Unmanned Agricultural Robotics Techniques for Enhancing Entrepreneurial Competitiveness in Emerging Markets: A Central Romanian Case Study

Abstract

Recently, the market for miniaturized Unmanned Agricultural Robots has experienced rapid development worldwide, driven by advances in robotics, artificial intelligence and precision agriculture. These technologies are no longer confined to highly industrialized countries but are increasingly penetrating emerging economies, including Romania, where they hold significant potential for transforming farming practices and entrepreneurial competitiveness. The purpose of the present paper is to present strategies for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. This is achieved by leveraging competitive advantage through value creation, specifically by deepening strategies for the rapid integration of new miniaturized robotic products. The research employed a mixed-methods approach, combining qualitative and quantitative techniques to investigate the ability of key stakeholders—agricultural entrepreneurs, precision agriculture product/service providers, institutional representatives, and investors—to dynamically adapt to evolving market conditions. The study’s findings reveal a strong interest and readiness among precision agriculture stakeholders to adopt advanced technologies, supported by robust operational knowledge management practices including external knowledge acquisition, strategic partnerships and data protection. Although agricultural entrepreneurs exhibit considerable adaptive and absorptive capacities—evidenced by their openness to innovation and collaboration—persistent barriers such as high equipment costs and limited financing access continue to impede the broad adoption of miniaturized robotic solutions. The study concludes by emphasizing the need for supportive policies and collaborative financing models and it suggests future research on adoption dynamics, cross-country comparisons and the role of education in accelerating agricultural robotics.

1. Introduction

Currently, an increasing awareness of humanity’s future challenges is evident, particularly in the context of positive technological advancements and the presence of certain “barriers” that must be considered in the formulation of policies, programs and strategies. These barriers include population growth, increasing pressure on food resources, climate change, the finite nature of resources and the need to preserve or enhance biodiversity and environmental quality [1]. The agricultural sector reflects this trend, showing an increased focus on developing strategies that integrate emerging technologies and provide effective responses to these challenges. This study underscores the importance of innovation in agricultural ecosystem technologies that enhance resource efficiency, environmental protection, soil and food health and overall competitiveness. Within this context, growing attention is being directed toward the market for Unmanned Agricultural Robots, which represents the central focus of the present analysis.

Nathanael et al. [2] emphasize that the primary drivers behind the adoption of precision technologies in agriculture are the perceived economic benefits and improvements in operational efficiency. This is particularly relevant in the context of agricultural robotics, which play a transformative role in the advancement of digital agriculture [3,4]. By automating complex and repetitive tasks such as precision planting, targeted weeding, and harvesting, these robotic systems directly address key industry challenges related to labor shortages and inefficiency. As highlighted by Moshayedi et al. [5], their integration not only streamlines traditionally labor-intensive processes but also contributes to increased productivity, reduced operational costs and lower environmental impact through more efficient resource utilization.

In order to meet the practical demands for labor-saving and efficient agricultural production, the range of agricultural robots has been steadily expanding, leading to increasingly diverse application scenarios [3].

Farmers’ preferences for robot size can vary, primarily based on the type of production system (organic vs. conventional) [6]. According to [3], researchers have focused primarily on smaller-sized robots.

Unmanned Agricultural Robots (UARs) are autonomous machines designed to perform a wide range of agricultural tasks without direct human operation. These robots use advanced technologies such as Global Positioning System (GPS), artificial intelligence (AI), computer vision, and various sensors to navigate through farmland and execute specific functions like planting, watering, monitoring, and harvesting. Miniaturized Unmanned Agricultural Robots are smaller-sized robots that allow for more flexible operations, especially in small or delicate areas that larger machinery cannot access. The motivation for the shift toward miniaturized UARs stems from the need to enhance the competitiveness of agricultural entrepreneurs in the face of rising labor costs, the increasing incidence of climate change effects—particularly in facilitating vectors that destabilize agricultural ecosystems—and the necessity of improving decision-making processes to achieve healthier products and a more sustainable agricultural environment. Miniaturized UARs are the result of converging advances in digital technology, particularly in sensor miniaturization [7], wireless communication [8], robust computer vision systems [9], as well as artificial intelligence (AI) and machine learning algorithms [10]. These digital technology directions provide an inexhaustible source of ideas for developing relevant solutions to the challenges of agricultural digitalization and its transformation into smart agriculture [11,12].

Despite the potential, the development and integration of miniaturized UARs into farming technologies is relatively slow compared to other sectors of the economy, mainly due to the complexity of agricultural ecosystems. Nonetheless, the increasing interest in digital technology adoption in agriculture, along with the growth in agricultural tech startups, bodes well for improving the technical performance of these systems and gaining the trust of key stakeholders, including governments. The latter have launched various support programs, such as the European initiative “The Partnership for Robotics in Europe”, which aims to support the development and adoption of robotics in agriculture [13].

Research on miniaturized UARs demonstrates growing interest in their development and integration into agricultural practice, with a focus on addressing specific challenges: phenotyping [14], specialized operations in crop-based agroecosystems [15,16], livestock farming [17], horticulture [18], farm management [12,19], forestry [20] and the processing of agricultural raw materials [21].

The adoption of robotic technologies in agriculture brings numerous advantages, including increased efficiency, significant cost reductions and improved productivity [5,22]. Also, advanced sensors and algorithms enable precise robotic operations, minimizing resource use, reducing waste and lowering environmental impact. By optimizing inputs like water, fertilizers, and pesticides, these systems promote sustainable farming while reducing chemical use and supporting biodiversity [23]. Also, small-sized robots could also enhance the profitability of farming on small and irregularly shaped fields [24].

To enhance the efficiency of miniaturized robot use in agriculture, it is essential that technological developments consider the specific characteristics of biosystems, the ecological-geographical relationships between plants and their environment, as well as the interaction between biocenosis (variety) and biotope, along with the ease of use for farmers. These factors transform the agricultural ecosystem into a cybernetic system, enabling intervention and adjustment of the interactions between vegetation factors and plant varieties, in order to achieve healthy and competitive agricultural outputs.

Another critical aspect is the integration of miniaturized UARs with diverse datasets and sensor-derived information (e.g., multispectral cameras, visual sensors, thermal imaging, weather stations, etc.) to support the development of prediction models for microclimatic parameters, as well as for forecasting diseases and pests. Such integration enables precise estimation of geometric parameters, growth stages, vegetation health, yield potential, and the identification of zones with distinct characteristics that require variable-rate input applications.

Connecting UARs to data collected by various sensors and to the specific features of agricultural biosystems increases the level of knowledge available for decision-making processes. This, in turn, allows farm management to design relevant solutions, such as the following: accurate application of substances on tree canopies through optimized dosage rates based on canopy characteristics identified by digital sensors [25]; use of sensors to measure morphological parameters by mounting them at various heights and angles on machinery in accordance with optimal resolution and measurement frequency [26]; promotion of 3D sensors and smart 3D cameras with built-in processors [27]; development of tree canopy characterization systems to optimize spraying in orchards using ultrasonic sensors [28]; creation of real-time positioning algorithms for air-assisted orchard sprayers with variable geometry [29]; fruit recognition systems using RGB image data [30]; orchard management based on UAV-collected data [31]; ecosystem monitoring systems in horticulture to reduce labor and time in early disease detection using UAV images [32]; aerial monitoring of agricultural ecosystems to detect abusive practices [33]; and the use of UAV for pre-harvesting field inspection for trash, such as plastic in cotton field [34,35].

