Integrated ERP Systems—Determinant Factors for Their Adoption in Romanian Organizations

Abstract

This study examines the factors influencing the adoption of enterprise resource planning (ERP) systems within Romanian organizations. The objective is to develop a comprehensive framework for ERP adoption decisions, thereby advancing the field of knowledge and offering managerial insights. To accomplish this research goal, a questionnaire is envisioned, employing various research hypotheses, and distributed to a representative sample. Quantitative econometric regression analysis is employed, considering potential factors such as user training and education, competitive pressures, user involvement and participation, decentralized ERP features, top management support, data quality, the quality of the ERP system, cost and budget considerations, and business process reengineering. Of the 12 factors analyzed, 9 were found to be relevant in terms of influence on the decision to adopt ERP systems, in the context of the Romanian market. The other three factors were found to be irrelevant, thus obtaining results partially different from other areas of the world. By validating the hypotheses and answering the research questions, this work addresses a research gap regarding the lack of a comprehensive understanding of the influencing factors that shape the adoption process of ERP systems in Romania.

1. Introduction

ERP (enterprise resources planning) integrated systems play a particularly important role in the modern economy because they represent the information backbone of organizations. Initially started as individual applications at modular level, they were intended to cover distinct areas such as production, accounting, or inventory management [1]. Technological developments, in both software and hardware, have allowed organizations to integrate step by step more modules and to obtain integrated system models in which information is managed as a true asset of value [2]. According to the literature [3], the ERP adoption process is complex and depends on several variables that influence the decision makers and the final chosen solutions. Moreover, the adoption of ERP systems has different characteristics depending on the type of economy in which the economic agents of a country operate; thus, previous studies took into consideration these aspects for different countries: Italy [4], Mexico [5,6], India [7], China [8,9], South Africa [10], Saudi Arabia [11], Turkey [12], Sri Lanka [13], or the United States of America [14]. Our study aims to analyze and highlight the main determinants of the adoption of ERP systems for Romanian organizations. This research goal is motivated by several arguments related to economic, social and cultural aspects. Therefore, it is important to mention that Romania is an important country in the European Union, and the software industry is a major contributor to the gross domestic product (GDP).

Also, the national legislation is harmonized with the general rules of the European Union, which contributes to the concept of economic, legislative, commercial, and fiscal integration. From the point of view of the research gap, analyzing the precedents resulting from the existing works [15,16,17], we find that in Romania, there is not a clear and unified picture of the determinants that influence the process of adoption of ERP systems. Starting from this research gap and having an investigation goal to obtain a complete image of the factors influencing the adoption of ERP systems, we elaborate the following research questions:

• What are the main determinants influencing the decision to adopt ERP integrated systems?
• What is the influence of each determinant on the final ERP adoption decision?

In order to cover the research gap we discussed above and to fulfill this proposed goal, the answers to the research questions will be obtained by testing several research hypotheses. All these hypotheses are based on previous results from the literature, which have been analyzed and validated by various previous studies, thus constituting a solid and reasoned basis for our research model.

The main aim of our study is to establish a general framework for understanding the context in which ERP adoption decisions are made. By analyzing the answers to our research questions, we can derive managerial implications and make a significant contribution to the field of knowledge. Based on the objective results obtained in our research, we can deduce managerial implications and, in this way, make a significant contribution to the field of knowledge.

The article is organized in several sections. Section 2 covers the development of research hypotheses based on a literature review and research gap identification. In Section 3, the materials and methods are presented, based on answers obtained from a questionnaire administered on a representative sample and on quantitative econometric regression analysis. This is followed by Section 4, presenting the results of the research and then discussions and interpretations of these results. In Section 5, the main findings, the research limitations, and future research directions are presented.

2. Literature Review and Hypothesis Development

Romania has been a member of the European Union since 2007 and is an important pillar in the European context because it has made the transition from a centralized economy to a competitive economy based on market economy rules. The country’s inclusion in the European Union has facilitated the adoption and implementation of legal regulations that have provided access to an extensive market, economic partnerships, and various sets of best practices [18]. At present, Romania attracts a significant amount of foreign direct investment, and economic development has led to the migration of workers from outside the European Union (especially Asia) to areas such as agriculture, industry, and construction.

The population’s consumption and companies’ investments have determined the growth of the gross domestic product to about EUR 360 billion in 2024, according to International Monetary Fund estimates [19], which places the country in 12th place in the European Union in terms of the size of the economy. Romania’s economic growth over the last decade has been sustained by domestic consumption, public investments that have benefited from European funds, and exports of industrial goods and services in the IT sector. A very clear proof of Romania’s economic potential is the evolution of GDP per capita, which has reached 80% of the European average, a sustained increase compared with previous years. Although at the macroeconomic level, there is a convergence and a catching-up with Western European countries, at the microeconomic level, there are still regional disparities between the West (better developed) and the East (less developed).

In relation to a population of about 18.5 million inhabitants1, there are more than 1.2 million companies registered in Romania, most of them being micro-enterprises with fewer than 10 employees. Today’s entrepreneurial environment is very active, dynamic, and competitive, and there is an interest for growth in areas such as IT, e-commerce, creative industries, and financial services. At the moment, Romania is attractive to investors due to its relatively low labor costs, geographical position, and membership in international economic treaties. According to the National Bank of Romania [20], foreign direct investment has surpassed EUR 7 billion annually, mainly in the automotive, renewable energy, and information technology sectors.

A particularly relevant aspect is the fact that human capital has a significant number of specialists in the technical and IT fields, which explains the accelerated growth of the IT&C sector, which has come to contribute more than 5% of the gross domestic product (GDP) [21]. Partnerships between universities and business contribute significantly to increasing the employability of graduates. At the same time, through European funds, Romania is implementing programs to improve access to digital education.

