Flexibility in work location has become a demand in the shifting world of work, but the experience remains precarious across gender lines. Technology is a significant enabler in remote working, and therefore adoption of new technology plays a role in facilitating more engagement. This article considers technology adoption and the influence of inclusion on workers’ level of engagement.
Design/methodology/approach
A cross-sectional empirical survey was conducted in a shipping company based in South Africa, and data were analysed using the partial least squares structural equation modelling approach. In addition to gender, the authors also considered the effect of worker location.
Findings/results
Different experiences for men and women, and those on-site as opposed to those working in a hybrid way, were observed. The study validated the relationship between technology adoption and workers’ engagement and confirmed the experience of inclusion as a moderating trend. It furthermore contributes insights into gender differences in experience as well as differences in remote and on-site work arrangements.
Practical implications
In a male-dominated industry like the maritime industry and shipping, attention needs to be given to gender differences and how to create more inclusive and enabling work environments.
Originality/value
Crucially, the research highlights gender-specific dynamics, finding that job satisfaction fully mediates the technology-engagement relationship for women, while resources like autonomy and support provide a stronger moderating boost to their engagement compared to men.
remote worktechnology adoptionemployee engagementinclusionjob satisfactionshippingFunding information This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.Introduction
The evolution of remote work is reshaping the global work landscape, with flexible work models increasingly viewed as the norm among professionals, including in South Africa (Leonardi et al., 2024; Michael Page International, 2024). The trend towards remote working is primarily driven by rapid technological advancements that enable organisations to redesign and transform their work processes and allow employees the flexibility of choosing when, where and how they work (Demerouti et al., 2014). This shift highlights a pressing need for transformation within organisations, particularly in fostering inclusive work environments that address gender disparities prevalent in male-dominated industries (Ryals, 2023). Motivated by the continued need for autonomy and flexibility, emphasis on inclusion in the new world of work and desire to save time previously spent commuting, this study was undertaken. Previous research has established technology adoption and engagement among workers as critical factors for successful remote and hybrid work environments. Research indicates that 65% of workers globally are not engaged (Keating & Heslin, 2015). Engagement is the extent to which people are emotionally connected to their work (Kahn, 1990), understand and believe in their organisational goals (Rich et al., 2010) and display an enthusiastic, positive attitude and determination to perform (Chughtai & Buckley, 2011). More specifically, work engagement is defined as a positive, fulfilling, work-related state of mind that is characterised by vigour, dedication and absorption (Schaufeli et al., 2019). However, organisations are facing additional challenges in keeping their employees engaged (Wang et al., 2021), especially when people work remotely (Bocean et al., 2021), despite remote work being positively received by more organisations and employees (Ozimek, 2020). Technology adoption has played a pivotal role in enabling remote work and has been a driving force behind increased levels of engagement (Urbaniec et al., 2022). Technology adoption is the acceptance and use of emerging technologies to simplify work processes and communication (Salahshour et al., 2018; Tenakwah et al., 2022), including in remote work settings (Wang et al., 2021). The characteristics influencing technology adoption include usefulness, ease of use, belief in technology for work improvement (Tenakwah et al., 2022), individual choices, institutional drive and the social context(s) in which one uses technology (Yi et al., 2006). Even though Mohanty et al. (2022) found that technology adoption significantly impacts user satisfaction and engagement, Seberini et al. (2022) and Yi et al. (2006) indicate that the extent to which emotional factors determine technology adoption is still unclear. Nevertheless, the literature presents varying perspectives on the relationship between technology adoption and workers’ level of engagement in remote work settings (Singh & Verma, 2020). Therefore, it is essential to understand how technology adoption influences engagement when people work remotely. To understand the relationship, we posit the moderating role of the experience of inclusion because technology adoption creates social inclusion in remote work settings (Deepa et al., 2023), fostering a sense of inclusion and collaboration (Syed et al., 2020). The lockdown during the coronavirus disease 2019 (COVID-19) pandemic demonstrated that when people have no alternative to communicate, socialise or work, they will adopt technology (Tenakwah et al., 2022) to suppress the need for emotional support and a sense of community (Seberini et al., 2022; Wang et al., 2021). The experience of inclusion is the extent to which group or organisation members feel engaged with, valued by and free to express their opinions and be their authentic selves within that environment (Ferdman et al., 2010). Tang et al. (2015) identified two dimensions of inclusion: inclusive practices by organisations and the individual perceptions of inclusion by the employees. Therefore, the primary objective of this study was to advance knowledge by refining the understanding of the relationship between technology adoption and worker engagement in remote and hybrid work settings, as influenced by the experience of inclusion. Specifically, the study aimed to explain the complexities and nuances of this relationship by adding several control variables, following an exploration of the literature. These controls included job satisfaction, resistance to change, work-life interference and gender. This article contributes towards the understanding of worker engagement in remote working by explicating the nuances which explain how technology adoption influences engagement. Firstly, it incorporates psychological motivational controls like resistance to change and job satisfaction, which have been confirmed to influence engagement (Tang et al., 2015). Resistance to change also hampers technology adoption (Tenakwah et al., 2022), and when people feel excluded, they are more likely to resist change (Chawla & Kelloway, 2004). Scholars have also suggested that when workers feel included, job satisfaction is improved among remote workers (Meng et al., 2022) and the enhanced autonomy remote working offers also impacts levels of satisfaction (Jamal et al., 2023; Martin et al., 2022). However, loneliness may dilute that satisfaction (Sundermeyer, 2025). The understanding of the extent to which these emotional and motivational factors determine technology adoption and engagement, therefore answers calls in the remote working and engagement literature (Seberini et al., 2022; Yi et al., 2006).
