Systemic Assessment of IoT Readiness and Economic Impact in Postal Services

Abstract

This research develops and applies the IoTRIM model to assess the economic and operational implications of IoT integration in postal and courier enterprises in Slovakia. Combining a multi-criteria evaluation framework with an extended Cobb–Douglas production function, the analysis captures both readiness levels and their translation into output performance. The IoTRIM assessment reveals heterogeneous distributions of strengths across four strategic and technical pillars, with notable disparities between connectivity, data analytics, and interoperability capacities. Monte Carlo simulations under pessimistic, realistic, and optimistic scenarios highlight divergent digital trajectories among enterprises, with some demonstrating accelerated gains from IoT readiness while others face structural bottlenecks in infrastructure and process integration. Hypothesis testing indicates that while a positive and statistically significant relationship between IoT readiness and output is observed in selected cases, this effect is not universal across all enterprises and scenarios. However, the inclusion of IoT readiness consistently improves the explanatory power of the production function models. The findings underline that digital transformation outcomes depend not only on investment scale but also on systemic absorption capacity, including interoperability, data governance, and organizational alignment. The proposed approach offers both a methodological contribution for measuring digital readiness impacts and practical insights for strategic planning in the postal and courier sector.

1. Introduction

Digital transformation has emerged as one of the defining phenomena of contemporary economic development [1]. While the term itself is frequently associated with advanced analytics, automation, and real-time decision-making, its implications are far broader encompassing organizational restructuring, changes in business models, and new approaches to value creation [2,3,4]. Since the early 2000s, research and policy agendas have increasingly emphasized the potential of digital technologies to enhance productivity, drive innovation, and enable more resilient economies [5,6,7,8]. However, the benefits of digital transformation have not been evenly distributed. Sectors such as finance, telecommunications, and manufacturing have seen rapid adoption, whereas others including healthcare, education, and public services have lagged [9]. These disparities are particularly pronounced in traditional service sectors, where operational routines, legacy infrastructure, and regulatory frameworks often pose significant barriers to technological advancement [10].

Unlike manufacturing, where digitalization is often concentrated around automation and optimization of production lines, traditional service sectors face different challenges [11]. Services are typically labor-intensive, customer-facing, and context-dependent, which makes the integration of digital technologies more complex [12]. Moreover, digital transformation in services requires not only technological tools but also changes in workflows, staff capabilities, and service delivery mechanisms [13]. Sectors such as hospitality, transport, and postal services exemplify these dynamics. While technology can enhance efficiency for instance, through route optimization, self-service kiosks, or predictive maintenance the shift demands significant reconfiguration of internal systems, data flows, and organizational culture [14,15]. As a result, even when the technological components are available, full-scale digital integration remains elusive for many service providers [16].

Postal and courier services represent a particularly interesting case within this context. Despite being logistics-heavy and reliant on time-sensitive operations, the sector has historically evolved within a framework of physical infrastructure, standardized routines, and low-margin competition [17]. The pressure to digitalize has intensified over the last decade, driven by e-commerce growth, environmental regulation, and rising customer expectations around transparency, speed, and personalization [18,19,20]. In response, postal operators have begun adopting solutions such as track-and-trace systems, automated parcel sorting, and smart lockers [21]. Yet, many still operate with fragmented IT systems and lack long-term digital strategies. Small- and medium-sized enterprises often face challenges related to investment capacity, skills shortages, and interoperability with external platforms [22,23]. In this regard, digital transformation is not simply a matter of acquiring new technologies, but requires systemic readiness across technical, organizational, and strategic dimensions.

1.1. The Role and Potential of IoT in Postal and Courier Services

The Internet of Things (IoT) has become a central enabler of digital transformation across various domains, yet its application in postal and courier services remains underexplored [24]. In contrast to capital-intensive industries such as manufacturing or energy, where IoT adoption is often driven by automation of production processes, the postal sector offers a distinctive environment with highly dispersed operations, time-sensitive service requirements, and close interaction with both physical infrastructure and human labor [25]. This complexity creates a rich context for IoT deployment but also introduces a series of implementation challenges.

At its core, IoT refers to a network of interconnected physical devices sensors, actuators, vehicles, handheld terminals capable of collecting, transmitting, and processing data without human intervention [26,27,28,29,30]. In postal and courier operations, such technologies can fulfill a wide range of functions. Smart sensors embedded in parcels or vehicles can monitor temperature, humidity, or shock during transportation, ensuring compliance with handling standards [31]. Real-time vehicle tracking systems enhance operational control, enabling dynamic rerouting based on traffic patterns or delivery priorities [32]. Predictive maintenance of delivery fleets, supported by IoT data, reduces downtime and extends asset life cycles [33]. At the customer interface, IoT-based smart lockers and notification systems improve delivery convenience and reduce failed delivery attempts addressing one of the sector’s chronic inefficiencies [34].

Beyond these operational applications, the strategic potential of IoT lies in data-driven service design and managerial decision-making [35]. With a continuous stream of granular data, enterprises can forecast demand, personalize customer experiences, and monitor performance metrics in near-real time [36]. IoT can also support compliance with sustainability targets by enabling carbon tracking, energy monitoring, and route optimization [37,38]. This multidimensional functionality positions IoT as a transformative technology for the postal and courier industry capable not only of enhancing efficiency but also of generating new value propositions and service models. Nevertheless, the adoption of IoT in the sector is far from uniform. While some national operators and global logistics firms have made significant strides, many small- and medium-sized enterprises continue to face considerable barriers [39]. These include limited access to capital for technology investments, insufficient in-house digital competencies, and fragmented legacy IT systems that are difficult to integrate with modern IoT architectures [40]. Moreover, regulatory uncertainties around data privacy, cybersecurity, and interoperability further complicate large-scale deployment [41]. The sector’s traditional focus on reliability and cost containment also leads to a conservative approach toward technological innovation, making it difficult to justify upfront investments in the absence of proven short-term returns [42].

1.2. Systems Thinking and the Evaluation of Complex Innovations

The implementation of digital technologies in service sectors such as postal and courier services often unfolds within organizational environments characterized by high interdependence, operational complexity, and structural inertia [43]. These conditions challenge traditional models of innovation evaluation, which typically assume linear causality between technological adoption and performance improvement [44]. In contrast, systems thinking offers an alternative perspective one that emphasizes the interconnectedness of components, feedback loops, and emergent properties within organizations undergoing transformation [45,46,47,48].

Systems thinking views an enterprise not as a collection of independent units, but as a dynamic system in which changes in one area inevitably affect others [49]. This is particularly relevant in the context of digital transformation, where new technologies often require concurrent changes in processes, competencies, infrastructure, and culture. For example, the deployment of IoT devices in a courier operation affects not only delivery logistics but also data governance, customer service interfaces, employee training, and decision-making structures [50]. Ignoring these systemic relationships can lead to fragmented implementations and suboptimal outcomes.

