Developing a Practical Framework for Applying the Work from Home Concept to Technical Jobs in Electricity Utilities Using the Unified Theory of Acceptance and Use of Technology

Abstract

With the global rise of coronavirus disease 2019 (COVID-19), a significant change occurred, prompting employees across different countries to switch to remote work and work from home (WFH) instead of working in their usual physical workplaces. This research aims to improve the unified theory of acceptance and use of technology (UTAUT) by identifying the factors that affect the acceptance of the technical employees in the electricity utility sector during WFH and identifying technologies required for WFH. By this aim, this study contributes to creating inclusive and sustainable work environments, essential for fostering economic growth even during global crises like the COVID-19 pandemic, in alignment with Sustainable Development Goals (SDG) 8: Decent Work and Economic Growth. This study used a mixed research methodology, using a focus group of five industry experts in addition to a survey. It conducted a specific study within a United Arab Emirates (UAE)-based electricity utility. The focus group session resulted in finalizing and listing twelve factors affecting WFH for technical job employees. Three of them were newly introduced during the focus group, namely, “emotional well-being”, “cultural factor”, and “honesty of employees”. Those factors were used further to prepare the related hypothesis and prepare a questionnaire. The survey data were collected from 145 respondents and analyzed using structural equation modeling (SEM) using IBM SPSS Amos (Version 29.0). The analysis of the survey revealed that there were significant relationships between all the constructs; however, the hypothesis concerning perceived risk was not supported. Moreover, the analysis also provided a list of vital technologies required to WFH, resulting in insights for organizational managers on which factors to prioritize when implementing remote work strategies. The successful completion of this research has the potential to better prepare organizations for future pandemics and improve the balance between work and personal life for employees. Research limitations and future study recommendations are also highlighted.

1. Introduction

The global community is dedicated to fulfilling the 2030 Agenda for Sustainable Development, comprising 17 Sustainable Development Goals (SDGs) adopted by United Nations (UN) Member States in September 2015 [1]. These goals address a variety of global challenges. Between the coronavirus disease 2019 (COVID-19) pandemic and the environmental disasters posed by human activity, the balance between human needs and environmental sustainability is critical. Recent research highlights the potential of telecommuting to make a significant contribution to SDG 8: Decent Work and Economic Growth.

The COVID-19 pandemic, which has been destabilizing the world since the end of 2019, has had significant repercussions on various sectors including health, economy, society, and education. In response to these unprecedented challenges, the special issue “Post-COVID-19 Education for a Sustainable Future: Challenges, Emerging Technologies and Trends” seeks to explore and provide solutions to the evolving landscape of professional practices and sustainability [2]. This study contributes to this special issue by identifying the most influential factors affecting the inclination of technical job employees to embrace work from home (WFH) technologies within the utility sector. By addressing these factors, our research aims to enhance the sustainability and effectiveness of remote work practices, thereby aligning with the objectives of the special issue.

Telecommuting, which provides job freedom and promotes work–life balance, has shown promise for enhancing economic well-being, especially for groups such as young mothers and older workers, and thus can reduce challenges linked with an aging workforce. Furthermore, telecommuting can create a safe and secure work environment by facilitating family care responsibilities and fostering sustainable employment opportunities [3].

Many countries worldwide have advised people to reduce social contact and stay at home amid the appearance of the COVID-19 crisis [4]. Consequently, numerous organizations have started to encourage their employees to WFH, leveraging advanced communication technologies. This transition has resulted in notable changes in both work and social environments. The adoption of WFH presents an opportunity to mitigate the risk of disease exposure and economic disruptions linked with government measures to resist the COVID-19 pandemic [4]. While the concept of WFH originated in the 1970s [5], its significance has become increasingly apparent nowadays. WFH is synonymous with terms such as remote work, telework, and telecommuting. Olson [6] defines remote work as “organizational work that is performed outside the normal organizational confines of space and time”. Teleworking, as described by Madsen [7], emphasizes the use of technology, while Ellison [8] characterizes it as “any form of substitution of information technologies for work-related travel.” Furthermore, telecommuting is defined as “the use of telecommunications technology to partially or completely replace the commute to and from work” [9]. One of the primary advantages of WFH is the flexibility it offers employees, allowing them to perform their work at their preferred times [10,11].

Although WFH was established decades ago [5] and used to be applied in a limited space within specific non-technical applications such as simple administrative jobs, the questions now triggered in this research are how far WFH applies to technical domains in general and the factors affecting technical job employees’ intentions to WFH. In a survey conducted by Baert et al. [12], most of the respondents anticipated that remote work would become more prevalent in the future, even after the COVID-19 crisis subsides. It is therefore important to test technology acceptance if WFH is to become a norm in our life. There are few studies available in the realm of technology acceptance of WFH, especially during the COVID-19 crisis [13].

Technology is seen as a key factor in allowing the working from home concept; hence, proper studying and understanding of technology acceptance are vital because without them, implementing the technology may fail and result in economic losses [14,15,16]. Therefore, this research aims to propose a practical framework for applying the WFH model to the technical jobs in the electricity utility industries in the UAE by expanding the unified theory of acceptance and use of technology (UTAUT) and identifying a list of technologies required for WFH. The targeted population of electricity utility employees are mainly the office engineers with normal working hours, the site engineers, and the maintenance engineers who work both in the office and on site to conduct regular maintenance or deal with emergencies as they may arise. Based on the stated aim, the following goals are derived:

• Goal 1 (G1)—Selecting the factors that might be added to the UTAUT to study the technology acceptance and usage in the WFH concept for the technical job employees.
• Goal 2 (G2)—Proposing a framework for applying the WFH model to the technical jobs in electricity utility industries based on the UTAUT.
• Goal 3 (G3)—Identifying a list of technologies required for the technical job employees to WFH.

