Using the lens of assistive technology to develop a technology translation readiness assessment tool (TTRAT)™ to evaluate market readiness

Figures & data

Figure 1. Pitt translational canvas. Step by step technology translational canvas that includes the following domains: Problem, Stakeholders, Solution, Benefits, Competition, Differentiation and Action Plan.

Flow chart of the Pitt Translational Canvas that includes problem, stakeholders, solution, benefits, competition, differentiation, and action plan.

Table 1. Table of Key Words used in the literature review of current models of technology translation and readiness levels. Publications were searched for in PubMed, Google Scholar, and relevant journals such as the Journal of Technology Transfer.

Category	Key Words
Models, Principles	Technology Translation Models; Tech Translation Models; Tech Transfer Models; Assistive Technology Tech Transfer; Technology Transfer Principles; Knowledge Translation of Tech Transfer; Technology Translation Readiness
Domains	Technology Transfer Domains; Technology Transfer Categories; Stakeholder Involvement; Technology Stakeholders; Technology Transfer Principles
Settings	Translating from Academia to Market; Technology Translation from Academia to Market; University Tech Transfer

Table 2. Questions asked by developers of the internal review team when assessing the AT Readiness Assessment Tool.

Question Asked	Sampling of Replies
How can we improve the instructions?	Consider making this ‘strongly agree, agree, etc.’ As a rater, I may not know whether you mean ‘my’ confidence in the particular type of technology versus my confidence based on the limited information I gathered.
Recommend additional domains or items that are missing.	Would it be better to determine how the stakeholders would be impacted by the solution, or affected by the problem? In my experience and training these two are not overlapping. Meaning that a solution is a must have OR a nice to have. In reference to Assistive Technology Domain Items: -  ^ Same criteria under team…. Should be part of the problem  ^ Could these be incorporated in one or more of the sections above, since all of the prospective candidates’ solutions support assistive technology? Are some of the questions repetitive
How can we improve the scoring?	I understand the reasoning for this, but the confidence level is also subjective, so personal bias is still involved. I think just having a single score would be sufficient. The confidence level score is provided by the same evaluator. It will also have personal bias. Wouldn’t there still be bias based on the person’s or evaluator’s confidence level? If anyone selects 5 on condition met, I assume there is a very low likelihood that he/she would ever give a 1 for confidence. In reference to the minimum requirement of 80% in each domain:  ^ I am glad this is here because I was thinking that you could have 100% in most categories, but if one category was very low, the translation would likely fail.  ^ Is it 80% of each, the condition met and criteria score? If so, why apply the confidence criteria?  ^ Benefits – has only two benchmarks while others have 5 or 6 (e.g., solution and team)
How can we improve the readiness definitions?	Classification is easy to understand.
How can we make the tool more intuitive?	By virtue of the number of items in each section, the solution, team and assistive technology sections are the three highest in terms of possible Condition Met and Criteria Scores. Yet a person has to achieve an 80% in each domain to be ready.

(N = 4). Sampling of replies to questions.

Table 3: Content Validation Content validation was computed by CVI at the item level and scale level by six experts. (N = 6).

Table 4. Item reliability statistics for Revised TTRAT.

Domain/ Subscale	Mean ± (SD)	Corrected Item- Total Correlation	Squared Multiple Correlation	Cronbach-α if Item Deleted	Subscale Cronbach’s α
Problem
Prob2	4.5 (0.8)				.749^a
Prob3	4.1 (1.1)
Stakeholders
Stake3	3.5 (0.9)				.652^a
Stake4	3.3 (1.0)
Solution
Sol1	3.7 (1.3)	.61	.48	.418	.624
Sol2	3.5 (1.0)	.55	.52	.473
Sol3	4.3 (0.8)	.05	.19	.697
Sol4	4.5 (0.8)	.62	.41	.463
Benef3	3.9 (0.7)	.13	.07	.665
Competition & Differentiation
Comp1	4.0 (0.9)	.72	.60	.630	.753
Comp2	3.7 (0.9)	.72	.57	.634
Comp3	3.9 (1.0)	.51	.43	.715
Diff1	4.2 (1.1)	.52	.35	.713
Diff2	4.4 (0.6)	.14	.13	.804
Team
Team1	4.2 (1.1)	.41	.44	.914	.880
Team2	3.8 (1.1)	.88	.84	.820
Team3	3.6 (1.2)	.78	.74	.839
Team4	2.8 (1.5)	.78	.79	.837
Team5	2.7 (1.6)	.77	.75	.844
Sustainability
Sus1	3.3 (1.3)	.59	.55	.843	.857
Sus2	3.3 (1.4)	.66	.58	.831
Sus3	3.4 (1.2)	.75	.60	.815
Sus4	3.1 (1.2)	.49	.29	.860
Sus5	3.1 (1.3)	.66	.53	.831
Sus6	2.8 (1.3)	.74	.66	.816

N = 24. Each subscale item is numbered and appears as an abbreviated domain name (where, Prob: Problem; Stake: Stakeholders; Sol.: Solution; Benef: Benefit; Comp or Diff: Competition & Differentiation; Team: Team; Sus: Sustainability) followed by a number, such as Prob2. ^aFor subscales with less than 3 items, such as Problem and Stakeholders, item-level statistics were not interpretable.

Data availability statement

Data is available upon request.