in the industry (Mercer et al. 2016).

This exoskeleton technology is evolving very quickly in industries, however there is research

that still needs to be conducted in areas such as kinematic capability and effective human-robot

interaction (Gull et al. 2020). There is a lack of product standards which can create a barrier for

exoskeleton development in the industry (Lowe et al. 2019). Most of the back-support

exoskeletons are heavy and bulky which tend to be less comfortable than soft exoskeletons

(Toxiri et al. 2019). There is a lack of soft exoskeletons developed now, which can be worn

under clothing or smart textiles, and are more comfortable for the user (Toxiri et al. 2019).

There is an importance of analyzing task demands of tasks such as working posture when

determining which exoskeleton to use (de Vries et al. 2019). For back-support exoskeletons,

performance tends to decline with tasks that required increased agility (Kermavnar et al. 2021).

2.2.7 Summary of Evidence

Table 2.3 Summary of Evidence – Wearable Exoskeletons

Exoskeletons

• Can be used as an ergonomic tool to analyze biomechanics and complex movement

related to construction activities

• Can assist in non-traditional movements and complex tasks

• Physically prevents awkward postures and movements

• Studies show acceptable usability, reduced discomfort, and significant decreases in

muscle tension

• Researchers can focus on the posture of the individual, the mass of the load, or both

simultaneously

Passive Back Exoskeletons

• Can be used to determine muscle activity, energy expenditure, and risk assessment

during human-centered tasks such as repetitive or manual lifting

• Endurance during lifting and static bending are improved

• Used as a risk assessment tool to relieve stress in the lower back during rebar and

flooring work

Upper Limb Exoskeletons

• Shoulder support exoskeletons have been used for overhead tasks, specifically

evaluating static and dynamic movements

• Shoulder support exoskeletons can reduce muscle activity in the shoulder, and create

more balanced patterns of shoulder movement

• Upper limb exoskeletons have been used to assist refractory construction workers by

assisting them with heavy lifting

• Less expensive and lightweight devices are available which can assist construction

workers in load lifting and carrying

• Small devices can handle 8 kg loads, more complex systems can handle as high as

50kg

Lower Limb Exoskeletons

• Support the action and movement of lower limbs

• Has been used in rehabilitation fields to assist disabled people

• Lower leg exoskeletons have been used for below hip height panel work in the

construction industry

• Has assisted in reduced pain in comfort in tasks that require squatting

• Has been used in the construction industry to assist workers in shotcrete projection

Table 2.3 (Continued)

Knee Assistive Exoskeletons

• Can be used to evaluate construction workers knee flexors, muscle activity, and

stiffness when combined with wearable sensors such as IMUs

• Have been used to analyze kneeling tasks in the construction industry

• Can be used to analyze tasks such as awkward kneeling postures, kneeling in general,

or crawling for long periods of time

• There is an opportunity for future studies to use this technology to prevent hazards

while walking, and for slip and fall mitigation

Exoskeletons as Personal Protective Equipment (PPE)

• Passive exoskeleton safety jacket in the construction industry

• Exoskeletons can be combined with heart beating sensors and global positioning

systems (GPS) for real time risk assessment of tasks in the construction industry

• Small and soft exoskeletons have to opportunity to be used as smart PPE

Limitations

• Perceived risk and compatibility are some of the main issues associated with these

devices

• Privacy concerns, acceptance of the technology, and protection motivation play crucial

factors

• Exoskeleton technology is emerging, however there is a lack of research in areas such

as kinematic capability and effective human interaction with the device

• Lack of product standards which can hinder exoskeleton development

• Most of these devices are bulky and heavy which effect comfort for the user

• Lack of soft exoskeletons which promote more comfortability for the user

• Performance can decline with tasks that require increased agility for back-support

exoskeletons

Future Studies

• There is an opportunity to incorporate soft exoskeletons and smart textiles more often

in studies, which has been used in healthcare and mining studies

• Smart anklets have been used in other industries in which researchers in the

construction industry can have similar studies for slip and fall prevention for example

• Future studies should incorporate a higher number of human participants in studies, as

this is currently lacking

2.3 Questionnaire Design

To receive the best feedback from the user, the design of the questionnaires will play a major

factor of how the user will answer questions. Questionnaire content can allow workers to answer

more questions freely and honestly, which can result in more information or a wider range of

results (Villalobos et al. 2013). Based on the results of the questionnaire, intervention may be

needed. If an intervention is needed, resilience and perceived social support play a role in

increasing morale (Rodriguez-Fernandez et al. 2016). Management can initiate a changing work

environment, along with increasing social support from management and other workers.

One example of a type of questionnaire that has been used widely in studies is a psychosocial

questionnaire. These are surveys that are designed to reach out to front-line workers or users to

receive feedback based on their work environment (Nistor et al. 2012). Psychosocial

questionnaires can be used a risk assessment tool for supervisors in a variety of different

industries (Nistor et al. 2012). For example, the Copenhagen questionnaire is a popular

questionnaire in which organizations have used since it is free of charge (Kristensen and Borg,

2003). For work-related questionnaires, the National Institute of Occupational Safety and Health

(NIOSH) has questionnaires that are specific to the workplace and hazards (Kristensen and Borg,

2003).

There are limitations to these questionnaires. Workers must self-report these questionnaires

which can imply biases (Useche et al. 2019). Worker attitude can also negatively impact data and

results if the worker is not fully truthful (Brenner and DeLamater, 2016). Since this data can be

perceived as subjective, there are other external factors that should be evaluated to gather more

accurate data.

2.3.1 Questionnaires in the Construction Industry

In the construction industry, there is an importance to enhance safety through innovative

strategies (Gao et al. 2015). For example, wearable technology can be utilized to collect

psychological data to evaluate behavior-based safety (Guo et al. 2017). Along with this, there are

opportunities to collect physiological data and physical data simultaneously through on-site

experiments by identifying correlations in the data (Guo et al. 2017). Wearable technology

studies involving human participants have been successful in trial studies (Gao et al. 2015).

The Copenhagen questionnaire has been used in the construction industry to identify

environmental factors (Kristensen, 2001). Looking at an industry such as the construction

industry, there are many occupational stressors that can lead to injury (Ikuma and Aghazadeh,

2011). External factors such as the work environment and mental health factors can result in

physical injury in this industry (Molen et al. 2013). The psychosocial questionnaire has also been

used to determine workers willingness to work past 65 years of age (Blatter et al. 2012).

In an online survey reaching out to 4385 construction workers, results showed that the worker

would be open to sharing and utilizing data collecting on them if it could help identify personal

risks and promote safety (Häikiö et al. 2020). Based on this research, there are opportunities to

reach out to individual workers utilizing questionnaire surveys.

A pilot study questionnaire was conducted to reach out to project managers that worked for

general contractors in the United States and the results showed that trialability, useful features,

and the availability of qualified staff were the most important factors for wearable technology

(Gao et al. 2015). Results from questionnaires outline that educating and training workers,

promoting wearable devices, and conducting detailed assessments are key strategies to improve

the implementation of these devices regardless of subjective perception (Nnaji and Awolusi,

2021).

Questionnaire surveys have been used in Malaysia which reached out to 133 construction

practitioners, which found that the most important technology in the industry is Building

Information Modeling (BIM) and wearable safety technologies such as robotics and automation

(Yap et al. 2021). The practitioners believed that these technologies are best suited for pre-

planning, safety monitoring and hazard identification (Yap et al. 2021).

There have also been questionnaire surveys reaching out to older construction workers on smart

wearable technology use to understand their perception of the device (Callejas and Kwon, 2019).

The research team reached out to 15 construction workers utilizing a survey and identified that

perceived risk was the main factor in their decision making (Callejas and Kwon, 2019). Other

results of this study had more even data in reasons such as perceived cost, perceived trust, lack of

knowledge, perceived usefulness, facilitating conditions, and more (Callejas and Kwon, 2019).

Social influence was the least important factor in most of their responses (Callejas and Kwon,

2019).

Studies have also been completed on entry-level construction workers to identify general fatigue

status and identify early interventions that can be used to mitigate fatigue during tasks such as

manual material handling (Lee et al. 2021). One main goal of this study was to identify a

wearable sensor that can actively measure risk factors such as fatigue (Lee et al. 2021). There is

an opportunity to incorporate questionnaires in a study like this to understand the user’s

perception of the device. Results may differ based on the change of age demographic compared

to many different studies that reach out to older construction workers.

There are opportunities to implement smart personal protective equipment (PPE) to minimize

work injuries on construction sites (Lee et al. 2020). For example, a research team reached out to

Malaysian construction workers, and findings show that respondents understand that smart

personal protective equipment (PPE) can be better to reduce injuries than standard personal

protective equipment (PPE) (Lee et al. 2020). In a similar study, the researchers reached out to

413 construction workers based on their acceptance of personal protective equipment (PPE) in

Hong Kong (Man et al. 2021). The researchers were able to develop a technology acceptance

model and used this questionnaire to identify risk perception for construction workers (Man et al

2021).

A research model was also conducted for smart vests and wristbands in the construction industry,

reaching out to 120 construction workers on their perception of these potential devices (Choi et

al. 2017). Results of this study showed that perceived usefulness, social influence, and perceived

privacy risk were the main factors associated with workers incentive to use the device (Choi et

al. 2017). Workers experience with devices can positively impact the use of using the smart vest

(Choi et al. 2017).

There have also been questionnaires that have reached out to consumers accepting wearable

technology within solar-powered clothing (Hwang, 2014). In this study, a pretest design was

completed to evaluate the conciseness of the questionnaire (Hwang, 2014). Future studies can

utilize these types of questionnaires, as smart textiles and smart clothing can be used widely

across many industries due to its accessibility. Future studies can also utilize a pretest design, as

this will give the researcher more immediate feedback on the questionnaire design itself.

There are also questionnaires available that have been completed in other industries for the user’s

preference of what is being tracked on them (Koo et al. 2017). By gaining results during a

baseline pre-test design, research teams can identify what data can be collected that workers

would be comfortable with.

A questionnaire was sent out to Indian construction companies identifying the gaps of business

growth (Bhattacharya et al. 2021). Results of this study showed that anticipation of new demands

and capabilities was one of the leading factors of enabling growth in the construction industry

(Bhattacharya et al. 2021). An example of a new capability would be wearable technology. There

is an importance to understand why there is a gap in perception and motivation to utilize these

unique capabilities to enhance worker safety.

Studies have also shown that participants participating in these questionnaires that work for

smaller companies have a lower perception and motivation to use the wearable devices in general

versus participants working for medium to larger companies (Nnaji et al. 2019). In this study in

particular, the research team was evaluating influence in technology can influence decision

making in the construction industry (Nnaji et al. 2019). The most influential factors were

reliability of the technology, effectiveness, and durability (Nnaji et al. 2019). Evidence shows

that questionnaires of wearable technologies should be geared towards employees at bigger

organizations, as they are more likely to have a higher baseline understanding of the technology

itself.

There are opportunities for researchers to reach out to major organizations via questionnaires.

For example, a questionnaire was sent out to the North American Association of Transportation

about wearable technology that can be used to protect bridge maintenance workers from fall

protection exposures (Santa, 2018). Future studies can adopt this technique as these public

organizations have outreach that can reach out to many different employees of different business

sectors.

2.3.2 Questionnaires in the Manufacturing Industry

The technology acceptance model (TAM) can be used when implementing a new technology

within the manufacturing environment (Gresham, 2020). While this model has been used within

a wide range of industries, there are still gaps from a demographic standpoint. For example, there

is little evidence of technology acceptance modeling being used in the South Africa

manufacturing industry (Taherdoost, 2018). Along with this, technology can be considered

complicated for the user and mass sampling can be difficult to obtain (Lee et al. 2011). As a

result, there can be mistaken conclusions of the data set (Lee et al. 2011).