To achieve this, the researchers conducted focus group discussions to select the factors that might be added to the UTAUT model; structural equation modeling (SEM) and analysis of moment structures (AMOS) were used to analyze the survey data, validate, and test the proposed model.

The rest of the paper presents the literature review in Section 2, Section 3 presents the research methodology, while the results analysis is presented in Section 4 followed by the results discussion in Section 5, and the research ends in the last two sections with research implications and conclusions.

2. Literature Review

This section contains a review of the literature related to the UTAUT and WFH covering mainly the following objectives:

• Identifying the factors that can be added to the UTAUT to study the acceptance of technical job employees to WFH.
• Identifying the technologies required for the technical job employees to WFH.
• Proving the novelty of the research approach.

2.1. Unified Theory of Acceptance and Use of Technology (UTAUT)

The UTAUT stands as a theoretical framework that is used to explain technology acceptance behaviors. Venkatesh et al. [17] crafted it by scrutinizing and evaluating variables across eight distinct models of technology acceptance. These models include the technology acceptance model (TAM), extended technology acceptance model (TAM2), theory of planned behavior (TPB), social cognitive theory (SCT), model of PC utilization (MPCU), diffusion of innovations (DOI), and decomposed theory of planned behavior (DTPB). The UTAUT is known for its simplicity [17]. The UTAUT consists of four main factors for predicting the intention and utilization of technology. These factors are performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI). Performance expectancy is “the degree to which using technology will provide benefits to consumers in performing certain activities”, effort expectancy is “the degree of ease associated with consumers’ use of technology”, social influence is “the extent to which consumers perceive that important others (e.g., family and friends) believe they should use a particular technology”, and facilitating conditions “refers to consumers’ perceptions of the resources and support available to perform a behavior” [17]. The model also includes the user intention (behavioral intention (BI)) and the actual usage of technology (use behavior). The UTAUT’s theoretical framework is presented in Figure 1.

2.2. Factors That Can Be Added to the UTAUT

Several factors identified in the literature could potentially enrich the UTAUT framework. See

Table 1. Additional factors from the literature review.

Factors	Reference
Access to resources, work environment, emotional well-being	[18]
Environmental concern	[13,19]
Cultural factor	[20]
Perceived risk	[21,22,23,24]
Trust	[21,22,23,24,25,26]
Innovativeness	[21,25]

.

2.3. Working from Home (WFH)

Most governments worldwide encouraged enterprises to introduce and encourage WFH to limit the spread of the COVID-19 pandemic [27]. WFH can be defined as “doing organizational work outside the working area provided by an employer” [28]. According to a Gartner survey of 229 human resource (HR) leaders, approximately 88 percent of organizations promoted remote work for employees amid the COVID-19 pandemic. Their study also revealed that around 30 percent of the respondents mentioned that employees worked from home at least part of the time before the pandemic. Moreover, this study stated that it is expected that post-pandemic, around 41 percent of employees may work at least part of the time remotely [29]. Moreover, Barrero et al. [30] investigated whether WFH will continue after COVID-19, by conducting a survey of more than 30,000 Americans over multiple waves. Their results stated that 20 percent of full workdays will be conducted from home after the pandemic, compared with just 5 percent before. A study was conducted by Alipour et al. [4] to check the possibility of working from home using a survey and administrative data. Their results revealed that around 56 percent of their employees may WFH. This result is compared with the result discovered by Dingel and Neiman [31] and Del Rio-Chanona et al. [32], where the percentages were calculated to check the WFH possibility in the U.S. economy to be around 37 percent and 43 percent, respectively. These differences could stem from various factors, such as employing different methodologies for measurement.

An online survey was conducted for the United States workers by Brynjolfsson et al. [33], who produced a real-time measure of WFH during the first week of April 2020. The results revealed that around half the survey respondents were working from home, while Bick and Blandin [34] stated that their survey resulted in more than 60 percent of their respondents’ working hours being conducted at home. Kłopotek [11] stated that this form of work should be seen as one of the numerous mechanisms devised to address the requirements of a modern working environment.

Table 2. Positive aspects of WFH.

Sr.	Positive Aspects	Reference
1	Better work–life balance	[35,36]
2	Fewer interruptions and more focus	[37]
3	Less physical monitoring of employees	[38]
4	Flexible working hours	[10,11]
5	Improve productivity and performance	[14,39]
6	Employees more willing to put in additional effort	[40]
7	Beneficial impact on the environment, including decreased NO2 levels and enhanced Air Quality Index	[41,42,43]
8	Cost reduction through minimized overhead expenses, decreased healthcare costs, enhanced mental and physical well-being of employees, and lowered travel expenses	[35,44]
12	Saving travel time	[11]

and

Table 3. Negative aspects of WFH.

Sr.	Negative Aspects	Reference
1	Difficulties in sharing knowledge	[35,36]
2	Difficulty in teamwork	[45]
3	Fewer communication skills, social isolation, and less cohesive organizational culture	[11,36]
4	Employees’ fear of fewer promotions and rewards	[46]
5	The challenge of distinguishing between personal and professional life	[11]
6	Negative impact on team performance	[35]
7	Limited access to resources, issues related to workspace, and the emotional well-being of employees	[18]

summarize the positive and negative aspects of WFH, respectively.