Secondly, advancing the understanding of the distinct experiences of men and women, particularly within the context of a male-dominated industry. Previous studies have highlighted gender as a moderating factor in workplace behaviour (De Laat, 2023), emphasising the necessity for a deeper understanding of the gender dynamics influencing work design. Studies in remote working contexts suggest that social interaction styles and preferences across gender identities may differ; however, this has not yet been concluded (Deepa et al., 2023). Finally, clarifying the role of inclusion. Existing suggestions are that technology adoption can indeed create social inclusion in remote work settings (Deepa et al., 2023), but do not explicate how this may be influenced. By investigating the influencing role of worker’s experience across gender identities as well as work modality, this article makes a contribution. The study was located within the shipping industry, at a Port operation in South Africa. Shipping is pivotal to the world’s economy as it can facilitate the flow of goods within a globalised community (Ryals, 2023). It is an important industry to study because (1) it has not been immune to changes in the world of work and (2) the labour market in the merchant shipping industry remains overwhelmingly represented by the male gender (Ryals, 2023; Wu et al., 2017). Even though the influx of women in the managerial sector of the maritime domain has substantially increased over the last decade, the maritime sector is still considered as a male-dominated working environment (Ryals, 2023). This presented a unique opportunity to conduct research in the industry. Two aspects were particularly interesting: (1) the shifting world of work and specifically remote working and (2) employees’ experience of inclusion in a predominantly male-dominated industry. In summary, by exploring the influence of resistance to change, job satisfaction, gender and inclusion, this article provides specific, contextual insights crucial for leaders and employers aiming to create inclusive and effective remote and hybrid workplaces that align with modern worker expectations for autonomy and flexibility.
Literature review
Remote work is a practice that allows an employee to work from home or elsewhere instead of from an organisation’s usual place of business. This working arrangement and work modality differ from the hybrid work model that enables an employee to work some days at home and some days in the office. Although the COVID-19 pandemic popularised remote work, the trend towards this form of work is primarily driven by rapid technological advancements that enable organisations to redesign their work processes and allow employees the flexibility of choosing when, where and how they work (Demerouti et al., 2014).
Remote work
Remote working is:
[A] flexible work arrangement whereby workers work in locations, remote from their central offices or production facilities, and the worker has no in-person contact with co-workers but can communicate with them using technology. (Wang et al., 2021, p. 15)
Remote working is also referred to as new ways of working (Demerouti et al., 2014), working from home, teleworking, virtual working, telecommuting, flexible work, mobile working, agile work or working from anywhere (Bocean et al., 2021). Although certain challenges have been identified, remote working holds benefits to both workers and employers. Among those are cost savings for companies on rental expense (no offices or reduced space), flexibility for both employees and organisations, cost savings for employees on transportation and office attire, fewer office conflicts, more efficiency, increased productivity with fewer office interruptions, better work-life balance and a better work lifestyle, which results in a more fulfilling life as employees experience more time at home and with their families (Nicholas, 2014). Moreover, the benefits of remote working extend beyond organisations and the employees; the environment benefits from less commuting and thus reduced carbon emissions (Bocean et al., 2021). But irrespective of the work modality, all organisations are interested in performance. Engagement is an important outcome for organisations as it has been positively linked to performance (Saks, 2019).
Work engagement
Engagement has become a priority for organisations and has been linked to performance, customer satisfaction and loyalty, organisational citizenship behaviour, innovation and positive financial turnover (Chughtai & Buckley, 2011; Tanwar, 2017). When employees are not engaged, they isolate themselves and block their mental and emotional energies, resulting in automatic responses, lack of work ownership and reduced proactivity (Rich et al., 2010), which affect organisational output (Ferdman et al., 2010). Conversely, engaged employees are satisfied with their job, have a deep connection with their organisation and actively contribute to the progress and innovation of the company (Keating & Heslin, 2015).
Self-determination theory (SDT) has been positioned as a consolidating framework to capture the various dimensions of workers’ level(s) of engagement. Self-determination theory emphasises the importance of intrinsic and extrinsic motivators, which should not be overlooked in engagement (Ryan & Deci, 2020). Revisiting the antecedents and consequences of workers’ engagement, Saks (2019) posited that skill variety of workers, variance in social support, reward and recognition, fairness and opportunity for growth are important. As the world of work keeps shifting, workspace design (Gagné et al., 2022), social interaction (Deepa et al., 2023), individual wellness and flexibility in time, space and digital tools for work are critical (Zapata et al., 2024). In the context of remote working, researchers found links between levels of worker engagement and several variables that may influence organisational outcomes, such as job satisfaction, resistance to change (Chawla & Kelloway, 2004; Tang et al., 2015) and work-home interferences (Wang et al., 2021). Resistance to change has been widely mentioned as one of the main reasons for the failure of organisational changes such as the adoption of new processes (Chawla & Kelloway, 2004) and new technology, especially when the user is not the chooser (Tenakwah et al., 2022).
