Determining the usability and technology acceptance of a powered and automated cargo management system during ladder lifting tasks: A pilot study

Abstract

BACKGROUND:

Evidence for the adoption and acceptance of assistive devices for ladder lifting tasks by workers is scarce.

OBJECTIVE:

This study aims to investigate the technology acceptance and usability of a powered and automated cargo management system (RazerLift^®) used by workers who need to lift ladders as part of their daily duties, as compared to mechanical cargo management systems (traditional).

METHODS:

We used a one-way repeated measures design in this study. Our primary outcome variable was a usability performance measurement measured as time (in seconds) for unloading and loading ladders using both systems. Our secondary outcome was technology acceptance, measured using questionnaires with a 5-point Likert scale: “strongly disagree (1)” to “strongly agree (5)”.

RESULTS:

The participants conducted the combined unloading and loading time using the powered and automated system (RazerLift^®) 20.85 seconds faster than the traditional system (p-value = 0.000, t-value (df) = –5.730 (6), d = 2.713). Overall, the RazerLift^® system (mean = 44.28, SD 5.58) had a higher technology acceptance compared to the traditional system (mean = 30.00, SD 7.91), (p = 0.041, t-value (df) = 6.589 (6), d = 4.60).

CONCLUSIONS:

The RazerLift^® was more time efficient compared with the traditional system, and (2) the RazerLift^® was superior in terms of technology acceptance compared to the traditional system.

Keywords

Technology acceptance UTAUT model ergonomics human factors prevention of work-related musculoskeletal injuries

1 Introduction

Manual lifting is a task that many workers perform on multiple occasions. Low back pain (LBP) and injuries attributed to manual lifting activities comprise one of the main occupational health and safety issues [1, 2]. Approximately one in every four Canadians whose jobs involve manual material handling experience pain due to a back injury [3]. Chronic low back pain leads to almost 85%of the overall LBP-associated costs [4]. In Canada, the costs of medical expenditure for LBP are estimated to be between $6 billion and $12 billion annually [5]. Preventing lower back injuries could have a large societal impact on businesses of all sizes [6]. Portable ladders are used by workers in industries such as construction, electrical, and telecommunications [7]. In most cases, ladders are mounted on mechanical structures on commercial vehicles or vans for transportation purposes. There are risks of back injuries and muscle strains associated with this exertion, as well as the slip and fall hazards, of climbing on bumpers or tires to lift or remove the ladders from these vehicles. Therefore, the use of an assistive device when lifting ladders manually from vehicles may be beneficial for workers in any industry. An assistive device that aids with lifting ladders may also have a positive physiological effect (e.g. $\dot{v}$ O₂ consumption, heart rate) and reduce the time spent in awkward positions when the workers are conducting these tasks [8]. However, the literature shows that for a variety of assistive technologies, information, and telecommunication devices, the abandonment rate is high, and as a result, the technological adoption rate is low. For example, Ledger and McCaffrey [9] surveyed U.S. consumers who owned wearable devices, such as wristbands, smartwatches, and wearable bio-monitors. This study showed that 50%of the participants surveyed had stopped using their devices. In the area of assistive technologies, a study that aimed to explore the adoption of patient lift hoists found that only 24%of healthcare workers used these hoists during patient transfer tasks [10]. Likewise, a study that aimed to evaluate the adoption of ceiling lifts implemented in nursing facilities to reduce the physical demands associated with patient-handling activities showed that this technology was not regularly adopted by its users [11]. Some authors claimed that one of the reasons certain technologies have not been widely adopted is because the efficacy of these technologies has not yet been fully demonstrated [12]. Although this is true for certain settings such as the healthcare industry [13], this technology should be accepted first by its potential users, and once it has been accepted by these users, then its effects could be demonstrated both in a short-term experiment and a long-term field study. Therefore, technologies are often abandoned because concerns over acceptance and usability issues arise first. In general, most of the ergonomics and technological intervention research has been focused on the development stage; as a result, other phases such as implementation (acceptance and adoption) remain under-researched [14]. The adoption and acceptance of using assistive devices for ladder lifting tasks by workers are scarce; as a result, the barriers and facilitators in the adoption of this type of technology thus remains an open question. In order to fill this gap, the current study was designed to investigate the technology acceptance and usability of a powered and automated cargo management system (RazerLift^® [15]) used by workers who need to lift ladders as part of their daily duties, as compared to mechanical cargo management systems (traditional).

