Experience over Hierarchy: Industry Tenure, Technology Exposure, and Fourth Industrial Revolution Awareness Among Hotel Managers in Gauteng, South Africa

1. Introduction

The post-COVID-19 era has increased the urgency for digital innovation across service industries (Baum et al., 2020). In hospitality, artificial intelligence (AI), the Internet of Things (IoT), big data analytics, contactless systems, and automation are redefining guest experiences, operational efficiency, and organisational structures (Ivanov & Webster, 2019; Rathjens et al., 2025; Wang et al., 2025). These technologies offer opportunities for service innovation and competitiveness, but they also introduce concerns about job redesign, workforce resistance, privacy, and the loss of the human element in hospitality (Binesh & Baloglu, 2023; Solnet et al., 2022; Tan et al., 2025).

For South Africa, the potential of digital transformation is accompanied by structural and capability constraints. The national Digital and Future Skills Strategy identifies coordinated capability building and skills development as central to participation in the digital economy (Department of Communications and Digital Technologies, 2020), while the Presidential Commission on the Fourth Industrial Revolution (2020) identifies tourism and related service industries as potential beneficiaries of 4IR. Nevertheless, uneven access to infrastructure, training, organisational support, and investment can limit the conversion of technological potential into sustained operational use (International Labour Organization, 2023; Ndhlovu & Dube, 2023; Olaitan et al., 2021).

Managerial employees are a critical segment of the hotel workforce because they influence technology-related decisions and the operational implementation of digital change. Technology acceptance is not automatic; it is mediated by perceived usefulness, ease of use, social influence, facilitating conditions, and experience (Davis, 1989; Venkatesh et al., 2003). Recent hospitality research similarly shows that employees’ attitudes and competencies affect whether technologies are adopted and used effectively (Guo et al., 2023; Im & Kim, 2022; Somera & Petrova, 2024; Wang et al., 2025).

South African accommodation research has already shown that digital readiness is a managerial issue beyond large hotels. Among 253 guest-house managers in Gauteng, Hader et al. (2023) reported strong acceptance of business-intelligence systems, a relationship between digital literacy and effective system use, and a need for continuing training and user-friendly interfaces. However, empirical evidence remains limited on whether awareness among hotel managers is associated primarily with formal hierarchy and hotel classification or with accumulated industry experience and direct exposure to technologies already embedded in operations.

This article therefore moves beyond a broad baseline description of 4IR readiness and examines the distribution of awareness within the managerial sample. It tests whether awareness is associated with hotel star grading, years of industry experience, job level, perceived operational benefits, and implementation of specific technologies. The central contribution is the comparison between formal organisational position and career-stage experience, together with the role of actual exposure to AI and IoT. The study asks: Which experiential, organisational, and technology-exposure factors are associated with 4IR awareness among managerial employees in Gauteng’s graded hotels?

2. Literature Review and Conceptual Framing

2.1. Fourth Industrial Revolution Technologies in Hospitality

The Fourth Industrial Revolution involves the merging of digital and physical systems through technologies such as AI, IoT, robotics, and big data (Schwab, 2017). In hotels, the COVID-19 pandemic accelerated investment in touch-free check-in, mobile keys, automated service points, and cashless interactions (Rathjens et al., 2025; Wang & Chan, 2025). Smart hotels increasingly use digital concierges, chatbots, self-service kiosks, sensor-based room management, and data-driven personalisation (Leung, 2019; Mariani & Borghi, 2021).

The operational promise of these technologies must be considered alongside the people-intensive character of hospitality. A recent systematic review links AI to service performance across operational, customer-facing, and strategic domains, but also identifies employment, training, employee outcomes, and psychological responses as central research themes (Wang et al., 2025). Research on a Singaporean hotel chain similarly found that AI can improve effectiveness and customer experience while human intervention remains necessary for personalised service (Tan et al., 2025). The balance between technological efficiency and human service therefore remains a central managerial concern.

2.2. Awareness, Perceived Usefulness, and Direct Exposure

The Technology Acceptance Model (TAM) explains technology acceptance through perceived usefulness and perceived ease of use (Davis, 1989). The Unified Theory of Acceptance and Use of Technology (UTAUT) extends this logic through performance expectancy, effort expectancy, social influence, facilitating conditions, and moderators that include experience (Venkatesh et al., 2003). Diffusion of Innovations further emphasises relative advantage, compatibility, trialability, observability, and communication channels (Rogers, 2003). Together, these frameworks suggest that employees become more willing and able to engage with technology when its benefits are visible, its use is supported, and exposure converts an abstract innovation into an operational tool.

