When Readiness Doesn’t Lead to Adoption: A TOE-TAM Analysis of AI-Enabled HR Digitalization in a Resource-Constrained SIDS

When Readiness Doesn’t Lead to Adoption: A TOE-TAM Analysis of AI-Enabled HR Digitalization in a Resource-Constrained SIDS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When Readiness Doesn’t Lead to Adoption: A TOE-TAM Analysis of AI-Enabled HR Digitalization in a Resource-Constrained SIDS Jecha Jecha, Ally Ali, Adilu Mussa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9472562/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines barriers to adopting AI-enabled human resource management (AI-HR) in Zanzibar’s public and private sectors, focusing on how adoption dynamics in resource-constrained, mandatory settings diverge from those theorized in developed economies. An explanatory sequential mixed-methods design was used: a structured survey of HR professionals and managers (n = 133) was followed by semi-structured interviews (n = 15) conducted to explain unexpected quantitative results. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested an integrated Technology-Organization-Environment and Technology Acceptance Model (TOE-TAM) framework. Only technological readiness predicted AI-HR adoption readiness (β = 0.203, p = 0.047); organizational readiness, environmental readiness, perceived usefulness, and perceived ease of use were non-significant. The organizational readiness construct itself exhibited measurement failure negative Cronbach’s alpha (α = -0.105) and opposing outer loadings across items a pattern substantively consistent with institutional decoupling manifesting at the measurement level, where management support and responsiveness to change operated as opposing rather than unified organizational dimensions. The qualitative phase identified four mechanisms behind these null results: vendor lock-in and external IT control; digital and energy infrastructure functioning as a threshold rather than a continuous predictor; institutional decoupling between formal policies and operational practice; and cultural resistance to automating HR judgment. These mechanisms not visible to survey instruments challenge the assumed universality of the Technology Acceptance Model in mandatory, resource-constrained public sector contexts and indicate that AI-HR implementation requires resolving hard infrastructure deficits before organizational and attitudinal levers become operative. The study offers a refined adoption model for Small Island Developing States and practical guidance for Zanzibar’s digital transformation agenda. AI adoption human resource management developing countries TOE-TAM institutional decoupling SIDS PLS-SEM mandatory adoption Figures Figure 1 Figure 2 1. Introduction The global HR function is changing. Administrative roles are giving way to strategic ones, increasingly mediated by AI-enabled systems. AI in HR promises real efficiency gains and better workforce analytics, but implementation runs into compounding institutional barriers. Without standardized AI risk-governance frameworks, organizations struggle to prioritize risks and maintain ethical oversight in HR practice (Madanchian & Taherdoost, 2025 ). Research on digital transformation leadership shows that relationship building and operational innovation matter for aligning technological change with organizational capability (Van Roekel et al., 2025). Most of this research, however, has been conducted in private-sector organizations in Global North economies that already have functioning regulatory and digital infrastructure. Public administrations face different pressures they prioritize citizen welfare and procedural fairness over innovation (Yao, 2024 ; Wittmann & Meynhardt, 2025 ). These differences produce distinct governance requirements and slower adoption trajectories, especially in resource-constrained countries. Small Island Developing States (SIDS) face additional structural problems: limited fiscal capacity, underdeveloped ICT infrastructure, and dependence on external donor funding, all of which complicate digital transformation (Faustine & Rachmawati, 2024 ; Mwita & Kitole, 2025 ; Venugopal et al., 2024 ). Zanzibar, a semi-autonomous SIDS with stated policy ambitions around AI and digital government, illustrates these tensions clearly. Territories in this position must invest simultaneously in “hard” infrastructures systems, connectivity, data architectures and “soft” infrastructures regulation, standards, workforce capability typically exceeding available policymaking capacity. Many developing economies, unlike the European Union, lack comparable institutional scaffolding for AI governance (Wittmann & Meynhardt, 2025 ). The situation is further complicated by AI integration’s negative externalities in HR: reduced collaboration, concerns about bias, privacy risks, and job reconfiguration (Ara and Ahmad, 2025 ). These outcomes threaten HR performance and employee trust precisely where institutional safeguards are weakest. Several research gaps follow. Technology adoption literature treats adoption primarily as a leadership and organizational challenge (Adiazmil et al., 2024 ; Van Roekel et al., 2025), but there is limited evidence on how organizational and environmental readiness conditions shape AI adoption in public and private sectors within SIDS and the Global South. The effects of digital transformation leadership, institutional constraints, and governance pressures on AI adoption readiness and HRM effectiveness under resource constraints are poorly understood. This limits the transferability of existing frameworks to organizations in small, donor-dependent economies such as Zanzibar, Tanzania. This study examines how technological, organizational, and environmental readiness shapes AI adoption and HRM effectiveness in Zanzibar’s public and private sectors, and tests whether standard TOE-TAM predictors remain operative under mandatory, resource-constrained conditions. It uses an integrated TOE-TAM framework built around seven constructs: Technological Readiness (TR), Organizational Readiness (OR), Environmental Readiness (ER), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), AI Adoption Readiness (AIR), and HRM Effectiveness (HRME) (Islam et al., 2023 ; Qu & Kim, 2025 ). This lens allows assessment of both institutional conditions and user perceptions in a resource-constrained context (Gong et al., 2025). Unexpectedly, the organizational readiness construct exhibited measurement failure negative reliability coefficients and opposing item loadings across management support and responsiveness-to-change indicators a finding substantively interpretable within institutional decoupling theory and indicative that standard Western readiness constructs may not transfer cleanly to Sub-Saharan SIDS contexts. The focus is on HR professionals, IT managers, and administrators responsible for digital transformation in Zanzibar’s key public and private institutions. 2. Literature Review and Conceptual Framework 2.1 Integrating TOE (Macro) and TAM (Micro) The Technology-Organization-Environment (TOE) framework explains organizational technology adoption through three contexts: technological (existing and emerging technologies, infrastructure), organizational (resources, structure, culture), and environmental (industry conditions, policy, regulation). Tornatzky and Fleischer ( 1990 ) argued that adoption decisions reflect both internal organizational capabilities and external constraints. The Technology Acceptance Model (TAM) operates at the individual level. It holds that system acceptance is shaped by perceived usefulness (PU) whether a system improves job performance and perceived ease of use (PEOU) whether a system is cognitively effortless (Davis, 1989 ). Across a range of systems and contexts, PU and PEOU predict usage intentions, with PEOU functioning as an antecedent to PU (Sek et al., 2010 ). In Zanzibar public and private sectors, both frameworks are relevant in different ways. TOE captures macro-level conditions: infrastructure, organizational resources, and policy. TAM captures the micro-level attitudes of HR officers who actually use the tools. Studies integrating both frameworks confirm that contextual and psychological factors jointly shape AI use in HR (Islam et al., 2023 ; Tambe et al., 2019 ). In Tanzania specifically, Faustine and Rachmawati ( 2024 ) showed that technological, organizational, and environmental factors together determine SMEs’ AI uptake. Zanzibar AI-HR adoption is constrained by infrastructure and policy at the TOE level, while success at the individual level depends on HR professionals finding the tools useful and manageable. 2.2 Technological Context: Infrastructure as Binding Constraint → H1 The technological context in TOE includes technologies in use and available for use: ICT infrastructure, system reliability, integration capacity, and cybersecurity. Poor connectivity, unreliable power, legacy systems, and weak security are documented obstacles to AI adoption in developing economies. For AI-enabled HR, technological readiness means internet bandwidth, stable electricity, secure servers, compatible HRIS platforms, and functional technical support. Faustine and Rachmawati ( 2024 ) found that in medium-sized Tanzanian enterprises, technological factors drive both AI adoption and HRM effectiveness. A 2023 study from Ghana found that e-HRM tools improved HR systems only where the technology infrastructure was fully in place (Asamoah-Appiah & Kesari, 2023 ). In Zanzibar, where infrastructure is uneven, technological readiness is a binding constraint: without reliable connectivity and platforms, other adoption drivers’ culture, policy cannot function. H1 Technological readiness positively affects AI-enabled HR adoption readiness in organizations in Zanzibar. 2.3 Organizational Context: Budget, Culture, and Management Support → H2 The organizational context in TOE includes firm size, structure, financial resources, HR capabilities, culture, and management commitment to ICT initiatives. Management support and organizational readiness determine whether AI projects get funded, receive strategic alignment, and become embedded in core HR workflows. A study of AI adoption in Malaysian tourism organizations found that technological competency, management support, and competitive pressure all predicted adoption (Islam et al., 2023 ). Hierarchical cultures and low digital skills among HR staff obstruct e-HRM implementation even when basic technologies are available (Muzaffar et al., 2024 ). H2 Organizational readiness positively affects AI-enabled HR adoption readiness in organizations in Zanzibar. 2.4 Environmental Context: Policy and Regulatory Pressure → H3 The environmental context in TOE is the external environment: government policies, regulations, and competitive pressures. In developing countries, governments shape ICT and AI adoption through digital strategies and data protection regulations (Shahadat et al., 2023 ). Research in sub-Saharan Africa shows that coherent digital policies accelerate e-government adoption, while fragmented policies drive ICT project failure (Asamoah-Appiah & Kesari, 2023 ). In Zanzibar, where AI-HR is tied to the broader digital economy agenda, environmental readiness through effective policy frameworks is expected to generate institutional pressure for adoption at the organizational level. H3 Environmental readiness (including government policies and competitive pressures) positively affects AI-enabled HR adoption readiness in organizations in Zanzibar. 2.5 TAM: User Perceptions of AI-HR Tools → H4, H5, H6 TAM holds that PU and PEOU shape attitudes toward a system, behavioral intention, and eventual use (Mohd Amir et al., 2020 ). Davis ( 1989 ) established that PEOU influences PU, because systems seen as easier to use tend to be seen as more useful. This relationship has been replicated across applications and contexts (Venkatesh & Davis, 2000 ). In AI-enabled HRM research, studies show that HR professionals’ perceptions of usefulness and ease of use predict their intention to adopt AI tools (Xing et al., 2025 ). In Malaysia, both PU and PEOU were significant alongside TOE factors in predicting AI adoption in HR (Islam et al., 2023 ). In Zanzibar, where HR officers’ digital skills and language preferences vary (Swahili versus English interfaces), PU and PEOU are expected to shape whether AI-HR systems gain traction once deployment becomes organizationally possible. H4 Perceived ease of use of AI-enabled HR tools positively affects perceived usefulness. H5 : Perceived usefulness of AI-enabled HR tools positively affects AI-enabled HR adoption readiness. H6 : Perceived ease of use of AI-enabled HR tools positively affects AI-enabled HR adoption readiness. 2.6 From Adoption Readiness to HRM Effectiveness → H7 Properly implemented e-HRM tools improve HRM effectiveness and organizational performance (Agarwal, 2022 ). A Ghanaian study found that e-HRM tools improved HRM effectiveness, with downstream effects on organizational effectiveness (Asamoah-Appiah & Kesari, 2023 ). Faustine and Rachmawati ( 2024 ) reported a positive effect of AI adoption on HRM effectiveness in Tanzanian enterprises. AI-enabled HR adoption readiness (AIR) captures an organization’s intent and capability to implement AI in HR processes; HRM effectiveness (HRME) refers to improvements in HR service quality, data reliability, and strategic contribution. H7 AI-enabled HR adoption readiness positively affects perceived HRM effectiveness in organizations in Zanzibar. 2.7 Conceptual Framework Summary Combining TOE and TAM, the conceptual model proposes that TR, OR, and ER shape AIR, while PU and PEOU influence AIR both directly and indirectly through the classical TAM pathway. AIR is expected to improve HRME, consistent with evidence from AI and e-HRM studies in developing countries (Faustine & Rachmawati, 2024 ; Asamoah-Appiah & Kesari, 2023 ). This integration tests how macro-level constraints (infrastructure, budgets, policy) and micro-level user perceptions jointly determine AI-HR readiness and its downstream effect on HRM effectiveness in Zanzibar. 