In this rapidly expanding and modernizing economic context, integrated ERP systems have become a very important component for the development of business, at both business-to-business and business-to-consumer levels. In recent years, the ERP systems market in Romania has seen significant growth, amid the accelerated digitalization of business and the increasingly high requirements for operational efficiency and business process integration. ERP systems have become the information backbone of organizations, having dedicated modules and processing data and information from sales, production, accounting, logistics, human resources, etc. The ERP market has surpassed the incipient stage of development and is on a maturing trajectory. This is confirmed by the fact that in the last year, the market for business software solutions has exceeded EUR 250 million, an increase compared with previous years. The ERP integrated systems segment has a share of over 40% of this major ecosystem. The demand for ERP systems is driven by factors such as the need for traceability; compliance with national tax regulations; the need for access to real-time data; integration with external modules; and applications such as business intelligence (BI), customer relationship management (CRM), and supply chain management (SCM).

According to the data of the National Agency for Fiscal Administration [22], in Romania there are over 1500 companies offering ERP implementation, development, maintenance, and customization services. In terms of human resources, more than 15,000 employees are included in the ERP industry in various positions of business analysts, developers, programmers, consultants, and support staff. Official reports [23] emphasize the fact that in Romania, there are both international ERP solutions and locally developed solutions. International ERP solutions are mainly adopted by multinational companies and corporations, while local solutions are preferred by small and medium-sized enterprises with Romanian capital.

Among the most important international ERP solutions found on the Romanian market are the following:

• SAP S/4HANA—adopted by large companies in retail, manufacturing, and public areas;
• Oracle NetSuite and Oracle E-Business Suite—mainly used in the financial, industrial, and service sectors;
• Microsoft Dynamics 365—flexible solution, preferred in the service and trade sectors;
• Infor ERP—with a notable presence in the manufacturing area;
• IFS Applications—complex solution, mainly used in the energy industry and construction sector.

In terms of locally produced ERP solutions, the market is predominantly divided between suites such as WinMentor Enterprise [24] (developed by WinMentor Software), an authoritative and versatile modular solution, adapted to national tax regulations; Socrate ERP [25] (developed by BITSoftware), a flexible platform, in the cloud or on premises, adapted to local requirements; SeniorERP [26] (developed by Senior Software), having extensive functionalities for distribution, retail, manufacturing, and services; Clarvision ERP [27] (developed by QBS), particularly oriented toward manufacturing SMEs; and Transart ERP [28], having a strong presence in distribution and focused on fast-moving consumer goods.

Existing studies in the literature have tried to determine what are the reasons why ERP integrated systems are adopted and what are the determinants of this decision. Thus, top management support has been identified as a determining factor in the successful adoption and implementation of ERP systems. In this regard, the role of top management in allocating material, financial, and human resources is mentioned as essential [29]. Leadership is essential for the alignment between the strategic goals of the organization and the ERP adoption initiatives [30]. This is largely justified by the fact that the decision to adopt an ERP system is by its very nature a strategic decision, as it affects the long-term (sometimes the whole life cycle) evolution of the organization and involves significant resources both at the beginning of the process and beyond [31]. Strategic alignment involves correctly identifying digitalization and process integration needs, setting long-term goals, and selecting the ERP vendor in line with the organization’s vision.

In general, research to date has shown that organizations that prioritize this dimension tend to have higher success rates in the ERP implementation process [32]. This factor is considered not only fundamental but also integrative as it has a significant impact on most of the steps related to ERP implementation. Previous studies [33,34] show that variations in information systems adoption outcomes are frequently correlated with discrepancies in leadership and top management support. Consequently, the ERP projects with active top management support have higher success rates than those with a rather passive management attitude. Also, companies such as SAP and Oracle emphasize that successful implementation depends on strong CEO and CIO leadership [35].

Based on the above previous results from the literature, we formulate the following research hypothesis:

H1. 

Top management support has a significant impact on the decision to adopt ERP systems.

Another important factor in the adoption process of ERP integrated systems is user training and education because without proper training, end-users may reject or even sabotage the system. User training has the role to improve possible resistance to change and to familiarize users with the graphical user interface [36] and with the functions of the new system, thus reducing the fear of replacing the human factor with a data processing machine [37]. Studies [38] show that organizations that invest in ERP user training programs experience higher employee adoption rates, increased efficiency because users understand how to operate the system correctly, and significantly reduced errors. User education is aimed at providing details on why and how to justify and explain why a particular ERP system was chosen, how the system integrates into existing processes and how processes need to be modified to fit into the system, and what the specific responsibilities of each individual user are so that they fit with the overall goals of the organization. In general, user training and education also play an important role in motivating and retaining employees involved in the process of using the software system [39]. Relevant examples [40] confirm the importance of user training and education in the successful adoption of ERP systems as there are several categories of key users who are involved in such a process: system administrators, managers, and operational employees. The general user training and education process can be done in different ways [41], depending on the specifics of each company and each type of user: face-to-face (instructor-led) training, e-learning and online tutorials, practical workshops, guides, and manuals. Therefore, in the case of Coca-Cola, massive investment in user training has led to rapid adoption and increased productivity [42]. On the other hand, an Oracle Netsuite study [43] shows that more than half of ERP failures are due to lack of training rather than technical problems. In the same vein, Oracle Insights [44] shows that companies with well-structured training programs have reduced onboarding time from 6 to 2 months, which is a truly significant advantage. These actual examples, as well as specialized research, highlight the fact that investing in user training and education is not just a mere expense but can be a differentiator between success and failure, allowing us to build the following research hypothesis:

H2. 

User training and education have a significant impact on the decision to adopt the ERP systems.

The implementation of an ERP system is a major transformation for any organization, involving the migration of a large volume of data from legacy systems to the new platform. Data quality and data migration are critical factors that can influence not only the decision to adopt ERP but also the success of the implementation over the long term [45]. An ERP system is only as good as the data it processes, and if the migrated information is incomplete, inaccurate, or redundant, the system will create several major problems for the organization and users. Consequently, erroneous reports will lead to wrong decisions, altered or inaccurate data will generate operational inefficiencies, and users at various levels will lose trust in the system.