Questionnaires have been geared to both management and front-line employees. When looking

at the organization’s perspective on technology acceptance, there is a need to look at the overall

system and processes of the organization. One area where this can be done is by collecting

meaningful data. For example, a technology acceptance model was used to collect big data

analytics (Verma et al. 2018).

For example, a survey was adopted to an audience of management for shop floor advanced

manufacturing technologies (Scannell et al. 2011). Results of this study found that supplier

support did not have significant influence on the behavioral control of the technology being used

by the front-line employees (Scannell et al. 2011).

In the automation manufacturing industry in China, results of a technology acceptance model

found that perceived norms significantly influence the overall organization’s intention to use the

automation technology applications for both perceived usefulness and intention to use (Cao et al.

2018). In a similar study in the food manufacturing sector in Kenya, found that ease of use,

usefulness, and intentions to use significantly impacted food innovations (Okumua and Faith,

2018).

In another study geared towards management, green manufacturing technology was looked at

further in the manufacturing industry in China (Ivan et al. 2010). Results of this study found that

current jobsite conditions, social implications, government regulations were driving factors of

accepting this technology (Ivan et al. 2010). Results of this study also found that performance,

social influence, and effort expectancy did not play a significant role in the overall results.

For front-line employees, user perspective was studied in the Indonesian manufacturing sector

(Chin and Lin, 2015). 258 questionnaires were sent to front-line employees and found that

compatibility and technology complexity were the biggest influencers to the employees’

intention to use technology (Chin and Lin, 2015). In a similar study, a technology acceptance

model was used to evaluate the user’s behavioral intention of a new plasma technology (Lee et

al. 2010). Results of this study found that perceived ease of use, subjective norm, and experience

were the biggest driving factors of accepting the technology (Lee et al. 2010).

Technology acceptance models have also been used to identify the relationship between

technology acceptance and organizational agility in Malaysia to identify an organization’s value

as a competitor in the marketplace (Zain et al. 2005). Organizational structure and culture play

critical roles in technology being utilized and accepted in the industry. For example,

organizational structure will play a role as financial stability and management personnel able to

collect and analyze data will be important. Culture is important, as it is important to have front-

line workers understand the value of the technology itself.

2.3.3 Questionnaires for One Specific Device

To utilize wearable technologies, the consumer must approve the technology itself. In research, it

is known that researchers use survey questionnaires, hypothesis testing, and modeling to collect

data (Liao et al. 2020). Organizations can utilize questionnaire surveys to reach out to this

audience to understand their perspective. For example, a survey was used to identify factors

influencing wearable technology adoption in the healthcare industry (Zhang et al. 2017). The

main results of this study showed that technology attributes, health, and consumer attributes

influenced the adoption of this technology the most (Zhang et al. 2017).

Many questionnaires have been pointed towards the consumer. A questionnaire survey in the

healthcare industry reached out to physiotherapists on their perspective on healthcare technology

such as mHealth (Blumenthal at al. 2018). Results of this study showed that perceived usefulness

and perceived ease of use were the most influenced factors (Bluementhal et al. 2018). Along

with this, the researchers found no evidence of age, gender, or experience playing a factor on

their perspectives of the device (Bluementhal et al 2018). In a similar study, a questionnaire

reached out to nurse practitioners as they will have a major responsibility on how patients will

use devices (Wilson, 2017). Results showed that nurses had a lot of experience in the latest

technologies such as electronic records and simulation experiences, and wearable technology

will be the next step in technology applications in this field (Wilson, 2017). More research is

needed to coach patients and caregivers on the benefits of these devices (Wilson, 2017).

There have been studies reaching out to audiences for specific devices. For example, a research

team reached out to 1412 consumers in Turkey on the acceptance of wearable technology such as

smart T-shirts and undershirts (Turhan, 2013). Results of this study found that subjective norms

and attitude behaviors are direct influences to the consumer buying the product, but perceived

usefulness was not a significant factor (Turhan, 2013).

Along with this, there are questionnaires that reach out to consumers for activity trackers. For

example, a research team reached out to 150 middle-aged and elderly adults around using a

health tracking device (Ku et al. 2020). The results of this study showed that attitude, perceived

normal, enjoyment, and concentration have positive effects on consistently using the device (Ku

et al. 2020). Results of this study also showed that perceived control did not affect this audience

(Ku et al. 2020). In a similar study, the researchers wanted to understand the underlying factors

affected consistent use with smartwatches (Dehghani et al 2018). The research team reached out

to 383 smartwatch users to find out the main reasons why the user would not consistently use the

device (Dehghani et al. 2018).

There have also been survey questionnaires that reached out to runners regarding user experience

since wearable technologies can be used for injury prevention (Clermont et al. 2020). The results

of the study showed that basic metrics were helpful for injury prevention, whereas the more

advanced metrics weren’t as helpful for the consumer (Clermont et al. 2020).

Perceived usefulness of smart wearable devices in disaster situations has been studied and

evaluated in Japan (Cheng et al. 2017). The research team was able to reach out to 647

respondents and found that if the user can appreciate the functions of smart wearable devices,

they would see how it would be useful in these disaster situations (Cheng et al. 2017). Like many

results, privacy issues were the main concerns with the respondents (Cheng et al. 2017). There

are many scenarios in which wearable technology can be used in disaster situations, such as

workplace emergencies or natural disasters. Questionnaires can be used to identify the gaps of

acceptance for the user.

The following are the main research questions that are looking to be identified through this

study:

1. What are the most important factors for wearable exoskeleton adoption in the

construction and manufacturing industries from the front-line workforce’s perspective?

2. Is there statistical significance between construction and manufacturing participant

groups when it comes to wearable exoskeleton acceptance and adoption?

3. Is the front-line workforce generally in favor of wearable exoskeleton adoption within

their industry?

2.4 Methodology

2.4.1 Participants

All respondents will have to be employees working within the construction and manufacturing

industries. These participants must be 18 years of age or older. These participants can range from

0 years of experience to retired professionals. To identify participants, a survey panel was used.

Participants were reached via social media and email, so they had access to a computer or smart

phone during the time of the survey.

Participants had to accept to have their anonymous data recorded by answering “Yes” to the

voluntary survey. After this, participants were asked what industry they currently work in.

Respondents who answered “Other” rather than construction or manufacturing were

automatically removed from the survey and were not able to answer any further questions.

2.4.1.1 Estimation of Sample Size

A minimum sample size was calculated considering the margin of error and confidence interval

for this study. A literature review was also conducted to determine the minimum number of

participants for the study, as these examples are outlined as industry standard best practices.

• 5% margin of error – this can remain low because there are few questions that are open-

ended

• 95% confidence interval

To identify the minimum number of participants needed for the study, a literature review was

conducted that involved data collection via questionnaires. More specifically, these studies

involved both specifically the technology acceptance model for exoskeleton acceptance. These

studies range from 24 to 60 participants. These studies focused specifically within exoskeletons

and technology acceptance models.

For example, Shore et al. 2022 used 24 participants to develop a technology acceptance model of

a robotic assistive device for an older adult population. Additionally, Siedl and Mara, 2021 used

31 participants to identify exoskeleton acceptance by utilizing two questionnaires. Goffredo et al.

2019 utilized a TAM model for a wearable powered exoskeleton with 46 participants. For studies

in this specific area, best practices for sample size have been established based on these study

results.

The participant count exceeded the minimum acceptable participant count. This study had 110

total participants within the construction and manufacturing sectors across a wide variety of roles

within their organization.

2.4.1.2 Tools

A minimum number of tools were used in this study as the purpose of this study is to gather

information via an online survey. Qualtrics was used to collect data. The internet machine

generated the results, and then the numerical data was exported to Microsoft Excel. This data

was then transferred to IMB SPSS Statistics to run a Chi Square test.

2.4.1.3 Procedure

The survey was generated through Qualtrics and was electronically recorded. Respondents were

reached out to by a survey panel in which a wide net of different job titles and experiences were

yielded. More specifically, there was an emphasis on gaining enough participants who perform

the task (i.e labor, floor worker, etc), and management roles that can help influence change

within an organization.

A survey panel was used to recruit participants. An initial trial collection was collected to ensure

that quality data was being collected in terms of the participant audience and responses.

2.4.1.4 Demographic Information

While there were no “set” boundaries for ruling out individuals within the study, there was an

importance to receive diversified responses. This questionnaire was geared towards the front-line

workforce, however, receiving responses from management to gain their perspective was useful

to the overall data results. The questionnaire was broken down to answer questions such as, but

not limited to:

• Current organization – which will help me compare different sizes of organizations to see

if there is a correlation of responses based on this demographic

• Job title

• Age and overall experience

• Experience with wearable technology

Exclusion Criteria:

• At least 18 years of age

• Able to read and write

• Access to computer and internet

None of these questions ruled out an individual as part of this study, however, there was an

opportunity to look at trends in the data such as:

• One industry may be more susceptible to using wearable exoskeletons in the workplace

• Younger individuals are inepter with wearable technology; older demographic and larger

tenure may want to do things the way they always have

• People with more experience utilizing technology may understand its value more

2.4.1.5 Data Analysis

IBM SPSS Statistics was used to analyze data for this study. As mentioned above, given the level

of variability expected from using a survey for the purpose of collecting data, a confidence level

of 95% with a 5% margin of error was used to evaluate significance in the data collected. In

addition, a descriptive summary was provided for each question.

A Chi-Square test was used to compare the two different population groups. This method was

used to compare the role of the organization versus the questions answered within the survey.

These results are mentioned mainly within the Likert scale questions, along with the results of

some demographic data (i.e role within organization).

Through Qualtrics, descriptive statistics such as the mean, SE mean, standard deviation,

variance, minimum/maximum values, along with quartile ranges were provided. Data is

presented in a table and/or graph format, including percentages and main trends in the data.

2.5 Results

As an output of the data through Qualtrics, numerical data was downloaded and converted into

Microsoft Excel. Statistical significance was tested through IBM SPSS Statistics. To test the

participant groups, a Chi Square test was completed with all the numerical data provided.

There were 110 participants who completed the questionnaire survey. The following is the

survey demographic.

The participants included 87 construction and 23 manufacturing employees. The % breakdown

between each industry was 79.09% and 20.91% respectively. Participants who answered

“Other”, were removed from the survey.

Participants were then asked to answer what best described their role within the organization.

They were able to choose from the following options listed in table 4 below. Craftsman,

Laborers, or Floor Workers yielded the most results with 40% of participants.

Table 2.4 Percentage Breakdown of Participants by Job Title

Job Title

% of Results

Craftsman, Laborer, or Floor Worker

40.00%

Foreman or Field/Plant Supervisor

15.00%

Project or Quality Management

22.73%

Safety Management

4.55%

Other

17.27%

Table 2.5 Breakdown of Participants by Job Title and Industry

Job title

Craftsman, Laborer, Floor

Worker

Construction

Manufacturing

Foreman or Field/Plant

Supervisor

Construction

Manufacturing

Project or Quality

Management

Construction

Manufacturing

Safety Management

Construction

Manufacturing

Other

Construction

Manufacturing

Total

Number

n=44

n=37

n=7

n=17

n=16

n=1

n=25

n=17

n=8

n=6

n=2

n=4

n=18

n=15

n=3

n=110

% of Results

40.00%

33.63%

6.36%

15.45%

14.54%

0.91%

22.73%

15.45%

7.27%

5.45%

1.81%

3.63%

16.36%

13.63%

2.73%

100%

As shown in table 5 above, the biggest demographic for this study was construction laborers.