2.4. List of Technologies Required for WFH

Regarding the technologies that can be used during WFH, there are many studies covering various technologies as per

Table 4. Technologies that can be used during WFH.

Technology	Reference
Video conferencing apps (Zoom, Teams, Google Meet, etc.)	[19,47]
High-speed internet	[48]
Management software	[48]
Virtual private network (VPN) client	[49]
Laptop computer	[48,49]
Smartphone and/or tablet	[48,49]
Instant message (IM) software	[49]
E-mail	[49]
Unmanned drones/robots	[26]
Artificial intelligent (AI)	[50]
Closed-circuit television (CCTV)	[51]
Metaverse	[52]

.

2.5. Summary of the Literature Review

The literature review fulfilled the main objectives as follows:

• Identifying the factors that can be added to the UTAUT to study the acceptance of the technical job employees of WFH: The additional factors include access to resources, work environment, and emotional well-being [18]; environmental concern [13,19]; cultural factors [20]; perceived risk [20,21,22,23]; trust [20,21,22,23,24,25]; and innovativeness [21,25]. Adding different factors to the UTAUT may enrich the theory of technology acceptance with other factors related to the study field, as Venkatesh et al. [17] stated that across different contexts, extensions to the original UTAUT could be considered. Hence, studying WFH in a specific industry in depth would help in widening the research area and outcomes. It was observed from the literature review conducted that the available research has not covered WFH for technical industries using the UTAUT. Hence, the following research question was formed linking it to G1:

  -

  What are the factors that might be added to the UTAUT to study the technology acceptance and usage in the WFH concept for the technical job employees?

• Proving the novelty of the research approach: This research aims to make a substantial contribution to knowledge by enhancing the framework of the technology acceptance theory for technical job organizations. Notably, it addresses a gap in the existing literature by investigating the feasibility of WFH in electricity utilities in the UAE using the UTAUT model. The study endeavors to illuminate the concerns of organizations regarding WFH and shed light on the key variables influencing employees’ intent to utilize WFH technologies. The research also aims to introduce a list of the technologies required for technical job employees to WFH. The potential outcomes of this study hold paramount importance for organizational strategic planning. It seeks to practically identify critical factors affecting technical job employees during WFH, which can be adopted by similar organizations or replicated across various sectors. Furthermore, the research’s success can contribute to better sector preparedness for future pandemics, offering employees an improved work–life balance and heightened job satisfaction. The study also offers theoretical contributions by augmenting the UTAUT framework with additional variables, enriching the theory of technology acceptance with factors relevant to a broader spectrum of technology users. It also introduces a list of technologies required to WFH. Researchers can leverage these findings for further exploration in this field or apply similar approaches to different industries. Hence, the third research question was formed linking it to G2 as follows:

  -

  What is the proposed framework for applying the WFH model to the technical jobs in electricity utility industries based on the UTAUT?

• Providing a list of technologies required to WFH: Several technologies are highlighted in the literature, such as video conferencing applications [19,47], autonomous systems [26], and a variety of communication tools [48,49]. However, these were not specific to technical jobs; hence, the following research question was formed linking it to G3:

  -

  What are the technologies required for the technical job employees to WFH?

3. Research Methodology

This research aimed to select the most critical factors affecting the intentions of employees who perform technical jobs to apply the WFH approach and use its technologies to conduct those jobs, using focus group discussions and a survey. It also aimed to introduce a list of technologies required for WFH. This was achieved by following the methodology mentioned in Figure 2 and briefly discussed here. As a first step, a focus group session was conducted to confirm/remove/identify other factors and add them to the original UTAUT theory factors. A total of five experts were selected from the electricity transmission and distribution utility, from different departments. This number was chosen to allow for more time for each individual’s contribution [53,54]. The ideal size for most noncommercial topics is typically between five and eight participants [55], as larger groups can be difficult to control and may limit individual participation, while smaller groups facilitate deeper insights and are more comfortable for participants [55,56]. Therefore, inviting five participants to the focus group session aligned with the recommendation for optimal group size [54,57]. All five experts were experienced employees, with effective communication skills and active engagement in the discussion sessions. They were either management or senior levels with 18 to 21 years of experience with ages between 40 and 50 years old and qualifications of either bachelor’s or master’s degrees in engineering. In this context, focus groups served as a valuable means to solicit diverse perspectives and opinions on the critical factors affecting the intentions of technical job employees to embrace the WFH approach and its associated technologies; thus, they were assigned to confirm the extracted factors from the literature or/and select additional factors that could be added to the UTAUT.

The final list from the focus group was further used to prepare a hypothesis and, hence, a survey. A pilot study was conducted to check the consistency of the survey questions, assessing the feasibility of the planned methods before undertaking a larger investigation [58]. Moreover, conducting a pilot study prevented potential flaws in the main study, thereby saving time and resources [59] and allowing researchers to proceed with the main study after the necessary modifications, if required [60]. The sample size was calculated for the main questionnaire using an online sample size calculator called the “Raosoft sample size calculator” [61,62]. The analysis of the survey was conducted using the SEM approach and utilized AMOS tools using Statistical Package for the Social Sciences (SPSS). At the final stage, the results were presented and discussed.