Before adopting an ERP system, organizations evaluate concrete aspects such as the degree of data cleansing already in place, the complexity of the migration process, and the cost of data cleansing. Paradoxically, in the case of legacy systems [46], the cost of data quality and data migration can sometimes be higher than the cost of the system itself. The specialized literature [47] identifies several common problems in data migration, among which the most important are duplicate or contradictory data, inconsistent formats, complex relationships between data structures that can be mapped with great difficulty in the new system, and data loss in cases of faulty migration processes. Data migration plays a very important role in the business continuity of an organization, and there are several examples of how to approach this [48]. For example, Unilever [49] invested time and resources in data cleansing before migrating to SAP ERP, reducing errors by 40%. In the same paradigm, a ThreadGold Consulting study [50] shows that over 40% of ERP failures are related to data migration issues. Based on this premise, Nestlé [51] used an incremental approach to migration, avoiding major disruptions in operations. The research cited above, as well as practical examples from business, lead to the conclusion that data quality and migration are factors to be taken into account when deciding to adopt ERP integrated systems, and the following research hypothesis can be expressed:

H3. 

Data quality and the migration process have a significant impact on the decision to adopt ERP systems.

In the contemporary business environment, characterized by globalization and accelerated digitalization, the pressure of competition becomes a determining factor in strategic decision making. Recent specialized studies [52] highlight that the adoption of integrated ERP systems is a complex choice that is strongly influenced by the competitive dynamics in the industry. Companies worldwide are facing increasingly strong global competition due to the entry of international players into the market. Competition is also emerging on the technological front as new technologies diversify and become widely adopted. This dynamic is forcing companies to look for solutions that offer competitive advantages, and ERP systems occupy a central place due to their integration and optimization abilities. Findings like [53] show that an important contribution to the decision to adopt ERP systems is made by the practices of direct competitors, the need to achieve up-to-date industry and commercial standards, and the pressure to remain relevant in the global marketplace. Several key factors influencing the ERP adoption decision have been highlighted in the literature [54,55,56]—especially factors related to the pressure of competition. Thus, the loss of market share is a strong driver because competitors who have adopted ERP systems can offer more competitive prices, shorter response times, and superior customer experiences. Also, the pressure to reduce costs relative to competitors is a strong determinant because ERP systems enable optimizations and automation of repetitive processes that can reduce operational costs by 15–25% and thereby reduce waste. Also, the modern competitive business environment increases the need for agility of companies through the ability to adapt quickly, real-time visibility of operations, and flexibility in decision making. In Romania, a number of large companies have had remarkable results under the influence of competitive factors. Therefore, according to [57], at the automotive company Dacia Renault, the implementation of SAP ERP has reduced order processing time from 7 days to 24 h, and at Dedeman, the largest home improvement and do-it-yourself goods national company, the migration to an integrated ERP system led to a 40% increase in stock management accuracy. Based on the previous results from theory and practice, the following research hypothesis is taken into consideration in our study:

H4. 

The pressure of competition has a significant influence on the decision to adopt ERP systems.

ERP system quality is not only about the technical aspects but also about how the system aligns with business needs, the user experience, and the ability to adapt to future changes. Thus, ERP system quality can be viewed from several perspectives [58,59]: technical quality, functional quality, and service quality. Technical quality considers aspects such as system stability and performance, data security, compatibility with other applications, etc. On the other hand, functional quality refers to the coverage of business needs, flexibility in configuration, and ease of customization [60]. Very important is also the service quality, which includes vendor support, quality of training, and documentation available for system maintenance. Relevant studies [61] show that system performance directly influences employee productivity in high-quality systems, while transaction processing time can also be reduced. The quality of an ERP system [62] has a direct impact on the user experience, with immediate influences on the speed of adoption in the organization, the level of employee satisfaction, and staff training costs. Another important aspect of ERP system quality relates to scalability, which affects the organization’s ability to adapt to growth, future upgrade costs, and compatibility with emerging technologies. ERP system quality currently also means elements related to the integration of real-time analytics tools, migration to cloud-native models, and the need for system stability regardless of external conditions.

In Romania, the impact of ERP system quality has been felt in various types of business. As a result, in the field of petroleum products processing, the company Rompetrol felt an improvement in the recruitment processes with the migration to a quality ERP product SAP [63]. In the financial–banking services domain, at Transilvania Bank (the largest in Romania in terms of assets), the implementation of Oracle ERP [64] reduced the financial reporting time, thus highlighting the importance of ERP system quality on the decision to adopt integrated business systems. Taking into consideration all these aspects, we formulate the following research hypothesis:

H5. 

The quality of ERP system has a significant influence on the decision to adopt such integrated systems.

Adopting and implementing an ERP system is one of the most complex organizational transformations, involving profound changes in business processes, workflows, and company culture. Change management plays a critical role in the success of this transition, directly influencing the organization’s decision to adopt ERP solutions and the results of this implementation [65]. In the context of integrated systems, change management covers several aspects of major interest such as preparing the organization for the digital transition, managing change resilience, and ensuring operational continuity during implementation. Various papers such as [66] have shown that ERP projects with well-structured change management have higher success rates, shorter time to adoption, and faster return on investment. Change management is about assessing the organization’s readiness for ERP adoption through digital maturity analysis, adaptive capacity assessment, and identification of key stakeholders. The cost of change [67] is also assessed by estimating the investment in human resources, calculating training costs, and assessing the impact on productivity. Within change management, a risk management component is also highlighted by identifying critical points and establishing KPIs for monitoring the ERP adoption and implementation project. Specialized studies [68] have shown that change management faces specific challenges such as resistance to change from employees, ineffective communication of benefits, or underestimation of the complexity of the transformation. In Romania, the change management process has been successfully manifested in the decision to adopt ERP in companies in the field of extraction and distribution of petroleum products (OMV Petrom) [69] and the sale of construction materials (Dedeman) [57]. At the same time, the scientific literature [70] indicates that many organizations without a change management plan experience significant delays in the ERP adoption process. Currently, the trend is to adopt the change management 4.0 paradigm, which incorporates agile approaches for implementation, the use of predictive analytics, and the personalization of strategies by target groups. Considering all these theoretical studies and practical examples, we can conclude that change management is an important determinant in the adoption of ERP systems, and we will test the following hypothesis:

H6. 

Change management has an important impact on the decision to adopt ERP systems.