This included 36 construction laborers and 8 floor workers in manufacturing. This was followed

by 16 construction supervisors (foreman), 15 construction project managers, and 15 construction

other (unspecified). Although these are unspecified, it is assumed that these participants are in

roles such as but not limited to: project executives, estimating/purchasing, and/or human

resources (HR). The strongest participant groups in manufacturing were within project

management and floor workers with 10 and 8 participants.

There was a diverse background among participants. The goal of the data collection was to

ensure that there would be a wide range of roles and responsibilities in the construction and

manufacturing sectors. There was an emphasis on ensuring that there were enough participants

that physically perform the task, rather than just management. Both were important in this sense

because the researchers wanted to gather data on the perception of employees that would

ultimately wear the device, along with management personnel that could influence upper

management within an organization.

Since the Chi-Square test was used to compare both participant groups, this was an area that had

statistical significance. Within the role of the organization, the following table below shows the

breakdown of the results.

Table 2.6 Chi-Square Results – Role within Organization

Value

Degrees of Freedom (df)

Asymptotic Significance (2-

sided)

11.991

.017

Shown in table 6 above, the P value is 0.017. This value is > 0.05, demonstrating that there is

statistical significance between the participant groups as it pertains to their role within the

organization. This was the only Chi-Square result that fell into this category.

Of the 110 total participants, 74.55% were male, and 25.45% were female. There were 82 total

male participants in the study. There were 62 male construction participants and 20

manufacturing participants. There were 28 total female participants in the study. 20 females in

construction, and 8 females in manufacturing. Although there is evidence to show that there is a

strong gender gap both the construction (Nakabonge, 2022) and manufacturing industries

(Abegaz and Nene, 2018), sufficient data was collected among female participants.

28 construction and 7 manufacturing professionals were between the ages of 18 and 34 years old.

46 construction and 18 manufacturing professionals were within the age range of 35 and 50 years

old. 15 construction and 3 manufacturing participants were between 51 and 65 years old. This

distribution was considered strong due to many of the participants (83.64%) being further away

from retirement, which means their perception of the device can carry more weight as these

devices continue to be rolled out in organizations.

There was an emphasis on getting participants with a wide range of backgrounds and experiences

within the construction and manufacturing sector. Over half of the participants (54.55%) were in

the age range of 35-50 years old. This is mid-career for most in this participant group. 29.09%

were between the ages of 18-34 years old, which is considered early career for this participant

group. 16.36% of participants were between the ages of 51 and 65 years old, or late career

towards retirement. There were no participants who were 66 years or older, so it was with the

assumption that all participants are active employees within the construction and manufacturing

sector. Experience in the industry for each of the participants is unknown.

42 total participants have suffered an injury during their career. 39 of these participants were

within the construction industry and 3 participants were within the manufacturing industry. 16

Craftsmen, Laborers, or Floor workers have suffered an injury, which had the most yielded

results in terms of job title. This was followed by 12 Project/Quality Management employees, 6

other, 5 Foreman/Plant Supervisors, and 3 Safety Managers.

Participants were then asked to classify what their experience and understanding is with

wearable technology in general, such as Fitbits, Apple Watches, etc. The graph below shows the

distribution of responses in regard to their experience and understanding of wearable technology.

Figure 2.1 Knowledge and Understanding of Wearable Technology, Qualtrics

Shown in figure 1 above, strong understanding had the most respondents. The table below shows

the number of participants and the % of responses. 54 participants had a strong understanding of

wearable technology such as owning a device or having experience using one. 36 participants

had some understanding of wearable devices, and 20 participants did not have any experience

with wearable technology.

Table 2.7 Breakdown of Knowledge and Understanding of Wearable Technology

Classification

Count

None – have never

used any of these

devices

18.18%

Some understanding

– have heard of these

devices and

understand what they

are designed for

32.73%

Strong understanding

– either own one of

these devices or have

had experience using

them

49.09%

Total

100%

110

Table 8 below shows the descriptive summaries of the participants’ understanding of wearable

devices.

Table 2.8 Descriptive Summaries – Knowledge and Understanding of Wearable Technology

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

2.00

1.62

0.49

0.24

Participants were then asked if they knew what a wearable exoskeleton is and what it is designed

for. 46 participants knew what a wearable exoskeleton was, and 64 participants did not know.

For participants that did not know what an exoskeleton was and what it is designed for, an about

exoskeleton page was provided as part of the survey questionnaire. This is listed below as an

example.

Exoskeletons are a device that has joints and links to mirror a human body (Perry et al. 2007), to

assist in human body motion (Kong & Jeon, 2006). These devices are used in human-centered

risk factors such as, but not limited to overexertion due manual material handling, unsafe

working postures, strains, and sprains, and more. Some examples of exoskeletons are: Full body

exoskeletons, passive back exoskeletons, upper limb exoskeletons, lower limb exoskeletons,

knee assistive exoskeletons. The following image is an example:

Figure 2.2 About Exoskeletons

“Exoskeletons: A Promising Development for Construction Site Safety.” Capitol Technology

University, Capitology Blog, 2023, https://www.captechu.edu/blog/exoskeletons-promising-

development-construction-site-safety. “

After all participants understood what a wearable exoskeleton device was, they were challenged

to rank the following factors in order of importance for the user:

• Physical Comfort

• Appearance of Device

• Mobility

• Technical Expertise

• Perceived Usefulness

• Personal Privacy

Figure 3 below provides a visual of the result for this study. This graph shows each rank from the

top priority to the lowest priority. This is color coded and labeled so the distribution of responses

is easy to interpret.

Figure 2.3 Ranking the Importance of Factors for Wearable Exoskeletons – Qualtrics

Figure 3 above shows the results of question 9 of the survey, which were critical factors for

wearable exoskeleton adoption in the construction and manufacturing industries. Physical

comfort was the most critical factor for employees in the workplace (63). Mobility was second

(24), followed by perceived usefulness (8) and apperance of the device (7).

Table 9 below gives a breakdown of how this question was answered by all participants.

Table 2.9 Breakdown of Participants Responses – Factors for Wearable Exoskeleton

Adoption

Rank

Topic

Total

Physical

Comfort

57.27%

27.27%

6.36%

5.45%

2.73%

0.91%

110

Appearance

6.36%

12.73%

17.27%

15.45%

20.00%

28.18%

110

Mobility

21.82%

37.27%

25.45%

9.09%

4.55%

1.82%

110

Technical

Expertise

4.55%

14.55%

20.91%

33.64%

19.09%

7.27%

110

Perceived

usefulness

7.27%

5.45%

21.82%

24.55%

27.27%

13.64%

110

Personal

privacy

2.73%

8.18%

11.82%

26.36%

48.18%

110

As shown in table 9 above over half of the participants ranked physical comfort as their most

important factor (57.27%). Additionally, 93/110 or 84.5% ranked physical comfort in their top

two factors. This was followed by mobility with 59% of respondents falling within the top two

factors. This made these factors unanimously the most important among all factors for wearable

exoskeleton adoption. Physical comfort also had the least amount of participants ranking it last

with only 1 participant (0.91%). This was followed by mobility with 24 participants (21.82%),

which also had the 2nd least responses for ranking it last (2 participants or 1.82%).

Personal privacy appeared to be the least valued factor for exoskeleton adoption within the

participants. 53 participants or 48.18% ranked personal privacy as the least valued factor in

wearable exoskeleton adoption. Personal privacy also had the least participants ranking it as the

most important and 2nd most important factor, as both only had 3 participants or 2.73%.

The appearance of the device did not appear to be important to the participant group. Only 7

participants or 6.36% of respondents ranked this as the most important factor. Additionally, 31

participants or 28.18% of respondents ranked this as the least important factor when it comes to

wearable exoskeleton adoption. This factor also had 22 participants or 20% of respondents

ranking it as its 5th most important factor or 2nd to least important. This means 53 participants or

48.18% ranked this factor as their bottom 1/3 or 33% of factors.

Technical expertise and perceived usefulness had participants ranking these factors in the middle

and bottom quartile of choices (i.e 3,4,5). For example, 60 participants or 54.5% ranked technical

expertise as 3rd or 4th most important. Perceived usefulness had 51 participants or 46.4% ranking

this category as 3rd or 4th most important. Results of this study show that many of the participants

feel indifferent or have a small opinion on this factors when it comes to wearable exoskeleton

adoption.

Table 2.10 Descriptive Summaries – Factors for Wearable Exoskeleton Adoption

Factor

Minimum

Maximum

Mean

Standard

Deviation

Variance

Count

Physical

Comfort

1.00

6.00

1.72

1.08

1.17

110

Appearance

of Device

1.00

6.00

4.15

1.60

2.56

110

Mobility

1.00

6.00

2.43

1.16

1.35

110

Technical

Expertise

1.00

6.00

3.70

1.26

1.59

110

Perceived

Usefulness

1.00

6.00

4.00

1.38

1.91

110

Personal

Privacy

1.00

6.00

5.01

1.27

1.61

110

Table 10 above shows the descriptive summaries for the results for this question. Physical

comfort had the lowest variance and mean, making it the most consistent answer among

participants followed by the mobility of the device.

Mobility had a mean of 2.43, followed by technical expertise of 3.70. Mobility had the 2nd lowest

variance of the responses, which shows conistency of agreement on the importance of this factor

among participants. Technical expertise and personal privacy had variance of 1.59 and 1.61,

respectively.

The next set of questions as part of the survey were similar in nature, and referred to concepts of

the technology acceptance model by Davis, 1989. More specifically, the Perceived Usefulness

(PU) and Perceived Ease-of-Use (PEU) sections will be added along with subjective norm and

behavioral intention-related questions such as Lewis, 2019 and Buabeng-Andoh, 2017. These are

critical components of technology acceptance models in previous studies. A likert scale was used

to rank each response in the following categories:

• Strongly Disagree

• Moderately Disagree

• Slightly Disagree

• Neutral

• Slightly Agree

• Moderately Agree

• Strongly Agree

This began on question 10 of the survey. Each question will follow a similar format. This will

include the question of what was asked, a bar graph representation (provided through a Qualtrics

output), a breakdown of how each question was answered by participants and the % for each

response, and the descriptive summaries for each question. This includes the minimum,

maxiumum, mean, standard deviation, and variance.

In addition, as part of the Likert scale questions, a Chi-Square test was completed to determine

statistical significance between both participant groups. Due to the heavy participant group

within the construction industry, there was a test needed to ensure that there was not a significant

statistical difference that could impact results in favor of a specific participant group. The

question regarding industry (question 2) was compared to the rest of the questions within the

questionnaire.

Table 2.11 Chi-Square Results – Likert Scale

Question #

Value

Degrees of Freedom

(df)

Asymptotic

Significance (2-

sided)

2.107

.834

3.274

.774

3.679

.720

5.476

.484

3.953

.683

1.857

.932

1.936

.925

4.714

.581

2.886

.823

4.032

.672

1.017

.985

4.064

.668

3.360

.763

1.105

.981

The designated alpha level with a 95% confidence interval is 0.05. In these cases, the P value is

>0.05, which shows that this is not significant. This means that we can’t conclude that there is a

statistical difference in responses between the construction and manufacturing industries. These

questions were based on attitude and acceptance of the device, meaning that the industry did not

dictate how participants responded to these questions.

The first question of the survey was: “Using an exoskeleton in my job would enable me to

accomplish tasks more quickly”. This question was geared towards the efficiency aspect of

participants tasks. Figure 4 below shows the distribution of responses to this question.