4. Research Analysis

4.1. Theoretical Background—Factors and Hypotheses

This section addresses G1 of the research as follows:

After the introduction, a literature review was conducted about the UTAUT and its main factors; it covered the literature related to additional factors that affected the intentions of employees to apply WFH technologies or other technologies in general using the UTAUT framework. Moreover, a list of technologies used to do their job from home was extracted. A list of six factors was selected from the literature, which included “innovativeness”, based on Slade et al. [21]; “environmental concern”, following Razif et al. [13] and Hafiar et al. [19]; “trust in the system” and “perceived risk”, used by Chayomchai et al. [24], Oktavia et al. [23], Li [22], and Slade et al. [21]. “Emotional well-being”, extracted from Ganguly et al. [18], however, was not covered as a factor in the UTAUT; also, based on the recommendations mentioned in several articles, “cultural factor” was added.

The focus group discussion with five utility experts was conducted on 22 March 2023. During the focus group session, the experts confirmed the factors that were extracted from the literature in addition to a new factor, namely, “honesty of employees”. Moreover, an additional list of technologies required for WFH was suggested by the experts, namely, autonomous calibration tools and Geographical Information Systems (GIS).

Based on the conducted literature review, each of the twelve factors’ analysis was summarized to show how significant each factor was in the used model for certain studies as per Appendix A.

The final list of the selected factors was further used to prepare a hypothesis as follows.

4.1.1. Performance Expectancy

To gauge the perceived improvement in job performance related to electricity management and operations through the use of WFH technologies, understanding employees’ expectations is crucial. Many studies show a positive effect on BI [13,19,20,21,23,24,25,26,63]. In this regard, the expected outcome is as follows:

H1. 

Within a technical-based organization, performance expectancy has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how useful their use of these technologies was.

4.1.2. Effort Expectancy

Assessing the ease of interaction between technical job employees and WFH technologies is essential to understand their perception of the effort required for effective utilization. Most studies report a positive significant effect, with some variations that show positive but not significant effects [13,19,20,21,23,24,25,26]. Thus, the current study assumed a positive effect using the following hypothesis:

H2. 

Within a technical-based organization, effort expectancy has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how easy was their use of these technologies.

4.1.3. Social Influence

Recognizing the impact of colleagues, supervisors, and industry peers on technical job employees’ decisions to embrace WFH technologies is vital in shaping the social dynamics influencing their adoption. Positive effects are consistent across studies, emphasizing the role of social factors in influencing BI [13,19,20,21,26]. Based on that, the expected outcome was as follows:

H3. 

Within a technical-based organization, social influence has a positive influence on the intention to use WFH technology.

This means that employees’ intentions to use WFH technologies were positively affected by how they perceived their social facilitating surroundings, opinions, and support regarding the use of these technologies.

4.1.4. Facilitating Conditions

Examining the availability of organizational and technical infrastructure supporting WFH technologies is critical to understand the facilitating conditions that contribute to or hinder the adoption process. The impact is positive in some studies, with variations in the direct effect of the actual use [20,23] and indirect effects through BI [13]. The current study suggested a direct effect on actual use; therefore, the following hypothesis was formed:

H4. 

Within a technical-based organization, facilitating conditions have a positive influence on the actual use of WFH technologies.

This means that the availability of necessary resources, knowledge, infrastructure, facility, and technical support was vital for employees to use WFH technologies.

4.1.5. Environmental Concern

Investigating employees’ awareness and consideration of environmental consequences related to WFH technologies is crucial, given the potential environmental impacts of electricity utilities. Limited studies involve this factor in the UTAUT and show a positive effect on BI [13]. Therefore, this study expected the following hypothesis:

H5. 

Within a technical-based organization, environmental concerns have a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how they reduced the environmental adverse impacts during their use of these technologies, as WFH technologies reduced gas emissions, reduced the amount of workspace required, reduced accidents, etc.

4.1.6. Perceived Risk

Recognizing technical job employees’ concerns regarding the security and safety of WFH technologies is vital in addressing potential barriers and building confidence in the adoption process. Some studies show positive effects through PE and EE but no direct positive significance [24], and others indicate negative effects [21,23]. This study expected negative effects; thus, this hypothesis was formed:

H6. 

Within a technical-based organization, perceived risk has a negative influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were negatively affected by how secure the system during their use of these technologies was.

4.1.7. Trust in the System

Understanding the level of trust technical job employees have in the safety and security of WFH systems is essential for fostering a positive perception and encouraging the adoption of this system. Positive effects are reported whether direct or through other factors like PE and EE [22,23,24,25]. Thus, this study assumed the following hypothesis:

H7. 

Within a technical-based organization, trust in the system has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how much trust occurred in systems safety and security during their use of these technologies.

4.1.8. Innovativeness

Assessing the acceptance of new technologies and innovations within the electricity utilities sector is crucial to understand the openness of technical job employees to embrace WFH technologies. In the limited evidence from the explored literature, Slade et al. [21] show a positive effect on BI, while Chayomchai [25] shows a positive effect on BI through PE and EE. This study expected to have a positive effect using the following hypothesis:

H8. 

Within a technical-based organization, innovativeness has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how new technologies and innovations were accepted among the employees.

4.1.9. Emotional Well-Being

Recognizing the impact of WFH technologies on the emotional well-being of technical job employees is essential to ensure that the adoption process aligns with their overall job satisfaction and mental health. This factor was a newly added factor in this study, and it was expected to have a positive impact on the BI.

H9. 

Within a technical-based organization, emotional well-being has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by how comfortable and emotionally well they were during their use of these technologies.