The system’s perspective is especially important in legacy-heavy service sectors, where existing infrastructure, institutional practices, and regulatory constraints limit the freedom to adopt and scale new technologies [51]. Here, innovation does not occur in a vacuum; it is shaped by the systemic configuration of the organization and its environment. Evaluating innovation in such contexts requires a shift from static benchmarking tools to frameworks capable of capturing dynamics, dependencies, and cross-domain effects [52]. Systems thinking thus provides a crucial foundation for developing meaningful evaluative models that reflect the real conditions under which digital transformation takes place.

1.3. Research Gap and Objective of the Research

While the discourse on digital transformation has produced a wide range of maturity models, readiness assessments, and strategic frameworks, most existing tools are either overly generic or insufficiently operationalized for the specific needs of service sectors. In the context of postal and courier services, this limitation becomes particularly pronounced. The sector combines logistical complexity, thin margins, and legacy systems yet is increasingly expected to integrate advanced technologies such as the IoT to remain competitive, responsive, and sustainable.

Despite growing attention to IoT adoption, few analytical models capture both the readiness of service providers to implement such technologies and the measurable economic impact of doing so. Existing frameworks tend to either focus narrowly on infrastructure and technical indicators or adopt a qualitative, checklist-style approach that lacks empirical depth. As a result, decision-makers in the sector often lack robust, data-driven tools to guide investment, benchmark progress, or quantify returns. Existing digital maturity and readiness frameworks, such as the Digital Capability Maturity Model (DCMM), the Industry 4.0 Maturity Index, and the Cisco Digital Readiness Framework, have been widely applied in manufacturing and ICT. While these models provide broad conceptual guidance, they remain relatively generic and are not sufficiently operationalized for the specific context of postal and courier services. Sector-specific dimensions such as logistics integration, regulatory constraints, and workforce adaptation tend to be underrepresented. By contrast, the IoTRIM model has been developed with a multi-level structure of pillars, criteria, and indicators tailored to this sector and embedded into a production function to allow for the quantification of economic effects. This methodological gap also hinders comparative research across firms and countries, limiting understanding of how digital transformation unfolds in heterogeneous organizational contexts.

Moreover, much of the research on digital maturity remains rooted in deterministic assumptions viewing technology adoption as a unidirectional process leading to performance gains. As discussed earlier, a systems perspective reveals that the relationship between digital readiness and economic outcomes is more complex and context dependent. It involves multi-layered interactions across infrastructure, human capital, management practices, and institutional settings. Evaluating such complexity requires a model that is both multidimensional and analytically tractable. In this research, IoT readiness is defined as the organization’s capacity to adopt, integrate, and leverage IoT technologies for strategic, operational, and performance-based outcomes. This definition captures both the enabling conditions and the actual ability to transform technological potential into measurable value.

To address this gap, the paper introduces the IoT Readiness & Impact Model (IoTRIM) a systems-informed evaluative framework specifically designed for the postal and courier sector. IoTRIM captures organizational readiness across four key pillars and links these dimensions to productivity outcomes via integration into an extended production function. The model was developed through a synthesis of existing literature, expert consultation, and empirical data from logistics enterprises. By combining qualitative structure with quantitative measurement, IoTRIM offers a novel approach to assessing digital transformation in a complex service environment.

The primary objective of this paper is to operationalize and validate the model through application in real-world settings. In doing so, the research contributes to both theory and practice: it advances methodological tools for evaluating digital transformation and provides actionable insights for postal and courier service providers navigating the path toward smarter, more connected operations.

2. Materials and Methods

To address the multifaceted nature of digital transformation in postal and courier services, a structured methodological approach is adopted. The aim is to evaluate not only the readiness of organizations to implement IoT technologies, but also the economic impact of such readiness on organizational performance. Given the systemic nature of innovation in service sectors, it is necessary to combine conceptual modeling, quantitative measurement, and simulation-based validation. The methods employed in this research are grounded in systems thinking, recognizing that digital transformation processes involve multiple interacting subsystems technological, organizational, and strategic.

The methodology is implemented in three complementary stages. First, a multi-dimensional evaluative framework, the IoTRIM is developed to capture the organizational conditions that enable effective IoT deployment. This model is constructed based on literature synthesis, expert input, and iterative refinement. Second, IoTRIM is integrated into an extended Cobb–Douglas production function to quantify the relationship between digital readiness and labor productivity. Third, Monte Carlo simulations are applied to validate the robustness of the model and assess the variability of outcomes under different configurations of readiness factors. Each stage of the methodology is described in detail in the following subsections.

2.1. IoTRIM Model: Readiness Assessment Methodology

The IoTRIM model is developed as a systemic and modular framework to assess both the organizational preparedness for IoT implementation and its economic impact in the postal and courier services sector. As digital transformation in logistics increasingly relies on interconnected devices and data-driven decision-making, there is a pressing need for a standardized and practically applicable tool to quantify this readiness across key strategic and operational dimensions. IoTRIM addresses this gap by offering a multi-level evaluative structure grounded in systems thinking.

The model builds on the conceptual definition of IoT adopted in this paper: “IoT constitutes part of the digital infrastructure that enables interconnection of devices and systems for the purpose of data collection and processing, through integration and interoperability of technologies, reflecting the strategic orientation of organizations towards efficiency and digitalization.” This definition forms the basis of the model’s architecture. IoTRIM is structured into four main pillars, each representing a critical domain affected by IoT implementation:

• Digital Infrastructure.
• Data Collection and Processing.
• Integration and Interoperability.
• Strategic Orientation.

Each pillar is broken down into two evaluation criteria, which further contain a set of quantifiable indicators. In total, the model consists of 8 criteria and 24 indicators. The hierarchy enables both detailed diagnosis and aggregated evaluation, allowing organizations to identify areas of strength and improvement.

The full structure of the model is summarized in

Table 1. Structure of the IoTRIM.