Considering the evolution of digital technologies and their growing impact on agriculture, along with the advancements in miniaturized agricultural robotics, the present study analyzes and proposes strategies for integrating innovative, scalable and widely applicable robotic solutions into agricultural ecosystems.

The research also addresses the issue of increasing competitiveness through the integration of miniaturized robots into agricultural ecosystems. It begins with the need to harmonize technological supply with the demands of agricultural entrepreneurs, tailored to the type of ecosystem involved and aims to gain competitive advantage through the adoption of new strategies better suited to current challenges. The focus is on accelerating the integration of robotic solutions—characterized by flexibility, accessibility and low costs—into the technologies promoted at the level of the agricultural enterprise.

To better understand and facilitate this integration, the study introduces the KMC-DC analytical framework, which links Knowledge Management Capabilities (KMC)—including knowledge acquisition, combination and protection—with Dynamic Capabilities (DC), enabling enterprises to sense opportunities, seize them and reconfigure resources to adapt to rapidly evolving technological and market conditions [36,37]. By operationalizing this framework, knowledge acquisition allows enterprises to identify and access relevant robotic technologies; knowledge combination facilitates the integration of these innovations with existing operational processes; and knowledge protection ensures that proprietary knowledge is safeguarded during implementation. At the same time, dynamic capabilities ensure that the companies remain agile, capable of rapidly adjusting strategies and resources to align with technological and market evolution. Figure 1 illustrates the transmission path through which KMC operational capabilities drive higher-order DC.

The KMC-DC framework is particularly relevant in this context because it connects knowledge-intensive practices with dynamic organizational capabilities, showing how agricultural enterprises can adapt to evolving technological landscapes. KMC-DC emphasizes the continuous transformation of knowledge into actionable strategies, ensuring that robotic solutions are not only implemented but also leveraged to create sustained competitive advantage within precision agriculture.

The justification for this research is anchored in several interrelated dimensions:

• Understanding the link between the supply of technological agricultural products and the growing sophistication of agricultural entrepreneurs, who increasingly require innovative, efficient and adaptable technologies in a rapidly evolving agrifood context.
• Analyzing opportunities to increase the competitive advantage of agricultural entrepreneurs in Romania’s Central Region, based on the premise that competitive advantage lies at the core of strategic management research and extends beyond local markets into the regional landscape. Here, competitive advantage—as a conceptual and strategic position—translates into superior value creation compared to competitors. The first-mover advantage becomes accessible through the targeted use of scalable, innovative technologies with high potential to reinforce the precision agriculture market in the region.
• Recognizing knowledge acquisition as an implicit competitive success factor for agricultural entrepreneurs—where competitive success can be measured through increased market share, improved product quality and diversity, and greater technological adaptability. A deeper understanding of regional competition and benchmarking practices facilitates the review and adaptation of both tactical and strategic plans to emphasize the role of innovative technologies in enhancing agricultural productivity and sustainability.
• Combining and integrating new knowledge supports adaptation to the changing dynamics of competition, particularly by aligning technological innovations with the practical needs and expectations of increasingly well-informed and demanding agricultural stakeholders.
• There is currently no research that clearly outlines how KMC—namely acquisition, combination and protection—influence competitive advantage in precision agriculture. This study anticipates the importance of detecting transformation processes, designing effective organizational leverage frameworks and building superior knowledge stocks aligned with technological change and market evolution.
• The literature has limited models that examine the interconnections between KMC operational capabilities and higher-order capabilities—namely adaptive, absorptive, and innovative capacities—and their mediating role in shaping competitiveness within precision agriculture in Romania’s Central Region.

The main objective of this contribution is to identify and decode strategies for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. This is achieved through the lens of competitive advantage creation, emphasizing value generation via the rapid integration of miniaturized robotic technologies in a way that fully aligns with the functional requirements of European precision agriculture systems. The aim of this research is to contribute to the development of strategic options for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. The study also aimed to contribute to a broader framework for regional development at the national level.

2. Materials and Methods

Achieving this aim required the formulation of several specific objectives, which also structured the research process, as presented in Figure 2.

This research employed a case study methodology, integrating both qualitative and quantitative approaches from the field of strategic management in accordance with the recommendations of [38]. The choice of this methodology was motivated by the need to conduct a holistic and in-depth investigation [39]. The investigation was oriented toward improving efficiency and strengthening competitive advantage, with implications extending beyond the Central Region of Romania.

The research methodology is based on the fusion of the concepts of KMC represented by the set of acquisition–combination–protection capabilities and DC as an expression of the ability to dynamically adapt to market change starting from the construction and modification of existing lower-order capabilities and expressed by the triplet of adaptive-absorptive-innovative capabilities. In this case, the new concept integrates capabilities of different levels and sequences in which KMC determines the response to change by building and synergizing with the set of superior capabilities (adaptive–absorptive–innovative).

2.1. Research Method and Representative Sample

The survey-based research employed two primary instruments: two distinct questionnaires for data collection, tailored to the specific target groups, and SPSS software, version 20.0, for data processing and statistical analysis [40]. The questionnaires were original instruments developed by the authors specifically for this study. To avoid ambiguity and ensure that respondents clearly understood the questions as intended, their design followed established guidelines from the literature, with an emphasis on clarity and concise phrasing [41].

The first questionnaire aimed to identify perceptions, adoption levels and interest in precision agriculture (PA) technologies among agricultural stakeholders in Romania’s Central Development Region: agricultural entrepreneurs, providers of products and services, institutional representatives, and investors. It was structured into several sections covering demographics, employment status, implementation intensity and opinions regarding technological tools in agriculture. To assess reliability, Cronbach’s alpha was calculated, obtaining a value of 0.919, which suggests good internal consistency among the questions measuring the same construct.