Recent hospitality evidence reinforces this emphasis on situated exposure. Hotel employees do not hold a single attitude towards fourth-industrial technologies; their perceptions range from readiness to accept change to caution about employment restructuring (Im & Kim, 2022). A meta-analysis also shows that workplace technology adoption depends on a combination of individual, technological, and organisational factors (Guo et al., 2023). A change-management perspective treats hotel technology adoption as a capability- and leadership-building process that progresses from detection and engagement to integration and continuity (Somera & Petrova, 2024).

2.3. Experience, Hierarchy, and Organisational Context

Formal managerial seniority may provide decision authority, but it does not necessarily indicate continuing interaction with new operational technologies. Experience may instead reflect repeated exposure to hotel operations, guest expectations, training, and technology-enabled workflows. UTAUT recognises experience as a moderator, yet a broad experience variable may conceal different career stages and combinations of operational knowledge and recent digital engagement (Venkatesh et al., 2003).

Hotel star grading can also act as a proxy for infrastructure, investment capacity, and innovation culture. Higher-grade hotels may have more resources to implement smart technologies, while smaller or lower-grade properties may face cost and capability barriers. However, grading is not itself evidence of employee awareness: training, management support, brand affiliation, and the visibility of implemented tools may be more immediate influences. The present analysis therefore compares years of experience, job level, hotel grade, perceived usefulness, and technology implementation rather than assuming that formal status or luxury classification determines digital awareness.

Digital readiness includes the skills, infrastructure, organisational support, and mindset required to adopt and adapt to new technologies. In hospitality, individual confidence may coexist with limitations in institutional systems, training provision, interdepartmental coordination, and investment capacity. Technology adoption must therefore be understood as both an employee-acceptance issue and an organisational change process. Without aligned human-capital development, technology investment may not produce its intended operational benefits (Hader et al., 2023; Somera & Petrova, 2024).

2.4. Integrated Conceptual Framework

The study developed an integrated conceptual framework from five theoretical foundations: TAM, UTAUT, Diffusion of Innovations, Service-Dominant Logic, and the Challenge-Hindrance Stressor Model (Cavanaugh et al., 2000; Davis, 1989; Rogers, 2003; Vargo & Lusch, 2004; Venkatesh et al., 2003). Rather than treating employee responses as the outcome of a single variable, the framework places the hospitality employee at the centre of five distinct but interrelated dimensions: awareness, perceived impact, perceived opportunities, perceived challenges, and acceptance. It thereby combines cognitive appraisal, organisational and facilitating conditions, innovation characteristics, service-role dynamics, and psychological appraisal of technological change.

The framework informed the research objectives, questionnaire, and interpretation of the findings. Awareness was linked to exposure, training, communication, and facilitating conditions; perceived impact to changes in roles, workloads, and value co-creation; perceived opportunities to relative advantage and enhanced service value; perceived challenges to complexity, insufficient support, and hindrance appraisal; and acceptance to perceived usefulness, ease of use, performance expectancy, and behavioural intention. Table 1 summarises these relationships.

The framework is applied in a deliberately bounded way in this article. The retained analysis centres on awareness and its bivariate associations with selected experiential, organisational, attitudinal, and implementation variables. The results can therefore indicate which proposed relationships receive support in this sample; they do not constitute statistical validation of the complete integrated framework.

3. Materials and Methods

3.1. Research Design and Setting

A quantitative, descriptive, cross-sectional design was used to assess managerial employees’ awareness and perceptions of 4IR technologies in the hospitality sector in Gauteng. The design provided a snapshot at a single point in time and enabled statistical examination of patterns and associations; it does not establish temporal order or causality (Creswell & Creswell, 2018).

The study focused on three- to five-star hotels graded by the Tourism Grading Council of South Africa (2019). Gauteng was selected because of its concentration of graded establishments and corporate travel activity. Data were collected from April to June 2024 through a combination of printed and electronic questionnaires.

3.2. Population, Sampling, and Respondents

Because a complete sampling frame of eligible hotel employees was unavailable and access depended on hotel gatekeepers, the study used non-probability convenience sampling. Survey packs and an electronic link were distributed through general managers and human-resource offices. Of 430 questionnaires distributed, 146 usable responses were obtained, a response rate of approximately 34%. The achieved sample consisted exclusively of employees in managerial roles across three-, four-, and five-star hotels.