3. Method 3.1 Research Design This study uses an explanatory sequential mixed-methods design quantitative data first, then qualitative data collected to explain the quantitative findings (Creswell & Clark, 2017 ; Ivankova et al., 2006 ). Two sequential phases are connected by a deliberate analytical logic: the qualitative phase exists because the quantitative phase produced unexpected results that a survey cannot explain on its own. Phase 1 (Quantitative) A structured survey (n = 133) using PLS-SEM to test the integrated TOE-TAM framework and identify statistical relationships among TR, OR, ER, PU, PEOU, AIR, and HRME. Phase 2 (Qualitative) Semi-structured interviews (n = 15) with HR directors, IT managers, and policymakers, selected to explain the specific null results from Phase 1. The phases connect at three points. First, the quantitative results determined what needed explaining specifically, why only H1 and H4 were supported while H2, H3, H5, H6, and H7 were not. Second, qualitative participant selection was purposively guided by the survey sample. Third, integration occurred during interpretation: interview excerpts were embedded directly in the hypothesis-by-hypothesis discussion to explain statistical patterns (Creswell & Clark, 2017 ). 3.2 Research Context and Sampling 3.2.1 Quantitative Phase Zanzibar is a Small Island Developing State with constrained ICT infrastructure and significant dependence on external donor funding for technology initiatives (Faustine & Rachmawati, 2024 ; Mwita & Kitole, 2025 ). The target population was HR professionals, IT managers, and administrators in public and private institutions involved in digital HR workflow transitions (Abane et al., 2023 ; Taher et al., 2025 ). Of 133 valid responses, 91 came from government agencies (68.4%), 32 from private-sector organizations (24.1%), five from SMEs (3.8%), and five from NGOs (3.8%). This distribution reflects Zanzibar organizational landscape, which is dominated by public-sector employment. Roles included 56 HR managers and staff (42.1%), 11 IT managers (8.3%), 18 general managers (13.5%), and 48 others (36.1%). Professional experience ranged from 1 to 53 years (Mdn = 6 years). The 100% response rate and cross-sector representation suggest minimal non-response bias and adequate coverage of the HR and IT populations in Zanzibar. 3.2.2 Qualitative Phase Following the quantitative phase, 15 respondents participated in semi-structured interviews lasting 45–60 minutes. The sample included six HR Directors and Officers, four IT Managers and Coordinators, three Finance Directors, and two machine-learning experts. Participants were selected based on their survey participation, their sector representation across universities, government, and private organizations, and their ability to speak directly to the non-significant quantitative paths. This selection logic is consistent with explanatory sequential design principles, where quantitative results guide purposive qualitative sampling (Ivankova et al., 2006 ). 3.3 Measures The survey instrument adapted scales from both TAM and TOE frameworks, rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) (Chiu et al., 2021 ; Jamil et al., 2025 ; Jia & Hou, 2024 ). The constructs were: Technological Readiness (TR) : Infrastructure, Internet connectivity, and system availability (4 items) Organizational Readiness (OR) : Management support, budget allocation, and strategic planning (4 items) Environmental Readiness (ER) : Government policies, regulatory pressures, and peer institutional influence (4 items) Perceived Usefulness (PU) : Belief that AI-HR tools improve job performance (4 items) Perceived Ease of Use (PEOU) : Belief that AI-HR tools are easy to operate (3 items after preliminary screening) AI Adoption Readiness (AIR) : Willingness to adopt AI-based HR systems (5 items) HRM Effectiveness (HRME) : Improvements in HR processes, speed, and accuracy (4 items) Item PEOU4 (“Internet connectivity issues do NOT hinder my use of HR tools”) was a reverse-scored item (Scholtz et al., 2016 ). Preliminary analysis confirmed that raw responses were inversely related to the other PEOU indicators. The item was reverse-coded before analysis to maintain directional consistency with the construct (Mason et al., 2021 ; Weijters et al., 2013 ). The qualitative interview guide targeted the specific patterns emerging from Phase 1. Questions covered HR management practices (manual versus digital), infrastructure constraints beyond Internet access, managerial understanding of AI-HR value, skill gaps among HR and IT staff, the effectiveness of government digitalization programs (particularly eGAZ), cultural trust in algorithmic HR decisions, software usability and language barriers, and the observed effects of digitalization on HR performance. 3.4 Data Analysis Strategy 3.4.1 Quantitative Analysis PLS-SEM was run in SmartPLS 4 (Ringle et al., 2024 ). PLS-SEM was chosen for its capacity to handle non-normal data distributions and its suitability for exploratory studies extending theory into new contexts rather than confirming established relationships (Arunraju Chinnaraju, 2025 ). Analysis followed two stages: measurement model assessment (reliability and validity), then structural model hypothesis testing using bootstrapping with 5,000 subsamples (Karimi et al., 2025 ; Murshid et al., 2019 ). 3.4.2 Qualitative Analysis Interview transcripts were analyzed through framework-guided thematic analysis in four stages. Deductive coding applied predefined categories from the quantitative TOE-TAM constructs: TR, OR, ER, PU, PEOU, AIR, and HRME. Inductive coding captured emergent themes from participants’ accounts, including institutional decoupling, vendor dependence, and brain drain concerns. Pattern matching then compared qualitative themes against quantitative results to find convergence and divergence. Finally, integration involved triangulating findings through participant validation: all 15 interviewees confirmed that the instrument reflected subject-matter relevance, language clarity, and content appropriateness. 3.4.3 Integration Procedures Following Creswell and Clark ( 2017 ), quantitative and qualitative strands were combined by connecting data collection and presenting statistical findings alongside explanatory quotations. Integration operated at three levels. At the sampling level, quantitative outcomes directed qualitative participant selection. At the interpretive level, qualitative findings were embedded in the hypothesis-by-hypothesis discussion to contextualize both significant and non-significant results. At the narrative level, participant quotations explained statistical patterns, clarified unexpected null findings, and revealed contextual factors the survey could not capture. 3.5 Ethical Considerations Ethical approval was granted by the Zanzibar University Research Ethics Committee (ZUREC). Participants received an information sheet covering the study’s purpose, procedures, risks and benefits, and their right to withdraw without penalty. Written informed consent was obtained before survey and interview participation. Data were anonymized through removal of identifiable information and assignment of unique participant codes. 4. Results 4.1 Measurement Model Construct reliability and validity were assessed following Hair et al. ( 2019 ) criteria for PLS-SEM. Indicator reliability was confirmed for five constructs: all outer loadings for TR, PEOU, PU, AIR, and HRME exceeded 0.708. For ER, items ER1, ER2, and ER4 had acceptable loadings (0.799, 0.743, and 0.771, respectively), but ER3 loaded at 0.347 below the threshold. ER3 was retained but flagged as a weak indicator, and the ER construct results should be read with corresponding caution. The OR construct presents a more serious problem. Cronbach’s alpha was negative (α = -0.105), rho_a was near zero (0.097), and composite reliability (rho_c) was 0.171, all well below acceptable thresholds. OR2 and OR4 produced negative outer loadings (-0.358 and − 0.217), while OR1 and OR3 loaded positively (0.619 and 0.731). This pattern suggests that OR items do not form a unidimensional construct in this context. Management support and responsiveness to change appear to operate as separate, possibly opposing, dimensions rather than as components of a single readiness factor. This finding is substantively interpretable and consistent with the institutional decoupling theme in the qualitative data, discussed below. OR is retained for theoretical completeness but treated as a failed measurement construct for analytical purposes. Internal consistency was acceptable for the remaining five constructs, with Cronbach’s alpha and rho_c values ranging from 0.710 to 0.904. Convergent validity was confirmed for TR, PEOU, PU, AIR, and HRME, with AVE values between 0.520 and 0.703. ER’s AVE was 0.476 marginally below the 0.50 threshold, again traceable to ER3. Discriminant validity was assessed via the heterotrait-monotrait (HTMT) ratio, with all values below 0.85 for the well-functioning constructs (Henseler et al., 2014 ). All 15 interview participants confirmed the instrument’s relevance, language clarity, and content appropriateness. Table 1 Measurement Model Results Construct Items Loading range α rho_c AVE Note TR TR1–TR4 0.620–0.832 0.788 0.859 0.606 — ER ER1–ER4 0.347–0.799 0.676 0.771 0.476 ER3 = 0.347 OR OR1–OR4 -0.358–0.731 -0.105 0.171 0.273 Measurement failure PEOU PEOU1–3 0.743–0.847 0.710 0.837 0.633 — PU PU1–PU4 0.741–0.838 0.806 0.873 0.633 — AIR AIR1–AIR5 0.632–0.776 0.770 0.844 0.520 — HRME HRME1–4 0.760–0.927 0.884 0.904 0.703 — 4.2 Structural Model All inner VIF values fell below 3.0 (range: 1.03–1.68), ruling out collinearity problems. Model fit: The saturated model SRMR (0.086) meets the Hair et al. ( 2019 ) threshold of < 0.10, confirming acceptable fit for the unconstrained solution. The estimated model SRMR (0.132) exceeds this threshold, reflecting model complexity introduced by the OR construct’s measurement irregularities rather than a fundamental structural misspecification consistent with Henseler et al. (2015), who note that fit indices in the estimated model are sensitive to construct-level specification problems. The gap between the two SRMR values is therefore informative: it indicates that the structural restrictions imposed by the proposed theoretical model diverge from the unconstrained saturated solution, an expected and interpretable outcome when a failed measurement construct (OR) is retained for theoretical transparency. The model explained roughly 30% of the variance in PU (R² = 0.30), 10% of the variance in AIR (R² = 0.10), and about 1% of the variance in HRME (R² = 0.01). These R² values are consistent with exploratory adoption research in complex public sector environments (Safie et al., 2025 ). Hypotheses were tested using bootstrapping (5,000 samples, two-tailed, α = 0.05). Results are in Table 2 . Table 2 Hypothesis Testing Results Hypothesis Path β p Supported? H1 TR → AIR 0.203 0.047 Yes H2 OR → AIR −0.159 0.321 No H3 ER → AIR 0.135 0.272 No H4 PEOU → PU 0.552 < 0.001 Yes H5 PU → AIR −0.056 0.588 No H6 PEOU → AIR −0.108 0.305 No H7 AIR → HRME 0.100 0.513 No Note. β = standardized path coefficient. Bootstrap subsamples = 5,000. The 95% CI for H1 is (− 0.003, 0.388), indicating marginal significance. All other non-significant paths crossed zero. Two paths were supported. TR significantly predicted AIR (H1: β = 0.203, p = 0.047) the only structural predictor that held. PEOU significantly predicted PU (H4: β = 0.552, p < 0.001), replicating the classical TAM cognitive mechanism. The five remaining hypotheses were not supported. OR (β = -0.159, p = 0.321), ER (β = 0.135, p = 0.272), PU (β = -0.056, p = 0.588), PEOU (β = -0.108, p = 0.305), and AIR (β = 0.100, p = 0.513) did not predict their respective outcome variables. These null results position the qualitative phase as analytically essential: they indicate that standard TOE-TAM predictors are not functioning as expected in this context, and the reasons need to be identified through in-depth accounts. 4.3 Qualitative Results Semi-structured interviews (n = 15) were conducted after the quantitative phase to explain the statistical findings. Thematic analysis guided by the explanatory sequential logic produced four themes that contextualize the quantitative null results. 4.3.1 Vendor Lock-In and External IT Control Multiple respondents reported that their organizations held no effective sovereignty over HR technology infrastructure. RS8 (IT Manager, Government Agency) described the problem directly: “Existing HR systems were not owned by the Revolutionary Government of Zanzibar. All access and infrastructure were possessed by one ICT company from Tanga, while Zanzibar was given minimum access, which endangered HR records, data, and the economy as well.” This external vendor dependence overrides organizational readiness management support, budget, and skills cannot compensate for the absence of technical control providing a proximate explanation for why OR was non-significant in the model. 