Pillar	Evaluation Criterion	Indicator
Digital infrastructure	Device connectivity	Share of IoT devices per employee
		Share of devices connected in real time
	Network availability	Share of company premises covered by high-throughput network
		Number of connection outages per month
Data collection & processing	Data collection automation	Share of sensor-acquired data in total data volume
		Frequency of data collection
	Data analytics utilization	Share of analytical reports generated monthly
		Share of automatically processed data
		Existence of an IoT data management platform
		Share of depts. using IoT data in decision-making
Integration & interoperability	System connectivity	Share of systems connected via API / middleware
		Share of IoT systems integrated with ERP/CRM/SCM
		Share of IoT devices connected via cloud services
		Existence of unified data interface for IoT data
	Solution adaptability	Existence of standardized protocol (MQTT, CoAP)
		Share of devices supporting remote updates
		Existence of centralized IoT device management across the entire network
Strategic orientation	IoT investment	Share of IoT-related expenditure in total investment
		Number of IoT projects implemented in last two years
	Governance & planning	Existence of a company-level IoT strategy
		Consideration of IoT data in management d.-making
		Share of employees trained in IoT-related topics
		Existence of a perf. monitoring system for IoT projects
		Existence of regular int. audits of IoT implementation

, which outlines the mapping of pillars, criteria, and indicators. A detailed definition of each indicator including units, data sources, and scaling methods is provided in Appendix A.

The development of IoTRIM followed a hybrid approach combining literature analysis, expert consultation, and practical validation. The criteria and indicators were designed to be:

• Collectible, favoring data that can realistically be obtained from internal reports, databases, or structured expert interviews.
• Comparable, allowing benchmarking across organizations or time periods.
• Representative, capturing technological, data, organizational, and strategic aspects of IoT readiness.
• Flexible, enabling adaptation to other sectors beyond postal services.

To ensure consistency and transparency in scoring, all indicators are scaled to a common 0–100 range. This includes direct percentage indicators, categorical indicators transformed via expert-based scoring scales, and binary indicators scored as 0 or 100.

Each pillar contributes equally (25%) to the overall readiness score. Within each pillar, the two criteria are weighted equally, and each indicator within a criterion is assigned equal weight. This bottom-up aggregation method facilitates clarity and interpretability, while allowing for proportional recalculation if data for a specific indicator are missing.

The model’s structure also supports its integration into broader economic analysis. The aggregated IoTRIM score is used in subsequent stages of this research as an input variable in a modified Cobb–Douglas production function, enabling the quantification of IoT’s economic impact.

The final version of the IoTRIM model was validated through structured expert interviews. Participants confirmed the feasibility of data collection and the relevance of each indicator to real-world IoT readiness. Feedback from these interviews, for example, led to the addition of the Strategic Orientation pillar and refinement of certain indicators to enhance interpretability and data accessibility.

2.2. Integration of IoTRIM into the Cobb–Douglas Production Function

To quantify the economic contribution of IoT implementation, this research employs an extended Cobb–Douglas production function, adapted to include IoT readiness as a distinct explanatory variable. The traditional Cobb–Douglas model has long been used to capture the relationship between input factors typically labor and capital and output, measured as gross value added or revenue [53,54]. While suitable for modeling physical production environments, this formulation can be extended to incorporate intangible and systemic inputs that increasingly drive performance in service-oriented sectors such as logistics and postal delivery. The extended version used in this research takes the following general form:

$$\mathrm{y}={\mathrm{A}\ast \mathrm{K}}^{\mathrm{\alpha }}{\ast \mathrm{L}}^{\mathrm{\beta }}{\ast \mathrm{I}}^{\mathrm{\gamma }},$$

(1)

where

• Y = value added (economic output),
• A = total factor productivity (TFP),
• L = labor input (e.g., number of employees or full-time equivalents),
• K = capital input (e.g., physical assets, IT infrastructure),
• I = IoT readiness index (composite score from IoTRIM),
• α, β, γ = elasticity coefficients of respective inputs.

In this formulation, the coefficient γ captures the marginal contribution of IoT readiness to output, holding other factors constant. This allows us not only to evaluate the significance of IoT as a production input, but also to compare its relative importance with that of labor and capital. Data for Y, L, and K were derived from internal financial and operational reports, while the I variable is the composite IoTRIM index constructed in the previous phase.

For estimation purposes, the production function is log-linearized, enabling the use of linear regression techniques while preserving the multiplicative structure of the original model. This transformation simplifies the interpretation of parameters, as the elasticities can be directly estimated as regression coefficients. The analysis is performed using outputs and inputs collected from postal and courier service providers across various operational profiles.

The integration of the IoTRIM index into the production model represents a novel methodological contribution. It bridges qualitative assessments of digital transformation with quantifiable performance outcomes, advancing the current state of digital readiness evaluation. Furthermore, due to the internal structure of the IoTRIM comprising multiple pillars and indicators, the approach allows for disaggregated analysis, in which specific components of readiness (e.g., infrastructure, interoperability) can be tested for their individual contributions to productivity.

This modeling approach provides not only theoretical coherence, grounded in systems thinking, but also practical utility. The robustness of the estimates is tested and validated through Monte Carlo simulations, as presented in the following section.

2.3. Simulation and Estimation Approach

To assess the robustness of the extended production model and the statistical significance of the IoT readiness variable, a Monte Carlo simulation approach is applied. This method enables the generation of synthetic datasets under controlled assumptions, allowing for the evaluation of parameter stability, estimation variance, and sensitivity to input perturbations. Given the multidimensional nature of the IoTRIM index and the contextual heterogeneity across service providers, simulations serve as an important complement to empirical regression analysis.

Monte Carlo simulation involves repeated random sampling of input parameters from predefined distributions. In this research, probability distributions are assigned to the input variables L, K, and I, based on empirical ranges derived from real-world datasets and expert input. Output values (Y) are then generated using the extended Cobb–Douglas function, incorporating noise terms to reflect realistic uncertainty. The process is repeated across thousands of iterations to build a probabilistic understanding of how variations in digital readiness and traditional inputs affect overall productivity.

The simulation framework enables several key analytical outcomes. First, it allows for the testing of different elasticity scenarios, including both conservative and optimistic assumptions about the impact of IoT readiness. Second, it facilitates robustness checks under varying levels of data quality, such as incomplete reporting or skewed distributions. Third, it supports the identification of threshold effects points at which incremental improvements in readiness lead to disproportionately high productivity gains.

The parameters used in the simulations are calibrated based on descriptive statistics from operational data collected during model development, as well as secondary sources from sectoral reports from Finstat.sk portal. In the absence of full population-level data, this approach provides a statistically grounded basis for inference, particularly valuable in under-researched sectors such as postal and courier logistics.

All simulations are conducted using Python 3.11 and R environments 4.3.1, leveraging open-source statistical packages for reproducibility and scalability. Confidence intervals and sensitivity plots are generated to illustrate the relative stability of estimated elasticities, and to highlight the interaction effects among capital, labor, and IoT readiness. By combining simulation-based inference with classical regression analysis, the methodological design reinforces the credibility of findings and accommodates the systemic nature of digital transformation captured by the IoTRIM framework.