The sample included executive, junior, middle, and senior management. Executive respondents were few (n = 3), while junior management formed the largest group (n = 107). This distribution is reported transparently because small cells affect the choice and interpretation of inferential tests.

3.3. Instrument and Measures

The structured, self-administered questionnaire was adapted from validated instruments concerning Industry 4.0 awareness, technology acceptance, and technological knowledge renewal (Ejsmont, 2021; Rong & Grover, 2009). It included demographic and institutional items, five-point Likert items on awareness and perceived proficiency, perceived benefits and impact, acceptance, and binary questions on whether specific technologies were implemented in the respondent’s hotel. The instrument was pre-tested with 10 employees from TGCSA-graded hotels and refined for clarity and sequencing.

The awareness construct comprised five items addressing general technological proficiency, operational confidence in using common digital technologies, the ability to apply up-to-date technological developments in current work, competitiveness in the current job market, and future workplace effectiveness. The five-item scale demonstrated excellent internal consistency (Cronbach’s alpha = 0.9179). The five item scores were summed to form a composite awareness score (observed range = 7–25; M = 21.20, SD = 3.40). For the categorical cross-tabulations retained from the original analysis, the summed score was dichotomised at the sample mean. Because the composite score was integer-valued, scores of 21 or below were classified as ‘less aware’ and scores of 22 or above as ‘more aware’.

3.4. Data Analysis

Descriptive statistics were used to summarise awareness and the distribution of respondents. Cross-tabulations examined awareness by years of industry experience, job level, hotel grading, perceived benefits, perceived impact, and implementation of seven technologies: AI, IoT, intelligent detection and identification, robotics, augmented reality, blockchain, and intelligent tracking. Pearson’s chi-square test assessed associations where expected cell frequencies were adequate; Fisher’s exact test was used for sparse tables. Statistical significance was evaluated at p < 0.05. The analysis is bivariate and the results are therefore reported as associations rather than predictors or causal effects.

The preserved statistical output was generated using Stata 18 statistical software (StataCorp, College Station, TX, USA).

3.5. Ethical Considerations

The TUT Research Ethics Committee, a registered Institutional Review Board (IRB 00005968), granted final approval on 6 February 2024 (REC2023/12/029-MS). Participants received an information sheet, participation was voluntary, and informed consent was obtained. No names, email addresses, or hotel-identifying details were collected. Responses were stored on password-protected devices and handled in accordance with institutional requirements and the Protection of Personal Information Act.

4. Results

4.1. Overall Awareness

Among the 146 managerial employees, 0.68% rated their familiarity with 4IR as low and 25.34% as average, while 73.98% rated it as sufficient or more than sufficient. Across the five awareness items, between approximately 74% and 88% selected the higher familiarity categories. When the composite awareness measure was represented by the two categories used in the original analysis, 68 respondents (46.58%) were classified as less aware and 78 (53.42%) as more aware.

More than 70% of respondents also rated their digital skills as sufficient or more than sufficient. This high self-assessment indicates confidence in using digital tools, but it should be interpreted as perceived readiness rather than an objective competency test. Some respondents reported that departmental or institutional readiness lagged behind their individual capabilities, indicating that awareness and self-confidence do not remove the need for organisational systems, training, and implementation support.

4.2. Hotel Grade, Industry Experience, and Job Level

Awareness increased descriptively across hotel grades, from 44.26% more aware in three-star hotels to 58.00% in four-star and 62.86% in five-star hotels. However, the association was not statistically significant (chi-square(2) = 3.73, p = 0.155). Hotel star grading was therefore not a reliable discriminator of awareness in this sample.

Table 2. Awareness by hotel star grading.

Hotel star grading	Less aware, n (%)	More aware, n (%)	Total
Three-star	34 (55.74)	27 (44.26)	61
Four-star	21 (42.00)	29 (58.00)	50
Five-star	13 (37.14)	22 (62.86)	35
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(2) = 3.7301, p = 0.155.

Table 2. Awareness by hotel star grading.

Hotel star grading	Less aware, n (%)	More aware, n (%)	Total
Three-star	34 (55.74)	27 (44.26)	61
Four-star	21 (42.00)	29 (58.00)	50
Five-star	13 (37.14)	22 (62.86)	35
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(2) = 3.7301, p = 0.155.

Years of industry experience were significantly associated with awareness (chi-square(3) = 13.56, p = 0.004). The 11–15-year group had the highest proportion of more-aware respondents (78.38%), compared with 40.00% among those with 0–5 years and 44.78% among those with 6–10 years. The group with more than 15 years of experience was smaller (n = 12), with 58.33% classified as more aware.