Given that most companies purchase an ERP package from a specialized vendor, a critical factor influencing the decision to adopt these systems is the quality of vendor support and services. Vendor support in the ERP life cycle manifests itself in several directions [71]. In consequence, we can consider technical support that includes 24/7 assistance for critical problems, management of technical and functional updates, and rapid resolution of incidents. Another important direction is consulting services for auditing and optimizing processes, specific customizations adapted to business needs, and reconfiguring workflows. Vendor services [72] also refer to services delivered for continuous training, services that include onboarding programs for new company employees, advanced training for key users, and the provision of resources for online learning. Existing findings [32] show that there are many organizations that consider service support and services to be more important criteria than even price when selecting an ERP system. This vision [73] is due to the complexity of the implementation (average duration is 12–18 months), the need for continuous maintenance, and the importance of regular updates. Given the proliferation of vendor support and services, the current trend is to implement the hybrid support model, which includes local support for rapid interventions, global expertise for complex issues, and dedicated teams for enterprise clients. Specialized studies [74,75] have highlighted the fact that local providers offer better response times than global ones, which can be a differentiating element in the decision to adopt an integrated ERP system. The local market is experiencing trends in terms of increasing demand for managed services and migration to remote premium support. Also, support flows integrate AI conversational software agents (chatbots) that add value to the concept of vendor support [76]. Based on elements from previous research, we can conclude that the quality of vendor support and services is a decisive factor in the adoption of ERP systems, influencing both the initial decision and the long-term success of the implementation. Accordingly, we hypothesize the following:

H7. 

Vendor support and services have a significant influence on the decision to adopt ERP systems.

In the process of selecting and implementing an ERP solution, cost analysis and appropriate budget allocation are elements of major interest. In general, cost and budget considerations take into account the entire life cycle of the software solution, from the adoption decision to ongoing maintenance. An integrated system suite involves various cost categories, which can be synthetically grouped into initial costs, recurring costs, and hidden costs [77]. Initial costs include software licenses (20–30% of the total budget), hardware and infrastructure (15–25%), and consulting and implementation services (25–40%). During the use of the integrated system, recurring costs include maintenance and support (10–20% annually of the license value), updates and upgrades, and ongoing staff training. Although initial and recurring costs seem to cover the entire range of costs associated with an integrated system, in reality, during the life cycle, so-called hidden costs [78] also appear that are more difficult to quantify directly. We are talking here about productivity losses during the transition from the old system to the new system, costs with various internal resources reallocated, as well as additional customizations that can have a significant cost. It was noted [79] that cost and budget considerations can lead to significant limitations regarding the decision to adopt an ERP system because many companies postpone implementation due to high initial costs. Also, due to budgetary restrictions, it is possible that some organizations opt for limiting the scope of the ERP system. Another limitation occurs when companies opt for suboptimal solutions for financial reasons. In order not to end up in such restrictive situations, companies adopt various strategies designed to reduce costs and keep them under control [80]. Thus, a common approach is the one in which the ERP system is implemented in phases, prioritizing the modules critical to the operational activity. Another strategy involves a sometimes-aggressive negotiation with the ERP solution provider or using the cloud version to avoid investing in its own hardware infrastructure [81]. Poor financial risk management can lead an organization into various types of traps: underestimating staff training costs, neglecting infrastructure needs, or unexpected customization costs. Based on the data available in the literature, it appears that cost and budget considerations can have a significant influence on the decision to adopt ERP systems, and we formulate the following research hypothesis:

H8. 

Cost and budget considerations have a significant impact on the decision to adopt ERP systems.

In the context of the digital transformation of the economy, Business Process Reengineering (BPR) and the implementation of ERP systems represent two interdependent strategic components. BPR represents [82] a radical redefinition of business processes to achieve significant improvements in organizational performance, based on three fundamental pillars: results orientation, elimination of non-value-added activities, and the use of information systems as enablers. BPR is based on the full rewriting of processes, instead of incremental optimization. It also aims to develop a holistic, integrated vision of the workflows within the organization. The literature [83] has concluded that for an ERP system to be effective, business processes must be optimized and very clearly defined. In many organizations, it has been found [84] that ERP does not solve the problem of inefficient processes if the processes are defective; paradoxically, their automation through ERP can lead to an amplification of inefficiencies. In practice, BPR prepares the ground for ERP by identifying and eliminating redundancies and aligning workflows to best practices. Thus, organizations that apply BPR before ERP identify real business needs, avoid excessive customizations, and try to ensure compliance with industry standards because many ERP solutions are built on standard processes. Recent studies [85] show that ERP projects also fail due to unadjusted processes, and BPR can reduce this risk by involving employees in process transformation, reducing complexity by simplifying workflows before automation and increasing the acceptance rate because employees are already familiar with the new processes. Internationally, there are relevant examples of the influence of BPR on the decision to adopt and implement ERP. For instance, the Ford company [86] implemented BPR in the 1990s to restructure its procurement processes. By eliminating unnecessary steps, the organization reduced administrative staff by a significant percentage. Subsequently, adopting an ERP was much easier because the processes were already optimized. In a similar manner, Dell company used BPR to redefine its supply chain, reducing delivery time [87]. The subsequent implementation of ERP allowed for easy integration between production and logistics. Considering the aspects highlighted in the specialized literature, we conclude that BPR is a factor of interest for analyzing the influence on the decision to adopt integrated ERP systems, and we will analyze the following research hypothesis:

H9. 

Business process reengineering has a significant impact on the decision to adopt ERP systems.

