Abstract
The digitalization of product development in small and medium-sized enterprises (SMEs) increasingly depends on the effective integration of IT tool stacks. However, the deployment of such tools often entails unpredictable and underestimated effort, especially in resource-constrained SME environments. This study investigates the multifaceted drivers of IT tool deployment effort through a mixed method approach by combining a structured literature review, expert workshops, qualitative interviews, and a quantitative survey. The results reveal five overarching effort dimensions - Integration and Implementation, Human Factors, Environmental Influences, Methodological Approaches, and Cost-Related Considerations - each containing multiple specific influencing factors. Emphasis is placed on usability, technical integration, stakeholder involvement, and the availability of methodological support. A prioritized factor hierarchy was derived from empirical expert input and validated through the quantitative survey process involving 44 professionals. The findings provide a practical framework for SMEs to estimate and manage implementation effort more effectively and serve as a foundation for the future development of decision-support models. The study extends existing technology adoption theories by centering effort as a critical lens for analyzing digital transformation in SME product development.
Introduction
Digital transformation has become a cornerstone of competitive advantage, even survival, for companies across all sectors. For small and medium-sized enterprises (SMEs) the effective adoption of digital technologies is increasingly essential to maintain innovation, agility, and market responsiveness. 1 Among the most impactful elements of this transformation is the integration of IT tools into product development processes. These tools, ranging from communication tools over data management tools and design tools, significantly enhance operational efficiency, team coordination, and decision-making quality.2,3,4
In practice, the IT setup in SMEs varies widely in terms of maturity, standardization, and governance. Many SMEs operate with heterogeneous system landscapes, often consisting of legacy tools, isolated software solutions, and minimal automation. Unlike large enterprises with centralized IT infrastructures, SMEs frequently lack dedicated IT departments or comprehensive support frameworks. As a result, digital tools are often introduced in a bottom-up, decentralized manner, with limited alignment across business units or with broader organizational goals.4,5,6
However, the successful introduction of IT tool stacks in SMEs is far from straightforward. It is not merely a technical upgrade, but a deep organizational shift that demands strategic planning, change management, and a reconfiguration of internal processes. 7 For many SMEs, such efforts are constrained by limited financial capital, insufficient technical expertise, and a lack of dedicated personnel. Unlike large enterprises, SMEs cannot typically rely on specialized departments or external consultants to manage these transitions, making them especially vulnerable to unexpected implementation challenges.6,8
Crucially, the effort required to introduce IT tools is often underestimated or poorly scoped. The resulting inefficiencies can lead to project delays, misalignment with business goals, or even abandonment of digital initiatives. While the benefits of digitalization are widely acknowledged, there is a lack of empirical research focusing specifically on the effort dimension of IT tool deployment in SME settings. More specifically, there is no comprehensive framework to guide SMEs in identifying, evaluating, and prioritizing the factors that drive implementation effort.1,6,8,9
This study addresses this critical gap by exploring the multifaceted influences that affect IT tool stack deployment in SME product development environments. It recognizes that successful digital adoption depends not only on tool selection or technical integration, but also on an organization’s ability to anticipate and manage the associated effort across all relevant dimensions - technical, organizational, human, and strategic importance of digital transformation in SMEs.
Problem statement
Despite the recognized importance of digital tools in modern product development, SMEs often encounter unpredictable and highly variable levels of effort when introducing IT tool stacks. These implementation efforts are influenced by a wide range of technical, organizational, and contextual factors that are not yet systematically understood or prioritized. Current research lacks a clear and structured framework to guide SMEs through the complexity of effort estimation during the digitalization process. This gap impedes effective decision-making, particularly in resource-constrained environments where planning precision is crucial.
Research objectives and questions
The primary objective of this study is to identify and prioritize the factors that influence the implementation effort of IT tools in product development projects within SMEs. By developing a structured understanding of these factors, the study aims to support effort estimation and enable better planning and resource allocation for digital transformation initiatives. To achieve this goal, the following research questions are addressed:
This empirical study adopts a multi-method approach, integrating insights from literature, experts, and a Delphi study, to derive a validated and practical framework tailored to the needs of SMEs.
State of the art
This chapter provides the theoretical foundation for the empirical investigation.