4.3.2 Hard Infrastructure as Threshold Condition Respondents described layered infrastructure failures that blocked adoption regardless of measured readiness levels. RS14 (power) “We have power problems caused by frequent power outages, limiting HR systems and making AI adoption difficult.” RS3 (technical support) “Insufficient technical support capacity and limited in-house expertise for system maintenance, cybersecurity monitoring, and system optimization leads to slow issue resolution and underutilization of available digital platforms.” RS7 (expertise) “AI needs greater preparation and investment. Problems such as lack of finance, experts, awareness, and institutional readiness affect AI-HR. The government lacks expertise in AI. In the private sector too, we have more than 200 employees, but very few possess AI or digital skills.” Taken together, these accounts suggest that infrastructure operates as a threshold condition, not a continuous predictor: organizations must first reach minimum infrastructure thresholds before other adoption factors become operative. This explains why TR was significant while OR and ER theoretically important but dependent on functioning infrastructure were not. 4.3.3 Institutional Decoupling: Policy vs. Practice Respondents consistently described a gap between formal digitalization policy and what actually happens in their organizations a pattern consistent with the institutional decoupling literature (Boxenbaum & Jonsson, 2017). RS1 “Our management’s understanding of AI in HR is mixed. Some leaders clearly understand its value, but others still see it mainly as a buzzword, with limited practical understanding of how AI can support HR operations.” RS2 “Formal policies exist supporting digitalization, but limited implementation; there is discrepancy between policy intention and operational reality.” RS6 “Just policy documents no change I have seen from these initiatives.” RS7 “The intention behind initiatives such as eGAZ and the Digital Economy Blueprint is good. However, in practice, their impact is not yet fully realized. Much of the support remains at the policy and strategic levels, with limited practical implementation or resources reaching organizations.” This decoupling explains why OR and ER were non-significant despite their theoretical importance. Formal commitments and policy pronouncements do not translate into operational capacity or resource allocation at the point of actual adoption. 4.3.4 Cultural Resistance: The Human Touch Question Several respondents expressed normative objections to automating HR functions that they saw as requiring human judgment. RS3 “HR cannot be integrated by AI. Our HR system depends on rules, regulations, acts, and policies. These all depend on human touch, human thinking, discussion, and group decisions rather than automation.” RS7 “Human resources in Zanzibar depend much on policies, Acts, provisions, which could not be interpreted perfectly through any digital system.” RS9 “Zanzibar can adopt AI-HR tools under special selection such as AI-Training, AI-Recruitment and Selection, AI-Staff Leave, while employees’ records, payroll, reporting, and contract management should remain manual.” RS5 “Many teachers resist using digital systems for examinations, results, and biometric machines. They believe manual systems are better than automatic or AI systems.” These four themes vendor lock-in, infrastructure thresholds, institutional decoupling, and cultural resistance are not detectable in survey data, but they account for the bulk of the unexplained adoption variance. They extend existing adoption theory by naming context-specific mechanisms: external IT control as an unmodeled constraint on organizational agency (RS8); nonlinear threshold effects in technological readiness (RS14); policy-practice decoupling in the organizational and environmental constructs (RS2, RS6, RS7); and systemic human capital flight (“brain drain”) as a structural skills deficit that standard adoption models do not address (RS7). 5. Discussion 5.1 Overall Pattern: What the 90% Gap Tells Us The structural model explained 10% of variance in AI Adoption Readiness and roughly 1% of variance in HRM Effectiveness. Read in isolation, these figures suggest the model failed. They should instead be read as a finding: traditional TOE-TAM factors explain only about one-tenth of adoption variation in Zanzibar context. The qualitative phase, designed explicitly to address this gap, identified four mechanisms absent from the model vendor lock-in, infrastructure failure, institutional decoupling, and systemic skill deficits. These mechanisms appear to explain more of actual adoption behavior than the standard predictors. The explanatory sequential design is what made this pattern identifiable: the low R² triggered the qualitative phase, and the qualitative phase named the missing variables. 5.2 Hypothesis-by-Hypothesis Analysis H1: Technological Readiness → AI Adoption Readiness (SUPPORTED) TR predicted AIR (β = 0.203, p = 0.047). The effect is modest but is the strongest predictor in the model, and the finding is theoretically interpretable: in Zanzibar, organizations cannot move to the “soft” problems policy alignment, management culture, user attitudes without first solving the “hard” problem of basic infrastructure. This is consistent with the Digital Transformation Strategy for Africa (2020–2030), which argues that without foundational connectivity and data centers, policy initiatives remain aspirational (Aker & Cariolle, 2022 ). The qualitative data specify what “infrastructure problems” actually means in this context: it is not simply slow internet. It includes vendor lock-in (RS8), chronic power outages (RS14), absent technical support (RS3), and an expertise gap driven in part by brain drain (RS7). Together, these suggest that TR functions as a threshold condition rather than a continuous predictor below a minimum infrastructure level, the other TOE and TAM factors are inert. This departs from standard TOE modeling, where TR is treated as one predictor among several rather than as a prerequisite for the others to matter (Saghafian et al., 2021 ). Implication Infrastructure investment reliable connectivity, power systems, secure servers should come before, not alongside, investments in management training or policy reform in resource-constrained SIDS contexts. H2: Organizational Readiness → AI Adoption Readiness (NOT SUPPORTED) OR did not predict AIR (β = -0.159, p = 0.321). Both TOE and institutional theory predict that management support and resource allocation drive adoption (Baharuddin and Omar, 2024 ), so this null result is the most theoretically surprising in the model. The explanation lies in institutional decoupling (Boxenbaum & Jonsson, 2017; Hensel & Guérard, 2019 ). Organizations in Zanzibar adopt formal digital HR policies in response to external pressure government mandates, donor requirements, international frameworks but these commitments remain on paper rather than in practice. Three mechanisms sustain this decoupling. First, policy proliferation without resourcing: governments announce digital transformation strategies to signal modernity and attract development finance, without adequate budgets or monitoring systems (Yukhno, 2022 ). Second, organizational buffering: agencies absorb new policy commitments while shielding core HR operations from actual change staff continue using paper-based systems despite digital HR mandates. Third, external vendor control (RS8) removes organizational agency: formal readiness is meaningless if critical HR infrastructure is owned by an outside ICT company that controls access. Implication Management declarations of support and nominally allocated budgets do not produce adoption readiness when technology is externally controlled and when policy commitment is not backed by operational resourcing. H3: Environmental Readiness → AI Adoption Readiness (NOT SUPPORTED) ER did not predict AIR (β = 0.135, p = 0.272). Government initiatives such as eGAZ exist in formal terms, but as RS7 observed their impact “remains at the policy and strategic levels, with limited practical implementation or resources reaching organizations.” RS2 specified that available government support targets task simplification through established technology, not AI integration specifically. This is consistent with broader research on implementation gaps in African public administration, where strategic plans routinely diverge from operational realities (Badghish & Soomro, 2024 ). Environmental readiness in Zanzibar is symbolic: policy signals create institutional visibility but not the organizational capacity, technical support, or resource flows that would drive actual adoption. Implication Policy announcements (eGAZ, Digital Economy Blueprint) require accompanying capacity transfers, monitoring mechanisms, and resource allocation to produce adoption at the organizational level. The signal alone is insufficient. H4: Perceived Ease of Use → Perceived Usefulness (SUPPORTED) PEOU significantly predicted PU (β = 0.552, p < 0.001), replicating the classical TAM mechanism in a developing-country public sector context. This is the strongest path in the model and confirms that individual cognitive evaluation of technology tools works as theorized. Respondents acknowledged usability as relevant RS4 raised data security concerns; RS5 emphasized training but these concerns were secondary to infrastructure and governance questions. The TAM cognitive logic holds at the individual level; the problem is that it does not propagate to organizational adoption decisions. H5 and H6: Perceived Usefulness and Ease of Use → Adoption Readiness (NOT SUPPORTED) Neither PU (β = -0.056, p = 0.588) nor PEOU (β = -0.108, p = 0.305) predicted AIR. PEOU influences PU (H4), but neither attitude variable reaches the organizational adoption decision. The cognitive TAM mechanism functions internally at the individual level but breaks down at the point of organizational action. This outcome makes most sense in a mandatory adoption context. In Zanzibar public and private sectors, individual HR professionals do not choose to adopt systems based on their personal evaluation of those systems’ utility. Adoption is centrally mandated as RS3 stated, “Adoption is a government directive, not a choice.” Under these conditions, individual attitudes toward usefulness and ease of use are decoupled from organizational adoption decisions because: systems are imposed hierarchically; concerns over data sovereignty (RS4) and job security (RS5) dominate over usability judgments; and infrastructure failures power outages, connectivity problems make system usability irrelevant regardless of design quality (RS14). This is a theoretically substantive finding. The TAM has been applied predominantly in voluntary-use contexts (Dinev & Hu, 2007 ). In mandatory, resource-constrained public sector settings, the attitude-to-adoption link does not hold. Future adoption models for SIDS contexts need to treat adoption voluntariness as an explicit moderator rather than an assumed background condition. Implication Improving system usability or communicating usefulness will not drive adoption in mandatory contexts where adoption is governed by institutional directive, infrastructure constraints, and job security concerns. H7: AI Adoption Readiness → HRM Effectiveness (NOT SUPPORTED) AIR did not predict HRME (β = 0.100, p = 0.513). This null result does not mean AI-HR systems are ineffective. It means the readiness-to-effectiveness relationship is temporally lagged and organizationally contingent. Technology adoption research describes a three-stage trajectory: installation (systems deployed, staff trained, processes formalized), routinization (systems embedded in workflows, workarounds reduced), and value realization (measurable improvements in efficiency, data quality, and strategic HR outcomes). Zanzibar appears to be in the early installation phase, where being ready to adopt is necessary but not sufficient for performance gains. Respondents confirmed this: systems “exist but are underutilized” (RS1, RS5, RS6), though some improvements in data accessibility were visible (RS7). A cross-sectional design cannot detect effectiveness improvements that take 12–24 months to materialize as implementation moves from installation to routinization. Implication Near-term non-significance does not indicate AI-HR ineffectiveness. It indicates that readiness-to-effectiveness requires sustained training, process reengineering, organizational restructuring, and data quality investment over time. 5.3 Theoretical Contribution: A SIDS-Specific Adoption Model This study shows that the TOE-TAM framework operates differently in Small Island Developing States. Five specific implications follow. First, technological readiness functions as a threshold condition rather than a continuous predictor. Below a minimum infrastructure level, the other TOE and TAM factors do not matter. Second, technology sovereignty is central to organizational agency. External vendor controls unaddressed by the TOE framework as originally formulated functionally overrides organizational readiness and blocks local capacity building. Third, in mandatory public and private sector contexts, the TAM attitude-to-adoption mechanism breaks down. Individual attitudes toward usefulness and ease of use are decoupled from adoption decisions made centrally and hierarchically. Adoption voluntariness is not a background assumption; it is a moderating variable. Fourth, institutional decoupling explains why OR and ER were non-significant despite their theoretical relevance. Organizations satisfy external legitimacy requirements through symbolic policy adoption while insulating operational practice from actual change. Fifth, the readiness-effectiveness relationship (H7) is likely temporal. Cross-sectional designs cannot detect adoption’s performance effects, which require longitudinal tracking from installation through routinization to value realization. Table 3 TOE-TAM in Developed vs. SIDS Contexts Factor Developed economies Zanzibar (SIDS) Infrastructure Assumed reliable Threshold condition Organizational Readiness Operationally meaningful Decoupled from practice Environmental Readiness Policy drives behavior Symbolic compliance TAM (PU/PEOU) Predicts adoption Irrelevant in mandatory context Adoption → Effectiveness Contemporaneous Temporally lagged Vendor sovereignty Assumed internal Externally controlled 6. Conclusion This study examined AI-enabled HR system adoption in Zanzibar public and private sectors using an explanatory sequential mixed-methods design. PLS-SEM identified technological readiness as the only significant predictor of AI-HR adoption readiness. Organizational readiness, environmental readiness, perceived usefulness, and perceived ease of use all produced null results. The subsequent qualitative interviews identified four mechanisms behind these findings: vendor lock-in and external IT control; digital and energy infrastructure functioning as a threshold condition; institutional decoupling between formal AI policies and operational practice; and cultural resistance to automating HR judgment. These mechanisms, invisible to standardized survey instruments, account for the roughly 90% of adoption variance left unexplained by the TOE-TAM model. They also explain why the TAM’s core assumption that individual attitudes predict adoption behavior does not hold in mandatory, centrally governed public sector settings. 