Figure 2.4 Distribution of Responses, Question 10 – Qualtrics

Table 12 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.12 Distribution of Responses, Question 10

Answer

Participants

Strongly Disagree

10.91%

Moderately Disagree

3.64%

Slightly Disagree

3.64%

Neutral

17.27%

Slightly Agree

30.00%

Moderately Agree

15.45%

Strongly Agree

19.09%

Total

100%

110

Table 13 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.13 Descriptive Summaries – Question 10

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.75

1.80

3.24

Shown in figure 4 and table 12 above, “Slightly Agree” was chosen the most with 33

participants. This was followed by “Strongly Agree” and “Neutral”. The mean of the data was at

4.75, showing that the average response is leaning towards “Slightly Agree”. The standard

deviation of this response was 1.80, while the variance was 3.24. 64.54% of respondents were in

agreement, whether that was slightly, moderately or strongly. 17.27% of respondents were

neutral or indifferent. 18.19% of respondents were in disagreement, whether that was slightly,

moderately, or strongly.

Question 11 was “Using an exoskeleton would improve my job performance”. The following

graph below shows the distribution of responses to this question. This question again, was geared

towards the perception of how wearable exoskeletons can impact efficiency in a positive way for

the user.

Figure 2.5 Distribution of Responses, Question 11 – Qualtrics

Table 14 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.14 Distribution of Responses – Question 11

Answer

Participants

Strongly Disagree

9.09%

Moderately Disagree

2.73%

Slightly Disagree

5.45%

Neutral

18.18%

Slightly Agree

25.45%

Moderately Agree

16.36%

Strongly Agree

22.73%

Total

100%

110

Table 15 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.15 Descriptive Summaries – Question 11

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.88

1.78

3.16

Shown in table 15 above, “Slightly Agree” was chosen the most with 28 respondents. This was

followed by “Strongly Agree” with 25 respondents, and “Neutral” with 20 respondents. The

mean of this response was 4.88, similar to question 11, learning towards “Slightly Agree”. The

standard deviation for this response was 1.78, while the variance was 3.16. 64.54% of

respondents were in agreement, whether that was slightly, moderately, or strongly. 18.18% were

neutral or indifferent. 17.27% of respondents were in disagreement, whether that was slightly,

moderately, or strongly.

Question 12 was “Using an exoskeleton in my job would increase my productivity”. The

following graph below shows the distribution of responses to this question.

Figure 2.6 Distribution of Responses, Question 12 – Qualtrics

Table 16 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.16 Distribution of Responses – Question 12

Answer

Participants

Strongly Disagree

9.09%

Moderately Disagree

1.82%

Slightly Disagree

7.27%

Neutral

18.18%

Slightly Agree

26.36%

Moderately Agree

14.55%

Strongly Agree

22.73%

Total

100%

110

Table 17 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.17 Descriptive Summaries – Question 12

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.85

1.77

3.12

29 respondents chose “Slightly Agree”, which was the most common response. This was

followed by “Strongly Agree” with 25 participants, and “Neutral” with 20 participants. The

mean was 4.85, standard deviation of 1.77, and a variance of 3.12. 63.64% of respondents were

in agreement, whether that was slightly, moderately, or strongly agree. 18.18% were neutral or

indifferent. 18.18% of participants were in disagreement whether that was slightly, moderately,

or strongly.

Question 13 of the survey was “Using an exoskeleton would enhance my effectiveness on the

job”. The following graph shows the distribution of responses.

Figure 2.7 Distribution of Responses, Question 13 – Qualtrics

Table 18 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.18 Distribution of Responses – Question 13

Answer

Participants

Strongly Disagree

8.18%

Moderately Disagree

1.82%

Slightly Disagree

7.27%

Neutral

16.36%

Slightly Agree

20.91%

Moderately Agree

24.55%

Strongly Agree

20.91%

Total

100%

110

Table 19 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.19 Descriptive Summaires – Question 13

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.97

1.73

3.01

27 respondents chose “Moderately Agree”, followed by “Strongly Agree” and “Slightly Agree”

with 23 respondents. The mean for this response was 4.97, had a standard deviation of 1.73, and

a variance of 3.01. 73 participants or 66.37% were in agreement, whether that was slightly,

moderately, or strongly. 18 participants or 16.36% were neutral. 19 participants or 17.27% were

in disagreement, whether that was slightly, moderately, or strongly.

Question 14 of the survey was “Using an exoskeleton would make it easier to perform job-

related tasks”. The following graph below shows the distribution of responses.

Figure 2.8 Distribution of Responses, Question 14 – Qualtrics

Table 20 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.20 Distribution of Responses – Question 14

Answer

Participants

Strongly Disagree

6.36%

Moderately Disagree

5.45%

Slightly Disagree

1.82%

Neutral

17.27%

Slightly Agree

25.45%

Moderately Agree

20.91%

Strongly Agree

22.73%

Total

100%

110

Table 21 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.21 Descriptive Summaries – Question 14

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.04

1.69

2.87

“Slightly Agree” yielded the most results with 28 respondents, followed by “Strongly Agree”

with 25, and “Moderately Agree” with 23. 76 or 69.09% of respondents were in agreement,

whether that was slightly, moderately, or strongly. 19 or 17.27% of respondents were neutral or

indifferent. 15 or 13.63% of respondents were in disagreement, whether that was slightly,

moderately, or strongly. The mean for this response was 5.04, had a standard deviation of 1.69,

and a variance of 2.87.

Question 15 was “I would find an exoskeleton useful in my job”. The following graph shows the

distribution of responses.

Figure 2.9 Distribution of Responses, Question 15 – Qualtrics

Table 22 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.22 Distribution of Responses, Question 15

Answer

Participants

Strongly Disagree

8.18%

Moderately Disagree

3.64%

Slightly Disagree

6.36%

Neutral

21.82%

Slightly Agree

20.00%

Moderately Agree

19.09%

Strongly Agree

20.91%

Total

100%

110

Table 23 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.23 Descriptive Summaires – Question 15

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.83

1.76

3.11

“Neutral” had the most responses with 24 participants. This was followed by “Strongly Agree”

with 23 participants, and “Slightly Agree” with 22 participants. 66 responses or 60% of

participants agreed whether that was slightly, moderately, or strongly. 24 responses or 21.82% of

participants were neutral or indifferent. 20 responses or 18.18% of participants disagreed

whether that was slightly, moderately, or strongly. The mean for this response was 4.83, had a

standard deviation of 1.76, and a variance of 3.11.

Question 16 was “Learning to operate an exoskeleton would be easy for me”. These set of

questions focused primarily on the learning and training portion of the product. The following

graph shows the distribution of responses.

Figure 2.10 Distribution of Responses, Question 16 – Qualtrics

Table 24 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.24 Distribution of Responses – Question 16

Answer

Participants

Strongly Disagree

2.73%

Moderately Disagree

1.82%

Slightly Disagree

4.55%

Neutral

15.45%

Slightly Agree

33.64%

Moderately Agree

18.18%

Strongly Agree

23.64%

Total

100%

110

Table 25 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.25 Descriptive Summaires – Question 16

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.25

1.42

2.00

“Slightly Agree” yielded the most responses with 37. 83 participants or 75.46% of responses

agreed, whether that was slightly, moderately, or strongly. 17 participants or 15.45% of the

responses were neutral or indifferent. 10 participants or 9.1% of responses disagreed, whether

that was slightly, moderately, or strongly. The mean for this response was 5.25, had a standard

deviation of 1.42, and a variance of 2.00.

Question 17 was “I would find it easy to get an exoskeleton to do what I want it to do”. The

graph below shows the distribution of responses.

Figure 2.11 Distribution of Responses, Question 17 – Qualtrics

Table 26 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.26 Distribution of Responses – Question 17

Answer

Participants

Strongly Disagree

4.55%

Moderately Disagree

5.45%

Slightly Disagree

9.09%

Neutral

21.82%

Slightly Agree

20.00%

Moderately Agree

25.45%

Strongly Agree

13.64%

Total

100%

110

Table 27 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.27 Descriptive Suammaries – Question 17

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

4.78

1.60

2.55

“Moderately Agree” yielded the most responses with 28. This was followed by “Neutral” with

24 and “Slightly Agree” with 22. 24 participants or 21.82% of respondents were neutral or

indifferent. 21 participants or 19.09% of respondents disagreed, whether that was slightly,

moderately, or strongly. The mean for this response was 4.78, had a standard deviation of 1.60,

and a variance of 2.55.

Question 18 was “My interaction with an exoskeleton would be clear and understandable”. The

graph below shows the distribution of responses.

Figure 2.12 Distribution of Responses, Question 18 – Qualtrics

Table 28 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.28 Distribution of Responses – Question 18

Answer

Participants

Strongly Disagree

1.82%

Moderately Disagree

3.64%

Slightly Disagree

4.55%

Neutral

27.27%

Slightly Agree

22.73%

Moderately Agree

17.27%

Strongly Agree

22.73%

Total

100%

110

Table 29 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.29 Descriptive Summaries – Question 18

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.08

1.47

2.15

“Neutral” yielded the most responses with 30. This was followed by “Slightly Agree” and

“Strongly Agree”, with 25 responses each. 69 participants or 62.73% of responses agreed,

whether that was slightly, moderately, or strongly. 30 participants or 27.27% of responses were

neutral or indifferent. 11 participants or 10.01% of responses disagreed, whether that was

slightly, moderately, or strongly. The mean for this response was 5.08, had a standard deviation

of 1.47, and a variance of 2.15.

Question 19 was “It would be easy for me to become skillful at using an exoskeleton”. The graph

below shows the distribution of responses.

Figure 2.13 Distribution of Responses, Question 19 – Qualtrics

Table 30 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.30 Distribution of Responses – Question 19

Answer

Participants

Strongly Disagree

1.82%

Moderately Disagree

0.91%

Slightly Disagree

5.45%

Neutral

13.64%

Slightly Agree

30.91%

Moderately Agree

20.91%

Strongly Agree

26.36%

Total

100%

110

Table 31 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.31 Descriptive Summaries – Question 19

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.39

1.36

1.84

“Slightly Agree” was the most yielded result with 34 participants. This was followed by

“Strongly Agree” with 29, and “Moderately Agree” with 23. 86 participants or 78.18% of

responses agreed, whether that was slightly, moderately, or strongly. 15 participants or 13.64%

of responses were neutral or indifferent. 9 participants or 8.18% disagreed, whether that was

slightly, moderately, or strongly. The mean for this response was 5.39, had a standard deviation

of 1.36, and a variance of 1.84.

Question 20 was “I would find an exoskeleton easy to use”. The following graph shows the

distribution of responses.

Figure 2.14 Distribution of Responses, Question 20 – Qualtrics

Table 32 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.32 Distribution of Responses – Question 20

Answer

Participants

Strongly Disagree

1.82%

Moderately Disagree

2.73%

Slightly Disagree

3.64%

Neutral

20.91%

Slightly Agree

28.18%

Moderately Agree

23.64%

Strongly Agree

19.09%

Total

100%

110

Table 33 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.33 Descriptive Summaries – Question 20

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.18

1.36

1.86

“Slightly Agree” yielded the most responses with 31 participants. This was followed by

“Moderately Agree” with 26, and “Neutral” with 23. 78 participants or 70.91% of respondents

agreed, whether that was slightly, moderately, or strongly. 23 participants or 20.91% of

respondents were neutral or indifferent. 9 participants or 8.18% of respondents disagreed,

whether that was slightly, moderately, or strongly. The mean for this response was 5.18, had a

standard deviation of 1.36, and a variance of 1.86.

Question 21 was “I am interested in learning more about how exoskeletons can assist me in my

job efforts”. The graph below shows the distribution of responses.

Figure 2.15 Distribution of Responses, Question 21 – Qualtrics

Table 34 below is a breakdown of the responses with a breakdown of the number of participants

who answered in a particular category, and the % breakdown of each response.

