In Pakistan's ICT sector, internal readiness — not outside pressure — drives AI uptake; firms with compatible infrastructure and stronger technical capability are far more likely to adopt AI, while regulatory or market pressures show little effect.
Drivers of AI Adoption: The Role of Innovation Attributes, Organizational Capability, and the External Environment
Among Pakistani ICT professionals, perceived compatibility with existing systems and organizational technical capability strongly predict AI adoption intentions, while external environmental pressures do not.
Artificial Intelligence continues to reshape the ICT sector in Pakistan, yet organizations differ widely in how and why they adopt this technology. This study explores the key drivers of AI adoption by focusing on national ICT professionals who work directly with digital systems and emerging technologies. A total of 110 valid responses were collected through an organized online survey using purposive sampling. The investigation was guided by Technology Organization Environment framework combined with innovation characteristics from Diffusion of Innovation theory. The variables examined include the perceived suitability of AI to current systems, the benefits and complexity of adopting AI, organizational technical capability, and external environmental pressures. Data analysis involved Smart PLS-SEM, which facilitated reliability and validity assessment along with hypothesis evaluation. The outcomes highlight that seamless compatibility with existing infrastructure plays a key role in encouraging AI adoption, offers clear operational value, and is not overly difficult to implement. Technical capability also demonstrates a strong influence, indicating that firms with mature digital systems are better prepared to integrate AI solutions. In contrast, external environmental pressures did not show a significant role in the adoption process. These findings highlight that internal technological perceptions and readiness are stronger predictors of AI adoption than external forces in operating ICT firms in Pakistan. The study provides insights that can help organizations strengthen their technical readiness and make more confident decisions when transitioning toward AI enabled transformation. This study contributes to AI adoption literature by isolating organizational technical capability and providing national level evidence from an emerging ICT economy.
Summary
Main Finding
In Pakistan’s ICT sector, AI adoption is driven primarily by technology-internal factors: perceived innovation attributes (especially compatibility with existing systems) and organizational technical capability significantly increase firms’ likelihood of adopting AI. External environmental pressures (government policy, competitive pressure, vendor ecosystems) did not show a significant direct effect on adoption in this sample.
Key Points
- Sample and focus: 110 purposively sampled ICT professionals (IT engineers, AI specialists, developers, technical managers) across major Pakistani cities; survey Aug–Oct 2025.
- Theoretical framing: Technology–Organization–Environment (TOE) framework augmented with Diffusion of Innovation (DOI) attributes (compatibility, relative advantage, complexity).
- Main hypotheses:
- H1: Innovation attributes → AI adoption — supported.
- H2: Organizational technical capability → AI adoption — supported.
- H3: External environment → AI adoption — not supported (no significant direct effect).
- Most influential innovation attribute: compatibility (fit with current networks, data and software environments). Perceived benefits (productivity, customer service, resource utilization) and manageable complexity also helped adoption.
- Organizational technical capability was treated separately from managerial capability and showed a strong positive effect (standardized measures indicate high internal consistency).
- External factors (competitive pressure, government support, vendor trends) showed limited direct explanatory power for AI adoption in this national ICT sample.
- Reliability/validity (reported): Cronbach’s alpha: AI adoption .821; Innovation attributes .881; Organizational capability .881; External environment .751. Composite reliabilities and AVE reported in the paper indicate acceptable internal consistency, though some AVE numbers are marginal.
Data & Methods
- Design: Cross-sectional quantitative survey, self-administered online questionnaire using 5-point Likert items.
- Respondents: Predominantly younger professionals (20–35 yrs), mostly bachelor’s/masters, 1–6 years’ experience in ICT roles.
- Sampling: Purposive sampling to target respondents with direct AI/IT experience; N = 110 (authors note this meets common PLS-SEM 10× rule).
- Measures: Constructs for AI adoption, innovation attributes (compatibility, relative advantage, complexity), organizational capability (technical + some managerial items originally, though authors emphasize technical capability), and external environment. Some scale items were deleted during validation.
- Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping for hypothesis testing; measurement model assessed via Cronbach’s alpha, rho_A, composite reliability, AVE.
- Timeline & context: Pakistan ICT sector, data collected Aug–Oct 2025; paper published in a 2026 conference proceeding.
Implications for AI Economics
- For empirical models of AI diffusion and productivity:
- Firm-level technical capability and system compatibility should be key covariates/predictors. Models that focus only on external stimuli (policy, competition) may miss the strongest determinants of adoption in technologically intensive firms.
- Compatibility with legacy systems and data architectures mediates adoption costs and implementation speed — important when estimating adoption thresholds and aggregate diffusion curves.
- For policy and intervention design:
- Policies that only provide incentives or mandates may have limited direct impact unless paired with measures that build technical capacity (infrastructure grants, standardized data platforms, subsidized integration services, workforce upskilling).