6.1 Practical Implications Several priorities follow for the Revolutionary Government of Zanzibar and sector policymakers. Policy must shift from symbolic declaration to operational implementation. Linking digital and AI-HR strategies to specific budget lines, training programs, and monitoring mechanisms is the minimum needed to close the decoupling gap observed in this study. Priority investments should target connectivity, secure servers, reliable power supply, and hardware for HR units’ technological readiness is the only empirically validated predictor of adoption, and the evidence points to layered infrastructure failure, not simple Internet access. Capacity building requires targeted, credentialed upskilling in AI-enabled recruitment, analytics, and data ethics, alongside Swahili or bilingual interfaces where English fluency is a barrier. Data security and sovereignty concerns require regulatory guidance and organizational ownership of critical HR data. A phased adoption strategy makes sense: deploy AI tools in lower-sensitivity domains training, recruitment, leave management before moving to payroll and contract management. Longer-term, education policy must promote digital competence from primary through tertiary levels to build the human capital base that the current digital transformation agenda assumes but does not have. 6.2 Limitations Several limitations bound the interpretation. The cross-sectional design prevents examination of temporal dynamics; the non-significant H7 path may reflect a lag rather than an absence of effect, with performance improvements emerging 12–24 months post-implementation. Longitudinal designs are needed to track the installation-routinization-value realization trajectory. OR’s measurement failure negative Cronbach’s alpha, near-zero reliability, negative loadings on OR2 and OR4 means that management support, staff skills, and budget allocation operated independently in this sample rather than as components of a single construct. Future research should develop context-specific, multidimensional measures of organizational capacity for SIDS settings, rather than importing scales from developed-economy contexts. All quantitative data are self-reported, which introduces social desirability bias, particularly in ratings of organizational readiness and HRM effectiveness. Objective measures such as system usage logs and HR process cycle times would strengthen validity. The Zanzibar sample (n = 133) is broadly representative within the SIDS context but limits generalization to mainland Tanzania, other African countries, or middle-income economies. The qualitative sample (n = 15) was modest in size; data saturation was reached when no new themes emerged in the final interviews, consistent with established guidelines (Guest et al., 2006 ). Finally, AI applications in HR were nascent at the time of data collection; replication as AI-HR maturity increases in Zanzibar would clarify whether adoption drivers shift. 6.3 Future Research Longitudinal designs tracking organizations from installation through routinization to value realization are needed to test the lag hypothesis for H7. Comparative studies across SIDS and African contexts are needed to assess whether the infrastructure threshold effect and institutional decoupling mechanisms generalize. Systematic comparisons of mandatory and voluntary AI adoption settings would clarify when and how the TAM attitude-to-adoption link is severed. Intervention studies that pilot targeted infrastructure investments and measure their effects on adoption and HRM outcomes would provide stronger causal evidence. Finally, future work should develop a “Sociocultural Readiness” construct parallel to TR, OR, and ER that integrates cultural preferences for human judgment, data sovereignty concerns, and brain drain vulnerability, then tests this construct’s interactions with the TOE-TAM framework in SIDS contexts. The current study provides the qualitative evidence needed to specify such a construct; its psychometric development and structural testing are the natural next step. In resource-constrained public and private sectors, AI-HR adoption depends more on concrete digital infrastructure, institutional coherence, and sociocultural fit than on the abstract readiness constructs that dominate the adoption literature. By identifying how these factors interact in Zanzibar public and private services, this study refines technology adoption theory for SIDS and offers a more realistic basis for digital transformation policy in the Global South. Declarations Conflict of Interest The authors declare no conflicts of interest. Ethics This study was approved by the XX University Research Ethics Committee. Informed consent was obtained from all participants. Funding This study received no specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments The authors thank all survey respondents and interview participants who contributed time and insights to this study, and the Revolutionary Government of Zanzibar and eGAZ for facilitating access to key stakeholders. Data Availability The data supporting the findings of this study are available from the corresponding author upon request. References Abane JA, Brenya E, Agyapong AB (2023) Employee perception of electronic human resource management and COVID-19 restrictions in public organizations: The experience of Ghana Revenue Authority, Bono Region. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9472562","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626326566,"identity":"0bf1b765-a2d0-4c1f-ad83-44fb11824d3c","order_by":0,"name":"Jecha Jecha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACxgYGNhAtAyYZKoCYmbmBKC08EC1nQFoY8WsBAogWiAFtUGPwAeb25mcPfvw5zMPH3p34uXBebTR/O1DLj4ptuB3Wc8zcsLftMA8bz9nN0jO3Hc+dcZixgbHnzG3cWmbksEnwNgC1SORukObddiy3AaiFmbENj5b5b9gk/wAdxib/dvNv3jnHcucT1DKDh02ahw1kC+82ad6GmtwNBLX0pJlJy7alA/2Su82a59iB3I1ALQfx+cWw/fAzyTd/rOXk289uvs1TU5c77/zhgw9+VODR0oDKPwwmD+BUDwTyaPw6fIpHwSgYBaNghAIANU9WwHok/9QAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7784-0994","institution":"Zanzibar University","correspondingAuthor":true,"prefix":"","firstName":"Jecha","middleName":"","lastName":"Jecha","suffix":""},{"id":626326567,"identity":"d7ba1c58-93c1-468b-a1a0-f69bfd5e006d","order_by":1,"name":"Ally Ali","email":"","orcid":"","institution":"Zanzibar University","correspondingAuthor":false,"prefix":"","firstName":"Ally","middleName":"","lastName":"Ali","suffix":""},{"id":626326568,"identity":"e7ba7c79-2bcd-49a3-812e-d032d7e29604","order_by":2,"name":"Adilu Mussa","email":"","orcid":"","institution":"Zanzibar University","correspondingAuthor":false,"prefix":"","firstName":"Adilu","middleName":"","lastName":"Mussa","suffix":""}],"badges":[],"createdAt":"2026-04-20 12:58:21","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9472562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9472562/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107411925,"identity":"6f938b8d-0fcc-4816-b0fd-98e39da4ac01","added_by":"auto","created_at":"2026-04-21 09:13:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120104,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Structural Model of AI Readiness and HRM Effectiveness\u003c/p\u003e","description":"","filename":"Framework.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9472562/v1/5705bb97180d9a1f4a7ec350.jpg"},{"id":107411928,"identity":"61c8bb68-f542-48d3-9a54-12a807502216","added_by":"auto","created_at":"2026-04-21 09:13:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38375,"visible":true,"origin":"","legend":"\u003cp\u003ePLS Path Diagram from SmartPLS\u003c/p\u003e","description":"","filename":"PLSSEM.png","url":"https://assets-eu.researchsquare.com/files/rs-9472562/v1/83d80cf54cbedf7877cb36ed.png"},{"id":107412058,"identity":"4d399b8a-c083-498e-ba9d-4f9f0c9dbcd1","added_by":"auto","created_at":"2026-04-21 09:13:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":817012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9472562/v1/aad3c635-809b-49c0-8c93-3ef78f295bd5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eWhen Readiness Doesn’t Lead to Adoption: A TOE-TAM Analysis of AI-Enabled HR Digitalization in a Resource-Constrained SIDS\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global HR function is changing. Administrative roles are giving way to strategic ones, increasingly mediated by AI-enabled systems. AI in HR promises real efficiency gains and better workforce analytics, but implementation runs into compounding institutional barriers. Without standardized AI risk-governance frameworks, organizations struggle to prioritize risks and maintain ethical oversight in HR practice (Madanchian \u0026amp; Taherdoost, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research on digital transformation leadership shows that relationship building and operational innovation matter for aligning technological change with organizational capability (Van Roekel et al., 2025).\u003c/p\u003e \u003cp\u003eMost of this research, however, has been conducted in private-sector organizations in Global North economies that already have functioning regulatory and digital infrastructure. Public administrations face different pressures they prioritize citizen welfare and procedural fairness over innovation (Yao, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wittmann \u0026amp; Meynhardt, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These differences produce distinct governance requirements and slower adoption trajectories, especially in resource-constrained countries. Small Island Developing States (SIDS) face additional structural problems: limited fiscal capacity, underdeveloped ICT infrastructure, and dependence on external donor funding, all of which complicate digital transformation (Faustine \u0026amp; Rachmawati, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mwita \u0026amp; Kitole, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Venugopal et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZanzibar, a semi-autonomous SIDS with stated policy ambitions around AI and digital government, illustrates these tensions clearly. Territories in this position must invest simultaneously in \u0026ldquo;hard\u0026rdquo; infrastructures systems, connectivity, data architectures and \u0026ldquo;soft\u0026rdquo; infrastructures regulation, standards, workforce capability typically exceeding available policymaking capacity. Many developing economies, unlike the European Union, lack comparable institutional scaffolding for AI governance (Wittmann \u0026amp; Meynhardt, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The situation is further complicated by AI integration\u0026rsquo;s negative externalities in HR: reduced collaboration, concerns about bias, privacy risks, and job reconfiguration (Ara and Ahmad, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These outcomes threaten HR performance and employee trust precisely where institutional safeguards are weakest.\u003c/p\u003e \u003cp\u003eSeveral research gaps follow. Technology adoption literature treats adoption primarily as a leadership and organizational challenge (Adiazmil et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Van Roekel et al., 2025), but there is limited evidence on how organizational and environmental readiness conditions shape AI adoption in public and private sectors within SIDS and the Global South. The effects of digital transformation leadership, institutional constraints, and governance pressures on AI adoption readiness and HRM effectiveness under resource constraints are poorly understood. This limits the transferability of existing frameworks to organizations in small, donor-dependent economies such as Zanzibar, Tanzania.\u003c/p\u003e \u003cp\u003eThis study examines how technological, organizational, and environmental readiness shapes AI adoption and HRM effectiveness in Zanzibar\u0026rsquo;s public and private sectors, and tests whether standard TOE-TAM predictors remain operative under mandatory, resource-constrained conditions. It uses an integrated TOE-TAM framework built around seven constructs: Technological Readiness (TR), Organizational Readiness (OR), Environmental Readiness (ER), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), AI Adoption Readiness (AIR), and HRM Effectiveness (HRME) (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qu \u0026amp; Kim, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This lens allows assessment of both institutional conditions and user perceptions in a resource-constrained context (Gong et al., 2025). Unexpectedly, the organizational readiness construct exhibited measurement failure negative reliability coefficients and opposing item loadings across management support and responsiveness-to-change indicators a finding substantively interpretable within institutional decoupling theory and indicative that standard Western readiness constructs may not transfer cleanly to Sub-Saharan SIDS contexts. The focus is on HR professionals, IT managers, and administrators responsible for digital transformation in Zanzibar\u0026rsquo;s key public and private institutions.