2 Theoretical framework

This study used the Unified Theory of Acceptance and Use of Technology (UTAUT) [16] as a theoretical framework. The UTAUT has been widely used to explain users’ acceptance of information technologies. The UTAUT has its roots in the Theory of Planned Behavior [17], and its predecessor, the Theory of Reasoned Action [18], based on the premise that if an individual intends to perform a certain behavior, it is probable that this individual will perform the behavior in question. The UTAUT model identifies four core constructs (performance expectancy, i.e., perception of usefulness of the technology), effort expectancy (degree of ease associated with the use of the technology, as well as the degree of use free of effort), social influence (the degree to which an individual perceives that significant others believe he or she should use the technology), and facilitating conditions (the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the technology) as direct determinants of behavioral intention. The UTAUT2 is a modified version of the UTAUT. We decided to base our study on UTAUT instead of UTAUT2, as the UTAUT is a more established and validated model.

Fig. 2

Study design: one-way repeated measures.

3 Materials

The participants lifted an industrial aluminum extension ladder. This type of ladder was designed according to the Canadian Standards Association (CSA) Grade 1A, American National Standards Institute (ANSI) Type 1A standard. For our study, we used a ladder that weighed 4.8 kg. The weight of the selected ladder is safe for 75%of females and 90%of males [19]. The Canadian Centre for Occupational Health & Safety (CCOHS) follows the recommendations of The National Institute for Occupational Safety and Health in the United States (NIOSH). Two Dodge RAM 2500 Promaster cargo vans (Overall Length: 5410 mm, Overall Body Width: 2050 mm, Overall Roof Height: 2311) were used in this study. One cargo van was equipped with a mechanical cargo management system (traditional) and one cargo van was equipped with a powered and automated cargo management (RazerLift^®). The industrial aluminum extension ladder was mounted on the cargo management systems (see Fig. 1 for more details)

Fig. 1

Experimental setup. a) Cargo vans equipped with the powered and automated cargo management (condition A, RazerLift^®). b) Cargo vans equipped with the mechanical cargo management system (condition B, traditional).

4 Methods

4.1 Study design

One-way repeated measures design.

4.2 Sample size calculation

The sample size required to conduct this study was n = 12 participants with a power of 0.8, an alpha of 0.05, and an large effect size of 1.2 [20, p. 845]. Studies suggests that between three and twenty participants can provide valid results for usability and technology acceptance studies, and a good baseline is between five and ten participants [21, 22].

4.3 Participants and sampling strategy

Participants from construction, electrical, telecommunication, or maintenance guilds were recruited using a convenience sampling method.

4.4 Inclusion criteria

A potential participant was included in this study if he/she: (1) was between the ages of 18 and 50 years old; (2) was currently working in an industry (construction, electrical, and telecommunications) that requires regular ladder lifting tasks as part of their job duties, and (3) had at least 6 months of experience in ladder lifting tasks.

4.5 Exclusion criteria

A potential participant was excluded in this study if he/she: was on medical leave (with a musculoskeletal condition). If an individual is working in an industry that requires regular ladder lifting tasks, but he/she is currently on a medical leave due to a musculoskeletal condition (e.g. low back pain), the ladder lifting tasks for this participant may be risky. In addition, this musculoskeletal condition may bias the results of the study. We also excluded any potential participants that indicated that they were experiencing musculoskeletal pain at the time of data collection

4.6 The measures

Primary outcome variable. Usability performance measurement measured as the time taken (in seconds) for unloading and loading the ladders from the powered and automated cargo management system (RazerLift^®) and the mechanical cargo management system (traditional). According to Nielsen [23, p. 194] and Baxter et al. [24], the time spent on a task, defined as the length of time taken to complete a task, is a typical and quantifiable usability performance measurement.

Secondary outcome variables. Behavioral intention to use, performance expectancy, effort expectancy, social influence, and facilitating conditions of the powered and automated cargo management system (RazerLift^®), as well as the mechanical cargo management system, were used as the secondary outcome variables in this study [16].