Table 2.34 Distribution of Responses – Question 21

Answer

Participants

Strongly Disagree

8.18%

Moderately Disagree

2.73%

Slightly Disagree

3.64%

Neutral

19.09%

Slightly Agree

19.09%

Moderately Agree

18.18%

Strongly Agree

29.09%

Total

100%

110

Table 35 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.35 Descriptive Summaries – Question 21

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.09

1.80

3.23

“Strongly Agree” yielded the most results with 32 participants. This was followed by “Neutral”

and “Slightly Agree” with 21. 73 participants or 66.36% of respondents agreed, whether that was

slightly, moderately, or strongly. 21 participants or 19.09% of respondents were neutral or

indifferent. 16 participants or 14.55 disagreed, whether that was slightly, moderately, or strongly.

The mean for this response was 5.09, had a standard deviation of 1.80, and a variance of 3.23.

Question 22 was “I have positive feelings towards learning how to use exoskeletons in the

workplace”. The following graph shows the distribution of responses.

Figure 2.16 Distribution of Responses, Question 22 – Qualtrics

Table 36 below is a breakdown of the responses with a breakdown of the number of

participantswho answered in a particular category, and the % breakdown of each response.

Table 2.36 Distribution of Responses – Question 22

Answer

Participants

Strongly Disagree

4.55%

Moderately Disagree

1.82%

Slightly Disagree

3.64%

Neutral

20.91%

Slightly Agree

21.82%

Moderately Agree

23.64%

Strongly Agree

23.64%

Total

100%

110

Table 37 below provides the descriptive summaries, including the minimum, maximum, mean,

standard deviation, and variance.

Table 2.37 Descriptive Summaries – Question 22

Minimum

Maximum

Mean

Std Deviation

Variance

1.00

7.00

5.09

1.80

3.23

“Strongly Agree” and “Moderately Agree” yielded the most responses with 26. This was

followed by “Slightly Agree” with 24, and “Neutral” with 23. 76 participants 69.1% of

respondents agreed, whether that was slightly, moderately, or strongly. 23 participants or 20.91%

of respondents were neutral or indifferent. 11 participants or 10.01% of respondents disagreed,

whether that was slightly, moderately, or strongly. The mean for this response was 5.09, had a

standard deviation of 1.80, and a variance of 3.23.

Table 2.38 Summary of Evidence – Descriptive Summaries

Question #

Mean

Std Deviation

Variance

4.75

1.80

3.24

4.88

1.78

3.16

4.85

1.77

3.12

4.97

1.73

3.01

5.04

1.69

2.87

4.83

1.76

3.11

5.25

1.42

2.00

4.78

1.60

2.55

5.08

1.47

2.15

5.39

1.36

1.84

5.18

1.36

1.86

5.09

1.80

3.23

5.09

1.80

3.23

Average

5.01

1.64

2.72

Table 38 above is the summary of evidence of the descriptive summaries for the Likert scale

questions. This provides all the descriptive summaries for each of the questions. Along with this,

the average for each descriptive summary is shown. The average mean for all responses was

5.01, the standard deviation was 1.64, and the variance was 2.72.

The mean being at 5.01, was around “Slightly Agree” as a consensus for many participants

involved in the study. There was also a pattern of this, as 7 of these questions or 53.84% had

“Slightly Agree” as the most yielded response.

The standard deviation ranged from 1.36 to 1.80. The standard deviation for any question did not

exceed 2 points from the mean. This data shows that the answers to this study were dispersed

closely to the mean, which was “Slightly Agree”.

The variance ranged from 1.84 to 3.24, with an average of 2.72. The lowest variance for

questions was typically when there was the lowest standard deviation. There was a higher

variance due to some answers leaning towards neutral and moderately/strongly agree. However,

there were no questions in which there was a high frequency of negative implications or

disagreement towards the question being asked.

For example, the question with the most disagreement was Question 17 “I would find it easy to

get an exoskeleton to do what I want it to do”. This had 21 participants or 19.09% in

disagreement (47.6% of this were slightly disagreed, 28.6% moderately, and 23.8% strongly).

Question 15 “I would find an exoskeleton useful in my job” had 20 participants and 18.18% of

responses.

Table 39 below shows the breakdown of the main responses to the questions listed above.

Additional responses were neutral, moderately agree, and strongly agree. These responses were

always in the top 3 of most yielded responses.

Table 2.39 Breakdown of Main Responses by Participants

Slightly Agree

Neutral

Moderately Agree

Strongly Agree

N=7

N=2

Neutral responses were for Question 15: “I would find an exoskeleton useful in my job”, and for

Question 18 “My interaction with an exoskeleton would be clear and understandable”. There

were no trends in terms of topic for neutral being the most yielded result.

“Moderately Agree” also had 2 questions in which it had the most yielded response. These were

for Question 13 “Using an exoskeleton would enhance my effectiveness on the job” and

Question 17 “I would find it easy to get an exoskeleton to do what I want it to do”. After learning

more about exoskeletons and their functionality, participants of the study were confident that

wearable exoskeletons can help the industry from an efficiency standpoint.

Additionally, the usability of the device is something that participants agreed with. Participants

felt that the device could be flexible to perform human-centered tasks both in the construction

and manufacturing industries.

“Strongly Agree” also had 2 questions in which it was the most yielded response. This was for

Question 21 “I am interested in learning more about how exoskeletons can assist me in my job

efforts” and Question 22 “I have positive feelings towards learning how to use exoskeletons in

the workplace”.

For this result, it was demonstrated that the audience, after learning what an exoskeleton is and

what its functionalities were, was willing and wanted to learn more about the device. By

selecting “Strongly Agree”, evidence shows that these participants within these industries would

be enthusiastic about how these devices can be applied to their industries.

Table 2.40 Average Responses – Likert Scale

Answer

Participants (per

question)

Total Answers

Strongly Disagree

5.90%

6.5 or 7

Moderately

Disagree

2.90%

3.2 or 3

Slightly Disagree

4.80%

5.3 or 5

Neutral

19.09%

273

Slightly Agree

25.00%

27.5 or 28

357

Moderately Agree

19.80%

21.8 or 22

284

Strongly Agree

22.10%

24.3 or 24

316

As shown in table 40 above, “Slightly Agree” had the highest average responses per question

with around 28 participants per question or 25% of responses. This was followed by “Strongly

Agree” with 24 participants per question or 22.1% of responses. “Moderately Agree” was 3rd,

with around 22 participants per question or 19.8% of responses. The least chosen choice was

“Moderately Disagree” with an average of 3 participants per question or 2.9% of responses.

Evidence shows that each question on average was skewed towards agreement of wearable

exoskeleton adoption within their respective organization. Since the Likert scale questions were

focused on a previously validated model of the Technology Acceptance Model by Davis, 1989,

the front-line workforce generally would be in favor of adopting these devices. There was strong

encouragement for many of these participants to want to learn more about the device and its

functionalities.

2.6 Discussion

There were a wide range of questions for this survey, which included demographics such as the

industry participants are working in and what their roles are, experience with wearable

technology in general, age, and more. Additionally, participants were asked if they have suffered

an injury during their career.

If employees were not aware of a wearable exoskeleton’s purpose and functionalities, an about

exoskeleton page was provided. After participants learned about what it was and what it looked

like, they were able to rank different factors in order of importance. This gave participants a

chance to consider different factors and put themselves in the perspective of physically wearing

the device.

If the wearable exoskeleton were to be adopted in the workplace, physical comfort would be the

most important factor within these business sectors based on data results. For example, 63

participants or 57.27% chose physical comfort as their first option out of 6 possibilities.

Additionally, 93 participants or 84.5% had physical comfort as their top 2 choices. Physical

comfort was unanimously the most important factor out of all the different choices. This was

followed by mobility with 24 participants or 21.82% as their first choice and 59% of participants

putting mobility as their first two choices. Mobility had the most participants as its second choice

with 41 participants or 37.27%.

Personal privacy was the least important factor. 53 participants put personal privacy as their most

unimportant factor. This included 3 participants putting personal privacy as their first choice and

an additional 3 participants putting it as their second choice. This was an area that was assumed

to have important implications among the factors due to previous literature that has focused on

personal privacy as a critical limitation (Pote and Asbeck, 2023) and (Ajunwa, 2018).

The appearance of the device and technical expertise also did not appear to be an important

factor as well. Wearable exoskeletons can be considered personal protective equipment (PPE) by

many participants, in which appearance is not its critical function. However, design studies have

been completed regarding PPE. For example, Watson et al. 2019 studied the design of personal

protective equipment (PPE) in low hazard settings. In addition, Sazanova et al. 2021 studied

personal protective equipment use for dust mitigation from bulk materials. In circumstances

where additional personal protective equipment (PPE) was needed, appearance was not a critical

function of the mitigation of injuries.

Technical expertise was not chosen as well, due to the assumption that the user would be trained

on the device before using it. Additionally, the about exoskeletons page provided participants

technical information on the capabilities of the device. Many of the participants did not have

technical expertise with the device anyway as they have never used it.

When referring to the Likert scale questions, evidence shows that many participants agreed with

the questions being asked. The graphs, tables of responses and %, and descriptive summaries

provided evidence of this. For example, “Slightly Agree” was the most answered response out of

the Likert scale choices. This was followed by “Moderately” and “Strongly Agree”.

There were no questions in which any of the disagreements were chosen over being with

agreement of the question. Many of these questions were geared towards quality and efficiency.

Additionally, there were questions regarding their individual’s motivation and willingness to

learn more about the device, in which these had the most participants choosing “Strongly Agree”

as their response.

The chi-square results did not have statistical significance, as each Likert scale question was

>0.05. This meant that the industry did not play a role in how participants answered questions.

This result was not surprising, as the tasks between the construction and manufacturing

industries are so similar and these devices have recently been rolled out in these industries.

Future studies can gather further data such as the role within the organization in comparison to

the acceptance questions, along with gathering further data on whether a specific trade or type of

worker would influence the outcome of the study.

2.7 Conclusion

Based on the six human factors to acceptability by Gimhae, 2013, physical factors such as

physical comfort and mobility were among the most important. However, a physical factor

mentioned within this category was aesthetic and appearance, which was not a critical factor in

implementation for this specific wearable device. Behavior factors such as personal privacy and

perceived usefulness were not among the important factors to wearable exoskeleton adoption

within this participant group.

However, cognitive attitude was tested through the Likert scale questions utilizing the

Technology Acceptance Model by Davis, 1989, and subjective norms and behavioral intention

questions by Lewis, 2019 and Buabeng-Andoh, 2017. Results of this study showed that many of

the participants were in favor of adopting wearable exoskeleton devices. More importantly, the

eagerness to learn more about the device and its capabilities yielded “Strongly Agree” results,

which show the positive attitude that many participants had.

Age and gender did not play a critical role in respondents answers. The age group of 51-65 had

the most in terms of disagreement, however this was 5/18 or roughly 28% of participants.

Additionally, the answers in disagreement were the same participants. This was not considered

statistically significant. Along with this, technical expertise with wearable devices was wide

ranged, and technical experience with wearable devices did not play a critical role in participants

answers as there was even distribution between agreeing, neutral, or disagreement.

There were also a wide range of different roles and responsibilities within an organization. 40%

of participants were participants that would be wearing an exoskeleton, followed by the other

participants who are in management roles within an organization. Although these employees

may not physically wear the device themselves, they do provide an influence on upper

management, and other corporate programs regarding safety, quality, and efficiency of the front-

line workforce.

Wearable exoskeletons are still being rolled out across organizations across the construction and

industrial sectors, so this concept is new for most employees within these sectors. Although

many participants (58.18%) did not know what a wearable exoskeleton was prior to finishing the

survey, participants showed enthusiasm towards wanting to learn more about the device.