4.1.10. Cultural Factor

Investigating how cultural factors within the electricity utilities sector may influence the acceptance and integration of WFH technologies is crucial for tailoring strategies to the organizational culture. This factor was a newly added factor in this study, and it was expected to have a positive impact on the BI.

H10. 

Within a technical-based organization, the cultural differences factor has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by differences in culture between the users of these technologies.

4.1.11. Honesty of Employees

Assessing the commitment and honesty of technical job employees in using WFH technologies is vital for understanding potential barriers and ensuring the reliability of data related to adoption. This factor was added by the experts during the focus group discussion and was expected to have a positive impact on BI as follows:

H11. 

Within a technical-based organization, the honesty of employees has a positive influence on intention to WFH.

This means that employees’ intentions to use WFH technologies were positively affected by the commitments and honesty of the users of these technologies.

4.1.12. Behavioral Intention

Understanding the intentions of technical job employees to adopt and use WFH technologies provides valuable insights into their readiness and willingness to embrace remote work practices. Many studies reported a positive effect of BI on the AU [20,23,24]. Therefore, this research introduced the following hypothesis:

H12. 

Within a technical-based organization, behavioral intention has a positive influence on the actual use of WFH technologies.

This means that employees’ intentions to use WFH technologies were positively affecting the actual use of these technologies.

Each of these factors contributes uniquely to the complex landscape of technology adoption within the specific context of electricity utilities, providing a comprehensive understanding of the challenges and opportunities associated with the integration of WFH technologies in this sector. Based on the produced hypothesis, a questionnaire was generated based on Venkatesh et al. [64], Im et al. [20], Razif et al. [13], Slade et al. [21], Venkatesh et al. [17], Chao [65], and Khechine et al. [66]; moreover, questions were initiated for the additional factors (refer to Appendix B).

4.2. Empirical Analysis and Findings—Practical Framework

This section addresses G2 of the research as follows:

A pilot study was conducted with a sample size of 30 respondents in the period between 21 and 24 October 2023, and an analysis was performed to check the consistency. The initial results from the pilot study showed slightly low consistency; hence, necessary modifications were made by removing four of the questions from the survey to improve the Cronbach’s Alpha results to a better value as per Appendix B. The pilot study results after these modifications showed that all the factors were consistent, reaching the acceptable minimum threshold of 0.7 for Cronbach’s Alpha [67]. The sample size was calculated for the main questionnaire using an online sample size calculator called the “Raosoft sample size calculator” [61,62]. For this study, the sample size was calculated as 143 while keeping the margin of error at 5%, the confidence level at 90%, a population size of 300, and the response distribution at 50%. The questionnaire was distributed, and the data for 145 respondents were collected. After collecting the data, the next step was to analyze all the factors of the UTAUT including both the original factors and the additional ones using the collected data from the questionnaire; the data were analyzed using the SEM approach and utilized AMOS tools using SPSS (IBM SPSS Amos Graphics (Version 29.0)) software. In this study, SEM techniques were used to model the relationships between several dependent and independent parameters in order to test the relationships between the model constructs [68]. As per Gefen et al. [69], the SEM was recommended for use in behavioral sciences and IT/IS. For the analysis of SEM, the two-step method recommended by Anderson and Gerbing [70] was followed in this study, by first testing the reliability and validity of the constructed model, which was followed by the second step to check the model fit and test the research hypotheses. Finally, the results were presented and discussed.

4.2.1. Demographic Data

The questionnaire was distributed to the targeted population, technical job employees in an electricity transmission and distribution utility, with a population of 300 employees in the period between 26 October and 11 November 2023. The responses reached 145 respondents, with a response rate of 48.33%.

Table 5. Characteristics of respondents.

Variable	Sub-Category	Frequency	Percentage
Sex	Male	64	44.1%
	Female	81	55.9%
Age (years)	Less than 20	-	-
	20–30	31	21.4%
	31–40	64	44.1%
	41–50	33	22.8%
	Above 50	17	11.7%
Education	High school	4	2.8%
	Diploma	20	13.8%
	Bachelor’s	85	58.6%
	Master’s	36	24.8%
	Ph.D.	-	-
Years of experience	Fewer than 5	11	7.6%
	5–10	57	39.3%
	11–20	50	34.5%
	More than 20	27	18.6%
Number of dependents (e.g., children, older adult family members)	None	24	16.6%
	1–3	69	47.6%
	More than 3	52	35.9%
Is your workplace adequately equipped with work from home supporting technologies?	Yes	118	81.4%
	No	12	8.3%
	Maybe	15	10.3%
How frequently do you use work from home technologies?	Rarely	65	44.8%
	Often	58	40%
	Every workday	22	15.2%
Is there enough space available at your home to work from home?	Yes	132	91%
	No	13	9%
Distance between your home and work office/site, in kilometers	Fewer than 10	24	16.6%
	10–30	62	42.8%
	More than 30	59	40.7%

presents the analysis of the demographic data.

4.2.2. Descriptive Analysis, Reliability, and Validity Tests

A strong reliability and validity were indicated based on the results of the confirmatory factor analysis (CFA) of the measurement model. Across all constructs—performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), environmental concern (EC), perceived risk (PR), trust in the system (TR), innovativeness (IN), emotional well-being (EW), cultural factor (CF), honesty of employees (HE), behavioral intention (BI), and actual use (AU)—the Cronbach’s α values exceeded the recommended threshold of 0.7 [67], which indicated a high internal consistency. Factor loadings (FLs) for all items exceeded the conventional threshold of 0.7 [71], indicative of strong associations with their respective constructs. Moreover, the average variance extracted (AVE) values for each construct surpassed the recommended threshold of 0.5 [72], demonstrating adequate convergent validity. The composite reliability (CR) values exceeded 0.7 [72] for all constructs and were higher than the corresponding AVE values, meeting the criteria for convergent validity. Meanwhile, the convergent and discriminant validity (AVE) was also achieved, which was more than 0.4 [73].