Table 3. Awareness by years of industry experience.

Years in industry	Less aware, n (%)	More aware, n (%)	Total
0–5 years	18 (60.00)	12 (40.00)	30
6–10 years	37 (55.22)	30 (44.78)	67
11–15 years	8 (21.62)	29 (78.38)	37
More than 15 years	5 (41.67)	7 (58.33)	12
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(3) = 13.5623, p = 0.004.

Table 3. Awareness by years of industry experience.

Years in industry	Less aware, n (%)	More aware, n (%)	Total
0–5 years	18 (60.00)	12 (40.00)	30
6–10 years	37 (55.22)	30 (44.78)	67
11–15 years	8 (21.62)	29 (78.38)	37
More than 15 years	5 (41.67)	7 (58.33)	12
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(3) = 13.5623, p = 0.004.

Formal job level was not significantly associated with awareness (Fisher’s exact p = 0.236). Middle managers showed the highest descriptive proportion of greater awareness (68.97%), but the small executive (n = 3) and senior-management (n = 7) groups limit comparisons. The contrast between significant industry experience and non-significant job level supports an interpretation centred on career-stage exposure rather than formal hierarchy.

Table 4. Awareness by job level.

Job level	Less aware, n (%)	More aware, n (%)	Total
Executive management	2 (66.67)	1 (33.33)	3
Junior management	54 (50.47)	53 (49.53)	107
Middle management	9 (31.03)	20 (68.97)	29
Senior management	3 (42.86)	4 (57.14)	7
Total	68 (46.58)	78 (53.42)	146

Note. Fisher’s exact p = 0.236.

Table 4. Awareness by job level.

Job level	Less aware, n (%)	More aware, n (%)	Total
Executive management	2 (66.67)	1 (33.33)	3
Junior management	54 (50.47)	53 (49.53)	107
Middle management	9 (31.03)	20 (68.97)	29
Senior management	3 (42.86)	4 (57.14)	7
Total	68 (46.58)	78 (53.42)	146

Note. Fisher’s exact p = 0.236.

4.3. Perceived Benefits and Perceived Impact

Awareness was significantly associated with perceived operational benefits (chi-square(1) = 11.6135, p = 0.001). Among respondents who viewed 4IR as more beneficial, 65.85% were classified as more aware, compared with 37.50% among those who viewed it as less beneficial. This pattern is consistent with the perceived-usefulness component of TAM. In contrast, awareness was not significantly associated with perceived impact on respondents’ own roles (p = 0.124).

Table 5. Awareness by perceived benefit.

Perceived benefit	Less aware, n (%)	More aware, n (%)	Total
Less beneficial	40 (62.50)	24 (37.50)	64
More beneficial	28 (34.15)	54 (65.85)	82
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(1) = 11.6135, p = 0.001.

Table 5. Awareness by perceived benefit.

Perceived benefit	Less aware, n (%)	More aware, n (%)	Total
Less beneficial	40 (62.50)	24 (37.50)	64
More beneficial	28 (34.15)	54 (65.85)	82
Total	68 (46.58)	78 (53.42)	146

Note. Pearson chi-square(1) = 11.6135, p = 0.001.

4.4. Exposure to Implemented Technologies

Of the seven technologies examined, only AI and IoT implementation showed statistically significant associations with awareness. In hotels reporting AI implementation, 58.04% of respondents were more aware, compared with 38.24% in hotels without AI (chi-square(1) = 4.11, p = 0.043). In IoT-enabled hotels, 57.69% were more aware, compared with 18.75% in hotels without IoT (Fisher’s exact p = 0.006).

Implementation of intelligent detection and identification (p = 0.092), robotics (p = 0.598), augmented reality (p = 0.598), blockchain (p = 1.000), and intelligent tracking (p = 0.476) was not significantly associated with awareness. Robotics and blockchain were rarely implemented, which limits the interpretive value of their tests. The results therefore distinguish visible, operationally embedded AI and IoT systems from technologies that were absent, experimental, or minimally deployed in the sampled hotels.

Table 6. AI and IoT implementation by awareness.

Technology implementation	Less aware, n (%)	More aware, n (%)	Total
AI: No	21 (61.76)	13 (38.24)	34
AI: Yes	47 (41.96)	65 (58.04)	112
IoT: No	13 (81.25)	3 (18.75)	16
IoT: Yes	55 (42.31)	75 (57.69)	130

Note. AI: Pearson chi-square(1) = 4.11, p = 0.043. IoT: Fisher’s exact p = 0.006.