4.7 Demographics variables

We included demographic and anthropometric variables to describe our population. The demographic data included age (18–24, 25–35, 35–45, 45–50), sex (female or male), dominant hand (right, left, both), frequency of ladder lifting in the past month (daily, weekly, monthly, occasionally), industry (construction, electrical, telecommunication, maintenance, other), years of experience in performing ladder lifting tasks, previous use of/experience ladder lifting tasks other than traditional methods (yes, no), and whether the participant is taking pain medication (yes, no). The anthropometric data included weight (in kg), height (in cm), and Body Mass Index (BMI) (calculated as kg/m²).

4.8 Measures and instruments

Time was measured using a stopwatch, whereas the technology acceptance was measured using questionnaires. We designed and administered two paper-based questionnaires –a powered and automated cargo management system (RazerLift^®) one, and a mechanical cargo management system (traditional) one in order to understand and compare these systems’ technology acceptance. The questionnaires have two sections. Section 1 of the questionnaires used a 5-point Likert scale: “strongly disagree (1)” to “strongly agree (5)”. Section 1 included items which were already validated as having high levels of internal consistency in previous research for UTAUT constructs [16]. Section 2 of the questionnaires included two open-ended questions in order to capture qualitative information about what features’ the users liked and disliked about the systems. Before applying the questionnaires to the participants, they were carefully reviewed by an expert member of the evaluation team. After finishing this review process, each questionnaire had 10 items. These 10 items comprised: two items for performance expectancy (e.g. I think the RazerLift^® was useful in the ladder lifting tasks); two items for effort expectancy (e.g. I think the RazerLift^® was easy to use); two items for facilitating conditions (e.g. I feel that the RazerLift^® fits with my ladder lifting tasks at work); two items for social influence (e.g. I think that people whose opinions I value (my family, my friends) would approve of my use of the RazerLift^® to do my ladder lifting tasks at work); and two items for behavioral intention to use the system (e.g. I would always use the RazerLift^® in my ladder lifting tasks at work if I were able to). The individual responses were added up to yield a total technology acceptance score (min = 10, max = 50), with higher scores indicating more technology acceptance.

4.9 Procedures and data collection

This research proposal was approved by the University of Alberta ethics board. Every participant signed an informed consent form. A Research Assistant (RA1) randomly assigned participants to a condition prior to their arrival to the shop in order to ensure the proper set-up of the equipment before the participants’ arrival. A red ball indicated that the RazerLift^® condition be done first, whereas a blue ball indicated that the traditional method be done first. The RA1 gave the participant an identification number and informed which condition the participant had been assigned to first. The vans were positioned one in front of the other facing the same direction in the shop space. The vans were positioned so that the ladders could be loaded and unloaded from the same sides. The RA1 provided verbal instructions on how to use the technology if needed.

Unloading the ladder from the van procedure. The participants were asked to complete the first ladder lifting task (under the first condition assigned, RazerLift^® or traditional). The RA1 provided the following instruction to the participant: “Please remove this ladder from the van using the system provided. The task will be complete when the ladder is propped up against the tire of the van. You will start when I say “now.” Do you understand the task? If the participant responded with confirmation of their understanding, the first ladder lifting task was completed. Another Research Assistant (RA2) started the stopwatch when the participant made contact with the ladder/cargo management system and stopped the stopwatch once the ladder was placed on the ground.

Loading the ladder onto the van procedure. The participant was asked to complete the second ladder lifting task (under the first condition assigned, RazerLift^® or traditional). The RA1 provided the following instruction to the participant: “Now, please load this ladder onto the van using the system provided. The task will be complete when the ladder is placed on top of the van in its original position. You will start when I say “now.” Do you understand the task?” If the participant responded with confirmation of their understanding, the loading of the ladder onto the van task was completed. The RA2 started the stopwatch when the participant made contact with the ladder. The RA2 stopped the stopwatch once the ladder was placed on top of the van in its original position.

Application of the technology acceptance questionnaire. The RA1 administered the technology acceptance questionnaire corresponding with the van cargo management system that the participant was first assigned to complete (RazerLift^® or traditional). The technology acceptance questionnaire was repeated under the second condition assigned. The participants were given a 10-minute-break between loading and unloading ladder under the RazerLift^® or traditional conditions.

