## 3 Taxonomy
### Empirical Studies  
The final and largest category was empirical studies, with 44 articles (57.1%) grouped into five sub-categories based on the methodology used. In the _theoretical studies_ sub-category, there were 17 articles identified, in which the IoT adoption factors were empirically investigated based on theories and hypothesis testing. Several studies have examined IoT adoption factors  
Figure 6: Taxonomy of the literature on IoT adoption in organizations. based on a single theory, such as the technology-organization-environment (TOE) framework [13; 33; 35; 40; 42], the technology acceptance model (TAM) [68], complexity theory [15], and the poly social reality (PoSR) framework [11]. In contrast, others have integrated more than one theory in their models, such as TOE and human-organization-technology (HOT-fit) [56], TOE and diffusion of innovation (DOI) [38], TOE, DOI, and oil and gas value (OGV) framework [25], and DOI, TAM, and theory of reasoned action (TRA) [39]. Furthermore, three studies developed hypotheses and identified factors based on the literature to determine the factors affecting IoT adoption [45], short-term impacts of IoT adoption [36], and IoT impact on higher education [17]. Lastly, the authors of [21] used path-dependence theory to explain government IoT adoption behavior. Table 3 shows the theoretical models used in IoT adoption studies at the organizational level. Studying various models helps to better understand IoT adoption and the development of more unified models. However, the TOE framework is the most frequently used theory for this purpose. Eleven articles were identified in the _multi-criteria decision-making_ sub-category. These studies highlighted different multi-criteria decision-making methods for analyzing and identifying the relationships between IoT adoption factors. Several studies adopted a single method, such as the fuzzy analytic hierarchy process (FAHP) [51] or the analytical hierarchy process (AHP) [80]. Others combined multiple methods in their studies, such as the combination of grey relational analysis (GRA) and analytical hierarchy process (AHP) [6], interpretive structural modeling (ISM) integrated with matrice d'impacts croises multiplication appliques a un classement (MICMAC) [20; 61; 81], modified total interpretive structural modeling (m-TISM) and MICMAC methods [59], ISM and decision making trial and evaluation laboratory (DEMATEL) [22], techniques for ordering preference by similarity to ideal solution (TOPSIS) and AHP [54], DEMATEL method, maximum mean de-entropy (MMDE), and ISM [37], and total interpretiative structural modeling (TISM) approach, Fuzzy-MICMAC, and DEMATEL method [62]. As shown in Table 4, ISM and MICMAC are the most frequently used methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Method Name** & **Reference** \\ \hline AHP & Analytical Hierarchy Process & [6; 54; 80] \\ DEMATEL & Decision Making Trial and Evaluation Laboratory & [22; 37; 62] \\ FAHP & Fuzzy Analytic Hierarchy Process method & [51] \\ Fuzzy-MICMAC & Fuzzy Matrice d’Impacts Croises Multiplication Appliques a un Classement & [62] \\ GRA & Grey Relational Analysis & [6] \\ ISM & Interpretive Structural Modeling & [20; 22; 37; 61; 81] \\ MICMAC & Matrice d’Impacts Croises Multiplication Appliques a un Classement & [20; 59; 61; 81] \\ MMDE & Maximum Mean De-Entropy & [37] \\ m-TISM & Modified Total Interpretive Structural Modeling & [59] \\ TISM & Total Interpretative Structural Modeling Approach & [62] \\ TOPSIS & Technique for Order of Preference by Similarity to Ideal Solution & [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multiciteria decision-making methods are used in determining IoT adoption factors.