Table 6. AI and IoT implementation by awareness.

Technology implementation	Less aware, n (%)	More aware, n (%)	Total
AI: No	21 (61.76)	13 (38.24)	34
AI: Yes	47 (41.96)	65 (58.04)	112
IoT: No	13 (81.25)	3 (18.75)	16
IoT: Yes	55 (42.31)	75 (57.69)	130

Note. AI: Pearson chi-square(1) = 4.11, p = 0.043. IoT: Fisher’s exact p = 0.006.

5. Discussion

5.1. Experience Over Hierarchy

The central result is that years of industry experience were associated with 4IR awareness, while formal job level was not. Managers with 11–15 years of experience showed the highest awareness, whereas newer employees in the 0–5-year and 6–10-year groups showed lower proportions of greater awareness. Ongoing exposure to hotel operations, guest interactions, and technology-enabled workflows may therefore be more relevant to awareness than job title alone. Because the analysis is cross-sectional and bivariate, the finding does not establish that experience causes awareness; it identifies a career-stage pattern that warrants further testing.

This pattern provides a contextual refinement to the general experience moderator in UTAUT. The strong 11–15-year cohort suggests that a broad experience variable may be insufficient to capture the specific blend of operational maturity and recent digital engagement observed in this setting. ‘Mid-career professionalisation’ may describe this combination. The term is advanced here as an interpretive proposition rather than a validated construct: future studies would need to operationalise and test it directly.

The non-significant association between job level and awareness also challenges the assumption that digital knowledge is concentrated at senior levels. Middle managers showed a high descriptive level of awareness, but the uneven group sizes and very small executive sample require caution. The result nevertheless suggests that digital transformation strategies should identify operationally experienced technology champions rather than relying only on hierarchical authority.

5.2. Technology Exposure and Perceived Usefulness

AI and IoT were the only implementation categories significantly associated with awareness. These technologies are visible in booking platforms, chatbots, decision tools, smart rooms, occupancy sensors, and networked energy controls. Their operational presence makes 4IR concepts tangible. Technologies that were rarely implemented, including robotics and blockchain, did not show comparable associations. The pattern supports TAM’s emphasis on perceived usefulness and DOI’s emphasis on observability, while also corresponding with recent evidence that employee competence and practical interaction influence AI-related service outcomes (Davis, 1989; Rogers, 2003; Wang et al., 2025).

The significant association between perceived benefits and awareness reinforces this interpretation. Managers who regarded 4IR as more beneficial were substantially more likely to be more aware, while awareness was not significantly associated with perceived impact on their own roles. The sector may therefore be in a transitional phase in which managers recognise operational value before technologies have fully altered their day-to-day responsibilities. This is consistent with a change-management view in which adoption develops from recognition and engagement towards integration and continuity (Somera & Petrova, 2024).

The result also connects naturally with earlier Gauteng accommodation research. Hader et al. (2023) found strong acceptance of business-intelligence systems among guest-house managers and linked effective use to digital literacy, training, and usable interfaces. Together, the two studies indicate that visible operational value and supported interaction are important across both graded hotels and smaller accommodation establishments.

5.3. Hotel Grading and the South African Context

Awareness increased descriptively from three-star to five-star hotels, but the association with star grading was not statistically significant. Luxury classification may reflect resource availability, yet it is not a substitute for examining the actual technologies, training systems, and organisational routines to which managers are exposed. Other organisational factors, such as brand affiliation, management support, and specific training budgets, may be more influential than hotel grade and should be examined directly in future research.

The South African context remains important. National strategies recognise the potential of 4IR, but sector-wide transformation depends on capability building and inclusive access to digital skills (Department of Communications and Digital Technologies, 2020; International Labour Organization, 2023). The results show that awareness is neither uniform nor guaranteed by formal status. They therefore support targeted interventions based on experience level and actual workplace exposure rather than broad assumptions about digitally ready managers or technologically advanced hotel categories.

5.4. Theoretical Contribution of the Integrated Framework

The integrated framework is a theoretical contribution because it connects dimensions that are usually treated separately. TAM and UTAUT explain cognitive and organisational conditions of acceptance; DOI explains exposure, observability, and relative advantage; SDL locates technology within the employee’s service and value-co-creation role; and the Challenge-Hindrance Stressor Model distinguishes growth-oriented from obstructive appraisals of change. In combination, the framework treats awareness as part of a wider progression involving perceived opportunities, impacts, challenges, and acceptance rather than as a stand-alone measure of technological familiarity.