2.4. Hypotheses

In order to examine the relationship between IoT implementation and economic performance in the postal and courier services sector, two research hypotheses were formulated. These hypotheses reflect the dual objective of the paper: to assess the direct contribution of IoT readiness to output and to determine the explanatory power of an extended production model incorporating digital readiness indicators.

H1: 

The implementation of IoT has a positive effect on the output of postal and courier service providers.

H₀₁: 

The elasticity coefficient of IoT readiness (γ) is less than or equal to zero (γ ≤ 0).

H₁₁: 

The elasticity coefficient of IoT readiness (γ) is greater than zero (γ > 0).

This hypothesis is tested using regression results from the extended Cobb–Douglas production function introduced in Section 2.2. A statistically significant and positive value of γ is interpreted as evidence supporting the hypothesis.

H2: 

The Cobb–Douglas model extended by the IoT readiness index explains the variability of output better than the traditional model.

H₀₂: 

The inclusion of the IoT readiness variable does not significantly improve the explanatory power of the model.

H₁₂: 

The inclusion of the IoT readiness variable significantly improves the explanatory power of the model.

This hypothesis is evaluated by applying the F-test for nested models. Comparison is made between the baseline Cobb–Douglas function and the extended version including the IoT readiness index. A significant result of the F-statistic supports the hypothesis that digital readiness contributes meaningfully to output variability.

The testing of both hypotheses is carried out in the following sections using simulated datasets based on real-world parameter ranges observed in postal and courier logistics.

3. Results

3.1. IoTRIM Model Outcomes

The application of the IoTRIM framework to the case study enterprise resulted in a total readiness score of 66.66 in 2024, indicating a moderately advanced stage of IoT integration. This value reflects a balanced yet uneven distribution of performance across the four pillars, with certain strategic capabilities outpacing technical implementation.

Table 2. Case study criterion level scores.

Evaluation Criterion	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Device connectivity	3.50	5.00	6.50	11.00	17.50	21.50	25.00	33.50	41.50	52.50
Network availability	54.00	56.00	58.00	47.50	51.50	42.50	44.00	63.50	70.50	75.00
Data collection automation	2.50	3.00	14.50	17.50	31.00	45.00	46.00	64.00	68.00	72.00
Data analytics utilization	0.75	1.00	3.75	33.25	38.00	43.25	47.00	55.25	60.50	66.75
System connectivity	0.00	0.00	1.25	3.25	34.00	37.25	38.00	45.50	51.75	57.50
Solution adaptability	0.00	0.00	0.00	0.00	1.67	70.00	73.33	77.33	80.67	83.33
IoT investment	0.00	0.00	0.00	2.50	7.50	9.50	12.50	24.00	30.50	39.00
Governance & planning	0.00	0.00	0.00	1.00	2.40	1.00	1.60	83.60	85.60	87.20

presents the scores achieved at the criterion level. The highest-performing criteria in 2024 were Solution Adaptability (83.33) and Governance and Planning (87.20), suggesting that the enterprise has already established a solid strategic orientation and the capacity to integrate digital solutions into its operations effectively. These also results imply that the enterprise can capture relevant operational data at a high frequency and convert it into actionable insights for process optimization.

In contrast, in 2024 Digital Infrastructure (63.75) and Strategic Orientation (63.10) recorded the lowest values, as shown in

Table 3. Case study pillar level scores.

Pillar	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Digital infrastructure	28.75	30.50	32.25	29.25	34.50	32.00	34.50	48.50	56.00	63.75
Data collection & processing	1.63	2.00	9.13	25.38	34.50	44.13	46.50	59.63	64.25	69.38
Integration & interoperability	0.00	0.00	0.63	1.63	17.83	53.63	55.67	61.42	66.21	70.42
Strategic orientation	0.00	0.00	0.00	1.75	4.95	5.25	7.05	53.80	58.05	63.10

at the pillar level. These scores indicate persistent weaknesses in the modernization of technological assets and in the long-term strategic alignment of IoT initiatives, which may constrain the overall pace of digital transformation in the enterprise. Such constraints may slow the pace at which IoT-generated data can be utilized across departments.

From a systemic perspective, the disparity between the strategic and technical dimensions suggests that the enterprise is well-positioned in terms of vision and planning but must bridge the execution gap by reinforcing the underlying infrastructure. Addressing weaknesses in network availability and interoperability is likely to yield a disproportionate positive effect, as improvements in these areas would enable more effective use of the already well-developed device and analytics capabilities.

Figure 1 illustrates the temporal evolution of the IoTRIM score for the analyzed enterprise over the period 2015–2024. In the initial years, the enterprise displayed minimal readiness for IoT implementation, with only marginal year-on-year growth. A more pronounced increase is observable after 2020, corresponding to the adoption of more systematic integration practices for digital technologies. The year 2022 marks a clear turning point, after which the rate of improvement accelerated particularly in the domains of data collection and processing efficiency, interoperability, and the formalization of an IoT strategy at the managerial level.

The observed trajectory indicates that IoT adoption within the enterprise has transitioned from an experimental phase to a structured and strategically supported process. This sustained upward trend suggests that the enterprise has reached a level of maturity where further gains are likely to come from optimizing interoperability and network resilience, rather than from expanding basic device deployment. Consequently, future development should prioritize the consolidation of existing IoT assets into a unified operational ecosystem, ensuring that data flows seamlessly across departments and supports both operational efficiency and long-term strategic objectives.

3.2. Extended Cobb–Douglas Production Function

The Cobb–Douglas production function was applied to examine the relationship between output and the two primary production inputs: capital (K) and labor (L).

Table 4. Baseline input data for the Cobb–Douglas Model in €.

Year	Value Added (Y) in €	Depreciation of Fixed Assets (K) in €	Personnel Cost (L) in €
2015	3,951,482	681,104	2,301,879
2016	4,083,907	722,537	2,394,751
2017	4,051,199	741,688	2,298,172
2018	4,349,418	791,511	2,472,689
2019	4,593,821	810,346	2,485,994
2020	4,703,179	771,264	2,454,411
2021	4,682,109	759,383	2,430,178
2022	5,004,568	853,910	2,543,628
2023	5,002,311	943,207	2,582,742
2024	5,905,823	1,083,661	2,701,884

summarizes the annual values of gross value added, depreciation of fixed assets (proxy for capital), and personnel costs (proxy for labor) for the period 2015–2024. These values formed the basis for the estimation of the model parameters and reflect a gradual increase in all three variables over the observed period, with the most notable growth in capital after 2021.

Figure 2 presents the baseline model results, estimated without the IoTRIM index. The capital elasticity was 0.33, while the labor elasticity reached 1.47. The sum of coefficients (1.80) suggests increasing returns to scale, meaning that a proportional increase in both inputs leads to a more than proportional increase in output. This pattern highlights the enterprise’s strong labor intensity, where output growth is particularly sensitive to changes in personnel-related inputs.