4.10 Statistical analyses

We used descriptive statistics to summarize the participants’ demographic and anthropometric data. We conducted paired t-tests to evaluate differences in time (performance measure) and the UTAUT constructs (measures of the technology acceptance) of both systems (i.e. RazerLift^® compared to the traditional one). In order to determine the validity and reliability of the technology acceptance questionnaires, we calculated the intra-class correlation coefficient (ICC) (i.e. average and single measures) for the full scale of our questionnaires and Cronbach’s Alpha (CA). The alpha level of significance for every test was set at p≤0.05 (two-sided). We also performed an effect size calculation of the differences using Cohen’s d [25]. The SPSS^® V 27.0 and G^*Power V 3.1.9.4 [26] statistical packages were used to generate the descriptive, univariate, and statistical tests, and to calculate the power of the statistical tests, respectively.

5 Results

A total of 10 participants were invited to participate. Two of them did not meet the inclusion criteria and one participant needed to be rescheduled due to a delay on the part of the research team; this participant was then unable to reschedule his/her appointment. Thus, the final sample size who completed the study was seven participants, 58.33%of our target sample size.

5.1 Participants’ description

One hundred percent of the participants were male. Adults between 25 and 35 years of age (42.95%) was the most common age category among our participants. Eighty-five-point seven percent (85.7%) of them were right-handed. Overall, the participants had a mean BMI of 46.36 SD 3.99. They had an average of 16.36 (SD 11.26) years of experience in their respective industries, performing ladder lifting tasks. Most of the participants worked in the construction industry (71.4%) and none of them had used the RazerLift^® before the study was conducted (see Table 1 for more details).

Table 1
Demographics (n = 7)

Anthropometric measurements Mean (SD)

Weight (in kg) 84.77 (8.86)

Height (in cm) 182.71 (8.3)

Body mass index (BMI) 25.4 (2.2)

Years of experience Mean (SD)

Years of experience in the industry in which you perform lifting ladders 16.36 (11.26)

Age n (%)

18–24 1 (14.3)

25–35 3 (42.9)

35–45 1 (14.3)

45–50 1 (14.3)

+51 1 (14.3)

Sex

Male 7 (100)

Female 0 (0)

Right-Handed or Left-Handed

Right 6 (85.7)

Left 1 (14.3)

Both 0 (0)

Frequency of ladder lifting tasks in the past month

Daily 2 (28.6)

Weekly 2 (28.6)

Monthly 0 (0)

Occasionally 3 (49.2)

Type of industry

Construction 5 (71.4)

Electrical 0 (0)

Telecommunications 2 (28.6)

Maintenance 0 (0)

Other 0 (0)

Previous experience uses of/experience with ladders lifting other than traditional methods

Yes 0 (0)

No 7 (100)

Currently taking pain medication

Yes 0 (0)

No 7 (100)

Anthropometric measurements	Mean (SD)
Weight (in kg)	84.77 (8.86)
Height (in cm)	182.71 (8.3)
Body mass index (BMI)	25.4 (2.2)
Years of experience	Mean (SD)
Years of experience in the industry in which you perform lifting ladders	16.36 (11.26)
Age	n (%)
18–24	1 (14.3)
25–35	3 (42.9)
35–45	1 (14.3)
45–50	1 (14.3)
+51	1 (14.3)
Sex
Male	7 (100)
Female	0 (0)
Right-Handed or Left-Handed
Right	6 (85.7)
Left	1 (14.3)
Both	0 (0)
Frequency of ladder lifting tasks in the past month
Daily	2 (28.6)
Weekly	2 (28.6)
Monthly	0 (0)
Occasionally	3 (49.2)
Type of industry
Construction	5 (71.4)
Electrical	0 (0)
Telecommunications	2 (28.6)
Maintenance	0 (0)
Other	0 (0)
Previous experience uses of/experience with ladders lifting other than traditional methods
Yes	0 (0)
No	7 (100)
Currently taking pain medication
Yes	0 (0)
No	7 (100)

Notes: SD: Standard deviation.

5.2 Usability performance measurement measured as time (in seconds)

Tables 2a , and c) show the descriptive statistics and hypothesis tests (paired t-tests) for the usability performance measurement, measured as the time taken (in seconds) for unloading and loading the ladder into the motor cargo systems, and the total time taken (unloading + loading the ladder), respectively. The results showed that all, i.e. the time for unloading and loading the ladder and the combined unloading and loading time in using the powered and automated system (RazerLift^®), was lower and statistically significantly different compared with the traditional cargo system. The participants conducted the combined unloading and loading time using the powered and automated system (RazerLift^®) 20.85 seconds faster than the traditional system (p-value = 0.000, t-value (df) = –5.730 (6), d = 2.713). In order words, the powered and automated system was more time efficient compared with the traditional cargo system.