- Public programs should prioritize infrastructure interoperability, cybersecurity standards, and firm-level integration support (technical assistance, vendor matchmaking, incubation of middleware solutions).
- For firms and markets:
- Vendors and consultants should target interventions that reduce integration costs and complexity (prebuilt connectors, deployment templates, managed integration services), since compatibility and technical readiness strongly influence purchase decisions.
- Investment in internal IT standards, data governance, and scalable infrastructure can yield higher adoption likelihood and faster ROI from AI projects.
- For macroeconomic modeling and forecasts:
- Heterogeneity in firm technical readiness implies uneven productivity gains from AI across sectors and countries. Aggregate projections should incorporate the distribution of firm-level capabilities, not just aggregate demand or policy variables.
- Export performance and competitiveness gains from AI will depend on firms’ technical absorptive capacity; interventions that raise this capacity can change growth and trade outcomes more effectively than broad-based regulatory incentives alone.
Limitations & Suggestions for Future Research
- Cross-sectional, self-reported data from a purposive sample (N=110) limits generalizability; potential common-method bias.
- Some measurement inconsistencies are present in the paper (deleted scale items; AVE values reported near/under conventional thresholds).
- Recommendations: replicate with larger, representative samples; include objective adoption measures (investment amounts, deployed systems); test mediating/moderating roles (e.g., firm size, export orientation, managerial capability); run longitudinal studies and cross-country comparisons to see if external environment matters more in other contexts.
Assessment
Paper Typecorrelational
Evidence Strengthlow — Findings are based on a small (n=110), purposive, non-probability sample of self-reported survey responses collected at one point in time, analyzed correlationally; this leaves results vulnerable to selection bias, common-method variance, reverse causality, omitted variables, and limited statistical power for subgroup or robustness checks.
Methods Rigormedium — The study uses an appropriate structural equation modelling tool (Smart PLS-SEM) and reports reliability and validity assessments, which is suitable for theory testing with latent constructs and smaller samples; however, the borderline sample size, purposive sampling, lack of exogenous identification or longitudinal data, and limited discussion of robustness or endogeneity mitigation reduce overall rigor.
Sample110 valid responses from national ICT professionals in Pakistan collected via an organized online survey using purposive (non-probability) sampling; respondents are professionals working directly with digital systems and emerging technologies in ICT firms (no firm-level random sampling, timing not specified).
Themesadoption org_design
IdentificationCross-sectional online survey of ICT professionals analyzed with PLS-SEM to test associations implied by the Technology-Organization-Environment and Diffusion of Innovation frameworks; no experimental or quasi-experimental source of exogenous variation used, so causal identification is not achieved.
GeneralizabilityNon-probability purposive sampling limits representativeness of the broader ICT workforce in Pakistan., Findings are country- and sector-specific (ICT firms in Pakistan) and may not generalize to other industries or national contexts., Self-reported measures and cross-sectional design limit inference to actual adoption behavior over time., Small sample size constrains generalization across firm sizes, regions, or sub-sectors within ICT.
Claims (10)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| A total of 110 valid responses were collected through an organized online survey using purposive sampling. Other | null_result | high | sample_size / data_collection |
n=110
0.5
|
| The investigation was guided by the Technology-Organization-Environment (TOE) framework combined with innovation characteristics from Diffusion of Innovation theory. Other | null_result | high | theoretical framing / constructs selection |
n=110
0.5
|
| Data analysis involved Smart PLS-SEM, which facilitated reliability and validity assessment along with hypothesis evaluation. Other | null_result | high | analytical method used |
n=110
0.5
|
| Seamless compatibility with existing infrastructure plays a key role in encouraging AI adoption among ICT firms in Pakistan. Adoption Rate | positive | high | AI adoption |
n=110
0.3
|
| Perceived operational benefits (clear operational value) of AI encourage its adoption. Adoption Rate | positive | medium | AI adoption |
n=110
0.18
|
| Perceived complexity is not overly high (i.e., AI adoption was not seen as overly difficult to implement), which supports adoption. Adoption Rate | positive | medium | AI adoption (in relation to perceived complexity) |
n=110
0.18
|
| Organizational technical capability demonstrates a strong influence on AI adoption; firms with mature digital systems are better prepared to integrate AI solutions. Adoption Rate | positive | high | AI adoption |
n=110
0.3
|
| External environmental pressures did not show a significant role in the adoption process. Adoption Rate | null_result | high | AI adoption (in relation to external/environmental pressure) |
n=110
0.3
|
| Internal technological perceptions and readiness are stronger predictors of AI adoption than external forces in operating ICT firms in Pakistan. Adoption Rate | positive | high | AI adoption (relative importance of predictors) |
n=110
0.3
|
| This study contributes to AI adoption literature by isolating organizational technical capability and providing national-level evidence from an emerging ICT economy (Pakistan). Other | positive | high | scholarly contribution / evidence scope |
n=110
0.3
|