The second questionnaire contains four sections, each targeting critical dimensions relevant to the study’s objectives. The first section served to collect general background information about the respondents and, where applicable, their organizations. This included data regarding the type of enterprise, ownership structure, managerial level and the educational qualifications of the participants. These details were essential to contextualize the strategic orientation and decision-making capacities within the agricultural enterprises surveyed. The second section focused on assessing KMC, regarded as key operational capabilities. The items, measured using a 5- to 7-point Likert scale, were designed to capture three major sub-dimensions: (1) acquisition capabilities, reflecting the organization’s ability to generate new knowledge by learning from international experience and through collaboration at the local, national, and regional levels; (2) combination capabilities, referring to the capacity to integrate and apply internal and especially external knowledge—such as in the formulation of investor engagement strategies or the integration of innovative technologies; (3) knowledge protection capabilities, which indicate the ability to safeguard intellectual assets and knowledge resources within the firm. The third section evaluated the presence and intensity of DC, defined as higher-order capabilities that enable firms to adapt to environmental changes and remain competitive. Specifically, this section investigated the following: (1) adaptive capability, understood as the ability to detect the need for change and act strategically to close the performance gap relative to industry benchmarks; (2) absorptive capability, denoting the organization’s aptitude for identifying, assimilating, and applying external knowledge, especially through collaborative networks; (3) innovative capability, which refers to the development of new competencies that enhance managerial processes and generate unique, superior benefits for local and regional agricultural entrepreneurs compared to their competitors. The fourth section explored the respondents’ perceptions of organizational competitiveness in the context of precision agriculture, with a particular emphasis on identifying the ways in which competitive advantage can be accessed, sustained, and leveraged through technological innovation and strategic capability development. The reliability of the questionnaire, assessed using Cronbach’s alpha, was 0.665, indicating acceptable internal consistency for exploratory research purposes.

G*Power software (version 3.1.9.4; University of Düsseldorf, Düsseldorf, Germany) was used to calculate the sample size, considering a small effect size, as suggested by a survey assessing farmer perceptions of Precision Agriculture Technologies, which reported an effect of approximately 0.24 for the use of variable-rate fertilizer [2], power (1–β) of 0.85 and α level of 0.05. Accordingly, a sample size of 120 participants was necessary. 130 individuals were recruited from various categories, including agricultural entrepreneurs, providers of products and services, institutional representatives and investors. In the second phase of data collection, a refined sample of 125 eligible participants was targeted, excluding inactive or unqualified respondents. A total of 118 fully completed questionnaires were retained for analysis.

The central region of Romania was selected as the focus of this study due to its representative agricultural structure. The region is characterized by a mix of small, medium, and large farms, cultivating a variety of crops including cereals, vegetables and fruits, as well as maintaining livestock production. Leading agricultural industries in the region include dairy, poultry, and crop farming, reflecting typical farming practices found across Romania. These characteristics provide a relevant context for examining the adoption and impact of livestock robots and other agricultural innovations. While regional variations exist, the central region offers a representative setting for analyzing the dynamics of multiple stakeholders in the agricultural ecosystem.

2.2. Statistical Analysis

Data processing and analysis were conducted using SPSS Statistics for Windows (version 20.0, IBM Corp., Armonk, NY, USA), using different methods, such as frequency tables, descriptive statistics, ANOVA, post hoc tests, etc. The assumptions of normality and homoscedasticity were verified, respectively, with the Shapiro–Wilk and Levene’s tests. Cronbach’s alpha was used to test the reliability of the questionnaire. Tukey’s HSD was used when homogeneity of variances was confirmed, while Games-Howell was applied in cases of unequal variances. All statistical tests were conducted at a significance level of p < 0.05.

We hypothesized that the following:

H1: 

In Romania’s Central Development rural region, the perception of the need to implement precision agriculture is significantly influenced by the respondent’s professional role in the agricultural sector.

H2: 

In the rural areas of the Central Development Region, stakeholders perceive the acquisition of precision agriculture technologies as a key driver of competitive success, with significant differences across occupational categories.

H3: 

Within the Central rural region of Romania, perceived barriers to adopting precision technologies—particularly financial constraints—differ significantly depending on stakeholder background and role.

H4: 

Awareness of the importance of miniaturized UARs in precision agriculture varies significantly across professional groups in the Central Development rural region, depending on technological exposure.

H5: 

Among stakeholders in Romania’s Central Development rural region, the existence of an intelligent management framework is widely seen as essential to accelerating precision agriculture implementation, with significant differences observed across roles.

H6: 

Agricultural companies in the precision farming sector demonstrate high operational KMC by actively acquiring external knowledge, effectively combining it with internal expertise, and implementing measures to protect strategic knowledge assets.

H7: 

Agricultural entrepreneurs in the precision farming sector demonstrate high higher-order DC through effective adaptation to new technologies, practice alignment, capability development, innovation support, and collaborative training.

H8: 

Agricultural companies in the precision farming sector primarily enhance their competitive advantage through the adoption of new technologies, strategic partnerships and knowledge management, but face significant financial and training challenges that may limit their competitiveness.

3. Results

3.1. Analysis of the Issues of Interest from the Rural Area in Romania’s Central Development Region—The Perspective of Agricultural Digitalization

The study sample included 130 individuals (31.6 ± 8.83 years) recruited from various categories, including agricultural entrepreneurs (60.8%), providers of products and services (8.5%), institutional representatives (10%), investors (6.2%) and employees (110.8%) from the rural area in Romania’s Central Development Region. 3.8% of the participants were inactive, students in agricultural domain. Related to the activity sector, out of 130 respondents, 10.8% are not involved in agriculture, 53.8% work in crop farming, 1.5% work in animal farming and 343.8% work in both crop and animal farming. From the point of view of owned land area, 10.8% of respondents do not own any land. 13.1% own less than 5 hectares, while 43.8% own between 5 and 20 hectares. Additionally, 20% own between 21 and 100 hectares and 5.4% own more than 100 hectares. From the point of view of precision agriculture (PA) implementation, 48.5% of respondents stated that they do use it, while 51.5% do not. Among those who have implemented precision agriculture (63 respondents), the levels of technological intensity vary. 36.5% use consumption tracking tools, 22.2% apply precision seeding and 9.5% use precision fertilization or treatments. In addition, 22.2% use self-guidance systems, while 4.7% rely on sensor systems for crop monitoring and another 4.7% use aerial monitoring with UARs.

In order to find certain differences among stakeholders regarding the implementation of precision agriculture with respect to the type of category, ANOVA analysis was performed. The results are presented in

Table 1. ANOVA for testing H₁–H₅ hypotheses.