Technology plays a key role in enabling remote work, but users must adopt and engage with these technologies. Engagement and technology adoption play crucial roles in organisational success (Chughtai & Buckley, 2011; Singh & Verma, 2020) when employees work remotely (Ter Hoeven et al., 2016) and introducing technology presents the opportunity to influence engagement (Palumbo et al., 2022).
Technology adoption influences engagement
Increased engagement has been linked to employee adoption of technology, a factor that plays a pivotal role in enabling remote work (Urbaniec et al., 2022). Technology adoption is the acceptance and use of emerging technologies (Salahshour et al., 2018) to simplify work processes and communication (Tenakwah et al., 2022), including in remote work settings (Wang et al., 2021).
Technology adoption found its roots in the technology adoption model (TAM) (Venkatesh et al., 2003) which reports behavioural intentions fostered by two central beliefs: perceived ease of use and perceived usefulness (Venkatesh et al., 2003). Perceived ease of use refers to the extent to which new technology is seen as easy to learn and use, while perceived usefulness relates to how technology enhances productivity and performance (Tenakwah et al., 2022; Venkatesh et al, 2003). Technology adoption model was further revised into TAM2 to include subjective norm(s), adapted from the theory of planned behaviour (TPB) (Venkatesh et al., 2003), and TAM3 to address the shortcomings of the emotional variables assumed as being essential to the model (Salahshour et al., 2018). The theory was eventually refined into the unified theory of acceptance and use of technology (UTAUT), which has become one of the most used models to understand and predict technology adoption and usage behaviour (Taherdoost, 2018). While researchers have found a positive relationship between workers’ level(s) of engagement and technology adoption, some have posited a negative relationship (Singh & Verma, 2020), suggesting that there is no significant relationship. The first hypothesis of the current research was therefore articulated as follows:
H1: Technology adoption is significantly related to engagement.
Although Mohanty et al. (2022) found that while technology adoption significantly impacts user satisfaction and engagement, the extent to which emotional factors determine technology adoption is still unclear (Seberini et al., 2022; Yi et al., 2006). To this end, two factors were considered to explore the emotional experience, inclusion as suggested by Syed et al. (2020) and job satisfaction (Locke, 1969). As the nature of the context being studied presented an element of male domination which may lead to feelings of exclusion, we decided to turn our attention to employee experiences of inclusion and thereafter controlled for job satisfaction.
Employee experience of inclusion
The lockdown during the COVID-19 pandemic demonstrated that when people have no alternative means to communicate, socialise or work, they will adopt technology (Tenakwah et al., 2022; Wang et al., 2021) to address the need for emotional support and a sense of community (Seberini et al., 2022), fulfilling humans’ natural needs for autonomy, inclusion, relatedness, belongingness, competence and close relationships which are described by the SDT (Van den Broeck et al., 2016). Technology adoption, therefore, creates social inclusion within remote work settings, bringing employees and stakeholders closer and fostering a sense of inclusion and collaboration (Syed et al., 2020). Research has shown that technology adoption positively influences the experience of inclusion (Van Zoonen & Sivunen, 2022), as employees who adopt technology for remote work report higher levels of inclusion, satisfaction and sense of belonging to their workgroups (Tanwar, 2017; Ter Hoeven et al., 2016). The experience of inclusion is the extent to which group or organisation members feel engaged with, valued by and free to express their opinions and be their authentic selves within that environment (Ferdman et al., 2010). Research understood inclusion on two dimensions: inclusive practices by organisations and the individual perceptions of inclusion by the employees (Tang et al., 2015). In this study, the interest was centred on the employees’ experiences while working remotely and therefore the focus was on the individual experience of inclusion. Mor Barak (2015) posited that inclusion in the context of traditional or remote work settings refers to the sense of being part of the whole organisation system, in both formal processes (access to information and decision-making channels) and informal methods (social workplace networks for informal information and decision sharing). When remote workers are virtually connected, they feel included in the overall organisation (Keating & Heslin, 2015). Scholars suggest that such an experience of inclusion improves teamwork and collective benefit (Chung et al., 2020), job satisfaction, effective communication and organisational engagement among remote employees (Meng et al., 2022). Technology adoption contributes to social and professional inclusion (Syed et al., 2020) and therefore, in this study, the second hypothesis was articulated as follows:
H2: The experience of inclusion moderates the relationship between technology adoption and engagement.
Job satisfaction to control for the emotional experience
In remote working experiences, accepting technology that enables connection may create the experience of inclusion, and this, in turn, improves job satisfaction (Meng et al., 2022). Job satisfaction describes a pleasurable emotional state resulting from the appraisal of one’s job as achieving one’s values (Locke, 1969) and a positive attitude and motivation that workers experience towards their jobs (Mohammed et al., 2022). Remote working has been associated with higher levels of job satisfaction because of increased flexibility and autonomy, reduced commuting time and work-life management (Jamal et al., 2023; Martin et al., 2022). However, working remotely may also create a sense of disconnection from work and social relations which has been shown to reduce job satisfaction (Caranto et al., 2020; Davidescu et al., 2021) and over the long-term, higher levels of satisfaction may drop because of loneliness (Sundermeyer, 2025) and technostress (Seberini et al., 2022). Job satisfaction was therefore included in the model to control for the effect of technology adoption (TA) on engagement.