To account for technological factors, the model was extended by including the IoTRIM index as an additional explanatory variable.

Based on results shown in Figure 3 it is possible to compare the adjusted R² values of the baseline and extended models, showing an improvement in explanatory power when IoTRIM is included. The observed increase demonstrates that technological readiness has a measurable impact on the variability of output, even when traditional inputs are controlled for.

Regression analysis results in Figure 4 show potential multicollinearity, revealing only a weak correlation between capital and IoT readiness. This supports the interpretation that IoT adoption operates as an independent factor that complements, rather than substitutes, capital investment.

To verify the statistical significance of the linear dependence between the variables ln(K) and ln(I), the Farrar–Glauber test of multicollinearity was applied. This test is commonly used to detect correlations among explanatory variables through the transformation of the correlation matrix. In the first step, a correlation matrix between ln(K) and ln(I) was constructed in Microsoft Excel using the “Correlation” function within the “Data Analysis” add-in. The correlation coefficient between the variables was r = 0.820814. The determinant of the correlation matrix was then computed using the function = MDETERM(), resulting in |R| = 0.326264. Based on this, the test statistic X² was calculated according to the formula:

$${\chi }^2=-\left(n-1-{\displaystyle \frac{2p+5}{6}}\right)\ast \ln \left(\left|R\right|\right),$$

(2)

where n denotes the number of observations and p the number of variables in the correlation matrix. For n = 10 (the analyzed period 2015–2024) and p = 2, the result was:

$${\chi }^2=-\left(10-1-{\displaystyle \frac{2\ast 2+5}{6}}\right)\ast \ln \left(\left|0.326264\right|\right)=8.40,$$

(3)

The critical value at the 0.05 significance level with one degree of freedom is 3.841. Since the calculated test statistic exceeds the critical value, the null hypothesis of no correlation is rejected. This confirms that a statistically significant correlation exists between ln(K) and ln(I).

3.2.1. Variance Inflation Factor and Tolerance

To further assess the impact of this dependence on model stability, diagnostic measures of multicollinearity were calculated: the Variance Inflation Factor (VIF) and its inverse value, tolerance. Given that the regression produced R² = 0.674, the indicators were computed as follows:

$$VIF={\displaystyle \frac{1}{1-R^2}}={\displaystyle \frac{1}{1-0.674}}=3.065,$$

(4)

$$Tolerance={\displaystyle \frac{1}{IFR}}={\displaystyle \frac{1}{3.065}}=0.326,$$

(5)

According to standard methodological recommendations, VIF values below 5 and tolerance values above 0.2 are considered acceptable and do not indicate a serious multicollinearity problem. In this case, both indicators fall within the acceptable ranges, suggesting that while a correlation between the variables is present, it does not reach a level that would inflate standard errors of the estimated coefficients or reduce the reliability of the model.

In line with these calculations, a statistically significant linear dependence between ln(K) and ln(I) was identified and confirmed by both regression analysis and the Farrar–Glauber test. However, the results of VIF and tolerance confirm that the degree of correlation does not exceed methodological thresholds and does not destabilize the regression model. For this reason, both variables were retained in the extended Cobb–Douglas production function, with their statistical interdependence considered in the interpretation of results.

3.2.2. Comparison of Coefficients for the Basic and Extended Model

The results, presented in

Table 5. Comparison of coefficients.

No	Baseline Model	Extended Model	Difference
ln(K)	0.33	0.29	−0.04
ln(L)	1.47	0.67	−0.80

, reveal that after incorporating IoTRIM, the elasticity of labor decreased from 1.47 to 0.67 and the elasticity of capital from 0.33 to 0.29, while the IoTRIM coefficient was positive and statistically significant. This indicates that part of the output growth previously attributed to labor input is more accurately explained by the enterprise’s IoT readiness.

Finally, Figure 5 illustrates the marginal effect of IoT readiness on output. Holding capital and labor constant, an increase of 0.1 in the IoTRIM index corresponds to an estimated 2–3% increase in output. This finding reinforces the role of IoT readiness as a productivity-enhancing factor and suggests that further technological integration could yield substantial efficiency gains.

From a managerial standpoint, the results suggest that IoT readiness not only improves efficiency in existing processes but also changes the optimal allocation of resources between capital and labor. The observed reduction in labor elasticity in the extended model implies that technological tools can substitute for some labor-intensive activities, enabling staff to focus on higher value-added tasks. Similarly, the slightly lower capital elasticity indicates that part of the productivity gains from physical assets is now mediated through smart technology integration.

Moreover, the combination of increasing returns to scale and the positive marginal effect of IoT readiness points to an opportunity for synergistic growth: scaling operations while simultaneously deepening technological maturity. For the case study enterprise, this means that future investment strategies should not consider capital, labor, and IoT readiness in isolation, but as mutually reinforcing components of a modern production system. Such an integrated approach would maximize the long-term benefits of digital transformation and sustain competitiveness in a technology-driven market environment.

3.3. Scenario Analysis and Monte Carlo Simulation

To assess the longer-term impacts of digital transformation under conditions of uncertainty, the evolution of four variables capital (K), labor (L), IoT readiness (I), and output (Y)—was simulated for six postal and courier enterprises operating in Slovakia (DHL, DPD, GLS, Packeta, SPS, TNT). Baseline values from 2023 were used as starting points, reflecting the most recent operational and technological status of each enterprise. For each enterprise, 1000 Monte Carlo simulations per scenario were conducted over the 2024–2033 horizon. Annual growth rates were drawn from predefined intervals representing three distinct scenarios: pessimistic, realistic, and optimistic. In each simulation, the extended Cobb–Douglas model was re-estimated to obtain updated elasticities, and the resulting average trajectories and coefficients were subjected to comparative analysis. Detailed graphical trajectories for K, L, I, and Y are presented in Appendix B.

The design of scenarios reflects both macroeconomic uncertainties and the anticipated pace of digital adoption in the sector. Each scenario is constructed to capture a plausible pathway rather than a precise prediction, enabling the exploration of alternative futures and their strategic implications.

• Pessimistic scenario: Characterized by adverse macroeconomic conditions, weak investment activity, and slow technological uptake. Under these conditions, output (Y) remains stagnant or declines, capital investment (K) grows minimally, and IoT readiness (I) advances only marginally, while labor (L) changes little. This scenario may correspond to environments with technological debt, operational inefficiencies, or disruptive external shocks, such as regulatory hurdles or prolonged economic downturns.
• Realistic scenario: Represents a continuation of current sectoral trends, with balanced growth in all key variables. Output and capital expand moderately, labor demand remains stable with minor fluctuations, and IoT readiness rises gradually in line with steady digitalization initiatives. Such a trajectory aligns with historical averages and assumes a supportive but not exceptional macroeconomic environment.
• Optimistic scenario: Envisions strong macroeconomic performance, substantial digital investment, and accelerated organizational transformation. Capital and output increase at the highest rates, IoT readiness achieves the most substantial gains, and labor requirements decrease in certain areas due to process automation. This trajectory aligns with global best-practice adoption curves for advanced logistics and smart supply chain systems.