Variable	Category	N	M	SD	CI95% Lower	CI95% Upper	p	F	Levene Sig.
Rational use of inputs	Inactive	5	2.60	1.14	1.18	4.02	≤0.001	3.235	0.009
	Employee	14	4.86	0.36	4.65	5.07
	Farmer	79	4.81	0.55	4.69	4.93
	Product and service provider	11	4.55	1.50	3.53	5.56
	Institution representative	13	4.54	0.66	4.14	4.94
	Investor	8	4.88	0.35	4.58	5.17
	Total	130	4.68	0.81	4.54	4.82
PA acquisition success	Inactive	5	3.20	1.3	1.58	4.82	0.007	3.01	0.013
	Employee	14	4.86	0.36	4.65	5.07
	Farmer	79	4.58	0.79	4.4	4.76
	Product and service provider	11	4.45	1.5	3.44	5.47
	Institution representative	13	4.38	0.65	3.99	4.78
	Investor	8	4.88	0.35	4.58	5.17
	Total	130	4.55	0.87	4.39	4.7
PA acquisition difficulty	Inactive	5	3.00	1.00	1.76	4.24	≤0.001	1.28	0.276
	Employee	14	4.57	0.51	4.27	4.87
	Farmer	79	4.53	0.73	4.37	4.7
	Product and service provider	11	4.55	0.52	4.19	4.9
	Institution representative	13	4.23	0.59	3.87	4.59
	Investor	8	4.75	0.46	4.36	5.14
	Total	130	4.46	0.73	4.33	4.59
Robots use importance	Inactive	5	2.6	1.34	0.09	4.27	0.009	0.438	0.821
	Employee	14	4.21	0.69	3.81	4.62
	Farmer	79	4.37	0.98	4.15	4.59
	Product and service provider	11	4.18	0.98	3.52	4.84
	Institution representative	13	4.15	0.89	3.61	4.7
	Investor	8	4.25	0.88	3.51	4.99
	Total	130	4.24	1.00	4.06	4.41
Monitorization robots	Inactive	5	2.6	1.14	1.18	4.02	0.009	1.855	0.107
	Employee	14	4.07	0.61	3.72	4.43
	Farmer	79	4.22	1.08	3.97	4.46
	Product and service provider	11	4.18	0.98	3.52	4.84
	Institution representative	13	4.46	0.66	4.06	4.86
	Investor	8	4.63	0.51	4.19	5.06
	Total	130	4.18	1.02	4.01	4.36
Monitorization services	Inactive	5	3	1.41	1.24	4.76	0.146	0.557	0.733
	Employee	14	3.64	1.08	3.02	4.27
	Farmer	79	4.08	1.17	3.81	4.34
	Product and service provider	11	4.00	1.09	3.26	4.74
	Institution representative	13	3.54	1.39	2.70	4.38
	Investor	8	4.5	1.06	3.61	5.39
	Total	130	3.95	1.20	3.75	4.16
Variable rate robots	Inactive	5	3.20	0.83	2.16	4.24	0.698	3.105	0.011
	Employee	14	3.71	1.06	3.10	4.33
	Farmer	79	3.72	1.31	3.43	4.01
	Product and service provider	11	4.00	1.09	3.26	4.74
	Institution representative	13	3.54	1.26	2.77	4.30
	Investor	8	3.13	2.03	1.43	4.82
	Total	130	3.67	1.29	3.44	3.89
Variable rate services	Inactive	5	3.00	1.00	1.76	4.24	0.199	1.087	0.371
	Employee	14	3.64	1.15	2.98	4.31
	Farmer	79	3.97	1.12	3.72	4.23
	Product and service provider	11	3.55	1.57	2.49	4.60
	Institution representative	13	3.77	1.01	3.16	4.38
	Investor	4	4.50	1.06	3.61	5.39
	Total	130	3.88	1.16	3.68	4.08
Intelligent management framework	Inactive	5	2.60	1.51	0.72	4.48	0.002	1.115	0.356
	Employee	14	4.36	1.01	3.78	4.94
	Farmer	79	4.39	0.86	4.20	4.59
	Product and service provider	11	4.55	0.68	4.08	5.01
	Institution representative	13	4.23	1.09	3.57	4.89
	Investor	8	4.63	0.74	4	5.25
	Total	130	4.33	0.97	4.16	4.5

N—number of respondents. M—average. SD—standard deviation. CI—interval of confidence. p—significant level of probability. F—Levene test. Levene Sig.—Levene test level of significance.

.

The need to implement the concept of precision agriculture is strongly supported by the perception that rational use of inputs leads to healthier and more sustainable agricultural production. This is reflected in the high scores given to the variable Rational use of input by key stakeholder groups—including farmers (M = 4.81, SD = 0.55), employees (M = 4.86, SD = 0.36), and investors (M = 4.88, SD = 0.35). A statistically significant difference between respondent categories (p ≤ 0.001) confirms that this perception varies depending on professional background. Levene’s test indicated that the variability of scores for Rational use of input differed significantly between employee categories (F = 3.235, p = 0.009), and therefore the Games–Howell post hoc test was applied. No pairwise comparisons reached statistical significance, although inactive participants tended to report lower scores than other groups. These findings support Hypothesis H1, emphasizing the role of human capital in shaping attitudes toward the adoption of precision agriculture technologies.

The acquisition of precision agriculture technologies is perceived as a key driver of competitive success in the agricultural sector. This is supported by high average scores for the PA acquisition success variable, especially among employees (M = 4.86, SD = 0.36), farmers (M = 4.58, SD = 0.79) and investors (M = 4.88, SD = 0.35). The statistically significant difference between respondent groups (p = 0.007) confirms that stakeholders perceive the acquisition of precision agriculture technologies as a key driver of competitive success, thereby supporting Hypothesis H2. Levene’s test also showed significant variability between groups (F = 3.014, p = 0.013), and the Games–Howell test did not reveal significant differences, though inactive participants had slightly lower scores.

The acquisition of precision agriculture technologies is generally perceived as difficult and heavily dependent on financial resources. This is evident from the high scores for the PA acquisition difficulty variable across key stakeholder groups—including investors (M = 4.75, SD = 0.46), farmers (M = 4.53, SD = 0.73), employees (M = 4.57, SD = 0.51), and service providers (M = 4.55, SD = 0.52). The statistically significant difference between groups (p ≤ 0.001) highlights that challenges in technology adoption are unequally experienced, often reflecting disparities in financial access and institutional support, thus supporting Hypothesis H3. Tukey HSD was applied, showing that inactive participants reported significantly higher difficulty compared with employees, farmers, product and service providers, institutional representatives, and investors.

In precision agriculture, miniaturized UARs play an increasingly important role. Hypothesis H4 stated that awareness of the importance of miniaturized UARs varies significantly across professional groups depending on technological exposure. It is reinforced by the results.

This is reflected in the variable robots use importance, where most respondent categories show relatively high levels of understanding—farmers (M = 4.37, SD = 0.98), investors (M = 4.25, SD = 0.88), employees (M = 4.21, SD = 0.69) and service providers (M = 4.18, SD = 0.98). The significant differences across groups (p = 0.009) suggest that professional experience and exposure to agricultural innovation influence knowledge levels. Tukey HSD revealed that inactive participants had significantly lower knowledge of robot use than all other categories.

The use of miniaturized UARs enables crop monitoring and facilitates early warning alerts regarding potential vulnerabilities in the field. This perception is reflected in the monitorization robots variable, where most professional categories (e.g., farmers: M = 4.22, SD = 1.08, institutions: M = 4.46, SD = 0.66, investors: M = 4.63, SD = 0.51) reported high agreement. The statistically significant variation across groups (p = 0.009) underscores the importance of experience and technological integration in shaping how stakeholders view the value of robotic monitoring in precision agriculture. Tukey HSD showed that inactive participants reported significantly less engagement in monitoring robots compared with farmers, product and service providers, institutional representatives and investors.