Work-home interferences to control for levels of engagement
Work-home interferences have long been creating stress in many individuals, and this is because the conflicts of the role pressures from work and home are at some point incompatible (Carlson et al., 2000). Work-home interference has been found to be challenging in remote work (Wang et al., 2021). Moreover, researchers suggest that this challenge harms employees’ efficiency, effectiveness (Kim et al., 2019), well-being and engagement (Wang et al., 2021). Work-home interference was therefore included in the model to control for the effect of TA on engagement.
Resistance to change to control for technology adoption and engagement
Tenakwah et al. (2022) posited a relationship between resistance to change and technology adoption. Resistance to change pertains to attitudes or behaviours displayed by individual employees or groups that prevent organisations from achieving their change goals (Chawla & Kelloway, 2004). Disengaged employees, or those experiencing exclusion in the organisation and workgroups, might resist changes (Chawla & Kelloway, 2004) and lower their performances (Oreg, 2018). Furthermore, resistance to change could affect technology adoption because of satisfaction with existing working methods (Syed et al., 2020). Resistance to change was therefore included in the model to control for the effect of TA on engagement. Building on these initial reflections about technology adoption in remote work, and the limited scope of this study, the primary aim was to investigate the relationship between perceived usefulness and ease of use of technology, and how these factors predict engagement in remote settings. In this exploration, the role of inclusion was considered, while various aspects were controlled for. Given the nature of the industry, being predominantly male-dominated (Ryals, 2023), we also controlled for gender in the sample. While multiple options to select were presented in the survey, all employees in this data set selected either male or female as their gender. We will therefore refer to gender in this article on the basis of a male or female classification. This conceptual problem is presented in Figure 1.
Conceptualising the research problem.
MethodologySample profile and data collection
Maritime transport is a pillar of the global economy, with over 90% of world trade being carried by sea (Ryals, 2023), and 7% of the industry is represented by port operations and services. In port operations, which excludes seafarers, 16% of workers are women (IMO & WISTA, 2025). In a male-dominated industry like the maritime industry and shipping, it is necessary to pay attention to gender differences and how to create more engaging, inclusive and enabling work environments. The work context has shifted in the industry, and port logistics and operations are continuously being optimised because of technology (Presidential Commission on the Fourth Industrial Revolution, 2020). Linked hereto is a noticeable shift in the world of work context as more workers opt for greater flexibility, remote working being an example thereof. The study was conducted in the container shipping industry, specifically within a division of a multinational organisation. All employees who used technology to work remotely in a permanent or hybrid way were invited to participate. From the total staff complement of 237 employees, 193 responses were acceptable for analysis. The minimum sample size required for a partial least squares structural equation model (PLS-SEM) analysis is 200 (Hair et al., 2019), but strong findings have been reported with smaller samples (Marques et al., 2021). To control for common-method variance, the respondents’ anonymity was protected, and concise and simple items were used (Podsakoff et al., 2003). Additionally, common-method bias was analysed through the application of an exploratory factor analysis (Podsakoff et al., 2003). The data did not adjust to a single-factor model. It could not be concluded that common-method bias was not a major issue. The survey was completed during July 2023.
Measurement
For this study, a questionnaire was designed to collect data on demographics, and six scales were utilised to measure the variables of interest.
Dependent variable: Engagement
Engagement was measured using the shorter version of the Utrecht Work Engagement Scale (UWES) questionnaire. Although most researchers use UWES to measure work engagement, others prefer it to measure engagement (Shuck et al., 2017). The validated shorter version of the instrument has Cronbach’s alpha above 0.70 and consists of three items, for example, ‘At my work, I feel bursting with energy’ (Schaufeli et al., 2019, p. 588). This study reported a Cronbach’s alpha at a 95% confidence interval of 0.73.
Independent variable: Technology adoption
Technology adoption was measured using the UTAUT, which includes four main dimensions for technology adoption, namely, performance expectancy, effort expectancy, social influence and facilitating conditions (Venkatesh et al., 2003). The scale includes 14 items; however, in this study, only eight were used as the study interest revolved around two dimensions (performance expectancy and effort expectancy). The reason for this decision was to manage the risk of survey fatigue. When a survey is too long, mental fatigue and the risk of incomplete responses need to be managed (Sarstedt & Wilczynski, 2009). We were also interested in the individual participant’s perception of how technology influences performance and what effort the use of such technology required, which speaks to behavioural intention. We decided to drop the questions on social influence and facilitating conditions, as those questions had more to do with conditions relating to specific system usage. Social influence was about how others using a specific technology influence when someone accepts that specific technology. Given our research question, we wanted to understand social influence more in terms of how people feel included and therefore decided on the variable of inclusion. Questions relating to facilitating conditions are more reflective of use behaviour, exploring people’s intention to use a specific technology based on others’ behaviour or the technology’s appearance, which we did not deem critical for this research objective as we were not studying a specific technology use in this instance. Unified theory of acceptance and use of technology’s reliability was established by a Cronbach’s alpha of > 0.70 (Taherdoost, 2018; Venkatesh et al., 2003). Moreover, when measured for each dimension separately, Taherdoost (2018) found an alpha of > 0.8, which means that this scale has good internal consistency and has demonstrated discriminant validity with an average variance of 0.70. In the data analysis of this study, performance expectancy measured a Cronbach’s alpha of 0.83, effort expectancy was at 0.94 and overall technology adoption was at 0.85, at a 95% confidence interval.