These three scenarios provide a structured framework for the subsequent Monte Carlo analysis, allowing not only the estimation of potential output levels but also the assessment of how different investment and technology adoption patterns influence long-term performance.

3.3.1. Simulation Highlights

The simulation results reveal notable differences in the trajectories of the analyzed variables across enterprises and scenarios. Capital growth is led by DPD, which surpasses €13 million by 2033 in the optimistic scenario representing nearly a threefold increase from baseline. By contrast, TNT and DHL maintain a conservative investment profile even under favorable conditions, while SPS, Packeta, and GLS display more pronounced expansion in the optimistic path. Realistic trajectories are generally linear, reflecting steady but unspectacular growth.

Labor trends vary considerably—Packeta and SPS record growth in all scenarios, driven by service expansion, whereas GLS and TNT register slight declines in the optimistic case, consistent with automation reducing labor intensity. For SPS, the gap between optimistic and pessimistic labor cost paths approaches €5 million, illustrating the potential impact of technology-driven efficiency gains.

IoT readiness scores (I) offer the clearest scenario differentiation. In the optimistic case, multiple enterprises exceed 90 points on the IoTRIM scale, with Packeta and DHL nearly doubling their readiness levels compared to baseline. The realistic scenario produces gradual and stable improvements, while the pessimistic path yields only marginal gains.

Output trajectories (Y) follow a similar pattern, strongly reflecting the interplay between capital, labor, and IoT readiness. Packeta emerges as the top performer, surpassing €42 million in output by 2033 in the optimistic scenario more than 160% above the baseline. SPS and GLS also accelerate markedly under favorable conditions. In the pessimistic case, growth remains subdued, with the gap between optimistic and pessimistic outcomes reaching approximately €18 million for DHL.

3.3.2. Elasticity Patterns

Figure 6 summarizes the extended Cobb–Douglas coefficients by scenario and enterprise. Cell color represents both sign and magnitude (green = positive; red = negative). While a modest correlation between capital (K) and IoT readiness (I) must be acknowledged, the coefficients nonetheless provide meaningful comparative insights.

Enterprise-level patterns are heterogeneous. DHL exhibits the strongest potential responsiveness to improvements in IoT readiness under favorable technological conditions. DPD shows a balanced and mutually reinforcing effect of K, L, and I in the optimistic scenario. GLS remains labor-led but has clear scope for productivity gains from increased I. Packeta demonstrates the highest dependency on I, with K and L acting as supporting drivers. SPS is currently labor driven but experiences substantial improvements as I deepens. TNT remains primarily reliant on traditional inputs, with limited short-term gains from I, suggesting that modernization should be targeted before expecting significant digital returns.

These patterns highlight distinct digital transformation pathways: enterprises already engaged in data-driven operations can monetize IoT readiness more rapidly, whereas others must first develop the infrastructural and organizational foundations needed for I to contribute meaningfully to productivity. This reinforces the notion that returns to digital investment are convex for sector leaders and delayed for late adopters, underscoring the importance of synchronized upgrades in infrastructure, skills, and process integration.

3.3.3. Total Factor Productivity

In the extended Cobb–Douglas regressions estimated on simulated data, the intercept term corresponds to ln(A), where A denotes total factor productivity not explained by K, L, or I. Consequently, A = e^(intercept), offering a concise measure of residual efficiency linked to management quality, process design, and organizational agility.

Figure 7 relates TFP to the elasticity of IoT readiness across the three scenarios. In the realistic scenario, the relationship is clearly positive, indicating that incremental gains in I are associated with proportionate improvements in overall efficiency. In the optimistic scenario, the relationship becomes slightly negative, suggesting that rapid expansion of I may not automatically lead to higher TFP without parallel investments in complementary capabilities. In the pessimistic scenario, the association remains weak, consistent with the notion that constrained operating environments limit the payoff from digital tools.

Overall, the Monte Carlo simulations combined with the extended production function demonstrate that while the magnitude of digital investment is a decisive factor, its effectiveness ultimately depends on absorption capacity encompassing interoperability, data governance, and alignment of technological tools with operational processes. For decision-makers, this implies a dual-track strategy: maintain disciplined investment in IoT readiness while mitigating risks through standardization and workforce development, ensuring that positive scenario conditions translate into sustainable productivity rather than transient gains.

3.4. Hypothesis Verification

The verification of the research hypotheses established in Section 2 was carried out using the results of the IoTRIM evaluation model in conjunction with outputs from the extended Cobb–Douglas production function under different development scenarios. The analysis considered not only the statistical significance of individual parameters but also their economic interpretation and strategic implications.

3.4.1. Hypothesis H1

H1: 

Implementation of IoT has a positive impact on the output of enterprises providing other postal services and courier activities.

H₀₁: 

γ ≤ 0 (IoT does not have a positive effect on output).

H₁₁: 

γ > 0 (IoT has a positive effect on output).

The hypothesis was tested on a sample of 18 models (six enterprises and three development scenarios) by observing the coefficient γ, representing the elasticity of output with respect to the IoT input, together with its p-value at a significance level of α = 0.05. Values of γ and their p-values are presented in

Table 6. Verification of Hypothesis H1.

Enterprise	Scenario	γ		p-Value (γ)
DHL	pessimistic	0.1496	✔	0.7585	✘
	realistic	0.9454	✔	0.0413	✔
	optimistic	0.4721	✔	0.0625	✘
DPD	pessimistic	0.0730	✔	0.7982	✘
	realistic	0.2988	✔	0.2684	✘
	optimistic	1.0643	✔	0.0040	✔
GLS	pessimistic	1.3111	✔	0.2287	✘
	realistic	1.0083	✔	0.0022	✔
	optimistic	0.6620	✔	0.2513	✘
Packeta	pessimistic	0.4725	✔	0.1015	✘
	realistic	0.1668	✔	0.7233	✘
	optimistic	0.1863	✔	0.2393	✘
SPS	pessimistic	−0.4350	✘	0.4350	✘
	realistic	0.4965	✔	0.3346	✘
	optimistic	0.2235	✔	0.2240	✘
TNT	pessimistic	0.1503	✔	0.6176	✘
	realistic	0.1578	✔	0.7152	✘
	optimistic	0.6965	✔	0.0067	✔

.