\u003c/p\u003e"},{"header":"2. Literature Review and Conceptual Framework","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Integrating TOE (Macro) and TAM (Micro)\u003c/h2\u003e \u003cp\u003eThe Technology-Organization-Environment (TOE) framework explains organizational technology adoption through three contexts: technological (existing and emerging technologies, infrastructure), organizational (resources, structure, culture), and environmental (industry conditions, policy, regulation). Tornatzky and Fleischer (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) argued that adoption decisions reflect both internal organizational capabilities and external constraints.\u003c/p\u003e \u003cp\u003eThe Technology Acceptance Model (TAM) operates at the individual level. It holds that system acceptance is shaped by perceived usefulness (PU) whether a system improves job performance and perceived ease of use (PEOU) whether a system is cognitively effortless (Davis, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Across a range of systems and contexts, PU and PEOU predict usage intentions, with PEOU functioning as an antecedent to PU (Sek et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Zanzibar public and private sectors, both frameworks are relevant in different ways. TOE captures macro-level conditions: infrastructure, organizational resources, and policy. TAM captures the micro-level attitudes of HR officers who actually use the tools. Studies integrating both frameworks confirm that contextual and psychological factors jointly shape AI use in HR (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tambe et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Tanzania specifically, Faustine and Rachmawati (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that technological, organizational, and environmental factors together determine SMEs\u0026rsquo; AI uptake. Zanzibar AI-HR adoption is constrained by infrastructure and policy at the TOE level, while success at the individual level depends on HR professionals finding the tools useful and manageable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technological Context: Infrastructure as Binding Constraint \u0026rarr; H1\u003c/h2\u003e \u003cp\u003eThe technological context in TOE includes technologies in use and available for use: ICT infrastructure, system reliability, integration capacity, and cybersecurity. Poor connectivity, unreliable power, legacy systems, and weak security are documented obstacles to AI adoption in developing economies.\u003c/p\u003e \u003cp\u003eFor AI-enabled HR, technological readiness means internet bandwidth, stable electricity, secure servers, compatible HRIS platforms, and functional technical support. Faustine and Rachmawati (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that in medium-sized Tanzanian enterprises, technological factors drive both AI adoption and HRM effectiveness. A 2023 study from Ghana found that e-HRM tools improved HR systems only where the technology infrastructure was fully in place (Asamoah-Appiah \u0026amp; Kesari, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Zanzibar, where infrastructure is uneven, technological readiness is a binding constraint: without reliable connectivity and platforms, other adoption drivers\u0026rsquo; culture, policy cannot function.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eTechnological readiness positively affects AI-enabled HR adoption readiness in organizations in Zanzibar.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Organizational Context: Budget, Culture, and Management Support \u0026rarr; H2\u003c/h2\u003e \u003cp\u003eThe organizational context in TOE includes firm size, structure, financial resources, HR capabilities, culture, and management commitment to ICT initiatives. Management support and organizational readiness determine whether AI projects get funded, receive strategic alignment, and become embedded in core HR workflows. A study of AI adoption in Malaysian tourism organizations found that technological competency, management support, and competitive pressure all predicted adoption (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hierarchical cultures and low digital skills among HR staff obstruct e-HRM implementation even when basic technologies are available (Muzaffar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003eOrganizational readiness positively affects AI-enabled HR adoption readiness in organizations in Zanzibar.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Environmental Context: Policy and Regulatory Pressure \u0026rarr; H3\u003c/h2\u003e \u003cp\u003eThe environmental context in TOE is the external environment: government policies, regulations, and competitive pressures. In developing countries, governments shape ICT and AI adoption through digital strategies and data protection regulations (Shahadat et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research in sub-Saharan Africa shows that coherent digital policies accelerate e-government adoption, while fragmented policies drive ICT project failure (Asamoah-Appiah \u0026amp; Kesari, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Zanzibar, where AI-HR is tied to the broader digital economy agenda, environmental readiness through effective policy frameworks is expected to generate institutional pressure for adoption at the organizational level.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eEnvironmental readiness (including government policies and competitive pressures) positively affects AI-enabled HR adoption readiness in organizations in Zanzibar.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 TAM: User Perceptions of AI-HR Tools \u0026rarr; H4, H5, H6\u003c/h2\u003e \u003cp\u003eTAM holds that PU and PEOU shape attitudes toward a system, behavioral intention, and eventual use (Mohd Amir et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Davis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) established that PEOU influences PU, because systems seen as easier to use tend to be seen as more useful. This relationship has been replicated across applications and contexts (Venkatesh \u0026amp; Davis, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn AI-enabled HRM research, studies show that HR professionals\u0026rsquo; perceptions of usefulness and ease of use predict their intention to adopt AI tools (Xing et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In Malaysia, both PU and PEOU were significant alongside TOE factors in predicting AI adoption in HR (Islam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Zanzibar, where HR officers\u0026rsquo; digital skills and language preferences vary (Swahili versus English interfaces), PU and PEOU are expected to shape whether AI-HR systems gain traction once deployment becomes organizationally possible.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003ePerceived ease of use of AI-enabled HR tools positively affects perceived usefulness.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH5\u003c/b\u003e: Perceived usefulness of AI-enabled HR tools positively affects AI-enabled HR adoption readiness. \u003cb\u003eH6\u003c/b\u003e: Perceived ease of use of AI-enabled HR tools positively affects AI-enabled HR adoption readiness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 From Adoption Readiness to HRM Effectiveness \u0026rarr; H7\u003c/h2\u003e \u003cp\u003eProperly implemented e-HRM tools improve HRM effectiveness and organizational performance (Agarwal, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A Ghanaian study found that e-HRM tools improved HRM effectiveness, with downstream effects on organizational effectiveness (Asamoah-Appiah \u0026amp; Kesari, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Faustine and Rachmawati (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported a positive effect of AI adoption on HRM effectiveness in Tanzanian enterprises. AI-enabled HR adoption readiness (AIR) captures an organization\u0026rsquo;s intent and capability to implement AI in HR processes; HRM effectiveness (HRME) refers to improvements in HR service quality, data reliability, and strategic contribution.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH7\u003c/strong\u003e \u003cp\u003eAI-enabled HR adoption readiness positively affects perceived HRM effectiveness in organizations in Zanzibar.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Conceptual Framework Summary\u003c/h2\u003e \u003cp\u003eCombining TOE and TAM, the conceptual model proposes that TR, OR, and ER shape AIR, while PU and PEOU influence AIR both directly and indirectly through the classical TAM pathway. AIR is expected to improve HRME, consistent with evidence from AI and e-HRM studies in developing countries (Faustine \u0026amp; Rachmawati, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Asamoah-Appiah \u0026amp; Kesari, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This integration tests how macro-level constraints (infrastructure, budgets, policy) and micro-level user perceptions jointly determine AI-HR readiness and its downstream effect on HRM effectiveness in Zanzibar.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Method","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study uses an explanatory sequential mixed-methods design quantitative data first, then qualitative data collected to explain the quantitative findings (Creswell \u0026amp; Clark, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ivankova et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Two sequential phases are connected by a deliberate analytical logic: the qualitative phase exists because the quantitative phase produced unexpected results that a survey cannot explain on its own.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhase 1 (Quantitative)\u003c/strong\u003e \u003cp\u003eA structured survey (n\u0026thinsp;=\u0026thinsp;133) using PLS-SEM to test the integrated TOE-TAM framework and identify statistical relationships among TR, OR, ER, PU, PEOU, AIR, and HRME.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhase 2 (Qualitative)\u003c/strong\u003e \u003cp\u003eSemi-structured interviews (n\u0026thinsp;=\u0026thinsp;15) with HR directors, IT managers, and policymakers, selected to explain the specific null results from Phase 1.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe phases connect at three points. First, the quantitative results determined what needed explaining specifically, why only H1 and H4 were supported while H2, H3, H5, H6, and H7 were not. Second, qualitative participant selection was purposively guided by the survey sample. Third, integration occurred during interpretation: interview excerpts were embedded directly in the hypothesis-by-hypothesis discussion to explain statistical patterns (Creswell \u0026amp; Clark, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Context and Sampling\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Quantitative Phase\u003c/h2\u003e \u003cp\u003eZanzibar is a Small Island Developing State with constrained ICT infrastructure and significant dependence on external donor funding for technology initiatives (Faustine \u0026amp; Rachmawati, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mwita \u0026amp; Kitole, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The target population was HR professionals, IT managers, and administrators in public and private institutions involved in digital HR workflow transitions (Abane et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Taher et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf 133 valid responses, 91 came from government agencies (68.4%), 32 from private-sector organizations (24.1%), five from SMEs (3.8%), and five from NGOs (3.8%). This distribution reflects Zanzibar organizational landscape, which is dominated by public-sector employment. Roles included 56 HR managers and staff (42.1%), 11 IT managers (8.3%), 18 general managers (13.5%), and 48 others (36.1%). Professional experience ranged from 1 to 53 years (Mdn\u0026thinsp;=\u0026thinsp;6 years). The 100% response rate and cross-sector representation suggest minimal non-response bias and adequate coverage of the HR and IT populations in Zanzibar.