Moderating variable: Experience of inclusion
The experience of inclusion is widely measured by Mor Barak inclusion-exclusion scale (MBIE) presented by Mor Barak and Cherin (1998), which focuses on three main dimensions: workgroup involvement, access to information and resources and influence in decision-making. This scale consists of 15 items and uses a 6-point Likert scale. The reliability of this measurement tool has been proven with a Cronbach’s alpha of 0.92 (Mor Barak & Cherin, 1998; O’Keefe et al., 2020). Examples of items included in the MBIE scale within different dimensions are:
Participation: ‘I feel part of informal discussions in my workgroup.’
Access: ‘I have all the equipment and resources I need to do my job.’
Influence: ‘I am able to influence decisions that affect my organisation.’
Control variable: Job satisfaction
The job satisfaction scale is an overall measure of the degree to which employees are happy or satisfied (Carlson et al., 2000). Following Dolbier et al. (2005), who confirmed the single-item (SI) measure, a single question was used, as job satisfaction was not the key variable in the study. The item, ‘Overall, how satisfied are you with your job?’ was used as confirmed valid by Jordan and Turner (2008, p. 242) and rendered a Cronbach’s alpha of 0.70. Job satisfaction remain one of the major constructs to apply the SI measure (Fakunmoju, 2021). In this study, the survey became long and our motivation to use the SI measure is grounded in the logic of response behaviour and practical terms of ‘costs, nonresponse, scale development, mental fatigue, and flexibility’ (Sarstedt & Wilczynski, 2009, p. 216), and that it is only a control variable (Boyd et al., 2005).
Control variable: Work-home interference
This study used a scale adapted from Carlson et al. (2000) to control for work-home interferences in the relationship between technology adoption and engagement. The scale’s reliability for measuring work-home interferences included three items and was validated by a Cronbach’s alpha of 0.9 (Kim et al., 2019). An example item on this scale is ‘My job or career interferes with my home life’. The data analysis presented reliability at 0.90.
Control variable: Resistance to change
This study used the instrument proposed by Giangreco and Peccei (2005) as a measuring scale for resistance to change. Originally the instrument had 13 items covering the two dimensions: pro-change and anti-change behaviours (Giangreco & Peccei, 2005). This instrument was revised to a 7-item scale with an overall Cronbach’s alpha of 0.82. The measuring instrument used a 5-point Likert scale. Examples of items in this scale include pro-change behaviour, ‘I am doing much more than is required of me to help this organisation through the numerous changes’, and anti-change behaviour ‘I am critical about the change in public discussions’ (Peccei et al., 2011).
Ethical considerations
Ethical clearance to conduct this study was obtained from the Stellenbosch University Social, Behavioural and Education Research (SBER) Ethics Committee (No. 28851).
Results
A representative sample rendered 52% identifying as female and 48% as male. No other gender classification was selected. A representative age distribution ranged from 18 to 65 with a majority of employees aged between 25 and 54 years old. With a predominantly white-collar worker profile in this organisation, education was distributed between high school (17%), first-level formal qualification (22%), bachelor’s degree (46%) and master’s degree (15%). The majority (52%) worked in a hybrid manner – that is, sometimes remotely and some days in office – while only 6% always worked remotely, and the rest always in office.
Partial least squares (PLS) analysis was performed using SmartPLS 2.3.4 to test the hypotheses (Hair et al., 2019). Partial least squares is suitable for early-stage research model construction as it allows for interaction between the theory, empirical data and small samples, including when SI scales are used, as was done for one variable in the study (Hair et al., 2019; Marques et al., 2021). A two-stage model was built to make it possible to test the measurement model assessing the construct, discriminant and convergent validity, and to confirm the structural model to test the hypotheses. The first stage of the PLS model confirmed the reliability and validity of the measurement model. To indicate the extent of variance because of the underlying variables, some authors recommend reliability greater than 0.5, and others suggest standardised loads more significant than 0.7 for each scale item (Hair et al., 2019). In the present study, the latter criterion was followed.
Table 1 presents the variables and their first-order factors, reporting on the reliability of relevant measures. Reliability was acceptable because the composite reliability coefficient exceeded 0.7 (Hair et al., 2019). The average variance extracted for ‘organisational inclusion’ reported lower than the suggested value of 0.5 (Hair et al., 2019), while the other constructs indicated adequate convergent validity. Considering the high composite reliability (0.92), no items were removed. Following recommendations by Hair et al. (2019), the removal of items should only be seriously considered when loading is lower than 0.4, and given that the constructs used in this study were all previously validated, it was decided to not remove items for fear of changing the meaning of the construct.
The internal consistency of measures.