IT implementation in SMEs
SMEs play a critical role in industrial innovation and economic performance. However, their ability to adopt and implement IT tools is shaped by unique constraints that differentiate them significantly from large enterprises. While large organizations often rely on formalized IT departments, standardized processes, and extensive external support, SMEs tend to operate with leaner structures, limited budgets, and reduced access to specialized technical expertise.10,11
Typical IT tools introduced in SMEs include enterprise resource planning (ERP), customer relationship management (CRM), project lifecycle management (PLM), project and collaboration platforms, data and knowledge management systems, and sector-specific engineering tools. These tools are increasingly bundled into IT tool stacks that support end-to-end digital product development processes.2,3,4 In engineering context, the IT tool stack refers to the entirety of applications used throughout the product development lifecycle, supporting communication, data exchange, modeling, workflow coordination, and documentation. 12
Despite their potential, many SMEs face difficulty in adopting these tools effectively. 13 Low IT maturity, outdated infrastructure, and a lack of in-house capabilities often result in fragmented implementations or abandonment of digital initiatives. 1 Moreover, SME decision-makers often lack structured guidance on how to select tools compatible with business needs, scalability requirements, and collaborative demands.3,14 Improvised and ad-hoc approaches to tool selection remain prevalent in practice, despite a growing number of software selection frameworks.5,15
Understanding implementation effort and complexity
The concept of effort in IT implementation refers to the organizational, technical, and human resources required to successfully introduce, integrate, and sustain the use of digital tools. In the literature, effort is closely linked to project complexity and is typically analyzed across cost, time, training, customization, and change management dimensions. Complexity may arise from technical factors (e.g., system integration, infrastructure readiness), organizational dynamics (e.g., workflows, communication structures), or human-centered issues (e.g., resistance to change, digital competence).1,4,16,17,18
Implementation effort is particularly difficult to estimate in SMEs due to their often volatile resource planning, limited institutional memory, and overlapping roles among staff. Additionally, multiple studies have shown that underestimating implementation effort leads to poor tool adoption, frustration among employees, and even project failure.13,19
In collaborative engineering settings, effort is further magnified by the need to harmonize diverse toolchains, terminologies, and partner infrastructures. This includes coordinating heterogeneous IT systems across organizations, synchronizing tool versions, and ensuring data interoperability - particularly between design and simulation tools like CAD and CAE systems.20,21
Stakeholders and organizational perspectives
Effective implementation depends not only on the selected technology but also on the stakeholders involved in the process. 22 Internally, these include top management, project managers, IT administrators, and end users. Each group plays a distinct role in tool adoption, from strategic decision-making to operational use.13,19,23
Externally, consultants, tool vendors, and service providers influence the quality of support and customization options available during implementation. 24 The absence of aligned interests or unclear responsibilities among these actors often leads to effort inflation. Empirical studies underline the importance of stakeholder alignment and participative tool selection processes to ensure relevance, reduce resistance, and improve acceptance.17,18,25,26
Furthermore, the perception that SME implementations are simpler due to organizational size is misleading. In reality, their lower redundancy and flexibility increase the risk of failure and amplify the consequences of poor planning.11,13 SMEs often operate without dedicated IT change managers, making communication, training, and user motivation critical to reducing implementation friction.
Relevant frameworks and models
Multiple theoretical models have been developed to explain the adoption and diffusion of digital technologies. Among the most influential are the Technology – Organization – Environment (TOE) framework, 27 the Technology Acceptance Model (TAM), 28 the Unified Theory of Acceptance and Use of Technology (UTAUT). 29 Each offers a distinct lens on the drivers of digital tool adoption.
The TOE framework provides a broad view by grouping influencing factors into three contexts: technological (e.g., complexity, compatibility, relative advantage), organizational (e.g., size, IT readiness, leadership), and environmental (e.g., competitive pressure, external support).10,27 TOE is widely considered suitable for SME contexts, particularly in collaborative engineering domains, due to its emphasis on both internal capability and external conditions. However, critics argue that TOE lacks specificity, particularly in identifying factor interrelations and quantifiable thresholds. 30
The TAM model, in contrast, focuses on the individual’s intention to use a system, shaped by perceived usefulness and perceived ease of use. 28 While widely applied, TAM tends to underrepresent organizational and environmental factors - limiting its explanatory power in multi-stakeholder SME contexts.31,32
UTAUT extends TAM by integrating variables such as performance expectancy, effort expectancy, social influence, and facilitating conditions. This makes it particularly useful in modelling employee-level behaviour in tool adoption. 29 For example, performance expectancy reflects users’ belief that a tool will help them complete their tasks more effectively, a crucial dimension in engineering teams. UTAUT also incorporates social pressures from clients or competitors, which can be decisive in SMEs operating in supply chains with strict digital expectations. 33
While these frameworks offer valuable perspectives on adoption behaviour, none are designed to systematically capture and prioritize effort as a distinct analytical category. This represents a key gap in the literature, particularly for SMEs seeking to understand and plan for the multifactorial challenges associated with IT tool stack deployment. This study addresses that gap by building on these theoretical models to empirically derive a factor-based hierarchy of effort drivers, specifically tailored to the SME engineering context.