Specialized studies have shown that the success of ERP adoption and implementation depends not only on choosing the right software but also on the technological readiness of the organization. Technological readiness [88] is actually the extent to which an organization is prepared to adopt and integrate new technologies, and it actually includes several aspects such as IT infrastructure, technical skills, organizational culture, and financial resources. In the context of technological readiness, IT infrastructure refers to the presence within the organization of the necessary hardware equipment, software licenses, and networks [89]. Technical skills refer to the skills that employees have already acquired and that can be used in the ERP system implementation process. Regarding organizational culture, technological readiness specifically targets the company’s openness and availability toward innovation and change. At the same time, technological readiness also implies availability manifested in financial form because an ERP project actually constitutes an investment of considerable value. In this context, it is considered [90] that an organization with a high level of technological readiness is more likely to successfully adopt an ERP because it has the necessary basis for integration. On the other hand, organizations with outdated IT systems may encounter difficulties in ERP integration. If a stable and scalable platform already exists, then the ERP adoption and implementation become easier. A relevant example in this regard [91] is the Amazon company, which managed a rapid ERP implementation thanks to the already existing cloud infrastructure, employees with advanced technical skills, and an organizational culture oriented toward innovation. Several possible techniques are highlighted in the specialized literature [92] by which companies can improve their technological readiness. Thus, through the technological audit, existing gaps can be identified, and the technological infrastructure can be improved. Investments in education and training can lead to increased skills of employees who will be involved in the adoption and implementation of ERP, and careful budgeting of the ERP project leads to realistic and sustainable long-term financial planning. Considering all these aspects, it is obvious that technological readiness can be a decisive factor in the successful adoption of ERP systems. Organizations that improve their IT infrastructure, employee skills, organizational culture, and financial resources will have a smoother transition to ERP, while without adequate technological readiness, the risk of failure increases significantly. These are the reasons for analyzing the following research hypothesis:

H10. 

Technological readiness has a significant influence on the decision to adopt ERP systems.

In addition to the factors mentioned above, studies also analyze user involvement and participation as one of the most critical factors in ERP adoption. User involvement [93] refers to the degree to which end users are included in the ERP decision-making process, while user participation describes the level of active engagement in the implementation. These concepts are strongly interconnected and include aspects such as consulting employees from the ERP system selection phase, involving them in system customization and configuration, testing during implementation, and continuous post-implementation training. User involvement and participation [94] can bring benefits in the system life cycle because operational users know the organization’s internal processes best and can quickly and correctly identify specific requirements. At the same time, employees who participate from the early stages of ERP system adoption feel valued and usually accept the new system more easily [95]. Also, based on their previous experience, operational users can identify errors or inconsistencies even before the system is launched into production, and processes can be adjusted to fit real workflows. Previous research [96] has shown that early adopters engaged in the system testing phase learn new functionalities faster. Therefore, in general, organizations that include employees in decisions, configuration, and training achieve faster implementation, easier adaptation, and superior results. On the other hand, ignoring user participation can lead to costly failures and staff rejection. Considering all these aspects, it is clear that user involvement and participation is a factor that deserves to be included in an exhaustive analysis of the determinants of the decision to adopt integrated ERP systems. In consequence, we hypothesize the following:

H11. 

User involvement and participation have a significant influence on the decision to adopt ERP systems.

Due to the pressure on organizations to improve their operational flexibility and adaptability, ERP vendors have begun to build systems with decentralized features, which offer a modern alternative to traditional centralized models. Decentralized ERP systems actually refer to architectures that distribute data processing and functionalities across multiple nodes or processing units, allowing greater autonomy for departments or branches. Decentralized architectures also use emerging technologies [97] such as blockchain for security and transparency, hybrid cloud computing, and microservices for modularity. In the case of decentralized ERP systems, the architecture is distributed across multiple locations, and flexibility is improved [98]. On the other hand, in terms of implementation costs, they are higher at the beginning, but they allow for easier scaling of the system. Unlike “classic” ERP systems where control is centralized, decentralized architectures use distributed control [99]. The decision to adopt integrated ERP systems with decentralized architecture is influenced by the increased flexibility [100] because subsidiaries can adapt modules to local needs. At the same time, it is worth mentioning the improved resilience [101] because decentralization allows the system to function even in the event of local failures. Additionally, decentralization offers the possibility of scaling the existing system without major investments or modifications. On the other hand, decentralized ERP also faces major challenges that cannot be neglected due to the increased complexity of integration and the need for a robust IT infrastructure.

In addition to the initial costs that are usually 15–20% higher than “traditional” ERP solutions, decentralized ERP also faces cybersecurity issues specific to distributed environments [102]. In the context of the current economy, the decentralized features of ERP systems actually represent a response to modern needs for organizational agility and resilience. Although they present implementation challenges, their potential to transform corporate operations is significant. As a result, decentralized ERP is a factor that can be decisive in the process of adopting integrated business systems and allow us to test the following research hypothesis:

H12. 

Decentralized ERP features have a significant impact on the decision to adopt ERP systems.

The purpose of this research is to highlight the influence and contribution of the following 12 factors: (1) top management support, (2) user training and education, (3) data quality and migration, (4) the pressure of competition, (5) ERP system quality, (6) change management, (7) vendor support and services, (8) cost and budget considerations, (9) business process reengineering, (10) technological readiness, (11) user involvement and participation, and (12) decentralized ERP features.

The hypothesis is that the factors mentioned above have a significant effect on the decision to adopt integrated ERP systems in Romanian organizations.

Each of the twelve selected indicators will be evaluated as an individual hypothesis:

• H₁: Top management support has a significant impact on the decision to adopt ERP systems.
• H₂: User training and education have a significant impact on the decision to adopt the ERP systems.
• H₃: Data quality and the migration process have a significant impact on the decision to adopt ERP systems.
• H₄: The pressure of competition has a significant influence on the decision to adopt ERP systems.
• H₅: The quality of ERP system has a significant influence on the decision to adopt such integrated systems.
• H₆: Change management has an important impact on the decision to adopt ERP systems.
• H₇: Vendor support and services have a significant influence on the decision to adopt ERP systems.
• H₈: Cost and budget considerations have a significant impact on the decision to adopt ERP systems.
• H₉: Business process reengineering has a significant impact on the decision to adopt ERP systems.
• H₁₀: Technological readiness has a significant influence on the decision to adopt ERP systems.
• H₁₁: User involvement and participation have a significant influence on the decision to adopt ERP systems.
• H₁₂: Decentralized ERP features have a significant impact on the decision to adopt ERP systems.

Considering that previous studies in the scientific literature have focused on a single factor or on narrow groups of indicators, our contribution lies in the fact that we offer a result that is based on exhaustive factor analysis.