Variables
Composite reliability
Average variance extracted
CR score
95% lower
95% upper
AVE score
95% lower
95% upper
Technology adoption
0.93
0.88
0.96
0.87
0.79
0.92
Organisational inclusion
0.92
0.90
0.93
0.45
0.40
0.51
Resistance to change†
0.86
0.82
0.89
0.53
0.47
0.59
Work-home interference
0.94
0.90
0.95
0.83
0.75
0.87
Engagement
0.85
0.79
0.90
0.65
0.56
0.74
, Items for ‘Resistance to change’ were reverse scored.
Heterotrait–monotrait ratios were used to investigate discriminant validity and were found to be acceptable. Table 2 shows overall high reliability. In the individual experience of inclusion scale, two items were found to be problematic. While the model was fitted with and without these items, the path coefficient results did not have a major impact on the overall results. For the items measuring resistance to change, which were all reverse scored, the same was observed. Here, two items were scored low: item 6, ‘I am critical about the changes in public discussion’, and item 7, ‘I support the actions of my colleagues against the change’. It is recommended that future users of the instrument review these measures before applying the scale.
The relationship between constructs and measures.
Latent variable
Loadings
Indicators
Engagement
0.802***
WE1
0.886***
WE2
0.727***
WE3
Work-home interference
0.892***
WHI1
0.913***
WHI2
0.931***
WHI3
Resistance to change
0.849***
RC1 (reversed)
0.864***
RC2 (reversed)
0.869***
RC3 (reversed)
0.644***
RC4 (reversed)
0.706***
RC5 (reversed)
0.234***
RC6
Job satisfaction
1.000
JS1
Organisational inclusion
0.744***
OI1
0.603***
OI2
0.742***
OI3
0.674***
OI4
0.323***
OI5 (reversed)
0.752***
OI6
0.823***
OI7
0.470***
OI8 (reversed)
0.794***
OI9
0.796***
OI10
0.640***
OI11
0.696***
OI12
0.616***
OI13
0.553***
OI14
Technology adoption
0.924***
Performance expectancy
0.941***
Effort expectancy
, significant at 1%.
In the structural model, the coefficient of determination was assessed for each construct to evaluate the predictive capacity of the structural model. The coefficient should preferably be higher than 0.25 (Hair et al., 2019). However, there are no cut-off guidelines for R2. Technology adoption was confirmed to significantly influence engagement p = 0.029 (path coefficient 0.205*). While the experience of inclusion showed a trend, it did not significantly impact engagement at 5% p = 0.126 (0.151). Similarly, for work-home interference, p = 0.015 (–0.132**). As predicted, resistance to change p < 0.001 (–0.241***) showed a negative impact on engagement. While the experience of inclusion did not significantly influence engagement, a moderating trend was observed. We therefore considered the experience of inclusion among the participants. As shown in the interaction graph (Figure 2), the blue line indicates that for people who measured low on organisational inclusion, there was a stronger relationship between technology adoption and engagement. Therefore, it appears that greater adoption of technology leads to an improvement in engagement. As the green line indicates, for people who experience a sense of inclusion, this relationship is of little to no significance.
Interaction graph: Moderating effect of organisational inclusion.
Having a reasonably equal distribution between male and female participants made it possible to examine the results along gender lines. Table 3 presents a summary of the main results and the influence of gender.
The probability of obtaining the observed result.
Source
Target
Beta female
Beta male
p-value†
Technology adoption
Engagement
0.05 (p = 0.613)
0.29 (p = 0.014**)
0.908*
Organisational inclusion
Engagement
0.01 (p = 0.923)
0.27 (p = 0.029**)
0.916*
Job satisfaction
Engagement
0.52 (p < 0.01***)
0.15 (p = 0.305)
0.023**
Resistance to change
Engagement
−0.29 (p < 0.01***)
−0.22 (p = 0.029**)
0.633
Work-home interference
Engagement
−0.15 (p = 0.165)
−0.15 (p = 0.083*)
0.509
, Note that the p-value column is a two-sided test. At 5%, p > 0.95 is also significant.
, 10%;
, 5%;
, 1%.
As shown in Figure 3a, the multigroup analysis path coefficient illustrates that the effect of technology adoption on engagement tends to disappear for women. A similar pattern was observed regarding the moderating influence of inclusion in Figure 3b. However, the opposite trend was noticed for job satisfaction and as illustrated in Figure 3c, it had a greater significance for women.
Finally, work arrangement was considered. For both, those based at the office permanently and those following a hybrid work model, greater adoption of technology was seen to lead to higher levels of engagement. However, the influence of inclusion was considered, as illustrated in Figure 4.
Multigroup analysis coefficient for work arrangement.
For workers who were always on-site, the more they felt included, the higher their level of engagement. Those working in a hybrid manner were less influenced by the experience of inclusion. The more included they felt, less engagement was measured. Furthermore, job satisfaction showed significant results and affected engagement to a greater extent for those working in a hybrid manner. Therefore, it appears that when workers are not always in the office, the higher the job satisfaction experienced, the more engaged they are. It was hypothesised that technology adoption is a significant predictor of engagement in remote work settings, and that this relationship may be moderated by the individual’s experience of inclusion. The study confirmed this hypothesis and added insights that this effect was not as significant for women as it was predominantly men who needed to adopt technology to feel engaged. The individual’s experience of inclusion did not significantly influence this relationship. However, for individuals measuring low on inclusion, a moderating trend was observed, showing a greater spike between technology adoption and engagement.