While miniaturized UARs are acknowledged for their ability to monitor crops and provide early alerts about potential vulnerabilities, the interest in accessing such monitoring services remains moderate across stakeholder groups. The monitorization_services variable shows relatively uniform scores, with farmers (M = 4.08, SD = 1.17), investors (M = 4.50, SD = 1.06), and product/service providers (M = 4.00, SD = 1.09) expressing the most interest. However, the absence of statistically significant differences (p = 0.146) suggests that service-based solutions are not yet clearly differentiated or prioritized by role within the agricultural sector. Also, post hoc comparisons revealed no significant differences between the categories.

The use of miniaturized UARs also enables variable-rate application of treatments, a key feature of precision agriculture. However, the interest in investing in such systems remains moderate and relatively consistent across all stakeholder groups. According to the variable_rate_robots variable, farmers (M = 3.72, SD = 1,31), employees (M = 3.71, SD = 1.06) and providers (M = 4.00, SD = 1.09) express some willingness, yet no statistically significant differences were found (p = 0.698). Levene’s test indicated significant variance differences (F = 3.105, p = 0.011), but Games–Howell showed no significant differences between groups. The results suggest that barriers such as cost, perceived complexity or uncertainty may temper enthusiasm for adopting these advanced systems.

Miniaturized UARs make it possible to apply treatments at variable rates, enhancing precision and efficiency. However, when it comes to accessing such services externally, the interest among respondents is moderate, with farmers (M = 3.97, SD = 1.12), institutions (M = 3.77, SD = 1.01), and investors (M = 4. 50, SD = 1.06) showing slightly higher inclination. The absence of statistically significant differences between groups (p = 0.199) and the fact that Tukey HSD post hoc test did not reveal significant differences suggests that while the technology is promising, the service model for variable-rate applications may not yet be widely adopted or trusted across agricultural roles.

Hypothesis H5 claimed that the existence of an intelligent management framework is widely seen as essential to accelerating precision agriculture implementation, with significant differences observed across roles. The results reflect high agreement among all active stakeholder groups—including farmers (M = 4.39, SD = 0.86), product and service providers (M = 4.55, SD = 0.68) and investors (M = 463, SD = 0.74). It was a statistically significant difference (p = 0.002), suggesting that those with active roles in the agricultural sector strongly recognize the strategic value of intelligent systems in coordinating data-driven decision-making and operational efficiency. Variance was not significantly different (F = 1.115, p = 0.356), and Tukey HSD indicated that inactive participants reported significantly lower scores than all other categories.

3.2. KMC and DC

Of the 118 responses for this second stage, about 45.8% were from responders working at privately held companies, followed by public institutions (20.3%), mixed-ownership organizations (13.6%), cooperatives (93%), and individual enterprises (10.2%). Only 0.8% represented limited liability companies (SRL), and no responses came from joint-stock companies (SA).

Regarding management level, the majority held tactical roles (60.2%), such as department managers or assistant directors. 28.8% had strategic positions (e.g., owners or general directors), while 8.5% were consultants or external experts. Operational-level managers accounted for 2.5% of the sample.

3.2.1. KMC Operational Capabilities

The operational dimension of KMC was assessed across three main categories: acquisition, combination and protection of knowledge. Each category included multiple Likert-scale items (1 = Strongly Disagree to 5 = Strongly Agree), with results based on a sample of 118 valid responses, as presented in

Table 2. KMC statistics.

Variable	Category	N	M	SD	CI95% Lower	CI95% Upper	p
KMC_acquisition_international_trends	Employee	11	4.73	0.46	4.41	5.04	0.97
	Farmer	76	4.71	0.48	4.60	4.82
	Product and service provider	10	4.70	0.48	4.35	5.05
	Institution representative	13	4.62	0.65	4.22	5.01
	Investor	8	4.75	0.46	4.36	5.14
	Total	118	4.70	0.49	4.60	4.79
KMC_acquisition_partnerships	Employee	11	4.82	0.40	4.55	5.09	0.85
	Farmer	76	4.70	0.51	4.58	4.82
	Product and service provider	10	4.60	0.51	4.23	4.97
	Institution representative	13	4.62	0.65	4.22	5.01
	Investor	8	463	0.51	4.19	5.06
	Total	118	4.58	0.56	4.48	4.69
KMC_acquisition_networks	Employee	11	4.45	0.52	4.10	4.81	0.32
	Farmer	76	4.66	0.53	4.54	4.78
	Product and service provider	10	4.40	0.69	3.90	4.90
	Institution representative	13	4.38	0.65	3.99	4.78
	Investor	8	4.63	0.51	4.19	5.06
	Total	118	4.58	0.56	4.48	4.69
KMC_combination_experience	Employee	11	4.09	0.53	3.73	4.45	0.25
	Farmer	76	4.54	0.59	4.40	4.68
	Product and service provider	10	4.40	0.69	3.90	4.90
	Institution representative	13	4.46	0.66	4.06	4.86
	Investor	8	4.50	0.53	4.05	4.95
	Total	118	4.47	0.60	4.36	4.59
KMC_combination_investments	Employee	11	4.00	0.63	3.58	4.42	0.01
	Farmer	76	4.45	0.66	4.30	4.60
	Product and service provider	10	4.40	0.51	4.03	4.77
	Institution representative	13	3.85	1.06	3.20	4.49
	Investor	8	4.75	0.46	4.36	5.14
	Total	118	4.36	0.72	4.22	4.49
KMC_combination_inovative_technologies	Employee	11	4.27	0.46	3.96	4.59	0.07
	Farmer	76	4.41	0.65	4.26	4.56
	Product and service provider	10	4.40	0.51	4.03	4.77
	Institution representative	13	4.08	0.64	3.69	4.46
	Investor	4	4.88	0.35	354.58	5.17
	Total	114	4.39	0.62	4.28	4.50
KMC_protection_policies	Employee	11	4.09	0.53	3.73	4.45	0.04
	Farmer	76	4.43	0.57	4.30	4.57
	Product and service provider	10	4.30	0.67	3.82	4.78
	Institution representative	13	4.08	0.64	3.69	4.46
	Investor	8	4.75	0.46	4.36	5.14
	Total	118	4.37	0.59	4.26	4.48
KMC_protection_measures	Employee	11	4.82	0.40	4.55	5.09	0.49
	Farmer	76	4.58	0.57	4.45	4.71
	Product and service provider	10	4.50	0.70	3.99	5.01
	Institution representative	13	4.38	0.76	3.92	4.85
	Investor	8	4.63	0.51	4.19	5.06
	Total	118	4.58	0.59	4.47	4.68
KMC_protection_priority	Employee	11	4.27	0.90	3.67	4.88	0.46
	Farmer	76	4.38	0.67	4.23	4.54
	Product and service provider	10	4.70	0.67	4.22	5.18
	Institution representative	13	4.54	0.51	4.22	4.85
	Investor	8	4.63	0.51	4.19	5.06
	Total	118	4.43	0.67	4.31	4.55

KMC—Knowledge Management Capabilities. N—number of respondents. M—average. SD—standard deviation. CI—interval of confidence. p—significant level of probability.

.