The results provide partial, not complete, empirical grounding for this framework. The association between perceived benefit and awareness is consistent with TAM’s perceived usefulness and DOI’s relative advantage, while the AI and IoT results are consistent with observability and facilitating exposure. By contrast, perceived impact on respondents’ own roles was not significantly associated with awareness, and hindrance variables such as job insecurity were not directly quantified. The SDL and Challenge-Hindrance relationships therefore remain propositions for direct testing rather than confirmed pathways.

The experience finding also extends the framework contextually. UTAUT includes experience as a general moderator, but the concentration of greater awareness in the 11–15-year group suggests that career stage may matter more precisely than accumulated tenure alone. Mid-career professionalisation is proposed to capture the combination of operational maturity, repeated exposure, and continuing digital engagement. Its status is theoretical and exploratory: a future study would need to define the construct, develop measures, and test its relationships within the integrated framework.

5.5. Practical Implications

Hotels should combine technology investment with role-embedded learning. Introducing visible, useful, and interactive systems can create practical exposure, while structured training can help employees connect the technology to operational benefits. Mid-career managers may serve as change champions because they combine operational experience with high awareness, but the lower awareness among newer managers indicates that digital onboarding should begin early rather than being reserved for senior staff.

The need for early, experience-based engagement is also consistent with South African research showing that hospitality students’ attitudes towards industry careers were strongly influenced by their internship experiences (Mqwebedu et al., 2022). Although that study examined career perceptions rather than technology awareness, it supports the practical importance of formative workplace exposure. Digital orientation, supervised use of hotel systems, and mentoring can therefore be incorporated into internships, graduate programmes, and early-career onboarding without treating technology training as a separate event.

Leadership remains important even though hierarchy was not associated with awareness. Senior executives should provide a clear digital vision, resources, policies, and continuing support, while technology champions at different levels can assist with implementation. A phased approach that begins with high-visibility, operationally relevant systems may build confidence and capability before hotels progress to less familiar technologies.

The high level of self-reported digital skill should not be treated as evidence that human-capital constraints have been resolved. Confidence can exceed applied competence, particularly where employees have limited access to formal training or where systems have not been integrated into daily work. Hotels should therefore combine self-assessment with practical demonstrations, role-specific learning, and continuing support. This approach also addresses the reported gap between individual willingness and institutional readiness.

6. Conclusions

This study examined the distribution of 4IR awareness among 146 managerial employees in three- to five-star hotels in Gauteng. The results show that awareness was significantly associated with years of industry experience, perceived operational benefits, and implementation of AI and IoT. In contrast, formal job level and hotel star grading were not significantly associated with awareness.

The contribution is therefore more specific than a general profile of digital readiness. The integrated framework connects awareness with perceived opportunities, impacts, challenges, and acceptance through behavioural, organisational, service, innovation, and psychological lenses. Within this managerial sample, the strongest empirical support concerns perceived benefit, direct AI and IoT exposure, and career-stage experience. The non-significant perceived-impact result and the absence of direct challenge measures delimit the framework’s present evidential support.

The career-stage pattern provides a further theoretical refinement. Mid-career experience and direct exposure to implemented technologies were more closely associated with awareness than hierarchy or luxury classification. Mid-career professionalisation is therefore introduced as a proposition for future empirical testing, not as a validated new construct.

For practice, the findings support targeted, experience-level-appropriate capability building, early-career digital onboarding, and role-embedded exposure to technologies with clear operational value. For research, they demonstrate the need to distinguish career stage from job title and organisational classification when examining hospitality technology adoption.

7. Limitations and Future Research

The sample was limited to managerial employees in TGCSA-graded hotels in one province and was obtained through convenience sampling. The findings are therefore not statistically generalisable to all South African hotel employees or accommodation establishments. The uneven job-level groups, particularly the small executive and senior-management cells, limit comparisons. Self-reported awareness may also reflect confidence or social desirability rather than objectively tested competence.

The cross-sectional and bivariate analyses identify associations but do not establish causality or independent predictive effects. The implementation items indicate that a technology was reported in the respondent’s hotel; they do not confirm the frequency or depth of each manager’s personal use. Future research should use probability sampling where feasible, include frontline employees and ungraded properties, and apply multivariable models to test whether experience and technology exposure remain associated with awareness after controlling for age, education, department, brand affiliation, and organisational resources. Longitudinal and qualitative studies could examine how the proposed mid-career professionalisation pattern develops and whether technology exposure leads to sustained capability and changes in work roles.