Discussion
Remote work is changing the world of work (Leonardi et al., 2024), and in the public domain, the perception is that a flexible work model is becoming an expected norm among professionals in South Africa (Michael Page International, 2022). Furthermore, transformation and creating inclusive work environments continue to be a focus, especially along gendered lines in male-dominated industries. We are mindful that our female sample in this study, representing 52% of the respondents, does not reflect the current state for female representation in the industry. For this specific organisation, however, that is the reality in terms of representation. Roscoe (1975) posited that for comparative analysis, a minimum of 30 responses should be included to allow a central limit theorem (CLT) to hold. In this study, the sample size was just adequate to run the multivariate data analysis. We appreciate that this may influence the generalisability of the findings and would need to be tested further. While technology adoption and engagement are well-established concepts, to understand and predict successful remote and hybrid working conditions, the study contributes more nuanced insights for future exploration. In remote working contexts, worker engagement is dependent on various factors including organisation policy, leadership style, appropriate technology applications and the extent to which people feel connected to work and each other (International Labour Organisation, 2020). While the study aligns with other work in confirming the relationship between technology adoption and engagement (Chughtai & Buckley, 2011; Ter Hoeven et al., 2016), there is a lack of consensus as others have posited no significant relationship or even a negative one (Kim et al., 2019; Singh & Verma, 2020). Deepa et al. (2023) have called for more research to understand the nuances of this relationship in the context of remote working. The first major contribution explicated psychological and motivational controls in the relationship between technology adoption and engagement in remote working, through the influence of resistance to change and job satisfaction. As expected, resistance to change was negatively related to engagement. However, resistance should not be feared as it can be a natural reaction that dissipates over time. Research has confirmed job satisfaction (Chawla & Kelloway, 2004; Tang et al., 2015) as a key variable that may influence outcomes during remote working. In this study, job satisfaction showed significant results and was found to affect engagement to a greater extent for those working in a hybrid manner. Thus, when a worker is not always in the office, they experience higher job satisfaction and are more engaged. It seems possible that the engagement of people who are always working on-site extends beyond the job. They may enjoy broader organisational aspects and interpersonal interaction that allow for more engagement, whether they like their job or not. However, Singh et al. (2022) warn that one should look out for technostress brought on by over-exposure to technology during remote working, which may impact job satisfaction and similarly job autonomy may influence well-being when working remotely (Winkler-Titus et al., 2025). A second contribution of the study is understanding the experiences of men and women. The gendered influence in a male-dominated industry has made it possible to highlight insights that warrant further scrutiny, with larger samples that are more representative of the actual state of gender in the industry and perhaps across industries. However, it was found that the significant effect of the relationship between technology adoption and engagement in a remote working context was not significant for women, and it is mostly men who need to adopt technology to feel more engaged. In past studies of workplace behaviour, gender had a moderating role, and De Laat (2023) encourages a greater understanding of the gender dynamics underlying work design. In this study, job satisfaction presented differently and had more significance for women; the more satisfied they were in their jobs, the more engaged they were. The gender lens on past studies of remote working also suggests that social interaction styles and preferences would be different for men and women. However, no significant difference was found in the relationship between limited social interaction and remote work effectiveness (Deepa et al., 2023). Finally, inclusion has not been studied extensively in the context of remote working (Zapata et al., 2024), although most studies emphasise social support and elements of loneliness experienced (Wang et al., 2021; Winkler-Titus et al., 2025). Remote working could produce a sense of exclusion and feeling disconnected from others (Byrd, 2022). In addition, Schertler et al. (2023), found that an increase in remote working led to lower feelings of belongingness. Kim and Dirks (2023) confirmed that employees’ level of support for their organisation’s remote working strategy was mediated by a feeling of inclusion. Technology adoption positively influences the experience of inclusion, as employees who adopted technology for remote working also reported greater experience of inclusion (Tanwar, 2017; Ter Hoeven et al., 2016; Van Zoonen & Sivunen, 2022). The third contribution of this study is related to the role of inclusion. While the experience of inclusion showed a moderating trend, it was not significantly correlated to engagement. This trend, however, is important in explaining the relationship between technology adoption and engagement: workers who measured low on the experience of inclusion had a greater spike between technology adoption and engagement. Therefore, if they adopted the technology, they also experienced greater levels of engagement. This result supports suggestions that technology adoption can create social inclusion in remote work settings (Deepa et al., 2023). However, unlike Deepa et al. (2023) who did not report differences in male and female experiences of inclusion, this study did and therefore opens an opportunity for further research. Furthermore, the study revealed a trend in the predominant work models. For workers always on-site, the higher their experience of inclusion, the more engaged they were measured, while those working in a hybrid manner were less influenced by the experience of inclusion. The more included these hybrid workers felt, the less engagement was measured. Inclusion for a remote worker involves ‘continuous connection on technology platforms’, and they may experience this as disruptive and negatively influencing their job satisfaction. Thus, location should be considered, and Dalessandro et al. (2024) found gender inequities in worker perceptions of workspace choice, encouraging further research exploring gender differences in remote working.