In summary, our contribution can be highlighted in

Table 1. Research hypotheses.

Hypothesis	Hypothesis Description	Sources and Previous Results
H1	Top management support has a significant impact on the decision to adopt ERP systems	[29,30,31,32,33,34,35]
H2	User training and education have a significant impact on the decision to adopt the ERP systems	[37,38,40,41,42,43,44]
H3	Data quality and the migration process have a significant impact on the decision to adopt ERP systems	[45,46,47,48,49,50,51]
H4	The pressure of competition has a significant influence on the decision to adopt ERP systems	[52,53,54,55,56,57]
H5	The quality of ERP system has a significant influence on the decision to adopt integrated systems	[58,59,60,61,62,63,64]
H6	Change management has an important impact on the decision to adopt ERP systems	[57,65,66,67,68,69,70]
H7	Vendor support and services have a significant influence on the decision to adopt ERP systems	[32,71,72,73,75,76]
H8	Cost and budget considerations have a significant impact on the decision to adopt ERP systems	[77,78,79,80,81]
H9	Business process reengineering has a significant impact on the decision to adopt ERP systems	[82,83,84,85,86,87]
H10	Technological readiness has a significant influence on the decision to adopt ERP systems	[89,90,91,92]
H11	User involvement and participation have a significant influence on the decision to adopt ERP systems	[93,94,95,96]
H12	Decentralized ERP features have a significant impact on the decision to adopt ERP systems	[97,98,99,100,101,102]

.

Accordingly, by analyzing and testing this complex set of research hypotheses, we contribute to the field of knowledge with a detailed global picture of the indicators that truly influence the decision to adopt integrated ERP systems and provide useful results to both ERP software manufacturers and companies that adopt and implement such integrated suites.

The theoretical TOE (technology–organization–environment) model, developed by Tornatzky and Fleischer (1990), provides a comprehensive framework for understanding the factors that influence technology adoption in organizations [103]. In our paper, the 12 factors analyzed in constructing the linear regression model to explain the adoption intention of ERP systems can be integrated into the three dimensions of the TOE model. Thus, factors such as technological readiness, data quality, quality of the ERP system, and decentralized ERP features reflect the technological dimension, providing insight into the extent to which the technical features of the ERP solution influence adoption intention. The organizational dimension is represented by factors such as top management support, user involvement and participation, user training and education, cost and budget considerations, and business process reengineering, highlighting the importance of internal structures and resources of the organization. As for the environmental dimension, the factors denoting competitive pressures, vendor support and services, and change management emphasize external influences and market dynamics in the adoption decision-making process. Through this structuring, our article contributes to the literature by operationalizing the TOE model in a specific context—i.e., ERP adoption in organizations—and provides further empirical validation of the relevance of this framework. The relevance of our study lies in the extension of the TOE model by including ERP-specific factors (e.g., data quality or decentralized features), which are not explicitly addressed in the classic TOE literature. Our research also provides a holistic perspective on ERP adoption, integrating both barriers and facilitators from all three TOE dimensions.

3. Materials and Methods

In order to carry out this study and to test the research hypotheses, data obtained from adult respondents were processed, based on an electronic questionnaire. The survey was administered to respondents in accordance with the agreement obtained from the scientific research ethics committee of the Faculty of Economics and Business Administration, Alexandru Ioan Cuza University of Iasi, Romania. This is agreement no. 1135, based on application no. 1116 of May 2025 formulated by the authors of this scientific study. The scientific research ethics committee agreed to administer the questionnaire in online format to adult respondents. Respondents were informed at the beginning of the questionnaire that their answers were anonymous and that no personal data would be collected, only answers to questions regarding various perceptions regarding ERP systems.

Before the questionnaire questions, respondents were notified about informed consent and that the answers are anonymous and will be used only for scientific research purposes. They were also told that they can stop answering at any time. The authors did not have any physical contact with the respondents and did not affect their physical or mental integrity in any way. The respondents who were targeted in this study are graduates of accounting and business informatics degree programs as most of them work in various positions in companies where ERP integrated systems are adopted and used. Also, all respondents work in companies operating in Romania, which makes the sample representative for the purpose of this research. Respondents were contacted through alumni groups on social networks.

In total, approximately 900 requests were sent out, and valid responses were received to 440 questionnaires, which were subsequently used in this study. The effort of the people who answered the questionnaire was voluntary and was not rewarded in any way.

The questionnaire was developed in Google Forms, and the initial responses were downloaded and processed in Microsoft Excel. The data were collected in May–June 2025, and the respondents were contacted exclusively by remote electronic means. IBM SPSS version 21 and SmarPLS 4.0 were used as software tools for data analysis and testing the multiple linear regression model. A total of 440 responses to the questionnaire were collected, the sample being representative in terms of volume. With the help of software tools and based on the sample, the values necessary for testing the research hypotheses and the multiple linear regression model were determined.

The independent variables are the following indicators: Q1, top management support; Q2, user training and education; Q3, data quality and the migration process; Q4, the pressure of competition; Q5, the quality of an ERP system; Q6, change management; Q7, vendor support and services; Q8, cost and budget considerations; Q9, business process reengineering; Q10, technological readiness; Q11, end-user involvement and participation; Q12, decentralized ERP features.

The general model is expressed in Equation (1) below:

$$Y= {\beta }_0+ {{\displaystyle \sum }}_{i=1}^n{\beta }_i{x}_i$$

(1)

where Y is the dependent variable, β₀ is the free term of the model (interceptor), and x_i values correspond to the twelve independent variables (Q1–Q12). In the proposed model, the regression coefficients β₁, β₂, …, β_n measure the impact of each predictor.

The questionnaire was designed so that each indicator is measured using a seven-point Likert scale, where a score of 1 indicates that the factor has no importance for the respondent, while a score of 7 indicates that the indicator has maximum importance in the respondent’s decision or preference. The responses obtained are described using descriptive statistics.

4. Results

The descriptive statistics of the sample are presented in

Table 2. Descriptive statistics of the sample.