Table 2a
Usability performance measurement as time (in seconds). Hypothesis Tests (paired t-test). a) Unload ladder, b) Load ladder, c) Total time

Ladder lifting conditions Unload ladder (time in sec) Paired t-test statistic (time in sec)

Mean SD p-value (df) t-value 95%IC Effect size (d) Power

A-Moto Cargo system (RazerLift^®) 24.53 4.31 0.000 –7.610 (6) (–27.55, –14.14) 3.995 100

B-Traditional Cargo system 45.38 7.38

Ladder lifting conditions	Unload ladder (time in sec)	Paired t-test statistic (time in sec)
A-Moto Cargo system (RazerLift^®)	24.53	4.31	0.000	–7.610 (6)	(–27.55, –14.14)	3.995	100
B-Traditional Cargo system	45.38	7.38

Notes. SD: Standard deviation. Confidence interval (CI). n: 7

Table 2b

Table2b

Ladder lifting conditions	Load ladder (time in sec)		Paired t-test statistic (time in sec)
	Mean	SD	p-value	t-value (df)	95%IC	Effect size (d)	Power
A-Moto Cargo system (RazerLift^®)	25.32	2.05	0.014	–3.405 (6)	(–18.25, –2.99)	1.576	87.6
B-Traditional Cargo system	35.94	9.03

Notes. SD: Standard deviation. Confidence interval (CI). n: 7.

Table 2c

Table2c

Ladder lifting conditions	Combined (time in sec)		Paired t-test statistic (time in sec)
	Mean	SD	p-value	t-value (df)	95%IC	Effect size (d)	Power
A-Moto Cargo system (RazerLift^®)	49.86	6.06	0.01	–5.730 (6)	(–44.90, –18.02)	2.713	97.6%
B-Traditional Cargo system	81.32	16.4

Notes. SD: Standard deviation. Confidence interval (CI). n: 7.

5.3 Technology acceptance of the RazerLift^® vs the traditional system

Table 3a and b) show the descriptive statistics and hypothesis tests (paired t-tests) of the subjective measures of the technology acceptance of the powered and automated cargo management system (RazerLift^®) and mechanical management cargo systems in terms of a summative scale (all UTAUT constructs) and for each UTAUT construct, respectively. The results from Table 3a indicate that overall, the powered and automated system (RazerLift^®) (mean = 44.28, SD 5.58) had a higher technology acceptance compared to the mechanical management cargo system (mean = 30.00, SD 7.91), (p = 0.041, t-value (df) = 6.589 (6), d = 4.60).

Table 3
Subjective measures, user acceptance Moto Cargo (RazerLift^®) and Traditional Cargo system per UTAUT construct. Hypothesis Tests. a) Summative scale (all UTAUT constructs) (paired t-test). b) Per UTAUT construct (paired t-test statistics)

Technological acceptance scale A-Moto Cargo system (RazerLift^®) B-Traditional Cargo system Paired t-test statistic

Mean SD Mean SD p-value t-value (df) 95%CI Effect size (d) Power

Summative scale (every UTAUT construct item) 44.28 5.58 30.00 7.91 0.01 6.589 (6) (8.98, 19.59) 4.60 99.7

Technological acceptance scale	A-Moto Cargo system (RazerLift^®)	B-Traditional Cargo system	Paired t-test statistic
Summative scale (every UTAUT construct item)	44.28	5.58	30.00	7.91	0.01	6.589 (6)	(8.98, 19.59)	4.60	99.7

Notes. SD: Standard deviation. Min summative scale = 10, Max summative scale = 50. Confidence interval (CI). n: 7.