Information provided through this survey included the important factors to consider with

wearable exoskeleton adoption, along with the perception of the device through the Technology

Acceptance Model by Davis, 1989, and subjective norms and behavioral intention questions by

Lewis, 2019 and Buabeng-Andoh, 2017.

Data collected from this study can help research practitioners and organizations who are

interested in adopting wearable exoskeleton devices for tasks that have risk factors such as

repetitive motion and awkward posture movements that can result in overexertion related

injuries. Additionally, it can help 3rd party manufacturers and consultants who are looking to

adopt these devices in construction and industrial settings.

2.8 Opportunities for Future Research

In future studies, the Likert scale model could utilize more safety-related questions as part of the

questionnaire. For example, Wong et al. 2021 and Zhang et. al 2022 studied the acceptance of

personal protective equipment (PPE) in training and safe working practices. There is an

opportunity to incorporate some of these concepts into the previously validated scale and

questions to better understand these devices as they pertain to safety.

There is an opportunity to incorporate questionnaires and the technology acceptance model in

general to identify areas of potential training enhancement. For example, a study completed by

Wang et al. 2016 utilized an acceptance model to understand aviation students’ perceptions

towards using augmented reality for training instruction (Wang et al. 2016). This study was used

to enhance perceptions and the future use of utilizing this technology for training. Training users

will be important in future studies and applications as training in general has been shown to have

significant on the user’s motivation from an extrinsic and intrinsic standpoint (Rao, 2007).

There are also opportunities for an organization to examine enterprise resource planning (ERP)

more widely in studies. In a specific research study conducted by Amoako-Gyampah and Salam,

2004, the researchers found that training and project communication influence behaviors

attitudes towards wearable devices (Amoako-Gyampah and Salam, 2004). In a similar study, a

survey that reached out to 75 personnel found that subjective norms, perceived usefulness, and

education are the key critical components of behavior to utilize the technology (Gumussoy et al.

2007). There is evidence that ERP is not only a technical system, but there are social components

that are associated with the system (Pasaoglu, 2011).

While the technology acceptance model by itself has been proven to show value, future studies

can combine and compare technology acceptance modeling with other applications. For

example, a research study compared technology acceptance and the theory of planned behavior

and found that the technology acceptance model had an advantage as it is easier to apply

(Mathieson, 1991). However, one limitation from this study showed that it typically only collects

very general information about the perception of the current system (Mathieson, 1991).

Organizations can utilize this model, but there is also the opportunity to design questionnaires to

be more flexible to fit business needs.

Since financial decisions are so critical for organizations, wearable technology is an investment

where many would be hesitant. There are opportunities to incorporate the technology acceptance

model to a cost estimation system, as this has been conducted in the automotive manufacturing

industry (Bodendorf and Franke, 2022). Value stream costing using the technology acceptance

model has been used in the manufacturing environment (Eslami et al. 2019). Results of this study

found that there is not significant relationship between perceived usefulness and behavioral

intention when it comes to financial decisions, however, perceived-ease of use and behavioral

intention will affect value-stream costing (Eslami et al. 2019).

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CHAPTER III

UNDERSTANDING AND INCORPORATING WEARABLE TECHNOLOGY INTO AN

ENTERPRISE ORGANIZATION: FROM A HUMAN RESOURCES (HR)

PERSPECTIVE

3.1 Abstract

While wearable technology has had critical success in areas such as evaluating performance and

identifying potential precursors to injury, there is an importance of evaluating each stakeholder

who is involved in the process of implementation. Many research studies focus on either the

individual or the organization. Another important individual stakeholder is Human Resources

(HR) for organizations. With high turnover rates, competitive job markets, employee ethics, and

more, human resource management and performance plays a critical role in an enterprise

organization’s success. They are a critical factor in implementing wearable technologies in the

industrial setting. The following paper is a comprehensive literature review that outlines the

potential enhancements wearable technologies can provide from an HR perspective, and the

current limitations that can make wearable technology adoption difficult in industrial settings.

3.2 Introduction

Human Resources or HR is known as one of the most important elements especially in large

organizations (Zeebaree et al. 2019). There is an importance to have sustainable HR policies

since there is such a competitive working environment (Izvercian et al. 2014), as organizations

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must constantly adapt to changes and industry trends (Harzallah and Vernadat, 1999). To be a

leading enterprise organization, integration and adaptation are the main components have having

strategic human resource management (Schuler, 1992). Larger organizations tend to have larger

revenues and resources, which allow these types of companies to adopt wearable technology.

Wearable technologies are devices that track human performance activity, collect data, and can

be tailored to the needs of either the user itself or the organization (Thierer, 2014). There is

exponential growth of wearable technology in a variety of industries such as healthcare (Ferreira

et al. 2021). However, there is a reputation of wearable manufacturers not respecting privacy of

the customers who utilize the device (Saa et al. 2018). However, engineering advances have

assisted in wearable technologies that are much easier to use for the employee (Bowman and

West, 2019).

There are many reasons why human resources would be opposed to adopting wearable

technology into the workplace. For example, employee monitoring can lead to concerns

regarding employee privacy (Moussa, 2015), which in return can negatively impact morale

(Tambe et al. 2019). There is also evidence that shows that employee privacy has been a leading

cause of concern in American workplaces (Guffey and West, 1996). The Fourth Amendment of

the United States Constitution gives the employee right to privacy (Bowman and West, 2019).

There are also liability risks that are associated with organizations, making it difficult to keep

employees under surveillance (Cox et al. 2005). For example, if an employee is found to have a

disease or illness which eventually results in an employee getting fired, this raises negative

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implications to the organization (Bowman and West, 2019). Negative impacts of laws such as

HIPPA have been considered in wearable technology applications (Newman and Kreick, 2015).

There are many variables that Human Resources must encounter. Along with innovation and

staying in front of trends, employee training and production parameters are important for

technology adoption (Perez et al. 2002). Experience components has played a critical role in

technology acceptance, which has lacked in typical TAM models (Szajina, 1996).

Enterprise organizations are under the pressure of saving money and reducing cost, along with

meeting customer demands (Ferdous et al. 2015). Lack of human resources, technological, and

financial resources have been critical factors in resisting technology acceptance (Vajjhala and

Thandekkattu, 2017). This makes it difficult for organizations to offer financial rewards, which

can impact incentives and motivation for the employee (Bowman and West, 2019).

While evidence shows that wearable technology can be helpful, there are constant barriers that

prevent organizations from using the technology (Page, 2015). There are constant changes of

people trying to use the technology, and as a result, enterprises cannot keep up with current

trends (Raj and Ha-Brookshire, 2015). Financial implications and employee pushback are

important factors for enterprise organizations and upper management to be able to incorporate

this technology into their current process. For example, enterprises may not see the value of the

technology due to the initial cost of the technology. Employee feedback can also be a concern as

employees may not want data collected on them.

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Christensen (1997) defines wearable technologies into two main categories which is sustaining

and disruptive. Sustaining technology is defined as being an already established technology that

is continuously improving, as disruptive technology is known to have performance problems

because it is new or does not apply to a given audience or working task. These perceptions apply

to social acceptance of technology which is an important factor in implementing technology into

the workplace (Gimhae, 2013). There is an importance for organizations and Human Resources

to create a sustaining culture towards wearable technology, and not disruptive.

The following paper addresses the key concerns of wearable technologies from a human

resources perspective, along with opportunities where wearable technologies can be

implemented and provide success to organizations in the industrial sector.

To utilize wearable technologies, the consumer must approve the technology itself. In research, it

is known that researchers use survey questionnaires, hypothesis testing, and modeling to collect

data (Liao et al. 2020). For example, qualitative studies have been conducted to inquire on

behavior of wearable technology toward information security policies (Raymundo, 2020).

Evidence shows that if an individual’s perceived benefit is higher than the perceived privacy

risk, that individual is more likely to accept the device (Li et al. 2016). Employees entering the

workforce now are in an age group where they may have grown up with wearable technology

and may be more willing to utilize this technology (De Smet et al. 2016). To collect this data,

Human Resources can utilize questionnaires to reach out to the front-line workforce.

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Studies have also shown that employees that work for smaller companies have a lower

perception and motivation to use the wearable devices in general versus participants working for

medium to larger companies (Nnaji et al. 2019). In this study in particular, the research team was

evaluating influence in technology can influence decision making in the construction industry

(Nnaji et al. 2019). The most influential factors were reliability of the technology, effectiveness,

and durability (Nnaji et al. 2019). Evidence shows that questionnaires of wearable technologies

should be geared towards employees at bigger organizations, as they are more likely to have a

higher baseline understanding of the technology itself.

Privacy risk has been an on-going concern with employees regarding wearable technology.

However, the medical industry has been overcoming concerns about patient privacy (Sergueeva

and Shaw, 2016). With overall support from the front-line, this may allow Human Resources to

be more accepting to adopting wearable technology into the workforce. Reaching out to front-

line personnel should be a motivating factor, as there is a demand for strengthening human

resource practices due to staff shortages increasing (Tursunbayeva, 2019).

Wearable technology can engage employees in a unique way (Maltseva, 2020), mainly because it

is newly adopted. For example, millennials have a strong perception towards luxury wearable

technologies (Blazquez et al. 2020). Innovation and conventional fashion are currently being

evaluated by product development teams which typically include leaders in upper-management

positions (Raj, 2017). If employees are on-board with the appearance of the device or can

choose the design of the wearable, that may assist in technology acceptance concerns.

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While wearable technology has been proven to be useful in a variety of industries, one challenge

is gaining acceptance from organizations. Technology plays a critical role for enterprises to

complete, as this is a way to stay in front of trends and production best practices in the industry

(Hamzeh and Xu, 2019). There is also a demand to utilize wearable technology in the industry,

especially within facilities with large areas and more complex systems (De Felice, et al. 2022).

Organizations are currently trying to identify tools and best practices regarding risk analysis of

these devices. The integration of wearable technology has assisted in the inspection process for

organizations, which can positively impact organizations (Pray and McSweeney, 2018).

Since financial decisions are so critical for organizations, wearable technology is an investment

where many would be hesitant. There are opportunities to incorporate the technology acceptance

model to a cost estimation system, as this has been conducted in the automotive manufacturing

industry (Bodendorf and Franke, 2022).

Value stream costing using the technology acceptance model has been used in the manufacturing

environment (Eslami et al. 2019). Results of this study found that there is not significant

relationship between perceived usefulness and behavioral intention when it comes to financial

decisions, however, perceived-ease of use and behavioral intention will affect value-stream

costing (Eslami et al. 2019). Human body communication-based wearables have been used to

save unnecessary expenses on time (Shi et al. 2018), which can save enterprise organizations

money on costs.

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There are two stages of employee management, which are typically the hiring phase and the on-

the-job phase (Barzilay, 2018). Technology in general has facilitated important aspects of

Human Resources such as recruiting, on-boarding, and monitoring performance (Vyas and Jain,

2022). Wearable technology can support the employee on-boarding process, by enabling faster

job transition (Kohen-Vacs et al. 2019). Augmented reality (AR) has been successful in on-

boarding personnel in electrical utility operations (Sebok et al. 2020) and have resulted in less

on-boarding and training time for new employees (Matveuik, 2019).

Collecting data on employee performance can help the employer hire the right personnel,

schedule employees, provide adequate wages, and monitor human performance (Bodie, 2017).

For example, sensor enabled wearables have supported the production industry from an

education and on-boarding standpoint, as new employees in this industry have to learn new

production processes (Schonig et al. 2021). Wearable computing has also been used as a protype

for assisting in training within the automotive industry (Maurtua et al. 2007). There has also been

success utilizing wearable technology for restaurant food handler training (Clark et al. 2018),

which can translate to training for human-centered tasks such as manual material handling in

industrial applications during the on-boarding process.

