# Intent to Adopt Location Sharing for Logging Safety Applications
## 2 Materials and Methods
### Data Analysis  
Surveys from the three LEAP update locations were pooled for data summaries and statistical analyses. In order to address possible geographic biases among locations, we first used Analysis of Variance (ANOVA) to compare a subset of survey questions across workshops: LS privacy concerns, smartphones at work, importance of new safety solutions, intent to adopt LS, and age. All ANOVA tests were insignificant, indicating no response variation across location, except for one question with a \(p\)-value of 0.0009 (intent to use PLDs on all ground workers and heavy equipment). We believe this \begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute** & **Measure** & **Scale** \\ \hline Intent & Direct & 5-point, bipolar: unlikely–likely \\ Attitude (Instrumental) & Indirect & \\ Potential Outcome & & 5-point, bipolar: unlikely–likely \\ Value of Outcome & & 5-point, bipolar: bad–good \\ Perceived Norms (Descriptive) & Direct & 5-point, bipolar: unlikely–likely \\ Perceived Behavioral Control & Direct & 3-point, unipolar: none–a lot \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of measurements and scales for Theory of Planned Behavior attributes. result was an artifact of the data and assumed for the purposes of subsequent analysis that there were no within-group differences for the three locations. We report responses to each survey question as percentages in the Results section. The only question not represented in the final results is the ranking of LS uses, as only 152 respondents (about 54%) answered the question uniformly. We also report [PERSON]'s alpha for select sets of survey questions to measure internal consistency. The higher the Cronbach's alpha value, the more likely compared questions measure the same underlying concept. We completed statistical analysis for several specific questions using R open source statistical computing software (Version 1.0.44, 2016, The R Foundation, Vienna, Austria) [48]. First, we analyzed the question on the importance of finding new ways to improve safety on logging operations. Responses were recoded numerically as 1, 2, 3, 4, and 5 for \"very unimportant\", \"somewhat unimportant\", \"neutral\", \"somewhat important\", and \"very important\", respectively. We compared median response scores using the Wilcoxon Sign-Rank Test (_wilcox.test_ function) and the non-parametric Sign Test (_SignTest_ function). We applied both tests because Wilcoxon Sign-Rank assumes that medians are symmetrically distributed, an assumption only loosely met with our data set; whereas the comparable but less powerful Sign Test does not require a symmetrical distribution. With both methods, we tested the null hypothesis that the median response was less than or equal to 3 (\"neutral\"), or that loggers do not place a high importance on new logging safety solutions. To determine the likelihood that loggers will adopt the three LS safety practices, we again used Wilcoxon Sign-Rank and Sign procedures, testing the null hypothesis that the median intent score is less than or equal to 3, where 1 corresponds to \"very unlikely\", 2 is \"somewhat unlikely\", 3 is \"unsure\", 4 is \"somewhat likely\", and 5 is \"very likely.\" Additionally, we used two-way ordinal regression with Cumulative Link Models (_clm_ and _Anova_ functions in R) to determine (a) if intent varies by age or the frequency with which loggers carry smartphones on logging operations, and (b) if there is an interaction effect between the two independent variables. We assumed that older loggers would be less experienced and comfortable with technology generally and thus would be less likely to adopt technologically-based safety practices in the workplace. We also assumed that individuals who more frequently carry a smartphone at work would be more accustomed to regular technology use and thus more likely to adopt LS.