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Qualitative Phase\u003c/h2\u003e \u003cp\u003eFollowing the quantitative phase, 15 respondents participated in semi-structured interviews lasting 45\u0026ndash;60 minutes. The sample included six HR Directors and Officers, four IT Managers and Coordinators, three Finance Directors, and two machine-learning experts. Participants were selected based on their survey participation, their sector representation across universities, government, and private organizations, and their ability to speak directly to the non-significant quantitative paths. This selection logic is consistent with explanatory sequential design principles, where quantitative results guide purposive qualitative sampling (Ivankova et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Measures\u003c/h2\u003e \u003cp\u003eThe survey instrument adapted scales from both TAM and TOE frameworks, rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 5\u0026thinsp;=\u0026thinsp;Strongly Agree) (Chiu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jamil et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jia \u0026amp; Hou, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The constructs were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTechnological Readiness (TR)\u003c/b\u003e: Infrastructure, Internet connectivity, and system availability (4 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOrganizational Readiness (OR)\u003c/b\u003e: Management support, budget allocation, and strategic planning (4 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnvironmental Readiness (ER)\u003c/b\u003e: Government policies, regulatory pressures, and peer institutional influence (4 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePerceived Usefulness (PU)\u003c/b\u003e: Belief that AI-HR tools improve job performance (4 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePerceived Ease of Use (PEOU)\u003c/b\u003e: Belief that AI-HR tools are easy to operate (3 items after preliminary screening)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAI Adoption Readiness (AIR)\u003c/b\u003e: Willingness to adopt AI-based HR systems (5 items)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHRM Effectiveness (HRME)\u003c/b\u003e: Improvements in HR processes, speed, and accuracy (4 items)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eItem PEOU4 (\u0026ldquo;Internet connectivity issues do NOT hinder my use of HR tools\u0026rdquo;) was a reverse-scored item (Scholtz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Preliminary analysis confirmed that raw responses were inversely related to the other PEOU indicators. The item was reverse-coded before analysis to maintain directional consistency with the construct (Mason et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Weijters et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe qualitative interview guide targeted the specific patterns emerging from Phase 1. Questions covered HR management practices (manual versus digital), infrastructure constraints beyond Internet access, managerial understanding of AI-HR value, skill gaps among HR and IT staff, the effectiveness of government digitalization programs (particularly eGAZ), cultural trust in algorithmic HR decisions, software usability and language barriers, and the observed effects of digitalization on HR performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis Strategy\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Quantitative Analysis\u003c/h2\u003e \u003cp\u003ePLS-SEM was run in SmartPLS 4 (Ringle et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PLS-SEM was chosen for its capacity to handle non-normal data distributions and its suitability for exploratory studies extending theory into new contexts rather than confirming established relationships (Arunraju Chinnaraju, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Analysis followed two stages: measurement model assessment (reliability and validity), then structural model hypothesis testing using bootstrapping with 5,000 subsamples (Karimi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Murshid et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Qualitative Analysis\u003c/h2\u003e \u003cp\u003eInterview transcripts were analyzed through framework-guided thematic analysis in four stages. Deductive coding applied predefined categories from the quantitative TOE-TAM constructs: TR, OR, ER, PU, PEOU, AIR, and HRME. Inductive coding captured emergent themes from participants\u0026rsquo; accounts, including institutional decoupling, vendor dependence, and brain drain concerns. Pattern matching then compared qualitative themes against quantitative results to find convergence and divergence. Finally, integration involved triangulating findings through participant validation: all 15 interviewees confirmed that the instrument reflected subject-matter relevance, language clarity, and content appropriateness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Integration Procedures\u003c/h2\u003e \u003cp\u003eFollowing Creswell and Clark (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), quantitative and qualitative strands were combined by connecting data collection and presenting statistical findings alongside explanatory quotations. Integration operated at three levels. At the sampling level, quantitative outcomes directed qualitative participant selection. At the interpretive level, qualitative findings were embedded in the hypothesis-by-hypothesis discussion to contextualize both significant and non-significant results. At the narrative level, participant quotations explained statistical patterns, clarified unexpected null findings, and revealed contextual factors the survey could not capture.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003eEthical approval\u003c/strong\u003e was granted by the Zanzibar University Research Ethics Committee (ZUREC). Participants received an information sheet covering the study\u0026rsquo;s purpose, procedures, risks and benefits, and their right to withdraw without penalty. Written informed consent was obtained before survey and interview participation. Data were anonymized through removal of identifiable information and assignment of unique participant codes.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Measurement Model\u003c/h2\u003e \u003cp\u003eConstruct reliability and validity were assessed following Hair et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) criteria for PLS-SEM. Indicator reliability was confirmed for five constructs: all outer loadings for TR, PEOU, PU, AIR, and HRME exceeded 0.708. For ER, items ER1, ER2, and ER4 had acceptable loadings (0.799, 0.743, and 0.771, respectively), but ER3 loaded at 0.347 below the threshold. ER3 was retained but flagged as a weak indicator, and the ER construct results should be read with corresponding caution.\u003c/p\u003e \u003cp\u003eThe OR construct presents a more serious problem. Cronbach\u0026rsquo;s alpha was negative (α = -0.105), rho_a was near zero (0.097), and composite reliability (rho_c) was 0.171, all well below acceptable thresholds. OR2 and OR4 produced negative outer loadings (-0.358 and \u0026minus;\u0026thinsp;0.217), while OR1 and OR3 loaded positively (0.619 and 0.731). This pattern suggests that OR items do not form a unidimensional construct in this context. Management support and responsiveness to change appear to operate as separate, possibly opposing, dimensions rather than as components of a single readiness factor. This finding is substantively interpretable and consistent with the institutional decoupling theme in the qualitative data, discussed below. OR is retained for theoretical completeness but treated as a failed measurement construct for analytical purposes.\u003c/p\u003e \u003cp\u003eInternal consistency was acceptable for the remaining five constructs, with Cronbach\u0026rsquo;s alpha and rho_c values ranging from 0.710 to 0.904. Convergent validity was confirmed for TR, PEOU, PU, AIR, and HRME, with AVE values between 0.520 and 0.703. ER\u0026rsquo;s AVE was 0.476 marginally below the 0.50 threshold, again traceable to ER3. Discriminant validity was assessed via the heterotrait-monotrait (HTMT) ratio, with all values below 0.85 for the well-functioning constructs (Henseler et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All 15 interview participants confirmed the instrument\u0026rsquo;s relevance, language clarity, and content appropriateness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Model Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoading range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003erho_c\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR1\u0026ndash;TR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.620\u0026ndash;0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER1\u0026ndash;ER4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.347\u0026ndash;0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eER3\u0026thinsp;=\u0026thinsp;0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR1\u0026ndash;OR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.358\u0026ndash;0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMeasurement failure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEOU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u0026ndash;0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU1\u0026ndash;PU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.741\u0026ndash;0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIR1\u0026ndash;AIR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.632\u0026ndash;0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRME1\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.760\u0026ndash;0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Structural Model\u003c/h2\u003e \u003cp\u003eAll inner VIF values fell below 3.0 (range: 1.03\u0026ndash;1.68), ruling out collinearity problems. Model fit: The saturated model SRMR (0.086) meets the Hair et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) threshold of \u0026lt;\u0026thinsp;0.10, confirming acceptable fit for the unconstrained solution. The estimated model SRMR (0.132) exceeds this threshold, reflecting model complexity introduced by the OR construct\u0026rsquo;s measurement irregularities rather than a fundamental structural misspecification consistent with Henseler et al. (2015), who note that fit indices in the estimated model are sensitive to construct-level specification problems. The gap between the two SRMR values is therefore informative: it indicates that the structural restrictions imposed by the proposed theoretical model diverge from the unconstrained saturated solution, an expected and interpretable outcome when a failed measurement construct (OR) is retained for theoretical transparency. The model explained roughly 30% of the variance in PU (R\u0026sup2; = 0.30), 10% of the variance in AIR (R\u0026sup2; = 0.10), and about 1% of the variance in HRME (R\u0026sup2; = 0.01). These R\u0026sup2; values are consistent with exploratory adoption research in complex public sector environments (Safie et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHypotheses were tested using bootstrapping (5,000 samples, two-tailed, α\u0026thinsp;=\u0026thinsp;0.05). Results are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis Testing Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSupported?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTR \u0026rarr; AIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR \u0026rarr; AIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eER \u0026rarr; AIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU \u0026rarr; PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU \u0026rarr; AIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEOU \u0026rarr; AIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIR \u0026rarr; HRME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e β\u0026thinsp;=\u0026thinsp;standardized path coefficient. Bootstrap subsamples\u0026thinsp;=\u0026thinsp;5,000. The 95% CI for H1 is (\u0026minus;\u0026thinsp;0.003, 0.388), indicating marginal significance. All other non-significant paths crossed zero.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo paths were supported. TR significantly predicted AIR (H1: β\u0026thinsp;=\u0026thinsp;0.203, p\u0026thinsp;=\u0026thinsp;0.047) the only structural predictor that held. PEOU significantly predicted PU (H4: β\u0026thinsp;=\u0026thinsp;0.552, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), replicating the classical TAM cognitive mechanism. The five remaining hypotheses were not supported. OR (β = -0.159, p\u0026thinsp;=\u0026thinsp;0.321), ER (β\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.272), PU (β = -0.056, p\u0026thinsp;=\u0026thinsp;0.588), PEOU (β = -0.108, p\u0026thinsp;=\u0026thinsp;0.305), and AIR (β\u0026thinsp;=\u0026thinsp;0.100, p\u0026thinsp;=\u0026thinsp;0.513) did not predict their respective outcome variables. These null results position the qualitative phase as analytically essential: they indicate that standard TOE-TAM predictors are not functioning as expected in this context, and the reasons need to be identified through in-depth accounts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Qualitative Results\u003c/h2\u003e \u003cp\u003eSemi-structured interviews (n\u0026thinsp;=\u0026thinsp;15) were conducted after the quantitative phase to explain the statistical findings. Thematic analysis guided by the explanatory sequential logic produced four themes that contextualize the quantitative null results.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Vendor Lock-In and External IT Control\u003c/h2\u003e \u003cp\u003eMultiple respondents reported that their organizations held no effective sovereignty over HR technology infrastructure. RS8 (IT Manager, Government Agency) described the problem directly: \u003cem\u003e\u0026ldquo;Existing HR systems were not owned by the Revolutionary Government of Zanzibar. All access and infrastructure were possessed by one ICT company from Tanga, while Zanzibar was given minimum access, which endangered HR records, data, and the economy as well.\u0026rdquo;\u003c/em\u003e This external vendor dependence overrides organizational readiness management support, budget, and skills cannot compensate for the absence of technical control providing a proximate explanation for why OR was non-significant in the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Hard Infrastructure as Threshold Condition\u003c/h2\u003e \u003cp\u003eRespondents described layered infrastructure failures that blocked adoption regardless of measured readiness levels.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS14 (power)\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;We have power problems caused by frequent power outages, limiting HR systems and making AI adoption difficult.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS3 (technical support)\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Insufficient technical support capacity and limited in-house expertise for system maintenance, cybersecurity monitoring, and system optimization leads to slow issue resolution and underutilization of available digital platforms.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS7 (expertise)\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;AI needs greater preparation and investment. Problems such as lack of finance, experts, awareness, and institutional readiness affect AI-HR. The government lacks expertise in AI. In the private sector too, we have more than 200 employees, but very few possess AI or digital skills.