In summary, the measurement model satisfies established benchmarks for internal consistency, factor loadings, AVE, and CR, affirming the reliability and convergent validity of the constructs in representing their underlying relationships, as per

Table 6. Measurement model results.

Constructs	Items	Cronbach’s α	FLs	AVE	CR
PE	PE1	0.933	0.889	0.839	0.913
	PE2		0.925
	PE3		0.907
EE	EE1	0.951	0.909	0.873	0.954
	EE2		0.955
	EE3		0.939
SI	SI1	0.860	0.826	0.758	0.862
	SI2		0.913
FC	FC1	0.926	0.809	0.816	0.930
	FC2		0.943
	FC3		0.951
EC	EC1	0.950	0.912	0.864	0.950
	EC2		0.933
	EC3		0.943
PR	PR1	0.898	0.794	0.751	0.900
	PR2		0.941
	PR3		0.858
TR	TR1	0.975	0.990	0.954	0.976
	TR2		0.944
IN	IN1	0.949	0.937	0.863	0.950
	IN2		0.884
	IN3		0.964
EW	EW1	0.972	0.96	0.946	0.972
	EW2		0.985
CF	CF1	0.907	0.887	0.707	0.905
	CF2		0.914
	CF3		0.77
	CF4		0.782
HE	HE1	0.962	0.945	0.928	0.962
	HE2		0.981
BI	BI1	0.855	0.915	0.752	0.858
	BI2		0.816
AU	AU1	0.908	0.961	0.837	0.911
	AU2		0.866

.

4.2.3. Structural Model Analysis

The model fit analysis is represented in

Table 7. Model fitness indices.

Test Indices	Description	Accepted Fit	Reference	Results	Model Fit Verification
CMIN/df	Chi-square to the degree of freedom	≤3.00	[74]	2.023	Acceptable fit
RMSEA	Root means square error of approximation	<0.08	[75]	0.078	Acceptable fit
SRMR	Root means squared residual	<0.1	[76]	0.0391	Acceptable fit
IFI	Normative fit index	≥0.90	[75]	0.933	Acceptable fit
TLI	Tucker–Lewis’s index	≥0.90	[77]	0.914	Acceptable fit
CFI	Comparative fit index	≥0.90	[78]	0.931	Acceptable fit

, encompassing a variety of fit indices, and it affirms the adequacy of the hypothesized model in accurately capturing the relationships within the observed data. The chi-square to the degree of freedom ratio (CMIN/df) stood at 2.023, falling within the acceptable range (less than 3.00) and indicating a favorable relative goodness of fit. The root mean square error of approximation (RMSEA) was recorded at 0.078, below the recommended threshold of 0.08, suggesting a reasonable fit with the data. Furthermore, the root mean squared residual (SRMR) at 0.0391, the normative fit index (IFI) at 0.933, the Tucker–Lewis’s index (TLI) at 0.914, and the comparative fit index (CFI) at 0.931 all exceeded their respective thresholds (0.1, 0.90, 0.90, and 0.90, respectively), indicating a satisfactory to good fit of the model. These actual values collectively provided robust evidence supporting the reliability and validity of the model, affirming its appropriateness in accurately representing the relationships among the variables in the studied context.

The structural model results and hypothesis tests revealed substantial insights into the relationships among constructs (see

Table 8. Structural model results.

Hypothesis		Standardized Path Coefficient (Beta)	t-Value	Sig. (p < 0.001)	Hypothesis Testing Result
H1	PE⟶BI	0.808 ***	16.406	0.000	Supported
H2	EE⟶BI	0.849 ***	19.252	0.000	Supported
H3	SI⟶BI	0.677 ***	11.005	0.000	Supported
H4	FC⟶AU	0.735 ***	12.948	0.000	Supported
H5	EC⟶BI	0.814 ***	16.738	0.000	Supported
H6	PR⟶BI	0.129	1.561	0.121	Not supported
H7	TR⟶BI	0.534 ***	7.548	0.000	Supported
H8	IN⟶BI	0.705 ***	11.874	0.000	Supported
H9	EW⟶BI	0.849 ***	19.176	0.000	Supported
H10	CF⟶BI	0.849 ***	19.247	0.000	Supported
H11	HE⟶BI	0.829 ***	17.719	0.000	Supported
H12	BI⟶AU	0.899 ***	24.481	0.000	Supported

*** p < 0.001.

and Figure 3). The analysis supported the majority of hypotheses, indicating significant relationships between constructs. Specifically, PE, EE, SI, FC, EC, TR, IN, EW, CF, HE, and BI all demonstrated strong positive associations. Notably, these relationships were supported by standardized path coefficients with high t-values and significant p-values (p < 0.001). However, hypotheses concerning PR (H6) were not supported, indicating no significant relationships in these instances. In summary, the structural model affirmed most hypothesized connections, providing valuable insights into the factors influencing behavioral intention and actual use.

4.3. Empirical Analysis and Findings—Technologies Required

This section addresses G3 of the research as follows:

As per

Table 9. List of technologies.

