*2.5. Facilitating Condition (FC)*

FC primarily refers to support and assistance available towards a system/technology from the perspective of end-users. Numerous studies have examined FC and revealed some interesting discoveries. Ref. [20] argued that FC is in actual fact similar to perceived behavioural control in TPB and may not necessarily lead to actual usage. Ref. [25] explained FC as the perception consumers have towards support and resources in order to exhibit a behaviour, which relates to the actual usage of system/technology and stressed that FC may render its influence on intention and usage.

Ref. [19] investigated mobile learning systems amongst university students and found that FC contributes significantly towards intention. In the same study, availability of resources, which had a similar definition to FC, has a significant relationship with actual usage. Despite the contrary and colourful nature of FC, numerous studies have successfully operationalised FC in the behavioural intention context by defining it as the degree of belief in overall infrastructure, ranging from organisational to technical infrastructure towards fostering system usage [32,35]. The current research operationalises the definition of [25] and measures FC using five items, three of which were adapted from [32] and two from [44]. Thus, the following hypothesis is developed:

#### **H7.** *FC has a positive effect on intention to adopt solar services.*

#### *2.6. Methods*

Present research developed a questionnaire from validated studies. PE, EE, FC, and SI were adopted from the combined research of [24,32,44] and awareness was adopted from [10]. Intention items were adopted from [10,17] (refer to Appendix A). A selfadministered questionnaire was developed as well. Cooperation was solicited through a purposive sampling method because the study investigated the intention to adopt solar services. The target group comprises working adults in Malaysia who own a home or intend to purchase one soon. Homeowners residing in condominium, apartments, or any shared building or property were excluded from this survey. A total of 400 respondents were approached, out of which 273 responses were received; nevertheless, only 272 responses were used for the final data analysis owing to missing values in one response.

The profile of the respondents showed that approximately 60% were females, 70% were aged 35 and above, and the majority hold at least a bachelor's degree. A total of 76% were working full time, while the remainder were contract workers or self-employed. The majority were earning approximately RM 3000–RM 5000, 37% of whom were earning above RM 5000. The profile of our sample is representative of the population of the Malaysian population as according to the census report of the Department of Statistics Malaysia (DOSM), in terms of gender it is about a 50-50 split followed by age with 69.8% of working age and similar percentage for the working group (15–64 years). For income level it differs from city to city, the mean household income was RM 7901 in 2019 with a median of RM 5873, the income of a majority of the respondents in our sample is about RM 3000–RM 9000 a month [45].

#### **3. Data Analysis and Results**

Data analysis was done through variance based SmartPLS 3.3.6 [46], which is a secondgeneration analytical tool. The threat of method bias due to single source data collection was addressed in this study using the suggestion of [47] by testing the full collinearity. All the variables were regressed against a common variable. Single source data have no bias if VIF ≤ 3.3. The analysis yielded a VIF of below 3.3, refer Table 1, hence there was no threat of single source bias in this study.


**Table 1.** Full Collinearity.