There is also the opportunity to utilize wearable technology to support individuals with

intellectual and developmental disabilities (Paul et al. 2022), which has been done in a vocational

setting. Wearable technology has also been used for autism spectrum disorder interventions in

the workplace (Koumpourous and Kafazis, 2019). These technologies have been more useful

than standard wearable technology such as Fitbits, as these devices for individuals with

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disabilities, providing adequate work accommodations will motivate them to apply for positions,

and will not limit Human Resources from a recruiting and on-boarding standpoint.

For age discriminatory purposes or potential concerns in this area, wearable technology has been

designed to fit the aging population (Lewis and Neider, 2017). Critical success factors have been

identified in the adoption of wearables for the senior population in Thailand (Srizongkhram et al.

2021). Wearable technology has also been used to physical activity such as quick movements

and the risk of falling for older adults that have diabetes (Najafi et al. 2013). This is another area

that if potential employees feel discouraged, they will not apply. If there is a reputation that an

organization is utilizing wearable technology to assist in human performance for all individuals,

this will again, welcome more potential candidates to apply to open positions.

For example, in a dynamic environment such as the construction industry, wearable devices can

collect data to predict safety performance metrics (Awolusi et al. 2018). Specifically, collecting

physical human performance data has been proven to have positive effects on safety behavior in

this industry (Guo et al. 2017). Requiring employees to use wearable technology has driven

employees to complete more physical exercise, which can in return lower health insurance

premiums (Deranek et al. 2021). Similarly, wearable devices have helped employees track

fitness parameters and human performance in industry 4.0 (Sivathanu and Pillai, 2018).

Along with identifying physical performance risk factors, there are also insurance premiums

concerned with work-related respiratory health and disease risk factors. Examples such as this is

inhalation of silica or dust, asbestos, chemicals and more. Wearable technology has played a

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critical role in identifying potential risks of respiratory health and diseases (Aliverti, 2017). For

example, wearable technology has been used to detect and prevent heart attacks in the medical

field (Boopathi, 2021).

The Oil and Gas industry has also used wearable technology to collect physiological and location

tracking data (Nguyen et al. 2020). People analytics have been strengthened utilizing wearable

technology due to real-time data collection capabilities (Gaur et al. 2019). Data such as this can

improve efficiency, assist in decision making, and lower risk in the workplace (Nyugen et al.

2020). Advancements in wearable technology has also assisted stress detection with nurses in a

hospital setting (Hosseini et al. 2022). Quality has also been enhanced due to wearable

technology, as eye wearables have assisted in machine maintenance in general industry (Zheng et

al. 2015). Wearable devices have also served as a real time monitoring system in shipyard

fabrication workers (Pribadi and Shinoda, 2020).

Organizations who have utilized technology has been proven to motivate employee’s

communication by using smart watches to monitor supply chain activates (Shafique et al. 2019).

Communication technologies has also been used in the hospitality and tourism industries

(Gonzalez et al. 2020), which can be a positive enhancement to motivate Human Resources to

adopt wearable technologies.

Social dynamics monitoring and studying social interactions have been studied utilizing

wearable technologies (Montanari, 2019), which can be a useful tool for Human Resources to

evaluate behavior and morale. Engineering team interactions have also been captured utilizing

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wearable technology as well (Sjoman et al. 2015), and wearable technology can provide positive

behavioral support (Hansel et al. 2015). Work collaboration and morale can positively impact

culture, and this type of technique can improve organization’s documentation process as well.

While many of the wearable technologies look to collect physical performance data and will help

researchers and Human Factors Engineers identify potential pre-cursors to injury, there is also

the opportunity to incorporate this technology in lower-risk settings. For example, wearable

devices have been used to develop visual load intelligence monitoring when working on tasks

that require mobile phones, tablets, or computers (Wu et al. 2018). This can apply to front-line

supervisors, project managers, administrative staff, and more.

There are a wide range of trends in which wearable technology is being used to identify

cognitive fatigue risk factors (Kodithuwakku et al. 2022). For example, wearable technology can

monitor sleeping patterns (Rentz, 2021). Employers can be notified on how rested their

employees are, as vigilance and cognitive situational awareness can be affected by lack of sleep

especially in industries that have 3 working shifts in each day. Wrist worn wearables have been

used on night shift workers (Cheng et al. 2021), which can detect potential reaction and response

time concerns. Emotion monitoring for drivers on the road has been assessed utilizing wearable

technology (Kaur et al. 2019), which can be critical for organizations who have larger fleets and

have a lot of drivers on the road.

While wearable technology has been used in proactive approaches such as evaluating the risk of

falls (Pallavi et al. 2021) and the examples listed above, it can also serve a purpose in the

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reactive space. Predictive and prescriptive fatigue management practices have been identified

using wearable technology (Go et al. 2020). Wearable technology has also been combined with

smartphone apps to monitor individuals following hip replacement surgery (Bahadori et al.

2020). These show evidence of wearable technology having the potential to assist organizations

in return-to-work programs.

Wearable technology has also been used in crime scene investigation by collecting evidence at

the scene (Baber et al. 2005). Similar practices can be used to collect documentation to assist in

claims defense if litigation cases do occur against an organization. Wearable technology being

used for crime scene investigations has been studied and researchers have been identifying better

integration of wearables in this area (Lathoud et al. 2000), which shows the potential for even

further improvement and best practice incorporation.

There is also the opportunity to incorporate wearables to capture current jobsite and plant

conditions, as wearables have been used in police applications as body-worn cameras in Uruguay

(Ariel et al. 2020). Similarly, field-based assessments have been used to assess behavioral

patterns during shiftwork in the police academy using wearable technology (Erickson et al.

2022). Employers are always looking for creative ways to capture current jobsite conditions, and

while these practices may not always be ideal, similar practices could be used on a periodic basis

to capture conditions during inspections.

The main purpose of this research study is to identify the following:

1. What does HR see as the main positives of wearable technology adoption?

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2. What are the critical roadblocks of wearable technology adoption from an HR

perspective?

3. Does it appear that HR within the construction and manufacturing industries would be

open to wearable technology adoption within their organization?

3.3 Methodology

3.3.1 Participants

Participants for this study will have to be human resource employees working within the

construction and manufacturing sectors.

3.3.1.1 Estimation of Sample Size

A minimum sample size was calculated considering the margin of error and confidence interval

for this study. A literature review was also conducted to determine the minimum number of

participants for the study, as these examples are outlined as industry standard best practices.

• 5% margin of error – this can remain low because there are few questions that are open-

ended

• 95% confidence interval

To identify the minimum number of participants needed for the study, a literature review was

conducted that involved data collection via questionnaires. More specifically, these studies

involved both specifically the technology acceptance model for exoskeleton acceptance. These

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studies range from 24 to 60 participants. These studies focused specifically within exoskeletons

and technology acceptance models.

For example, Shore et al. 2022 used 24 participants to develop a technology acceptance model of

a robotic assistive device for an older adult population. Additionally, Siedl and Mara, 2021 used

31 participants to identify exoskeleton acceptance by utilizing two questionnaires. Goffredo et al.

2019 utilized a TAM model for a wearable powered exoskeleton with 46 participants. For studies

in this specific area, best practices for sample size have been established based on these study

results.

Since this study is geared towards industries that have adopted wearable exoskeletons and this

work demographic has not been studied often, a minimum sample size within this range would

be sufficient.

3.3.1.2 Tools

A minimum number of tools was used in this study as the purpose of this study is to gather

information via an online survey. Qualtrics was used to collect the data which included final data

results and descriptive summaries for each response.

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3.3.1.3 Procedure

The survey was generated through Qualtrics and will be electronically recorded. Respondents

were reached out to by a survey panel, which was able to collect participant data strictly in the

construction and manufacturing industries.

A survey panel was used to recruit participants. This included an initial trial collection to ensure

that the quality of responses and correct industry was being recorded.

3.3.1.4 Demographic Information

While there is no “set” boundaries for ruling out individuals within the study if they are in the

right industry, there is an importance to receive diversified responses. The questionnaire is

broken down to answer questions such as:

• Current industry

• Current tenure within the organization

• Has the organization adopted wearable technology

• Level of knowledge of wearable technology – i.e what wearables have they heard of

• What is seen as the main positives of wearables

• What is seen as critical roadblocks of wearables

None of these questions will rule out an individual as part of this study, however, there may be

an opportunity to look at trends in the data such as:

• If the organization has adopted wearables, HR may be more inclined to utilize these in

the workplace

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• If the organization is larger, there is a higher chance the organization has the financial

resources to have put wearables into the workplace

• HR may not have a strong understanding of wearables and what they can be used for; if

they learn this, there may be an opportunity to apply this to their organization

Exclusion Criteria:

• At least 18 years of age

• Able to read and write

• Access to computer and internet

3.3.1.5 Data Analysis

Qualtrics was used to analyze data for this study. As mentioned above, given the level of

variability expected from using a survey for the purpose of collecting data, a confidence level of

95% with a 5% margin of error will be used to evaluate significance in the data collected.

3.4 Results

86 total participants in the construction and manufacturing industries answered the survey

questionnaire. Construction professionals who are working for organizations that are general

contractors, electrical, plumbing, HVAC, carpentry, ironwork, and more participated in the

survey. Manufacturing personnel who were in welding operations, sheet metal fabrication,

logistics, product development, and more participated in the survey. The mean of responses was

1.40, standard deviation 0.49, and a variance of 0.24.

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There were 52 participants in the construction industry, and 34 participants within the

manufacturing industry. The tenure between employees varied in experience ranging from 0-5

years to 21+ years. This included a mean of 2.20, a standard deviation of 0.83, and a variance of

0.69. Table 41 below is a breakdown of the tenure of Human Resource professionals who

answered the survey questionnaire:

Table 3.1 Tenure of HR Professionals

Tenure

Participants

0-5 years

17 participants

6-11 years

41 participants

12-20 years

22 participants

21+ years

6 participants

In terms of tenure, seventeen participants reported 0-5 years of experience, forty-one participants

6-11 years of experience, twenty-two participants with 12-20 years of experience, and six

participants with 21+ years of experience.

Of the participants, 60.47% knew what an exoskeleton is and what its designed for, while

39.53% did not know what an exoskeleton is or what it would be designed for. The mean for this

response was 1.40, a standard deviation of 0.49, and a variance of 0.24. The following table is a

breakdown of the examples of wearable devices participants have heard of within their

organization, whether through adoption, or examples such as word of mouth within their

industry:

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Table 3.2 Knowledge of Wearable Devices Among Participants

Type of Wearable Device

# of Participants

Wearable Exoskeletons

22 participants

Smart Personal Protective Equipment (PPE)

23 participants

Wearable IMUs or Surface EMGs

3 participants

Augmented Reality

13 participants

None

25 participants

Shown in table 42 above, no wearable devices, smart personal protective equipment (PPE), and

wearable exoskeletons were among the most yielded results. While “none” had the most yielded

results, the table above demonstrates that a significant portion of the HR professionals had a

general understanding of what wearable devices are out in the market from hearing through word

of mouth or specific organization implementation. This accounted for 61 participants or 70.9% of

results.