Implications for practice
Workers today expect some level of autonomy and flexibility (Wilson et al., 2024). Employers should therefore be mindful of creating inclusive workplaces considering different employees’ needs. Based on the findings of the study, elements critical to consider for remote and hybrid work modes are highlighted. Leaders could consider aspects like internet connectivity and infrastructure, as well as personal circumstances and caregiving responsibilities, based on practical observations about workers’ experiences when working from home (see Figure 5).
Employee insights on remote working.
In a developing context, logistics may pose a challenge to remote working, and therefore participants were asked whether they feel adequately equipped when working remotely. The majority (80%) responded ‘yes’, 14% ‘to some extent’ and only 6% did not feel equipped. A smaller percentage (19%) reported experiencing work-home conflict, and this was mostly ascribed to caregiving responsibilities of minors. Employees experiencing this conflict reported the following mechanisms to cope: time management, having a support system and managing a flexible schedule.
A preference was observed for flexibility, whereby workers want the option, if possible, to work remotely some days of the week. However, in a developing context, this may create a form of marginalisation where the same workers that tend to spend much time in commuting to the office may also be the ones who lack the infrastructure to work from home effectively. In this study, 34% of the respondents took up to an hour, 25% up to 2 h and 13% more than 2 h travelling to and from work. Therefore, it is suggested that in organisations’ policy, they must address how they will support workers in having appropriate workspaces that enable effective working and ergonomic health (Wilson et al., 2024). While technology is crucial, the findings emphasise the importance of job satisfaction, and feeling included, for workers to feel engaged. For example, people should be encouraged to spend time together while undertaking their in-person working days and maintain regular interaction with colleagues and teams (Wilson et al., 2024). For workers who experience a low level of inclusion, the right technology experience could help them feel more engaged. The recommendations include prioritising organisational support systems and fostering a sense of inclusivity while harnessing the power of job autonomy.
Conclusion
The continued need for autonomy and flexibility in the new world of work, specifically also saving time from commuting, served as motivation for this study. While working remotely, technology is an important enabler and being physically distanced places the idea of inclusion under the microscope. While the relationship between technology adoption and engagement in the context of remote and hybrid working has been established, the nuances in how this relationship unfolds need to be refined. This study validated the relationship between technology adoption and engagement. In explaining the nuances of the relationship, we posited the influence of resistance to change, job satisfaction and the different experiences of men and women. The importance of the posited relationship was found not to apply to females, as a significant relationship between technology adoption and engagement was only observed in the male participants. Furthermore, job satisfaction had a greater influence on female participants than on their male counterparts: the more satisfied they were in their jobs, the higher the measured engagement.
The study also provided insights into the influence of employees’ experience of inclusion on the relationship between technology adoption and engagement. While it has been established that technology adoption contributed positively towards experiences of inclusion and engagement, it was found that experiencing inclusion did not always significantly relate to engagement. In this study, the moderating role of inclusion was only found to be significant in the main relationship for those people measuring low on their experience of inclusion. Therefore, if they adopted the technology, they also experienced a greater level of engagement. This supports literature indicating that technology adoption can create social inclusion in remote work settings (Deepa et al., 2023). However, unlike Deepa et al. (2023) who did not report differences in male and female experience, this study did report such differences and has therefore opened an opportunity for further research. Furthermore, it is important to note work modality. For those workers permanently on-site, inclusion became a significant moderator, while for those working in a hybrid modality, job satisfaction had greater significance.
Limitations and future research opportunities
This study was conducted in a specific industry and could be replicated with a broader sample across different industries. In the individual experience of inclusion scale and the resistance to change scale, two items were found to be problematic. While the model was fitted with and without these items, the path coefficient results did not have a major impact on the overall results. The inclusion scale average variance extracted was below 0.5, and while it was explained in the results of this study, a further recommendation would be to conduct follow-up research replicating this study. It is also recommended that future users of the instrument review these measures before applying the scale. Furthermore, it was found that job satisfaction plays a key role when working remotely, and technology is important in work design. Future research could build on research (Gagné et al., 2022; Saks, 2019; Wang et al., 2021) to explore how work design and job satisfaction influence engagement in remote work contexts.
Acknowledgements
This article is based on research previously presented in abstract form at the Equity Diversity and Inclusion Conference, held in Seville, Spain from 27 May 2024 to 31 May 2024. The abstract has since been developed into a full manuscript, which has been expanded and revised for journal publication. This republication is done with permission from the conference organisers.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Natasha Winkler-Titus: Conceptualisation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. Neusa Feirreira-Marcelino: Conceptualisation, Formal analysis, Investigation, Project administration, Resources. Martin Kidd: Data curation, Formal analysis, Methodology,. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Data availability
The data that support the findings of this study are available from the corresponding author, Natasha Winkler-Titus, upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or the publisher. The authors are responsible for this article’s results, findings and content.
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How to cite this article: Winkler-Titus, N., Feirreira-Marcelino, N., & Kidd, M. (2026). Technology adoption and worker engagement in remote working: Insights on inclusion. South African Journal of Business Management, 57(1), a5455. https://doi.org/10.4102/sajbm.v57i1.5455