Variable	Sample Size	Count	Percentage
Job position	440		100
Operational position		317	72.04
Middle management		108	24.55
Top management		15	3.41
ERP usage experience	440		100
Basic		89	20.22
Medium		109	24.77
Advanced		242	55.01
Company size	440		100
Small		90	20.45
Medium		130	29.55
Large		220	50.00

and they include the situations of job positions (operational position, middle management, and top management), ERP usage experience (basic, medium, and advanced), and company size (small, medium, and large).

The main descriptive statistics for indicators and dependent variable are presented in

Table 3. Descriptive statistics for indicators and dependent variable.

Variable	Sample Size	Mean	Standard Deviation
Q1	440	3.9341	1.6962
Q2	440	3.9227	1.7571
Q3	440	4.1227	1.7375
Q4	440	3.9773	1.7741
Q5	440	4.1341	1.7255
Q6	440	4.1432	1.7062
Q7	440	3.9977	1.7452
Q8	440	4.0659	1.6291
Q9	440	4.0886	1.8715
Q10	440	3.9068	1.7229
Q11	440	3.9795	1.7671
Q12	440	4.1386	1.7849
Dependent	440	4.0455	1.6122

. They are based on the 440 responses from the sample.

To test hypotheses H₁–H₁₂ within the model, multiple linear regression analysis was used. To check the internal consistency, we calculated the RMO (Revelle’s Omega Total) coefficient value and obtained a result of Ω = 0.71, which indicated a good reliability, confirming that the Q1–Q12 scales consistently measured the construct under consideration. Predictive validity was examined through item-total correlation, with the total score revealing a robust correlation (r = 0.65). Internal consistency reliability was supported by high internal consistency (Cronbach’s α = 0.82). Exploratory factor analysis (EFA) indicated a plausible multidimensional structure (Kaiser–Meyer–Olkin KMO = 0.72; Bartlett’s test of sphericity was significant with p < 0.001). The main goal was to predict the dependent variable (the decision to adopt integrated ERP systems) based on the set of independent variables that we presented earlier in this study. This method based on multiple linear regression had the main advantage that all potential variables within the general model were taken into account to the same extent.

The contribution of each variable is presented in

Table 4. Model’s coefficients and contribution of independent factors to dependent variable description.

	Coefficients	Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	1.1719 *	0.5755	2.0363	0.0423	0.0407	2.3031
Q1	0.1084 *	0.0436	2.4851	0.0133	0.0227	0.1942
Q2	0.1171 **	0.0420	2.7907	0.0055	0.0346	0.1996
Q3	0.0915 *	0.0423	2.1630	0.0311	0.0084	0.1746
Q4	−0.1163 **	0.0415	−2.8036	0.0053	−0.1979	−0.0348
Q5	0.0932 *	0.0427	2.1810	0.0297	0.0092	0.1772
Q6	0.0101	0.0432	0.2338	0.8153	−0.0749	0.0951
Q7	−0.0235	0.0422	−0.5571	0.5778	−0.1064	0.0594
Q8	0.1053 *	0.0452	2.3323	0.0202	0.0166	0.1941
Q9	0.0952 *	0.0392	2.4260	0.0157	0.0181	0.1723
Q10	−0.0005	0.0427	−0.0120	0.9904	−0.0844	0.0834
Q11	0.1172 **	0.0415	2.8255	0.0049	0.0357	0.1987
Q12	0.1107 **	0.0411	2.6964	0.0073	0.0300	0.1914
	R2 = 0.53371, F = 5.088, p < 0.001

Note: * Significant at the level 5%, ** significant at the level 1%.

.

According to the literature, a multiple linear regression model is valid if the dependent variable is normally distributed. To confirm this, we tested the multicollinearity of the explanatory variables using the VIF (variance inflation factor), and the data obtained are presented in detail in

Table 5. VIF values and tolerance values.

Variable	VIF Value	Tolerance
Q1	1.028	0.973
Q2	1.021	0.979
Q3	1.014	0.986
Q4	1.018	0.983
Q5	1.021	0.979
Q6	1.022	0.978
Q7	1.018	0.982
Q8	1.016	0.984
Q9	1.013	0.988
Q10	1.015	0.985
Q11	1.008	0.992
Q12	1.008	0.992

.

Analyzing the tolerance and VIF values in Table 5, we find that all VIF values are less than 5, which gives us the certainty that our model does not have collinearity problems. Thus, all the factors analyzed are independent and do not overlap. This fact is also confirmed by the tolerance values because they are all greater than 0.20.

The estimated linear regression model has a coefficient of determination R² = 0.53371, which indicates that 53.37% of the variation in the dependent variable is explained by the independent variables included in the model. This value suggests a moderate to high degree of model fit, which is appropriate in the context of social and economic research, where the investigated behaviors are influenced by multiple latent variables and contextual factors that are difficult to fully quantify. The overall test of the model’s significance, performed using the F-statistic, yielded a value of F = 5.088, with a significance level p < 0.0001. This result demonstrates that the model is statistically significant, with strong evidence in favor of the hypothesis that independent variables significantly influence the dependent variable. In consequence, it can be concluded that the linear regression model constructed is suitable for analyzing the data and provides a valid framework for interpreting the relationships between the variables under study.

Having the certainty of the validity of the model, based on the data regarding the coefficients in Table 4 and the p-values, we deduce that there are several relationships and associations between the independent variables and the decision to adopt integrated ERP systems in Romania. Thus, the results show that the decision to adopt ERP systems is significantly influenced (p < 0.01) by the following factors: Q2, user training and education (Beta = 0.1171); Q4, the pressure of competition (Beta = −0.1163); Q11, user involvement and participation (Beta = 0.1172); and Q12, decentralized ERP features (Beta = 0.1107). The following factors also exert a significant influence (p < 0.05): Q1, top management support; Q3, data quality and the migration process; Q5, the quality of ERP system; Q8, cost and budget considerations; and Q9, business process reengineering. Although they have been highlighted in the specialized literature as having a significant influence, in our exhaustive research we identified the following factors that do not have an impact on the decision to adopt integrated ERP systems (p > 0.05): Q6, change management; Q7, vendor support and services; and Q10, technological readiness.

