## 3 Results and Discussion
### Answering RQ1 and RQ2
For example, Quality function deployment mixed with an Analytic hierarchy process (QFD-AHP) methodology was applied for the selection of the appropriate AR visual technology in creating an implementation for the aviation industry in [123] or to support the decision-makers with quantitative information for a more efficient selection of single AR devices (or combinations) in manufacturing [87]. Technology transfer and adoption in the industry is relevant to articles that provide a holistic view about the current challenges, limitations and potential improvements that could support the adoption of AR technology in an industry context while satisfying the business requirements of companies. Literature review articles about AR in manufacturing usually contribute to the Technology Adoption category [77; 190; 235]. Figure 9: Number of articles in each layer from 2010 to 2021. After analyzing the data, it showed that there is a nearly balanced investigation into \"Conceptual Framework\" (48 articles, or 24% of the total) and \"Algorithm and Modelling\" (47 articles, or 23.5% of the total). Only 9 articles, or 4.5% of the total, contribute to the \"Evaluation framework\", while there is a high interest at the moment in \"Technology transfer/adoption\", with 57 articles, or 28.5% of the total. Considering this, all selected AR literature review articles can be considered to reuse for AR technology transfer and adoption in the long-term in a manufacturing context. In layer 4, the relevant articles are analyzed and divided into two categories based on their industry adoption stages: \"tested in the industry\" and \"novel stage.\" When an application has been tested in a real manufacturing context or field experiments are carried out, it is characterized as \"tested in the industry.\" The \"novel stage\" is more relevant to applications or implementations that focus on solving specific issues of AR technology, such as tracking, calibration, etc., and is only tested in the laboratory environment. Besides that, pilot projects are relevant to those that implemented AR in an industrial environment but with no comprehensive tests carried out. Those proposing and developing a comprehensive AR-based solution that has high potential to integrate further in a real manufacturing context are also considered as pilot projects. The results reveal that 84% of applications  
Figure 10: Number of articles in each category of layer 1 from 2010 to 2021. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Criteria** & **References** \\ \hline \multirow{7}{*}{Concept and Theory} & \multirow{7}{*}{Conceptual Framework} & [11,40,51,54–76] \\  & & [95,121,126,139,143,159] \\  & & [161,163–165,167,168,170,190] \\  & & [191,193,197,202,213–215,235] \\ \hline \multirow{7}{*}{Concept and Theory} & \multirow{7}{*}{Conceptual Framework} & [45,49,67,73,77,77,79,81,83,86,88–93] \\  & & [95,96,102,109,111,112] \\  & & [118,120,122,123,125,127,130–132] \\  & & [135,137,140,141,143,145,149,150,154] \\  & & [171,183,190,217,219,221–223,225] \\ \cline{2-3}  & Evaluation Framework & [45,63,82,83,107,123,128,133] \\ \hline \multirow{7}{*}{Technology Adoption} & [2,17,18,33–53] \\  & & [73,77,78,84,85,87,90–93,101] \\ \cline{1-1}  & & [102,106,114,116,117,122,124,130–132] \\ \cline{1-1}  & & [134–137,141,162,166,190] \\ \cline{1-1}  & & [219,221–223,235] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Articles on Layer 1 Concept and Theory Layer. are still in the novel stage, while the remaining 16% are tested in the industry and can be improved further for industry adoption (see Figure 11). Although the novel stage projects achieve potential results, user acceptance, human-centric issues, seamless user interaction and user interface are still challenges that need investigating for long-term industry adoption in manufacturing [2].