For participants that did not know what wearable exoskeletons were, an about exoskeletons page

was provided as part of the questionnaire. The following is an example of what was provided as

an educational resource for participants:

Exoskeletons are a device that has joints and links to mirror a human body (Perry et al. 2007), to

assist in human body motion (Kong & Jeon, 2006). These devices are used in human-centered

risk factors such as, but not limited to overexertion due manual material handling, unsafe

working postures, strains, and sprains, and more. Some examples of exoskeletons are: Full body

exoskeletons, passive back exoskeletons, upper limb exoskeletons, lower limb exoskeletons,

knee assistive exoskeletons. The following image is an example:

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Figure 3.1 About Exoskeletons

“Exoskeletons: A Promising Development for Construction Site Safety.” Capitol Technology

University, Capitology Blog, 2023, https://www.captechu.edu/blog/exoskeletons-promising-

development-construction-site-safety. “

Participants were asked what they would see as the main benefits of wearable technology in an

open-ended question. This gave participants the opportunity to respond in free form with their

thoughts rather than be provided with potential options. The following is a breakdown of the 3

main categories in which participants answered questions:

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Table 3.3 Summary of Results: Main Benefits of Wearable Technology

Topic

Safety

Construction

Manufacturing

Quality or Efficiency

Construction

Manufacturing

Body Tracking

Construction

Manufacturing

Number

n=49

n=26

n=23

n=11

n=8

n=3

n=9

n=7

n=2

% (Results)

56.98%

30.23%

26.75%

12.79%

9.30%

3.49%

10.47%

8.14%

2.33%

These 3 categories accounted for 80.24% of the total responses. Other examples consisted of

none/not sure, enhancing training, and answers that could not be pinpointed into subcategories

due to not understanding answers since it was free form. As shown in the table above, the main

benefits that HR professionals identified were regarding potential safety concerns in the

workplace (56.98%). This was followed by assisting with quality or efficiency (12.79%) and

utilizing the device for body tracking purposes (10.47%).

26 construction HR professionals and 23 manufacturing HR professionals answered the open

question with having safety as a benefit of a wearable exoskeleton. Some examples of this

included but was not limited to:

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Table 3.4 Examples of Responses – Safety

“it would make manual work safer and more ergonomic”

“making manual labor more bearable for people”

“to prevent injuries and workers comp issues”

“safety and decreased worked injuries”

“reduction in workplace injury”.

Safety was considered the highest yielded response in terms of main benefits of wearable

technology between both participant groups. There was for the most part, an even distribution

between construction (53%) and manufacturing (47%). Many responses were along the lines of

prevention of injuries, reduction of injuries, and making the manual task more bearable for the

employee. While making the task more bearable for the employee can be considered as

“efficiency”, these responses mentioned safety first and was the focus of the response.

8 construction HR professionals and 3 manufacturing HR professionals answered within the

subcategory quality or efficiency. Some examples of responses in this area included but were not

limited to:

Table 3.5 Examples of Responses – Quality and Efficiency

“it could improve job performance”

“more efficient and precise operations”

“good service and high quality”

“better details more precise”

“to help you do stuff better”.

Construction HR professionals (72.7%) answered quality and efficiency more in this response

than manufacturing professionals (27.3%). This was an interesting finding due to the heavy

emphasis of lean six sigma (Drohomeretski et al. 2014) and time studies (Duran et al. 2015)

being performed in industrial settings. As shown in the examples in the table above, these

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examples relate to quality control and production best practices in construction and industrial

settings.

7 construction HR professionals and 2 manufacturing HR professionals answered the open-ended

question with having tracking mentioned as part of their response. Some examples in this area

included but were not limited to:

Table 3.6 Examples of Responses – Tracking

“You get real time monitoring of essential movements and strain points”

“It monitors everything for us”

“to monitor an employees body conditions”

“The main purpose is so we can get data”

“Tracking limits of the human body”

Tracking human performance was mentioned more frequently within construction HR

professionals (78%) than manufacturing HR professionals (22%). This again, was an interesting

finding due to the heavy emphasis on tracking human performance data through time studies.

There were some varying responses of what data was mentioned throughout the responses, such

as tracking human physical capabilities, real time monitoring, ensuring people are working

during working hours, monitoring temperature, and general data collection that was unspecified.

There was one participant that mentioned that there would be no positive benefits to adopting

wearable technology within their organization. Almost all participants (98.84%) were able to

think of a potential benefit that would be able to help front-line employees within their respective

organizations. This was considered an outlier in the data.

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Of the collected data, there was no reference to employee communication or on-boarding, in

which literature shows that wearable technology can be beneficial in these areas. These are also

critical functions of what a human resource professional manages, so there is an opportunity to

demonstrate the potential positives of wearable technology in this specific area through training.

If human resource professionals knew of its ability to help with these critical functions such as

communication and on-boarding, there is an opportunity that wearable technology such as

wearable exoskeletons would be more strongly encouraged for organizations to implement the

devices. This is an opportunity for future research, and an area where research practitioners could

explore further.

Additionally, the financial implications of wearable exoskeletons were not seen as positives,

meaning that wearable devices were not being perceived as a return on investment or ROI.

However, the prevention of injuries can be seen as a return on investment due to the costs of

workers’ compensation claims or employee injuries in general.

Participants were also asked what they would see as the critical roadblocks of wearable

technology adoption. Table 47 below are the main categories of the responses recorded:

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Table 3.7 Summary of Results: Critical Roadblocks of Wearable Technology

Topic

Usability

Construction

Manufacturing

Cost of the Device

Construction

Manufacturing

Nothing or Unsure

Construction

Manufacturing

Number

n=31

n=15

n=16

n=24

n=12

n=16

n=13

n=3

% (Results)

36.00%

17.42%

18.58%

27.90%

13.95%

18.60%

15.11%

3.49%

The 3 main responses as critical roadblocks were usability (36%), the cost of the device (27.9%),

or no critical roadblocks (18.6%). These responses accounted for 82.56% of the total responses

from the participants. Additional responses were concerns with artificial intelligence (AI), and

the transitions from humans to robots. There were also concerns regarding the equipment

breaking and personnel wearing them. 16 participants were not able to identify a potential

shortcoming with the device after learning more about its functionality.

15 construction HR professionals and 16 manufacturing HR professionals answered their open-

ended question with usability as a critical roadblock. Some examples of responses in this area

included but was not limited to:

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Table 3.8 Examples of Responses – Usability

“It may not work as intended”

“Its bulky”

“uncomfortable bad design”

“Not being able to move as much”

“Some devices may have inconvenient size or weight which can limit certain activities for the

wearer”

Usability was recorded as the most yielded response for critical roadblocks of wearable

exoskeleton adoption. There was nearly a 50/50 split between construction HR professionals

(48.3%) and (51.6%), respectively. Responses were similar in nature and mentioned keywords

such as “bulky”, “uncomfortable”, and more.

Usability had a wide range of main benefit responses, and there was not much correlation

between the responses. Only 2 participants, or 6.45%, mentioned quality or production as their

main benefit. This was surprising due to poor usability having a direct correlation to impacting

human performance (Brauner and Ziefle, 2015).

12 construction HR professionals and 12 manufacturing HR professionals answered cost or

financial implications as part of their response. Some examples of responses in this area included

but was not limited to:

Table 3.9 Examples of Responses – Cost of the Device

“Cost”

“Financial limitations”

“Cost and implementation”

“Pricing expense”

“It’s expensive”

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Shown in the data above, there was a 50/50 split between responses regarding the financial

implications of wearable exoskeletons in the workplace. Responses in this section were similar in

response, and many did not elaborate much further than the concern of the initial cost of the

device. The cost of the device was not provided in the survey questionnaire, it was just assumed

that the device would be costly.

Of the participants who answered cost, 16 participants mentioned safety as their main benefit.

This demonstrates that many participants did not consider the return on investment or ROI this

device may have in preventing injuries in the first place.

13 construction HR professionals and 3 manufacturing HR professionals answered none or

unsure as part of their response. Some examples of responses in this area included but was not

limited to:

Table 3.10 Examples of Responses – Nothing or Unsure

“Nothing at all”

“Nothing really”

“None”

“I don’t see no critical roadblock”

“I don’t see any”

Based on the data demonstrated above, it appears that more construction professionals were less

opposed to wearable exoskeletons than manufacturing. 6/16 or 37.5% of responses in this section

had organizations that have already adopted wearable exoskeletons within their organization,

which demonstrates some level of acceptance for the wearable devices that have already been

rolled out in organizations.

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A few responses (2 participants) expressed some concerns with fitting into small areas (i.e

confined spaces) and having the device fitting around standard personal protective equipment

(PPE) requirements to perform daily tasks. Although this was not considered significant in terms

of the overall results, this is something that should be considered if organizations looking to

adopt these devices have these potential concerns within their industry.

3.5 Discussion

The questionnaire study was geared more towards open-ended questions in identifying the

perceptions of benefits and potential shortcomings. This was to allow participants to answer in

free form, and to not give participants potential options to choose from. Data collected through

this process at times varied due to different explanations and open responses, however, the

research team was able to see trends in the data focusing on similar critical themes that HR

professionals see as it pertains to the rollout of wearable exoskeleton devices. Since all data

collected was anonymous, there was no opportunity to compare sizes or organizational

complexity of the organizations in which the HR professionals worked for.

After participants were able to learn more about a wearable exoskeleton’s function, many

participants were able to see potential positive implications such as safety, quality and efficiency,

and body tracking. Additionally, usability and cost were demonstrated as potential critical

roadblocks from an HR perspective. There was also a significant sample size that could not come

up with a potential negative of the device (18.6%), whereas, only 1 participant mentioned

“nothing” as part of their response for potential benefits of wearable technology (1.16%).

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The results of this study align with current literature results. Limitations of wearable technology

and wearable exoskeletons through the literature show that financial implications, organization

resources, and liability risks are critical shortcomings of current wearable technology adoption.

Although human resources (HR) professionals have not been studied as in depth as other

participants in industrial sectors, there are similar trends through previous research and the data

that was collected as part of this study.

This data is important for research practitioners and manufacturers who are looking to either

research wearable exoskeleton use where tasks are human-centered or are looking to design and

adopt these wearable devices. These devices have been shown to assist the front-line workforce

with human-centered tasks, and this is a product that will be researched and tested much further

as these devices continuously improve.

There was correlation to show that many participants who answered cost as a critical roadblock

also mentioned safety as its greatest benefit (67% of respondents). Since many participants in

general valued safety as its main benefit, there is an opportunity for research practitioners and

management personnel to demonstrate the return on investment or ROI that wearable technology

such as wearable exoskeletons can save an organization with the mitigation of injuries.

Although 70.93% of participants were working in organizations that have not adopted wearable

technology to date, data shows that there is potential for HR professionals to be open to

implementing these wearable devices to enhance their employee’s performance. For example,

37.5% of respondents who answered “none” or “unsure” as a critical roadblock already had

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wearable exoskeleton devices rolled out in their organizations. If employees were complaining

about using the device, it is likely that HR has heard these complaints and would mention it as a

critical roadblock.

Since many participants had a general understanding of what wearable devices are out in the

marketplace through their respective organization, there is an opportunity for knowledge transfer

and training of wearable devices such as wearable exoskeletons in industries such as construction

and manufacturing that require human-centered tasks.

3.6 Conclusion

Overall, this paper gave a high-level understanding of the main components to be considered

when adopting wearable technology into the workplace from an HR perspective. Additionally,

data was collected through a significant sample size of 86 participants. This included a wide

range of employees working within the construction and manufacturing industries. With most

critical questions being open-ended, this allowed for participants to answer freely. This required

respondents to answer questions even if it was “none” or “nothing”. This is still data to

demonstrate that for example, there was no opposition to adopting the wearable device as shown

in the Table 7 – Critical Roadblocks of Wearable Technology from an HR Perspective.

Data demonstrated that there are concerns with usability and cost from an HR perspective.

Although these were concerns, there were positive implications such as safety, quality and

production. While this area hasn’t been studied enough, successful wearable technology

applications can have a direct positive enhancement to important HR aspects. Especially since

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