Technology	Important to Use	Not Important
Video conferencing apps (Zoom, Teams, Google Meet, etc.)	100%	0%
High-speed internet	97%	3%
Management software	94%	6%
Virtual private network (VPN) client	96%	4%
Laptop computer	99%	1%
Smartphone and/or tablet	91%	9%
Instant message (IM) software	78%	22%
E-mail	98%	2%
Unmanned drones/robots	34%	66%
CCTV	54%	46%
Document submittal platform	88%	12%
AI	70%	30%
Metaverse	59%	41%
Autonomous calibration tools	59%	41%
Geographical Information Systems (GIS)	86%	14%

, the analysis of technologies crucial for technical jobs to WFH underscores the fundamental importance of video conferencing apps (100%), high-speed internet (97%), and laptop computers (99%), with unanimous agreement on their significance. These technologies form the core infrastructure for seamless communication and task execution. Management software (94%), VPN clients (96%), and document submittal platforms (88%) are widely recognized as essential tools, facilitating efficient collaboration and secure access to organizational resources. While instant message software (78%) and e-mail (98%) continue to play crucial roles in communication, emerging technologies like artificial intelligence (AI) (70%) and GIS software (86%) are acknowledged by a substantial percentage, suggesting an increasing integration of innovative solutions for automation and spatial analysis in technical tasks. Interestingly, unmanned drones/robots (34%), CCTV (54%), metaverse (59%), and autonomous calibration tools (59%) exhibit varying degrees of importance, indicating a nuanced landscape where specific technologies may find application in certain technical scenarios. Overall, the diverse set of technologies underscores the complexity and adaptability required for effective remote work in technical domains.

An additional list of technologies suggested by employees during the survey included a diverse range of tools and solutions such as cloud computing, fault detectors for overhead lines (OHL) and cables, the adoption of augmented reality (AR) and mixed reality, distribution management systems (DMSs), distraction-drowning applications, and employee services for ensuring a supportive environment for remote workers. The diversity of ideas reflected a comprehensive exploration of technologies that could contribute to the efficiency and effectiveness of remote work.

5. Discussion

The focus group session was conducted to prepare a list of factors that affected the technical job employees’ acceptance of WFH technologies. The findings from the focus group discussion served as input for the subsequent steps, which comprehensively analyzed all UTAUT factors, original and additional, using the generated hypotheses and questionnaires. A pilot study was conducted to check on technical job employees in an electricity transmission and distribution utility; the results indicated that all factors met the acceptable minimum threshold for Cronbach’s Alpha. The questionnaire was distributed and received 145 responses, with a response rate of 48.33%. Demographic analysis revealed a diverse sample with 44.1% male and 55.9% female participants. Age-wise, the respondents were spread across various categories, with the majority in the 31–40 age range. The educational backgrounds were diverse, with 58.6% holding a bachelor’s degree. Professional experience ranged widely, ensuring a comprehensive understanding of work-related contexts. Around 81.4% reported workplaces adequately equipped for remote work, and 91% had enough space to WFH, suggesting a favorable environment.

The structural model results and hypothesis tests demonstrated significant relationships between various constructs, including performance expectancy, effort expectancy, social influence, facilitating conditions, environmental concern, trust in the system, innovativeness, emotional well-being, cultural factors, honesty of employees, and behavioral intention. These relationships were supported by standardized path coefficients with high t-values and significant p-values (p < 0.001). This may lead the organizations to focus on these factors to improve and smooth the implementation of WFH among their employees. For instance, for management to ensure their employees understand the expected productivity outcome of WFH, efforts should be made to encourage providing user-friendly technology platforms with the necessary training and support to employees, which may reduce the efforts required for remote tasks, ensure the availability of access to resources and tools among the employees for a smooth process, address the effects of WFH on the environment to encourage employees and management toward this concept, and build trust and confidence in online systems and processes by implementing robust cybersecurity system. For the three newly added factors, “honesty of employees”, “emotional well-being”, and “cultural factors’, it was concluded that understanding the impacts of cultural factors allows organizations to develop strategies that align technology initiatives with the cultural dimensions of their workforce, promoting smoother adoption and integration. Recognizing the importance of honesty emphasizes the need for trust-building measures. Organizations can focus on transparent communication, ethical practices, and fostering a culture of integrity to enhance trust in the technology adoption process. Implementing employee well-being programs becomes critical. These programs can contribute to a positive work environment, fostering employee satisfaction and, consequently, positive attitudes toward technology adoption. Hence, organizations prefer to invest in training and education programs that equip employees with the necessary skills and competencies, addressing any gaps identified in the context of emotional well-being, cultural awareness, and honesty among their employees. By focusing on these factors and addressing the significant relationships identified in the structural model results, organizations can enhance the implementation and effectiveness of this type of work, leading to improved productivity, employee satisfaction, and organizational success in this environment.

However, the hypothesis concerning perceived risk was not supported. This indicated that there was no significant negative relation between the perceived risk and the behavior intention and that the respondents did not feel risky while using WFH technologies. This meant that the lack of a negative relationship suggested that perceived risk may not be a significant barrier to technology adoption in this context. It suggested that organizations and policymakers may not need to prioritize addressing perceived risks in this organization as a primary concern when implementing WFH technologies, probably due to the availability of high security in their system. Instead, efforts may be better directed toward focusing on other factors to support mechanisms for WFH technologies to further encourage their adoption and usage among employees.