Table 3b

Table3b

Construct names	A-Moto Cargo system (RazerLift^®)		B-Traditional Cargo system		Paired t-test statistic

	Mean	SD	Mean	SD	p-value	t-value (df)	95%CI	Effect size (d)	Power
Performance expectancy	9.28	0.95	6.14	2.19	0.009	3.792 (6)	(1.11, 5.17)	1.53	86.6
Effort expectancy	9.85	0.38	6.43	1.90	0.003	4.768 (6)	(1.67, 5.18)	2.55	97.0
Social influence	8.57	1.39	6.57	1.90	0.001	6.481 (6)	(1.24, 2.76)	1.41	83.89
Facilitating conditions	8.14	1.95	5.28	1.70	0.001	5.620 (6)	(1.61, 4.10)	2.41	96.4
Behavioral intention to use the system	8.42	1.71	5.57	2.37	0.008	3.873 (6)	(1.05, 4.66)	1.70	89.7

Notes. SD: Standard deviation. “1” Strongly disagree to “5” Strongly agree, two items per UTAUT construct Min scale = 2, Max summative scale = 10. Confidence interval (CI). n: 7.

Regarding the results for each UTAUT construct, according to the participants’ responses, it is clear that they believed the powered and automated system (RazerLift^®) is more useful (high performance expectancy), is easier to use (low effort expectancy), is a better fit with the participants’ ladder lifting tasks at their work (high facilitating conditions), and the influence of the others toward their use is higher (social influence) compared to the mechanical management cargo system. We found statistically significant differences for every UTAUT construct (see Table 3b for more details). More importantly, the participants would be willing to use the powered and automated system (RazerLift^®) in the future if they were able to do so (average intention to use the RazerLift^® system, BI construct = 8.42 SD 1.71 (max = 10)). In fact, their willingness to use the RazerLift^® system (behavioral intention to use the construct) was higher and statistically significantly different (p = 0.008, t-value (df) = 3.873 (6), d = 1.70) compared to the mechanical management cargo system (average intention to use the system, BI construct = 5.37 SD 2.37). Overall, these results indicated the RazerLift^® is superior in terms of technology acceptance compared to the mechanical management cargo system.

5.4 Validity and reliability of the technology acceptance questionnaires

Table 4 shows the statistics of the reliability and internal validly of the technology acceptance questionnaires used in our study. The statistical significant values of the p values (i.e. p < 0.05) for the F-test of the intra class correlation coefficient (ICC) (i.e. average and single measures) for the full scale of our technology acceptance questionnaires tells us that the subjects are different from each other, which is a necessary condition for a reliability test. Note that the validity of the ICC measure is suspect if the F-test is not significant. Since ICC and Cronbach’s Alpha (CA) of the two full scale technology acceptance questionnaires are above 0.70 and the F-tests of the ICC (i.e. average and single measures) were statistically significant (p < 0.05), one can reach the conclusion that the instruments (technology acceptance questionnaires) used in this study has good levels of reliability and internal validly [27, p. 595].

Table 4
Validity and reliability of the technological acceptance questionnaires

Constructs A-Moto Cargo system (RazerLift^®) questionnaire B-Traditional Cargo system questionnaire

CA ICC C.I p-value CA ICC C.I p-value

Full-scale (PE + EE + SI + FC + BI) 0.893 0.871 (0.668, 0.974) 0.000 0.881 0.869 (0.664, 0.973) 0.000

Constructs	A-Moto Cargo system (RazerLift^®) questionnaire	B-Traditional Cargo system questionnaire
Full-scale (PE + EE + SI + FC + BI)	0.893	0.871	(0.668, 0.974)	0.000	0.881	0.869	(0.664, 0.973)	0.000

Notes. PE: Performance expectancy (two items). EE: Effort expectancy (two items). SI: Social influence (two items). FC: Facilitating conditions (two items). BI: Behavioral intention to use the technology (two items). CA: Cronbach’s Alpha. ICC: Intraclass correlation coefficient (average measures). CI: Confidence interval. p values for the F-test of the Intraclass correlation coefficient (ICC). n: 7.

6 Discussion

In this study, we investigated the technology acceptance and usability of a powered and automated cargo management system (RazerLift^®) used by workers who need to lift ladders as part of their daily duties at work as compared to a mechanical cargo management system (traditional). Overall, we found that (1) the RazerLift^® is more time efficient compared with the mechanical cargo management system (traditional), and (2) the RazerLift^® is far superior in terms of technology acceptance compared to mechanical management cargo systems. These results support the assertion that RazerLift^® usability and technology acceptance were high amongst this study’s participants. As a result, our study’s participants would be willing to use the RazerLift^® in the future if able to do so.