From the results, γ was positive and statistically significant in four out of eighteen cases. In these instances, the null hypothesis was rejected, confirming a positive IoT impact on enterprise output. In the remaining fourteen models, either statistical significance was not achieved despite a positive γ, or, in one case, the coefficient was negative. This implies that, for most models, the null hypothesis cannot be rejected, and the positive effect of IoT on output cannot be statistically confirmed.

Consequently, Hypothesis H1 cannot be accepted as universally valid across all tested models. Nevertheless, the results indicate that under certain conditions particularly in scenarios with stronger technological adoption the impact of IoT can be both positive and significant. This highlights the importance of complementary capabilities and enabling conditions in translating IoT integration into measurable performance gains.

3.4.2. Hypothesis H2

H2: 

The Cobb–Douglas model extended by the IoT readiness variable exhibits a higher explanatory power of output variability than the traditional model.

H₀₂: 

Adding the IoT variable does not increase the explanatory power of the model.

H₁₂: 

Adding the IoT variable increases the explanatory power of the model.

Verification was performed using the F-test to compare the baseline Cobb–Douglas model with its extended version for each of the 18 models. Differences in residual sum of squares (SSE) were calculated, followed by the computation of the F-statistic to determine whether the extended model provided a statistically significant improvement in explanatory power. Results are summarized in

Table 7. Verification of Hypothesis H2.

Enterprise	Scenario	SSE (Baseline)	SSE (Extended)	F	p-Value	Result
DHL	pessimistic	0.021485	0.000063	2732.39	1.99 × 10−11	H0 not accepted
	realistic	0.021485	0.000087	1964.88	7.41 × 10−11	H0 not accepted
	optimistic	0.021485	0.000063	2732.39	1.99 × 10−11	H0 not accepted
DPD	pessimistic	0.262331	0.000077	27,289.73	2.00 × 10−15	H0 not accepted
	realistic	0.262331	0.000067	31,184.77	1.22 × 10−15	H0 not accepted
	optimistic	0.262331	0.000026	80,958.41	1.11 × 10−16	H0 not accepted
GLS	pessimistic	0.080606	0.000110	5879.91	9.32 × 10−13	H0 not accepted
	realistic	0.080606	0.000023	27,883.19	1.89 × 10−15	H0 not accepted
	optimistic	0.080606	0.000134	4787.09	2.12 × 10−12	H0 not accepted
Packeta	pessimistic	0.114749	0.000018	26,016.16	8.86 × 10−9	H0 not accepted
	realistic	0.114749	0.000041	11,201.95	4.78 × 10−8	H0 not accepted
	optimistic	0.114749	0.000005	94,829.81	6.67 × 10−10	H0 not accepted
SPS	pessimistic	0.187342	0.000040	32,517.59	4.25 × 10−14	H0 not accepted
	realistic	0.187342	0.000081	16,199.02	4.88 × 10−13	H0 not accepted
	optimistic	0.187342	0.000007	187,334.64	1.11 × 10−16	H0 not accepted
TNT	pessimistic	0.088198	0.000023	30,510.25	1.33 × 10−15	H0 not accepted
	realistic	0.088198	0.000058	12,090.46	5.23 × 10−14	H0 not accepted
	optimistic	0.088198	0.000014	52,180.01	1.11 × 10−16	H0 not accepted

.

In all 18 cases, the inclusion of the IoT variable resulted in a statistically significant increase in explanatory power. The F-statistics reached very high values, and p-values in all cases were far below the α = 0.05 threshold.

Therefore, Hypothesis H2 is confirmed, indicating that the extended Cobb–Douglas model consistently explains output variability more effectively. These findings support the view that integrating digital readiness into economic models enhances their ability to capture real-world performance dynamics in postal and courier services.

The combined results suggest that while the inclusion of IoT readiness markedly improves model fit (H2), the direct and statistically significant positive effect of IoT on output (H1) is not universally observed. This asymmetry implies that IoT readiness contributes to the structural explanatory power of the model, but its realized effect on output depends on specific organizational and contextual factors. In strategic terms, IoT adoption appears to act as an enabler whose benefits materialize when coupled with adequate infrastructure, interoperability, and process alignment.

4. Discussion

The evidence derived from this research provides substantial insights into how digital transformation, and specifically IoT readiness, influences productivity in the postal and courier sector. While IoT readiness significantly enhanced the explanatory power of the extended Cobb–Douglas model in all evaluated cases (supporting H2), a statistically significant positive effect on output (H1) was confirmed only in selected scenarios and enterprises. This aligns with previous research [27] indicating that early-stage IoT adoption can yield real but modest productivity improvements, with the magnitude of impact dependent on organizational readiness and integration into core processes. More recent evidence confirms that IoT’s contribution to organizational performance is mediated by factors such as supply chain integration, digital skills, and competitive positioning, rather than stemming from technological deployment alone [28].

From a methodological perspective, embedding IoTRIM into the Cobb–Douglas framework offers a refined means of quantifying the economic role of intangible digital assets. Similar integration of digital readiness measures into production functions has been successfully applied in manufacturing, where the introduction of IoT significantly improved total factor productivity through innovation diffusion and process optimization [53]. The present findings echo these results, as IoT readiness emerges as a valuable intangible input, yet one whose impact on total factor productivity is contingent on complementary capabilities such as governance quality, workforce competencies, and interoperability of systems.

Enterprise-level differences observed in the simulations underscore the heterogeneous nature of digital transformation. In optimistic scenarios, enterprises with strong digital infrastructure such as Packeta and DHL translated higher IoT readiness into notable output growth and even TFP gains. In contrast, enterprises in less mature digital contexts demonstrated improved model fit after including IoT readiness but did not achieve immediate performance increases. This asymmetry mirrors broader empirical patterns, where digital investment returns are often delayed until organizations complete structural and process adjustments [26,35].

In the context of postal enterprises, several sector-specific constraints have been identified that influence the scope and pace of IoT application. These include limited financial capacity for large-scale digital investments, reliance on legacy infrastructure that reduces flexibility in adopting new technologies, strict regulatory and data protection requirements, and the need for workforce adaptation to digital tools. Such challenges highlight why a systemic assessment framework is required, as isolated technological initiatives may not sufficiently address the structural and organizational barriers present in this sector.