Moreover, the analysis of technologies essential for remote technical jobs highlights a list of technologies essential to WFH including video conferencing apps, high-speed internet, and laptop computers as unanimous necessities for seamless communication and task execution. Core infrastructure also includes widely recognized tools such as management software, VPN clients, and document submittal platforms, ensuring efficient collaboration and secure access to organizational resources. Traditional communication tools like instant messaging software and e-mail maintain crucial roles, while emerging technologies such as AI and GIS indicate a growing integration of innovative solutions for automation and spatial analysis. This diverse set of technologies emphasizes the complexity and adaptability required for effective remote work in technical domains.

6. Research Implications

The significant relationships identified in the structural model and hypothesis tests offer several important research implications. First, the strong associations between performance expectancy, effort expectancy, social influence, facilitating conditions, environmental concern, trust in the system, innovativeness, emotional well-being, cultural factor, honesty of employees, and behavioral intention provide a comprehensive understanding of the factors influencing the adoption of work from home (WFH) technologies in the context of electrical transmission and distribution. These findings can guide organizations in prioritizing interventions and strategies to enhance employee acceptance and utilization of remote work tools. Additionally, the non-supported hypotheses related to perceived risk highlight the need for further investigation into the factors contributing to perceived risks and how they impact the adoption process.

Moreover, the analysis of essential technologies for remote technical jobs underscores the critical role of video conferencing apps, high-speed internet, and laptop computers in facilitating seamless communication and task execution. The widespread recognition of management software, VPN clients, and document submittal platforms indicates the industry’s reliance on these tools for efficient collaboration and secure access to resources. The integration of emerging technologies such as AI and GIS suggests a progressive shift toward innovative solutions for automation and spatial analysis. These implications emphasize the dynamic and multifaceted nature of technology adoption in technical domains, urging researchers and practitioners to stay abreast of technological advancements and their implications for remote work practices.

The addition of emotional well-being, cultural factor, and honesty of employees as new factors to the UTAUT framework introduces significant implications for research in the context of technology adoption, particularly in the domain of working from home (WFH) technologies within the electrical transmission and distribution industry.

7. Conclusions

This study was conducted to address and deliver the aim of this paper to identify the most influential factors affecting the inclination of technical job employees to embrace WFH technologies. To achieve this, six factors were derived from the literature and subsequently validated by experts from the utility sector. Additionally, during a focused group session, an extra factor was introduced, which was “honesty of employees”, in addition to two factors used for the first time in the UTAUT, “emotional well-being” and “cultural factor”, as per the review conducted. These identified factors were further integrated into the existing UTAUT framework to test the structural model. For each construct a hypothesis was produced, and a questionnaire was established and distributed to the targeted employees in the utility sector, where the structural model results showed significant relationships among the following constructs: “performance expectancy”, “effort expectancy”, “social influence”, “facilitating conditions”, ”environmental concern”, ”trust in the system”, ”innovativeness”, ”emotional well-being”, ”cultural factor”, ”honesty of employees”, and ”behavioral intention”. The hypothesis related to “perceived risk” was not supported. Moreover, a list of technologies vital for remote technical jobs was provided including video conferencing apps, high-speed internet, and laptop computers as being indispensable for seamless communication and task execution. Additionally, the core infrastructure included well-established tools like management software, VPN clients, and document submittal platforms, ensuring streamlined collaboration and secure access to organizational resources. While conventional communication tools such as instant messaging software and e-mail play pivotal roles, the integration of emerging technologies like AI and GIS signals a growing trend in innovative solutions for automation and spatial analysis. This array of technologies underscores the intricate nature and adaptability necessary for effective remote work in technical domains.

This study supports SDG 8: Decent Work and Economic Growth, by increasing understanding of the factors affecting the acceptance of technology among technical workers when working remotely. By providing insights into the acceptance of remote work technologies and their impacts on organizational sustainability and employee well-being, this research contributes to the promotion of decent work practices and economic growth. Additionally, the identification of new factors and essential technologies for remote work offers actionable insights for organizational managers seeking to implement effective remote work strategies, thus promoting a healthy work–life balance for employees and better preparing organizations for future pandemics. Aligned with the aims of the special issue [2], this research provides concrete answers and solutions to key challenges in adapting professional practices in the utility sector to the post-pandemic world. Identifying critical factors and technologies that influence remote work in the utility sector, this study contributes to the ongoing discourse on enhancing digital literacy, promoting inclusive and sustainable work practices and addressing the diverse needs of workers in a rapidly changing environment. This alignment underscores the relevance of the work to the special issue and highlights its broader impact on fostering a resilient and adaptive professional landscape in the wake of the COVID-19 pandemic.

This research endeavor serves as a critical contribution to bridging an existing knowledge gap in the literature. It equips relevant enterprises, at both the employee and managerial levels, with essential insights into the key determinants impacting technical job employees during their WFH experiences and provides a list of technologies they may use. This newfound understanding holds the potential to inform strategic planning within organizations, particularly in the context of electricity utilities, as they navigate the future landscape of work. Future research could benefit from an extended time frame, allowing for a more extensive and diverse dataset. Future studies should consider incorporating a broader range of industries and sectors. Moreover, future work could employ longitudinal studies to track the evolving dynamics of remote work adoption over time, providing a more nuanced understanding of the sustained impact of technological changes. Combining these recommendations with an expanded sample size and a more diverse industry focus would contribute to a richer and more comprehensive exploration of technology adoption in remote work contexts.