Overall, the intervention (the RazerLift^® compared with a mechanical management cargo system) had a large effect size and high power for every statistical test, i.e. a mean effect size d = 2.49 SD 1.13 and a mean power = 93.17%SD 6.1%. These results allow us to assert that (1) this study’s small sample size did not obscure any difference, if one truly existed (type II error), for all of our outcome variables; and (2) our sample size of seven participant fits into the Faulkner [21] criteria that a good baseline for a usability study is between five and ten participants.

Our result that performance expectancy was the highest rated construct in the RazerLift^® technology acceptance is consistent with previous research, showing that among the four major UTAUT constructs, performance expectancy is the most important construct that predicts the behavioral intention to use a technology under study [28, 29]. In our study, this result was validated by comments obtained from participants written in answer to the open-ended questions in our RazerLift^® technology acceptance questionnaire, who unanimously stated that the RazerLift^® was very useful. Comments such as, “this system is useful because it brings the ladder level (so not at an angle)” or “this system is useful because it brings the ladder to lower heights” were examples of the usefulness of the RazerLift^® system.

In our study, effort-expectancy was the second-best rated construct in the RazerLift^® technology acceptance questionnaire. Our results are consistent with some previous acceptance study research [30, 31]. Comments obtained from our participants written in answer to the open-ended questions in to our RazerLift^® technology acceptance questionnaire corroborate with why effort-expectancy was rated as the second-best construct. Users stated that the “[RazerLift^® was] Easy to use”, and the “[RazerLift^® was] Easy to learn how to use”.

The fact that social influence was a construct was very well rated (third best rated) in the RazerLift^® technological acceptance questionnaire did not surprise us. Similar results were obtained in a study conducted by Liu et al. [32], with the aim of determining Global Positioning System (GPS) technology acceptance among dementia clients and family caregivers. In Liu and colleagues’ study, both caregivers and people with dementia encouraged each other to use the GPS in order to address the caregiver burden associated with worrying about people with dementia wandering off or getting lost. In our study, the participants had the perception that the RazerLift^® is useful in reducing the risk of getting low back pain and injuries, as well as being easy to use; therefore, they believed that if his/her boss were to suggest using this new device, he/she should use it. The probable net result that will happen next, as in Liu and colleagues’ study, is that if the bosses, co-workers, and family members suggest that the participants use the RazerLift^® (in the future) in order to address or reduce the risk of getting low back pain and injuries attributed to manual ladders lifting activities, they will do so.

6.1 Practical implications

Our results have two practical implications. First, we have demonstrated that the RazerLift^® has been well accepted by its users; second, we believe that the RazerLift^® is a technology that is ready to test its effectiveness in future research. For example, a study with the aim of investigating the effects of a powered and automated cargo management system on workers’ biomechanical variables during ladder lifting tasks compared to a mechanical cargo management system would be the next logical step. Our study supports the postulate that usability and technological acceptance studies can be conducted using low sample sizes. As a result, this type of research can be conducted with low research budgets. Finally, we have obtained two valid and reliable technology acceptance questionnaires that can be used by the research community in similar research. Technology acceptance questionnaires can be made available on request.

6.2 Study limitations and future research

Like any study, this research had its limitations. First, we did not reach our target sample size and although our statistical analyses demonstrated that it was not an issue, future studies should come up with better strategies to increase their participation rates. Second, the gender (100%males) bias evident in the study also restricted the generalizability of our results.

7 Conclusion

Despite these limitations, this study clearly shows that the RazerLift^® device was well accepted by its users and was time efficient. That is, (1) the RazerLift^® is more time efficient, and (2) the RazerLift^® is superior in terms of technology acceptance compared to the mechanical management cargo system. In conclusion, the results indicated that the participants would continue to use the RazerLift^® system if they were able to do so.

Footnotes

Acknowledgments

This study was supported by Mitacs-Accelerate in 2019-2020 with Grant number IT13961. We would like to thank Hank’s Plumbing & Gas Fitting Ltd., which provided the vehicles we used to conduct our study. We would like to thanks to Freeze Maxwell (Calgary) Ltd., which provided the location to conduct the data collection. We would also like to thank the following individuals who assisted with the data collection: Emily Murphy, Aaron Elock-Coyle, and Daniel Alejandro Quiroga.

Conflict of interest

None of the authors have any financial interest or benefit to declare in any regard in relation to the study described in this manuscript.

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