It is important to note that the extended Cobb–Douglas model was estimated on a limited sample of ten annual observations, which constrains the statistical power of the analysis. These represent the only consistent time-series data available for the enterprise under research. Accordingly, the results are to be interpreted primarily as a proof of concept, demonstrating the feasibility of integrating IoT readiness into a production function framework. It is acknowledged that an expansion of the dataset through higher-frequency data or multi-enterprise panel structures would increase the robustness and generalizability of the findings, which is recommended as a direction for future research. Moreover, the analysis is restricted to a single national context, which limits its geographic scope, and several estimated coefficients did not reach statistical significance. Therefore, future research should focus on broadening the geographical scope. The IoTRIM framework has been designed in a way that allows its adaptation and validation across different countries and service sectors, which also represents an important direction for future research.

A significant implication is that IoT readiness acts as a form of strategic optionality rather than an immediate productivity lever. Its presence allows enterprises to rapidly exploit favorable market or technological shifts but does not guarantee automatic gains in the absence of supportive organizational and process environments. This is consistent with the absorptive capacity perspective, which stresses the necessity of complementary capabilities for translating external knowledge and technology into economic performance [54].

The managerial and policy relevance of these findings is substantial. Effective digital transformation requires more than capital expenditure on IoT technologies; it depends on orchestrated investments in governance frameworks, workforce training, and cross-platform interoperability. The McKinsey IoT Value Report emphasizes that technology deployment alone is insufficient, with scalability, ecosystem support, and leadership engagement determining the extent to which potential value is captured [55]. In the postal and courier domain, recent applications of extended Cobb–Douglas models have confirmed that the realization of productivity benefits from IoT is highly context-dependent, requiring alignment of technology with strategic and operational objectives [26].

The distribution of statistically significant positive elasticities for IoT readiness across scenarios suggests that certain market conditions such as growth in e-commerce demand, logistics network optimization, and integration with advanced tracking systems can amplify IoT’s productivity effects. In pessimistic scenarios, macroeconomic stagnation, cost constraints, and technological debt appear to neutralize the benefits of IoT integration. This highlights the importance of scenario-based planning and strategic resilience, allowing enterprises to calibrate investment pace to economic conditions while preserving the option to accelerate when opportunities arise [56].

Another relevant finding is the varying role of labor across enterprises. While some experienced labor efficiency gains through automation in optimistic scenarios, others saw stable or even increasing labor requirements despite IoT integration. This reinforces the observation that automation’s impact is uneven and may require targeted workforce reskilling programs to avoid bottlenecks in adoption. Research in logistics and manufacturing confirms that technology-led labor displacement is rarely linear, and in many cases, labor is redeployed into higher-value activities rather than eliminated [57,58].

From a policy standpoint, there is an opportunity to design targeted incentives for IoT adoption in logistics, focusing not only on technology acquisition but also on interoperability standards, cybersecurity readiness, and training initiatives. Regulatory support for open data exchange within supply chains could also accelerate the integration of IoT-derived data into decision-making processes, maximizing the economic impact of digital readiness.

The IoTRIM model can be applied in managerial practice to support both decision-making and investment planning. By quantifying the scores across individual pillars, it can be identified which areas, such as digital infrastructure or system interoperability, represent the most critical bottlenecks to IoT adoption. This allows investment priorities to be directed towards the areas with the highest potential impact. Furthermore, the integration of IoTRIM into a production function framework enables the simulation of alternative investment scenarios, providing an evidence-based estimate of their potential contribution to productivity. In this way, the model offers managers a systematic tool for aligning digital transformation initiatives with strategic objectives and resource allocation.

Looking ahead, several research directions emerge. First, extending the model to cross-sectoral analyses could reveal how differences in industry dynamics and capital intensity influence IoT productivity effects. Second, integrating additional intangible variables such as organizational agility, innovation culture, and ecosystem engagement could refine the explanatory power of extended production functions. Third, exploring hybrid modelling approaches that combine econometric estimation with system dynamics or agent-based simulation may offer a more nuanced understanding of feedback effects in digital transformation. Finally, longitudinal research is essential for capturing the delayed but potentially exponential returns from IoT integration once enterprises reach full digital maturity.

Overall, this paper positions IoT readiness not merely as a technological metric but as a strategic resource whose productivity impact depends on timing, context, and complementary investments. The results support a dual-track approach in both managerial practice and policy design: maintain steady investment in IoT capabilities while simultaneously building the absorptive capacity required to translate readiness into sustained competitive advantage.

5. Conclusions

This paper set out to integrate IoT readiness into an extended Cobb–Douglas production function to evaluate its contribution to output variability in the postal and courier sector. The proposed IoTRIM-based approach provided a structured means of quantifying digital capability and incorporating it alongside capital and labor in productivity analysis. This integration yielded two key insights: first, that IoT readiness consistently enhanced the explanatory power of the model, and second, that its measurable impact on output is not universal but shaped by enterprise-specific conditions and broader economic contexts.

Both hypotheses addressed in the research were thus resolved in distinct ways. The first hypothesis that IoT implementation has a positive impact on enterprise output was confirmed only in selected scenarios and organizational settings, indicating that technological potential does not automatically translate into productivity gains. The second hypothesis that extending the Cobb–Douglas model with IoT readiness improves explanatory accuracy was strongly supported across all enterprises and scenarios, highlighting the analytical value of incorporating digital variables into established economic frameworks.

In comparison with existing digital maturity or readiness frameworks commonly applied in manufacturing, logistics, and the ICT sector, the IoTRIM model introduces two incremental contributions. First, the assessment of IoT readiness is embedded directly into an extended Cobb–Douglas production function, which allows not only the evaluation of the current state of adoption but also the estimation of its measurable economic impact on productivity. Second, the model has been structured into pillars, criteria, and indicators specifically tailored to the postal and courier services sector, thereby addressing contextual characteristics that are not captured in general-purpose maturity frameworks.

Beyond methodological contributions, the findings offer a practical message: IoT readiness should be treated as a strategic enabler whose economic benefits depend on complementary investments in interoperability, workforce skills, and process alignment. For policymakers, this underscores the need for measures that accelerate not only technology acquisition but also its effective absorption and integration into enterprise operations.

The framework and findings presented here create a basis for further research into the economic role of digital readiness in other segments of transport and logistics. Future work could extend the model to incorporate additional intangible factors, adopt longitudinal data for post-implementation tracking, and test cross-country differences in digital transformation outcomes.

In summary, IoT readiness is not merely a technological attribute it is a capacity whose productivity effects are contingent, context-dependent, and maximized only when supported by strong organizational foundations. Recognizing and acting on this dependency is essential for enterprises and policymakers aiming to achieve sustainable competitive advantage in an increasingly digital logistics ecosystem. In an era where digital transformation is both inevitable and uneven, the real measure of success will not lie in the mere presence of IoT technologies, but in the ability of enterprises to turn readiness into measurable, lasting performance gains. The results of this research suggest that the winners in the next decade’s logistics sector will be those who master not just the tools, but the systemic integration that makes those tools matter.