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eTaken together, these accounts suggest that infrastructure operates as a threshold condition, not a continuous predictor: organizations must first reach minimum infrastructure thresholds before other adoption factors become operative. This explains why TR was significant while OR and ER theoretically important but dependent on functioning infrastructure were not.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Institutional Decoupling: Policy vs. Practice\u003c/h2\u003e \u003cp\u003eRespondents consistently described a gap between formal digitalization policy and what actually happens in their organizations a pattern consistent with the institutional decoupling literature (Boxenbaum \u0026amp; Jonsson, 2017).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS1\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Our management\u0026rsquo;s understanding of AI in HR is mixed. Some leaders clearly understand its value, but others still see it mainly as a buzzword, with limited practical understanding of how AI can support HR operations.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS2\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Formal policies exist supporting digitalization, but limited implementation; there is discrepancy between policy intention and operational reality.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS6\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Just policy documents no change I have seen from these initiatives.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS7\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;The intention behind initiatives such as eGAZ and the Digital Economy Blueprint is good. However, in practice, their impact is not yet fully realized. Much of the support remains at the policy and strategic levels, with limited practical implementation or resources reaching organizations.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThis decoupling explains why OR and ER were non-significant despite their theoretical importance. Formal commitments and policy pronouncements do not translate into operational capacity or resource allocation at the point of actual adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Cultural Resistance: The Human Touch Question\u003c/h2\u003e \u003cp\u003eSeveral respondents expressed normative objections to automating HR functions that they saw as requiring human judgment.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS3\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;HR cannot be integrated by AI. Our HR system depends on rules, regulations, acts, and policies. These all depend on human touch, human thinking, discussion, and group decisions rather than automation.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS7\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Human resources in Zanzibar depend much on policies, Acts, provisions, which could not be interpreted perfectly through any digital system.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS9\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Zanzibar can adopt AI-HR tools under special selection such as AI-Training, AI-Recruitment and Selection, AI-Staff Leave, while employees\u0026rsquo; records, payroll, reporting, and contract management should remain manual.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRS5\u003c/strong\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Many teachers resist using digital systems for examinations, results, and biometric machines. They believe manual systems are better than automatic or AI systems.\u0026rdquo;\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThese four themes vendor lock-in, infrastructure thresholds, institutional decoupling, and cultural resistance are not detectable in survey data, but they account for the bulk of the unexplained adoption variance. They extend existing adoption theory by naming context-specific mechanisms: external IT control as an unmodeled constraint on organizational agency (RS8); nonlinear threshold effects in technological readiness (RS14); policy-practice decoupling in the organizational and environmental constructs (RS2, RS6, RS7); and systemic human capital flight (\u0026ldquo;brain drain\u0026rdquo;) as a structural skills deficit that standard adoption models do not address (RS7).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Overall Pattern: What the 90% Gap Tells Us\u003c/h2\u003e \u003cp\u003eThe structural model explained 10% of variance in AI Adoption Readiness and roughly 1% of variance in HRM Effectiveness. Read in isolation, these figures suggest the model failed. They should instead be read as a finding: traditional TOE-TAM factors explain only about one-tenth of adoption variation in Zanzibar context. The qualitative phase, designed explicitly to address this gap, identified four mechanisms absent from the model vendor lock-in, infrastructure failure, institutional decoupling, and systemic skill deficits. These mechanisms appear to explain more of actual adoption behavior than the standard predictors. The explanatory sequential design is what made this pattern identifiable: the low R\u0026sup2; triggered the qualitative phase, and the qualitative phase named the missing variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Hypothesis-by-Hypothesis Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eH1: Technological Readiness \u0026rarr; AI Adoption Readiness (SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTR predicted AIR (β\u0026thinsp;=\u0026thinsp;0.203, p\u0026thinsp;=\u0026thinsp;0.047). The effect is modest but is the strongest predictor in the model, and the finding is theoretically interpretable: in Zanzibar, organizations cannot move to the \u0026ldquo;soft\u0026rdquo; problems policy alignment, management culture, user attitudes without first solving the \u0026ldquo;hard\u0026rdquo; problem of basic infrastructure. This is consistent with the Digital Transformation Strategy for Africa (2020\u0026ndash;2030), which argues that without foundational connectivity and data centers, policy initiatives remain aspirational (Aker \u0026amp; Cariolle, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe qualitative data specify what \u0026ldquo;infrastructure problems\u0026rdquo; actually means in this context: it is not simply slow internet. It includes vendor lock-in (RS8), chronic power outages (RS14), absent technical support (RS3), and an expertise gap driven in part by brain drain (RS7). Together, these suggest that TR functions as a threshold condition rather than a continuous predictor below a minimum infrastructure level, the other TOE and TAM factors are inert. This departs from standard TOE modeling, where TR is treated as one predictor among several rather than as a prerequisite for the others to matter (Saghafian et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplication\u003c/strong\u003e \u003cp\u003eInfrastructure investment reliable connectivity, power systems, secure servers should come before, not alongside, investments in management training or policy reform in resource-constrained SIDS contexts.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: Organizational Readiness \u0026rarr; AI Adoption Readiness (NOT SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOR did not predict AIR (β = -0.159, p\u0026thinsp;=\u0026thinsp;0.321). Both TOE and institutional theory predict that management support and resource allocation drive adoption (Baharuddin and Omar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), so this null result is the most theoretically surprising in the model. The explanation lies in institutional decoupling (Boxenbaum \u0026amp; Jonsson, 2017; Hensel \u0026amp; Gu\u0026eacute;rard, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Organizations in Zanzibar adopt formal digital HR policies in response to external pressure government mandates, donor requirements, international frameworks but these commitments remain on paper rather than in practice.\u003c/p\u003e \u003cp\u003eThree mechanisms sustain this decoupling. First, policy proliferation without resourcing: governments announce digital transformation strategies to signal modernity and attract development finance, without adequate budgets or monitoring systems (Yukhno, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Second, organizational buffering: agencies absorb new policy commitments while shielding core HR operations from actual change staff continue using paper-based systems despite digital HR mandates. Third, external vendor control (RS8) removes organizational agency: formal readiness is meaningless if critical HR infrastructure is owned by an outside ICT company that controls access.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplication\u003c/strong\u003e \u003cp\u003eManagement declarations of support and nominally allocated budgets do not produce adoption readiness when technology is externally controlled and when policy commitment is not backed by operational resourcing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: Environmental Readiness \u0026rarr; AI Adoption Readiness (NOT SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eER did not predict AIR (β\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.272). Government initiatives such as eGAZ exist in formal terms, but as RS7 observed their impact \u0026ldquo;remains at the policy and strategic levels, with limited practical implementation or resources reaching organizations.\u0026rdquo; RS2 specified that available government support targets task simplification through established technology, not AI integration specifically.\u003c/p\u003e \u003cp\u003eThis is consistent with broader research on implementation gaps in African public administration, where strategic plans routinely diverge from operational realities (Badghish \u0026amp; Soomro, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Environmental readiness in Zanzibar is symbolic: policy signals create institutional visibility but not the organizational capacity, technical support, or resource flows that would drive actual adoption.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplication\u003c/strong\u003e \u003cp\u003ePolicy announcements (eGAZ, Digital Economy Blueprint) require accompanying capacity transfers, monitoring mechanisms, and resource allocation to produce adoption at the organizational level. The signal alone is insufficient.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH4: Perceived Ease of Use \u0026rarr; Perceived Usefulness (SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePEOU significantly predicted PU (β\u0026thinsp;=\u0026thinsp;0.552, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), replicating the classical TAM mechanism in a developing-country public sector context. This is the strongest path in the model and confirms that individual cognitive evaluation of technology tools works as theorized. Respondents acknowledged usability as relevant RS4 raised data security concerns; RS5 emphasized training but these concerns were secondary to infrastructure and governance questions. The TAM cognitive logic holds at the individual level; the problem is that it does not propagate to organizational adoption decisions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5 and H6: Perceived Usefulness and Ease of Use \u0026rarr; Adoption Readiness (NOT SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNeither PU (β = -0.056, p\u0026thinsp;=\u0026thinsp;0.588) nor PEOU (β = -0.108, p\u0026thinsp;=\u0026thinsp;0.305) predicted AIR. PEOU influences PU (H4), but neither attitude variable reaches the organizational adoption decision. The cognitive TAM mechanism functions internally at the individual level but breaks down at the point of organizational action.\u003c/p\u003e \u003cp\u003eThis outcome makes most sense in a mandatory adoption context. In Zanzibar public and private sectors, individual HR professionals do not choose to adopt systems based on their personal evaluation of those systems\u0026rsquo; utility. Adoption is centrally mandated as RS3 stated, \u0026ldquo;Adoption is a government directive, not a choice.\u0026rdquo; Under these conditions, individual attitudes toward usefulness and ease of use are decoupled from organizational adoption decisions because: systems are imposed hierarchically; concerns over data sovereignty (RS4) and job security (RS5) dominate over usability judgments; and infrastructure failures power outages, connectivity problems make system usability irrelevant regardless of design quality (RS14).\u003c/p\u003e \u003cp\u003eThis is a theoretically substantive finding. The TAM has been applied predominantly in voluntary-use contexts (Dinev \u0026amp; Hu, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In mandatory, resource-constrained public sector settings, the attitude-to-adoption link does not hold. Future adoption models for SIDS contexts need to treat adoption voluntariness as an explicit moderator rather than an assumed background condition.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplication\u003c/strong\u003e \u003cp\u003eImproving system usability or communicating usefulness will not drive adoption in mandatory contexts where adoption is governed by institutional directive, infrastructure constraints, and job security concerns.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH7: AI Adoption Readiness \u0026rarr; HRM Effectiveness (NOT SUPPORTED)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAIR did not predict HRME (β\u0026thinsp;=\u0026thinsp;0.100, p\u0026thinsp;=\u0026thinsp;0.513). This null result does not mean AI-HR systems are ineffective. It means the readiness-to-effectiveness relationship is temporally lagged and organizationally contingent. Technology adoption research describes a three-stage trajectory: installation (systems deployed, staff trained, processes formalized), routinization (systems embedded in workflows, workarounds reduced), and value realization (measurable improvements in efficiency, data quality, and strategic HR outcomes). Zanzibar appears to be in the early installation phase, where being ready to adopt is necessary but not sufficient for performance gains.