Related to knowledge acquisition capabilities, the results indicate a high level of agreement among respondents regarding the proactive efforts of agricultural companies to adopt international trends and apply precision technologies (M = 4.70, SD = 0.49). There is also strong agreement on the importance of participating in local partnerships for exchanging good practices (M = 4.69, SD = 0.51), and on using national or local networks and events to support ongoing learning and professional development (M = 4.58, SD = 0.56). These findings highlight a clear openness to external knowledge sources and collaboration within the agricultural sector.

In terms of knowledge combination, the data suggest that internal experience is effectively integrated with external technological knowledge to improve management processes (M = 4.47, SD = 0.6). Furthermore, strategies aimed at attracting investors appear to rely on both accumulated expertise and input from external sources (M = 4.36, SD = 0.72). The adoption and implementation of innovative tools—such as drones, soil sensors, and management software—also received moderate support (M = 4.39, SD = 0.62), indicating a positive but still developing capacity for technological integration.

With regard to knowledge protection, most respondents agreed that there are clear policies in place for safeguarding sensitive knowledge, including cultivation methods and fertilization strategies (M = 4.37, SD = 0.59). Stronger agreement was observed in relation to the implementation of measures that prevent data loss or theft (M = 4.58, SD = 0.59). Protecting intellectual capital and innovative technologies is also recognized as a development priority for many companies (M = 4.43, SD = 0.67), underlining the strategic importance placed on securing knowledge assets.

The results validate H6 hypothesis, showing that precision farming companies demonstrate high operational Knowledge Management Capabilities by actively acquiring external knowledge, effectively combining it with internal expertise, and implementing strong measures to protect strategic knowledge assets.

Statistical analysis revealed significant differences between respondent categories for two variables: KMC_combination_investments (p = 0.01) and KMC_protection_policies (p = 0.04). While the other variables do not show statistically significant differences, they still provide contextual insights relevant to the research.

In the case of investment-related knowledge combination, investors reported a substantially higher mean score M = 4.75 SD = 0.46 compared to other groups, particularly institution representatives (M = 3.85, SD = 1.07), indicating a stronger emphasis placed by investors on financial commitment as a mechanism for integrating knowledge. Post hoc tests indicated that, for KMC_combination_investments, farmers reported significantly higher scores than institution representatives (p = 0.037), while investors also rated investment attraction higher than institution representatives (p = 0.036). Employees showed a tendency to rate lower than investors, although this difference was not statistically confirmed. Overall, institution representatives expressed the lowest perception of investment attraction compared to other groups.

Similarly, for knowledge protection policies, investors again scored higher (M = 4.75, SD = 0.46), while employees (M = 4.09, SD = 0.53) and institution representatives (M = 4.08, SD = 0.64) reported lower levels. As for the post hoc tests results, no statistically significant differences were observed between groups. Nevertheless, investors tended to rate policy protection more positively than both employees and institution representatives. These differences did not reach the conventional level of significance, but they suggest a more favorable perception among investors. The findings suggest that strategic priorities regarding investment and knowledge protection vary significantly depending on the role of the stakeholder within the precision agriculture ecosystem.

3.2.2. Higher-Order Dynamic Capabilities (DC)

The data presented in

Table 3. DC statistics.

Variable	Category	N	M	SD	CI95% Lower	CI95% Upper	p
DC_AC_new_technologies	Employee	11	4.64	0.67	4.18	5.09	0.63
	Farmer	76	4.57	0.63	4.42	4.71
	Product and service provider	10	4.8	0.42	4.50	5.10
	Institution representative	13	4.46	0.77	3.99	4.93
	Investor	8	4.38	0.51	3.94	4.81
	Total	118	4.57	0.63	4.45	4.68
DC_AC_practices alignment	Employee	11	4.91	0.30	4.71	5.11	0.50
	Farmer	76	4.51	0.77	4.34	4.69
	Product and service provider	10	4.60	0.51	4.23	4.97
	Institution representative	13	4.62	0.50	4.31	4.92
	Investor	8	4.63	0.51	4.19	5.06
	Total	118	4.58	0.68	4.45	4.70
DC_IC_new_capabilities	Employee	11	4.64	0.50	4.30	4.98	0.75
	Farmer	76	4.42	0.65	4.27	4.57
	Product and service provider	10	4.50	0.52	4.12	4.88
	Institution representative	13	4.62	0.65	4.22	5.01
	Investor	8	4.50	0.75	3.87	5.13
	Total	118	4.47	0.63	4.36	4.59
DC_IC_competitive advantage	Employee	11	4.73	0.64	4.29	5.16	0.22
	Farmer	76	4.51	0.64	4.37	4.66
	Product and service provider	10	4.80	0.42	4.50	5.10
	Institution representative	13	4.23	0.83	3.73	4.73
	Investor	8	4.38	0.74	3.75	5.00
	Total	118	4.52	0.66	4.40	4.64
DC_IC_innovations_support	Employee	11	4.73	0.46	4.41	5.04	0.68
	Farmer	76	4.53	0.66	4.37	4.68
	Product and service provider	10	4.50	0.70	3.99	5.01
	Institution representative	13	4.31	0.85	3.79	4.82
	Investor	8	4.50	0.75	3.87	5.13
	Total	118	4.52	0.67	4.39	4.64
DC_AbC_collaborations	Employee	11	4.73	0.46	4.41	5.04	0.04
	Farmer	76	4.64	0.55	4.52	4.77
	Product and service provider	10	4.10	0.99	3.39	4.81
	Institution representative	13	4.31	0.75	3.85	4.76
	Investor	8	4.38	0.74	3.75	5.00
	Total	118	4.55	0.64	4.43	4.67
DC_AbC_training	Employee	11	4.73	0.46	4.41	5.04	0.46
	Farmer	76	4.63	0.65	4.48	4.78
	Product and service provider	10	4.50	0.70	3.99	5.01
	Institution representative	13	4.85	0.37	4.62	5.07
	Investor	8	4.38	0.74	3.75	5.00
	Total	118	4.64	0.62	4.52	4.75

DC—dynamic capabilities. AC—adaptive capabilities. IC—innovative capabilities. AbC—absorptive capabilities. N—number of respondents. M—average. SD—standard deviation. CI—interval of confidence. p—significant level of probability.

illustrate the DC of agricultural entrepreneurs, focusing on adaptive (AC), innovative (IC) and absorptive (AbC) capabilities. These capabilities are crucial for enhancing the management and competitiveness of agricultural operations through the adoption of new technologies, continuous learning and strategic collaboration.

Regarding adaptive capabilities, respondents demonstrated a high level of agreement with the importance of identifying the need for change and adopting new technologies to increase productivity, as reflected by the high frequencies in the upper Likert scale categories (M = 4.57, SD = 0.63). Similarly, the alignment with best practices was perceived positively, with most respondents choosing agreement or strong agreement (M = 4.58, SD = 0.68), indicating a well-established awareness of continuous improvement in agricultural management.