Based on the objective results obtained from the multiple linear regression analysis, we believe that future research should focus on identifying the reasons why the three variables (Q6, Q7, and Q10) are not significant in Romania’s decision to adopt integrated ERP systems.

The results from Table 4 and Table 5 lead us to validate the following hypotheses: H₁, H₂, H₃, H₄, H₅, H₈, H₉, H₁₁, and H_12.

Also, as indicated in

Table 6. Research hypothesis results.

Hypothesis	Hypothesis Description	Result
H1	Top management support has a significant impact on the decision to adopt ERP systems	Supported
H2	User training and education has a significant impact on the decision to adopt the ERP systems	Supported
H3	Data quality and the migration process has a significant impact on the decision to adopt ERP systems	Supported
H4	The pressure of competition has a significant influence on the decision to adopt ERP systems	Supported
H5	The quality of ERP system has a significant influence on the decision to adopt integrated systems	Supported
H6	Change management has an important impact on the decision to adopt ERP systems	Not Supported
H7	Vendor support and services have a significant influence on the decision to adopt ERP systems	Not Supported
H8	Cost and budget considerations have a significant impact on the decision to adopt ERP systems	Supported
H9	Business process reengineering has a significant impact on the decision to adopt ERP systems	Supported
H10	Technological readiness has a significant influence on the decision to adopt ERP systems	Not Supported
H11	User involvement and participation have a significant influence on the decision to adopt ERP systems	Supported
H12	Decentralized ERP features have a significant impact on the decision to adopt ERP systems	Supported

, the outcomes we obtained indicate that we should reject hypotheses H₆, H₇, and H₁₀.

5. Discussion

The study conducted in this article aims to carry out an exhaustive analysis of the factors that influence the decision to adopt integrated systems in Romania. This analysis contributes to the field of knowledge because it takes into account 12 potential factors, factors that have been filtered based on previous results validated in the specialized literature. The sample of 440 respondents is representative, and the lack of multicollinearity constitutes a significant foundation for the robustness of the model.

In line with previous results, our research confirms that the decision to adopt integrated systems is the result of several influencing factors, such as cost and budget considerations [77,78,79,80,81], user training and education [37,38,40,41], the pressure of competition [53,54,55,56], user involvement and participation [93,94,96], decentralized ERP features [98,99,101,102], top management support [29,30,31,33,34,35], data quality and the migration process [45,47,48], the quality of ERP system [58,59,60,61,62], and business process reengineering [82,83,84,85,86,87].

Concurrently, the results we obtained for the Romanian market within this exhaustive model reject some previous individual results, regarding potential influencing factors such as change management [57,65,66,67,69,70], vendor support and services [32,71,72,73,75,76], and technological readiness [89,90,92].

The objective results obtained through multiple linear regression analysis led to the confirmation of the research hypotheses H₁, H₂, H₃, H₄, H₅, H₈, H₉, H₁₁, and H₁₂ and the rejection of hypotheses H₆, H₇, and H₁₀. In this way, our study answers the research questions that were developed at the beginning of the article:

• What are the main determining factors that influence the decision to adopt integrated ERP systems?
• What is the influence of each determining factor in the final decision to adopt ERP systems?

Therefore, the main determining factors that influence the decision to embrace integrated ERP systems are as follows: user training and education, the pressure of competition, user involvement and participation, decentralized ERP features top management support, data quality and the migration process, the quality of ERP system, cost and budget considerations, and business process reengineering. The influence of each factor is given by the specific value of the Beta coefficient.

By validating research hypotheses and addressing research questions, this study addresses a research gap in Romania. The current understanding of the factors influencing the adoption of ERP systems is fragmented and lacks a comprehensive framework. Our study contributes to filling this gap by providing insights into the significant ERP market in Romania, as evidenced in this article.

The article contributes to the literature by operationalizing the TOE model in a specific context—that of ERP adoption in national organizations—and provides further empirical validation of the relevance of this framework. The relevance of our study lies in the extension of the TOE model by including ERP-specific factors (e.g., data quality or decentralized features), which are not explicitly addressed in the traditional TOE literature. The research also provides a holistic perspective on ERP adoption, integrating both barriers and facilitators from all three TOE dimensions.

The results of our research are useful for businesses because managers can allocate human, material, and financial resources according to the factors that have been validated as being of significant importance in this study. In this manner, in the multifaceted process of adopting and implementing integrated ERP systems, decision-makers have a compass with which to orient themselves scientifically, so as to focus on the factors that really matter and avoiding inefficient allocation of resources. Company managers and ERP adoption and implementation teams can significantly increase their success rate if they have a scientific approach to the factors determining the adoption decision, further contributing to increasing customer satisfaction and the financial profitability of the organization.

In our opinion, in the context of Central and Eastern Europe [104,105], the conducted study provides useful results and directions for the management of the companies to decrease the risks associated with new technologies. Success or failure factors for IT projects have been analyzed regionally in various similar countries such as Hungary [106] and Bulgaria [107]. Our research brings a specific clarification for the Romanian ERP domain, highlighting the relevant indicators for the national market.

Our study also contains several objective limitations. Therefore, the main limitation is that the model testing was carried out for the specific situation of Romania, a country that is in the economic system of the European Union. For this reason, although the sample is numerically representative, we manifest several concerns in generalizing the results obtained for other countries or geographic–economic regions.

Additionally, the analyzed data can be considered just a “snapshot” because it refers to the period in which this article was written. There is a possibility that the values of the Beta coefficients may change over time. One limitation of our study is that the independent variables were examined individually through separate indicators. Future research could address this aspect by employing more sophisticated methods such as structural equation modeling (SEM) or partial least squares SEM (PLS-SEM) for a comprehensive analysis. Additionally, our research is limited by the possibility of some unmeasured variables that were not accounted for.

Starting from the objectively identified limitations, the results of the conducted study can become a solid foundation for future research directions such as comparative analysis of the factors influencing the decision to adopt ERP systems in different countries or in different economic-geographical areas, cross-sectional analysis of the importance of influencing factors on the time axis, and separation of results according to different categories of respondents (operational users, top managers, middle managers, etc.).