\u003c/p\u003e \u003cp\u003eRespondents confirmed this: systems \u0026ldquo;exist but are underutilized\u0026rdquo; (RS1, RS5, RS6), though some improvements in data accessibility were visible (RS7). A cross-sectional design cannot detect effectiveness improvements that take 12\u0026ndash;24 months to materialize as implementation moves from installation to routinization.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplication\u003c/strong\u003e \u003cp\u003eNear-term non-significance does not indicate AI-HR ineffectiveness. It indicates that readiness-to-effectiveness requires sustained training, process reengineering, organizational restructuring, and data quality investment over time.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Theoretical Contribution: A SIDS-Specific Adoption Model\u003c/h2\u003e \u003cp\u003eThis study shows that the TOE-TAM framework operates differently in Small Island Developing States. Five specific implications follow.\u003c/p\u003e \u003cp\u003eFirst, technological readiness functions as a threshold condition rather than a continuous predictor. Below a minimum infrastructure level, the other TOE and TAM factors do not matter. Second, technology sovereignty is central to organizational agency. External vendor controls unaddressed by the TOE framework as originally formulated functionally overrides organizational readiness and blocks local capacity building.\u003c/p\u003e \u003cp\u003eThird, in mandatory public and private sector contexts, the TAM attitude-to-adoption mechanism breaks down. Individual attitudes toward usefulness and ease of use are decoupled from adoption decisions made centrally and hierarchically. Adoption voluntariness is not a background assumption; it is a moderating variable. Fourth, institutional decoupling explains why OR and ER were non-significant despite their theoretical relevance. Organizations satisfy external legitimacy requirements through symbolic policy adoption while insulating operational practice from actual change.\u003c/p\u003e \u003cp\u003eFifth, the readiness-effectiveness relationship (H7) is likely temporal. Cross-sectional designs cannot detect adoption\u0026rsquo;s performance effects, which require longitudinal tracking from installation through routinization to value realization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTOE-TAM in Developed vs. SIDS Contexts\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeveloped economies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZanzibar (SIDS)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssumed reliable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThreshold condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganizational Readiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperationally meaningful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecoupled from practice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Readiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolicy drives behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbolic compliance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAM (PU/PEOU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicts adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrelevant in mandatory context\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdoption \u0026rarr; Effectiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContemporaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemporally lagged\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVendor sovereignty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssumed internal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExternally controlled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examined AI-enabled HR system adoption in Zanzibar public and private sectors using an explanatory sequential mixed-methods design. PLS-SEM identified technological readiness as the only significant predictor of AI-HR adoption readiness. Organizational readiness, environmental readiness, perceived usefulness, and perceived ease of use all produced null results. The subsequent qualitative interviews identified four mechanisms behind these findings: vendor lock-in and external IT control; digital and energy infrastructure functioning as a threshold condition; institutional decoupling between formal AI policies and operational practice; and cultural resistance to automating HR judgment.\u003c/p\u003e \u003cp\u003eThese mechanisms, invisible to standardized survey instruments, account for the roughly 90% of adoption variance left unexplained by the TOE-TAM model. They also explain why the TAM\u0026rsquo;s core assumption that individual attitudes predict adoption behavior does not hold in mandatory, centrally governed public sector settings.\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Practical Implications\u003c/h2\u003e \u003cp\u003eSeveral priorities follow for the Revolutionary Government of Zanzibar and sector policymakers. Policy must shift from symbolic declaration to operational implementation. Linking digital and AI-HR strategies to specific budget lines, training programs, and monitoring mechanisms is the minimum needed to close the decoupling gap observed in this study. Priority investments should target connectivity, secure servers, reliable power supply, and hardware for HR units\u0026rsquo; technological readiness is the only empirically validated predictor of adoption, and the evidence points to layered infrastructure failure, not simple Internet access.\u003c/p\u003e \u003cp\u003eCapacity building requires targeted, credentialed upskilling in AI-enabled recruitment, analytics, and data ethics, alongside Swahili or bilingual interfaces where English fluency is a barrier. Data security and sovereignty concerns require regulatory guidance and organizational ownership of critical HR data. A phased adoption strategy makes sense: deploy AI tools in lower-sensitivity domains training, recruitment, leave management before moving to payroll and contract management. Longer-term, education policy must promote digital competence from primary through tertiary levels to build the human capital base that the current digital transformation agenda assumes but does not have.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations bound the interpretation. The cross-sectional design prevents examination of temporal dynamics; the non-significant H7 path may reflect a lag rather than an absence of effect, with performance improvements emerging 12\u0026ndash;24 months post-implementation. Longitudinal designs are needed to track the installation-routinization-value realization trajectory.\u003c/p\u003e \u003cp\u003eOR\u0026rsquo;s measurement failure negative Cronbach\u0026rsquo;s alpha, near-zero reliability, negative loadings on OR2 and OR4 means that management support, staff skills, and budget allocation operated independently in this sample rather than as components of a single construct. Future research should develop context-specific, multidimensional measures of organizational capacity for SIDS settings, rather than importing scales from developed-economy contexts.\u003c/p\u003e \u003cp\u003eAll quantitative data are self-reported, which introduces social desirability bias, particularly in ratings of organizational readiness and HRM effectiveness. Objective measures such as system usage logs and HR process cycle times would strengthen validity. The Zanzibar sample (n\u0026thinsp;=\u0026thinsp;133) is broadly representative within the SIDS context but limits generalization to mainland Tanzania, other African countries, or middle-income economies. The qualitative sample (n\u0026thinsp;=\u0026thinsp;15) was modest in size; data saturation was reached when no new themes emerged in the final interviews, consistent with established guidelines (Guest et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Finally, AI applications in HR were nascent at the time of data collection; replication as AI-HR maturity increases in Zanzibar would clarify whether adoption drivers shift.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Future Research\u003c/h2\u003e \u003cp\u003eLongitudinal designs tracking organizations from installation through routinization to value realization are needed to test the lag hypothesis for H7. Comparative studies across SIDS and African contexts are needed to assess whether the infrastructure threshold effect and institutional decoupling mechanisms generalize. Systematic comparisons of mandatory and voluntary AI adoption settings would clarify when and how the TAM attitude-to-adoption link is severed. Intervention studies that pilot targeted infrastructure investments and measure their effects on adoption and HRM outcomes would provide stronger causal evidence.\u003c/p\u003e \u003cp\u003eFinally, future work should develop a \u0026ldquo;Sociocultural Readiness\u0026rdquo; construct parallel to TR, OR, and ER that integrates cultural preferences for human judgment, data sovereignty concerns, and brain drain vulnerability, then tests this construct\u0026rsquo;s interactions with the TOE-TAM framework in SIDS contexts. The current study provides the qualitative evidence needed to specify such a construct; its psychometric development and structural testing are the natural next step.\u003c/p\u003e \u003cp\u003eIn resource-constrained public and private sectors, AI-HR adoption depends more on concrete digital infrastructure, institutional coherence, and sociocultural fit than on the abstract readiness constructs that dominate the adoption literature. By identifying how these factors interact in Zanzibar public and private services, this study refines technology adoption theory for SIDS and offers a more realistic basis for digital transformation policy in the Global South.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eThis study was approved by the XX University Research Ethics Committee. Informed consent was obtained from all participants.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study received no specific grants from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors thank all survey respondents and interview participants who contributed time and insights to this study, and the Revolutionary Government of Zanzibar and eGAZ for facilitating access to key stakeholders.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbane JA, Brenya E, Agyapong AB (2023) Employee perception of electronic human resource management and COVID-19 restrictions in public organizations: The experience of Ghana Revenue Authority, Bono Region. 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Asia-Pacific J Inform Technol Multimedia 4(2):69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17576/apjitm-2015-0402-06\u003c/span\u003e\u003cspan address=\"10.17576/apjitm-2015-0402-06\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zanzibar University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI adoption, human resource management, developing countries, TOE-TAM, institutional decoupling, SIDS, PLS-SEM, mandatory adoption","lastPublishedDoi":"10.21203/rs.3.rs-9472562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9472562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines barriers to adopting AI-enabled human resource management (AI-HR) in Zanzibar’s public and private sectors, focusing on how adoption dynamics in resource-constrained, mandatory settings diverge from those theorized in developed economies. An explanatory sequential mixed-methods design was used: a structured survey of HR professionals and managers (n = 133) was followed by semi-structured interviews (n = 15) conducted to explain unexpected quantitative results. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested an integrated Technology-Organization-Environment and Technology Acceptance Model (TOE-TAM) framework. Only technological readiness predicted AI-HR adoption readiness (β = 0.203, p = 0.047); organizational readiness, environmental readiness, perceived usefulness, and perceived ease of use were non-significant. The organizational readiness construct itself exhibited measurement failure negative Cronbach’s alpha (α = -0.105) and opposing outer loadings across items a pattern substantively consistent with institutional decoupling manifesting at the measurement level, where management support and responsiveness to change operated as opposing rather than unified organizational dimensions. The qualitative phase identified four mechanisms behind these null results: vendor lock-in and external IT control; digital and energy infrastructure functioning as a threshold rather than a continuous predictor; institutional decoupling between formal policies and operational practice; and cultural resistance to automating HR judgment. These mechanisms not visible to survey instruments challenge the assumed universality of the Technology Acceptance Model in mandatory, resource-constrained public sector contexts and indicate that AI-HR implementation requires resolving hard infrastructure deficits before organizational and attitudinal levers become operative. The study offers a refined adoption model for Small Island Developing States and practical guidance for Zanzibar’s digital transformation agenda.\u003c/p\u003e","manuscriptTitle":"When Readiness Doesn’t Lead to Adoption: A TOE-TAM Analysis of AI-Enabled HR Digitalization in a Resource-Constrained SIDS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 09:11:14","doi":"10.21203/rs.3.rs-9472562/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"27e7a922-bbdb-44c9-8b3f-04a30e48cb2b","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T09:11:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 09:11:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9472562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9472562","identity":"rs-9472562","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}