With regard to innovative capabilities, most respondents agreed that developing new capabilities is a managerial priority (M = 4.47, SD = 0.63). Implementing innovations to gain a competitive edge was also considered important (M = 4.52, SD = 0.66), as was the existence of an internal system to support long-term innovation (M = 4.52, SD = 0.67), which was confirmed by a large number of agreement responses. These findings reflect a strategic orientation toward continuous improvement and long-term competitiveness through internal innovation processes.

In relation to absorptive capabilities, the ability to quickly adopt new knowledge through strategic collaboration was also highly rated (M = 4.55, SD = 0.64), with a concentration of responses at level 3 and above. Additionally, continuous employee training was perceived as essential for enhancing competencies, showing a strong agreement among participants (M = 4.64, SD = 0.62). These results suggest a shared recognition of the value of both external collaboration and internal learning mechanisms in facilitating innovation and adaptability.

Agricultural entrepreneurs in the precision farming sector report consistently high mean values across all higher-order dynamic capability dimensions, including adaptation to new technologies (M = 4.57), practice alignment (M = 4.58), capability development (M = 4.47), competitive advantage (M = 4.52), innovation support (M = 4.52), collaborative activities (M = 4.55), and training initiatives (M = 4.64) on a 5-point scale. The low-to-moderate standard deviations suggest limited dispersion in responses, further indicating uniformly high capability levels. The results validate H7, demonstrating that agricultural entrepreneurs exhibit high higher-order dynamic capabilities through effective adaptation, practice alignment, capability development, innovation support, and collaborative training.

Statistical analysis revealed a significant difference across respondent categories for the variable DC_AbC_collaborations (p = 0.04), which reflects the dynamic absorptive capability related to establishing collaborations. Product and service providers reported a notably lower mean (M = 4.10, SD = 0.99), compared to employees (M = 4.73, SD = 0.46) and farmers (M = 4.64, SD = 0.55). This suggests that while most stakeholder groups view strategic collaboration as an important absorptive capability, product and service providers may engage in or prioritize such partnerships to a lesser extent. The post hoc analysis using Tukey HSD did not reveal any statistically significant differences between the professional categories regarding collaboration. Although farmers tended to report higher scores than product and service providers, this difference approached but did not reach significance.

3.2.3. Organizational Competitiveness in the Precision Agriculture Segment

The role of organizational aspects for gaining and developing a robust, sustainable competitive advantage is essential in the case of precision agriculture segment, especially in the case of an emerging market.

Table 4. Competitiveness—used strategies.

Variable	N	%
Adopting new technologies (e.g., drones, sensors, GPS)	46	39
Creating strategic partnerships with other farmers or organizations	21	17.8
Protecting and leveraging internal knowledge (cultivation techniques, soil data)	13	11
Digitalizing farm management processes	19	16.1
Investing in employee training and development	15	16.1
Total	114	100

N—number of responses, %—percent.

presents the main organizational strategies employed to gain a competitive advantage in the field of precision agriculture, as reported by the respondents. Among the 118 participants, 39% identified the adoption of new technologies (such as drones, sensors or GPS) as the most frequently used strategy, highlighting the emphasis placed on technological innovation.

Creating strategic partnerships with other farmers or organizations was reported by 17.8% of respondents, followed by digitalization of farm management processes (16.1%), investments in employee training and development (16.1%), and leveraging internal knowledge, such as cultivation techniques and soil data (11%). These findings suggest that both technological advancement and knowledge management play key roles in enhancing organizational competitiveness.

The main challenges considered essential for increasing company competitiveness in the precision agriculture sector were highlighted by respondents. The most frequently cited challenge was the high cost of precision equipment, selected by 39% of respondents, followed closely by the lack of access to financing for new technologies (34.7%). These findings underscore the significant financial barriers faced by stakeholders in adopting advanced solutions. Additionally, 12.7% of participants reported difficulties in training personnel, suggesting that human resource development remains a secondary but notable concern. A smaller portion of respondents pointed to limited adaptability to cutting-edge technologies (5.9%) and the impact of uncertain or frequently changing government regulations (7.6%).

The results emphasize that cost-related constraints and funding limitations are the most pressing issues hindering the competitiveness of agricultural enterprises.

Respondents expressed varied perceptions of their company’s competitive position within the precision agriculture sector. A significant proportion of participants (45.8%) consider their company’s performance to be similar to that of their competitors. Additionally, 43.2% believe their company is slightly more competitive, suggesting a moderate sense of advantage or differentiation in the market.

On the contrary, only a small percentage see their company at a disadvantage. Specifically, 5.9% rate their position as slightly weaker, and 3.4% perceive it as significantly weaker. These results indicate that although most stakeholders view their organizations as at least on par—or modestly ahead—of competitors, there remains a minority who recognize competitive gaps.

The data reflect a relatively balanced yet optimistic perception of competitiveness in the precision agriculture segment among stakeholders in the Central Development Region of Romania. H8 hypothesis is validated.

4. Discussion

The present study investigates the role of knowledge management capabilities and dynamic capabilities integration in the development and integration of an innovative ecosystem of precision agriculture based on miniaturized robotic products. The importance and actuality of this study for the case of an emerging application in an emerging market is sustained by the transformative opportunity for enhancing productivity, resource efficiency and long-term sustainability.

The concept of the smart farm is also promoted, centered on data collection, transmission and efficient analysis to enable corrective actions based on feedback from specific agricultural activities. This approach supports the development of an intelligent management framework capable of generating adaptive strategies in response to environmental and biological feedback by aligning resource allocation with real-time environmental conditions [42]. Hence, it contributes to farm sustainability and the well-being of rural communities [43,44,45,46,47,48].

First survey findings reveal a generally positive attitude toward the adoption of precision agriculture technologies among stakeholders from the Central Development Region of Romania. Nathanael et al. [2] found that perceived economic benefits and operational efficiencies are key motivators for technology adoption. Similarly, Spykman et al. [6] reported that farmers in Bavaria view agricultural robots favorably, especially when their utility in reducing labor and input costs is clearly demonstrated. The current findings confirm this, respondents emphasized the rational use of inputs and the need for healthier, more efficient production systems, with strong agreement particularly among farmers and investors.

While acquiring precision technology is perceived as a competitive advantage, it is also viewed as financially challenging, pointing to the importance of supportive funding mechanisms. The financial burden associated with acquiring advanced technology continues to be a significant limiting factor—as stated in some studies [3,22]. According to Gil et al. [22], low adoption rates are often not due to lack of interest, but due to limited access to affordable and scalable solutions. Therefore, while the strategic interest exists, the market remains segmented by economic capacity.

Participants acknowledged the critical role of miniaturized UARs in both variable-rate treatment and crop monitoring with early alert systems. However, their willingness to invest directly in such robotic systems or to access external services remains moderate, suggesting that barriers such as cost, infrastructure, or uncertainty about return on investment still persist, a finding supported by previous research [49]. This is also reinforced by recent studies on UAV use in Romanian horticulture, which underline the need for improved infrastructure, targeted training, and clearer regulatory frameworks to support adoption [50].

