## 2 Review of Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Geography Focus** & \
\begin{tabular}{c} **Technological** \\ **Focus** \\ \end{tabular} & **Research Objectives** & **Methodology** &
\begin{tabular}{c} **Identified Factors Influencing or** \\ **Hindering IoT Adoption** \\ \end{tabular} \\ \hline
[26] & India & Smart cities & \begin{tabular}{c} Investigate the impact of IoT barriers for \\ smart city waste management \\ \end{tabular} & \
\begin{tabular}{c} An analytic approach involving a hybrid \\ multi-attribute decision-making model \\ applied to 15 IoT barriers identified from the \\ literature and expert opinions \\ \end{tabular} &
\begin{tabular}{c} Security and privacy issues, lack of \\ regulatory norms and policies, operational \\ costs and extended payback periods, etc. \\ \end{tabular} \\ \hline
[28] & South Africa & Education & \begin{tabular}{c} Investigate and identify the determinants of \\ IoT adoption \\ by higher educational institutions (HEIs) \\ within the South African setting \\ \end{tabular} & \
\begin{tabular}{c} A quantitative (empirical) method involving \\ a survey of 250 respondents selected \\ randomly from some tertiary institutions \\ and the use of exploratory factor analysis \\ (EFA) and regression analysis to determine \\ factors affecting IoT adoption in HEIs \\ \end{tabular} &
\begin{tabular}{c} Behavioral intention to use the IoT was \\ positively influenced by \\ performance expectancy, social influence, and effort expectancy. \\ \end{tabular} \\ \hline
[00] & N.A. & Healthcare & \begin{tabular}{c} Analyze and statistically classify existing \\ knowledge about the factors \\ that influence medical professionals to adopt \\ IoT applications in the healthcare sector. \\ \end{tabular} & \
\begin{tabular}{c} Systematic review of relevant IoT adoption \\ studies from 2015 to 2021 carried out on nine \\ major scientific databases and leading to \\ 22 articles being selected as \\ per the inclusion criteria \\ \end{tabular} &
\begin{tabular}{c} Social influence, attitude, and personal \\ inautentiveness, perceived \\ usefulness, perceived \\ ease of use, performance expectancy, and \\ offer expectations, \\ perceived privacy risk, perceived \\ severely and perceived health risk, financial \\ cost, and facilitating conditions \\ \end{tabular} \\ \hline
[1] & Pakistan & E-government & \begin{tabular}{c} Explore the success factors of IoT service \\ orchestration to create public value \\ in smart government. \\ \end{tabular} & \
\begin{tabular}{c} A quantitative method for data collection \\ involving a field survey conducted through \\ online and paper-based questionnaires \\ administered to 87 respondents. Partial least \\ squares structural equation modeling was \\ used for data analysis \\ \end{tabular} &
\begin{tabular}{c} Decision transparency, government trust, \\ service collaboration, service effectiveness, \\ service transparency, public engagement, \\ perceived ease of use, public trust, and \\ are valid success measures of e-gov IoT \\ service orchestration \\ \end{tabular} \\ \hline
[17] & South Africa & Smart Agriculture & \begin{tabular}{c} Identify factors that may determine \\ the adoption \\ and use of IoT within the agricultural sector \\ in Limpopo province. \\ \end{tabular} & \
\begin{tabular}{c} A quantitative approach involving data \\ collected via interviews, \\ observations, and document reviews. \\ Thematic analysis was used for data analysis \\ \end{tabular} &
\begin{tabular}{c} Organizational support, cost, \\ knowledge gap, \\ security, policy, monitoring and control, and \\ perceived value and risk \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of related works on IoT adoption.