Methodology
This study follows a mixed-method, iterative, and exploratory-confirmatory research design.
34
The three phases combine qualitative and quantitative methods to build, refine, and validate a structured understanding of effort drivers (see Figure 1). Research structure.
2a. Qualitative Expert Interviews: Ten semi-structured expert interviews were conducted until theoretical saturation was reached to capture in-depth insights into the challenges of IT tool stack deployment and the specific drivers of implementation effort in the SME context. The interview structure were designed and evaluated following 35 (Collect, Check, Sort, Consolidate).
2b. Factor Extraction and Synthesis: For the analysis, a qualitative content analysis approach following the methodology of Philipp Mayring 36 was applied. This technique is particularly suited to practice-oriented research contexts, where rich, text-based data must be structured without losing context. 36 ’s method emphasizes a rule-governed process of reducing textual data through successive abstraction and categorization, enabling the identification of thematically relevant patterns while preserving the integrity of the original responses. This analytical strategy provided a robust foundation for the development of the hierarchy factor used in subsequent stages of the study.
2c. Quantitative Delphi Survey and Prioritization: A structured survey rated the relevance of each factor. The responses were used to build a prioritized, consensus-based hierarchy of effort drivers. The survey, following 37 structure, was designed to systematically capture expert judgments on the relevance and impact of each factor, enabling the development of a prioritized, consensus-based hierarchy of effort drivers. Methodologically, the survey was implemented in a single online survey round, using a standardized questionnaire composed primarily of closed questions. Respondents evaluated the factors using a five-point Likert scale, which allowed for nuanced assessments of perceived importance and implementation effort.
Results
The following presents the outcomes of the empirical study, including findings from the literature review, expert workshop, and Delphi study.
Literature review outcome
The literature review revealed five overarching factor groups that influence the implementation of IT tool stacks in SMEs. These categories were derived from empirical studies, theoretical models, and applied research in engineering and information systems. They reflect the complexity of aligning technical, organizational, and user-related conditions in resource-constrained environments.
Integration complexity
SMEs face significant challenges in integrating heterogeneous IT systems. Poor interoperability, lack of standard interfaces, and inconsistent data formats increase technical effort, particularly in engineering environments.21,37
Organizational readiness
Organizational readiness includes leadership commitment, internal IT expertise, financial capacity, and employee time availability.39,40
User acceptance and usability
The perceived usefulness and ease of use of tools remain critical for user-level adoption. 28 Literature shows that shared interaction paradigms and intuitive interfaces across tools reduce user resistance and training effort, particularly in environments requiring frequent tool switching. 38
Process alignment
Tools that fail to align with established workflows or engineering practices increase friction and rework. Structured onboarding, piloting, and process-aware design improve fit and reduce effort.2,25,41
External dependencies
The broader system and support environment plays a significant role in tool adoption. SMEs are often influenced by external stakeholders, such as customers or partners, whose systems impose specific integration or compliance requirements. Availability of support, including APIs, training materials, and integration documentation, also significantly affects effort levels. 14 Adopting data standards such as STEP can mitigate some of these challenges by facilitating interoperability. 21
Results from the expert workshop
To complement the literature review with practical insights, an expert workshop was conducted with eight participants: two from academia (industrial IT background) and six from industry, including SME representatives, IT consultants, and software developers. This diverse group brought together strategic, technical, and operational perspectives. The workshop began with an individual brainstorming session, followed by a group discussion to categorize identified factors using an Ishikawa diagram (see Figure 2). Over 50 factors were collected and clustered into six main categories: Human, Cost, Organization/Management, Environment, