## 3 Theoretical Framework
Multiple studies have also proven that the UTAUT model provides a highly accurate measure of the level of acceptance of electronic systems as shown in Figure 1. As such, the following eight basic constructs of user adoption of \(\Pi\) may affect the theoretical framework:  
* Performance expectancy (PE)--three factors;
* Effort expectancy (EE)--three factors;
* Social influence (SI)--three factors;
* Perceived threats (PTs)--three factors;
* Perceived business threats (PT-Bs)--three factors;
* Perceived private threats (PT-Ps)--three factors;
* Employer attractiveness (EA)--three factors;* Behavioural intention (BI)--three factors;  
The four moderators that can be implanted in the UTAUT framework include gender, age, level of experience, and willingness to use [43]. Many fields have recently taken to using the UTAUT model [28; 44]. As such, multiple studies have proven its efficacy in gauging the adoption of mobile innovations [45; 46; 47]. It has also been used to identify the potential factors that may motivate employee adoption of BYOD programmes in the future [48] as well as what organisations can do to increase employee willingness to adopt technical services [49; 50]. Therefore, it is recommended to develop a UTAUT framework that can potentially increase the level of acceptance while simultaneously examining BYOD-related security issues and the potential acceptance and consumer effects from a security and InfoSec standpoint. As seen, the model depicts the influence of individual BIs on technology use [43], which directly depends on the four main factors of the UTAUT theoretical framework. Apart from the main constructs, gender, age, level of experience, and willingness to use moderate the correlation between these constructs and BI. More recently, [51] made two main modifications to the UTAUT model to adapt it to BYOD programmes and their requirements. These modifications excluded facilitating conditions, as the UTAUT model does not examine BYOD use behaviours, and included the addition of perceived threats (PTs) to examine the negative aspects of BYOD adoption and the use of personal devices to access work-related systems [44]. This present study further extended these modifications [51] by also examining how the factors of the theoretical framework affect the BI to adopt BYOD programmes across multiple industries, such as the military, education, commercial, and non-commercial sectors, that have varying uses and knowledge of BYOD programmes. This adds to the existing body of BYOD-related literature by providing useful insights on how to increase BYOD adoption globally. According to the PTs, innovations may not always be beneficial. As the UTAUT model does not examine the negative factors that hinder BYOD adoption, the PTs were added according to the social cognitive theory (SCT) [52; 53] and other major resistance theo  
Figure 1: The modified UTAUT model for BYOD programmes. ries in information-system-related literature [54; 55; 56]. Based on the concept of technology adoption and use disincentives, PTs are the degree to which an employee believes that participating in a BYOD programme carries threats that elicit anxious or emotional reactions, which, consequently, negatively affect adoption behaviours and acceptance. According to [PERSON] et al., these threats can be distinguished into two main dimensions: threats that affect the PT-Ps and threats that affect the PT-Bs. Examples of PT-Ps include the loss of private data, the recovery of the private data of the organisation, and the blurring of the boundaries between private life and work. Therefore, any form of encroachment into the property of an individual could increase administrative corruption among the users [57]. Examples of PT-Bs include the loss of business data, damaging the corporate network with malware, and violating corporate policies [57]. As such, it is recommended to examine the PT-Ps and PT-Bs that generally affect intentions to adopt BYOD programmes.