Also, the results showed strong support for the establishment of an intelligent management framework as a facilitator of precision agriculture implementation. This perception is significantly more pronounced among professionals, underlining their recognition of the role that integrated digital systems can play in optimizing agricultural operations.

Together, these insights highlight the need for tailored policies, training, and accessible technological solutions to bridge the gap between interest and actual implementation in the precision agriculture landscape.

Second survey findings revealed a high degree of maturity in operational knowledge management capabilities, particularly in the domains of knowledge acquisition and protection. Respondents strongly agreed on the importance of adopting international technological trends (M = 4.70), engaging in partnerships (M = 4.69) and participating in networks that foster professional development (M = 4.58). These findings suggest a well-developed infrastructure for absorbing external knowledge, which aligns with the conclusions of [1,17], who advocate for embedding new technologies into existing knowledge ecosystems to enhance adoption outcomes. However, while knowledge combination processes such as the adoption of innovative tools (M = 4.39) and alignment with investment strategies (M = 4.36) received positive evaluations, they also highlight a still-evolving capacity for technological assimilation. This matches the argument made by [21], who found that successful implementation of robotic systems requires not only technical readiness but also managerial alignment and long-term strategic vision.

First order capabilities (FOC) expressed by KMC were analyzed and extracted based on the interests of end-users and their recent experiences in the field of precision agriculture. However, relying solely on FOC does not enable a deep or flexible adaptation within this rapidly evolving ecosystem, characterized by high environmental dynamism. Therefore, higher-order DC are introduced, offering a more contextually appropriate architecture for the research framework. Within this innovative structure, several relevant mechanisms are uncovered that hold practical value for real-world integration. Agricultural entrepreneurs demonstrated strong adaptive and absorptive potential. High mean scores were reported for the ability to identify technological changes (M = 4.57), align internal practices (M = 4.58) and adopt knowledge through strategic collaboration (M = 4.55). Moreover, the commitment to employee training (M = 4.64) and supporting innovation processes internally (M = 4.52) further reinforces their readiness to experiment with and incorporate advanced robotic tools. These results are in line with the findings of [24,51], who emphasize that continuous learning and adaptive behavior are essential for enabling the effective transition toward precision agriculture.

However, the main barriers to competitiveness, as highlighted by the respondents, remain the high cost of precision equipment (39%) and limited access to financing for new technologies (34.7%). These constraints, alongside difficulties in staff training (12.7%) and uncertain regulatory frameworks (7.6%), underscore the need for structured intervention strategies. These findings align with the conclusions of Iagăru et al. [1,50], who emphasize the necessity of regional and institutional support for enhancing smart agricultural integration and promoting resilience in agroecosystems. Similarly, a recent study on Agriculture 4.0 adoption in emerging economies [52] identified key barriers to implementing smart farming technologies, such as technological complexity, insufficient collaboration among stakeholders, inadequate government support, and the absence of clear action plans.

Based on our findings, five strategic options are proposed to support the rapid integration of miniaturized robotic products in precision agriculture. First, designing these products as modular and scalable systems enables gradual adoption, thereby reducing initial costs and facilitating smooth integration with existing machinery—an approach validated in modular robotics research such as the Thorvald II and BoniRob platforms [53,54]. Second, collaborative financing mechanisms, including leasing or equipment-sharing models, can alleviate the capital constraints of small and medium-sized farms. Empirical evidence shows that equipment-sharing cooperatives can significantly reduce investment requirements and enhance cost-effectiveness [55,56].

Third, establishing regional agricultural innovation hubs can provide demonstration sites, hands-on training, and platforms for knowledge exchange among farmers and technology providers. Similar initiatives, such as the SmartAgriHubs Digital Innovation Hubs in Europe, have proven effective in facilitating adoption of digital technologies in agriculture [57]. These centers, however, require significant upfront investment and depend on rural connectivity; therefore, cost estimates and infrastructure shortcomings (e.g., rural network coverage) must be carefully assessed to ensure feasibility. Fourth, integrating robotic systems into existing knowledge management platforms ensures continuity, traceability, and reduced learning curves, building on strengths already demonstrated by agricultural firms [58].

Finally, clear and coherent public policy frameworks and stakeholder involvement in standard-setting (covering data security, interoperability, operational safety and environmental impact) are essential to reduce uncertainty and support responsible implementation [59,60].

To strengthen feasibility, a phased implementation roadmap is proposed:

Short-term (0–2 years): Deploy modular systems through leasing models or cooperative sharing, minimizing initial capital outlay and allowing farmers to test systems in practice.

Medium-term (2–5 years): Establish regional innovation hubs with public–private support; conduct feasibility studies to assess infrastructure gaps (e.g., network connectivity) and estimate implementation costs.

Long-term (5+ years): Launch policy reforms and standardization initiatives, institutionalizing support mechanisms and creating interoperability and safety standards to foster scale-up and sustainability.

This phased approach ensures that the proposed strategies are not only innovative but also realistic, financially feasible and aligned with existing infrastructure and policy environments.

The present study contains some limitations that might be considered in future studies. First, the research is based on case studies within the central region of Romania. Although the region is representative in certain aspects, the conclusions may not fully capture the diversity of agricultural systems or stakeholder responses in other regions. Future research could expand the analysis to additional regions to assess the broader applicability of the observed patterns. Second, the sample composition is unbalanced, with agricultural entrepreneurs forming the majority and investors being underrepresented. This limits the ability to fully capture the perspectives of all stakeholder groups, particularly investors, and will be addressed in future studies by including a larger and more geographically diverse group.

5. Conclusions

This study examined strategies for enhancing the competitiveness of agricultural enterprises in Romania’s Central Region by investigating stakeholder readiness to adopt miniaturized robotic and precision agriculture technologies. The research combined qualitative and quantitative methods to assess how knowledge management practices and dynamic capabilities contribute to shaping adoption pathways.

The findings indicate a strong interest and preparedness among stakeholders, particularly supported by operational knowledge management practices such as external knowledge acquisition, strategic partnerships, and data protection. Agricultural entrepreneurs demonstrated solid adaptive and absorptive capacities—reflected in openness to change, collaboration, and innovation—yet significant barriers such as high equipment costs and limited access to financing continue to hinder widespread adoption. The study also revealed strong support for intelligent digital management frameworks, especially among professionals, underscoring the perceived importance of integrated systems for improving efficiency and facilitating technological implementation.

These results have important implications. To bridge the gap between interest and actual adoption, it is essential to promote modular and scalable robotic systems, develop collaborative financing mechanisms, strengthen regional innovation hubs, and establish coherent public policies and technical standards that address data security, interoperability and sustainability.

In conclusion, the integration of miniaturized robotic systems in Romanian agriculture represents both a technical and strategic challenge. Nonetheless, the evidence suggests that enterprises already possess the cognitive, collaborative, and adaptive infrastructure necessary to support such innovations. Future research will expand beyond the regional context to assess broader adoption patterns, while practical implementation will focus on creating accessible and sustainable pathways for technology uptake.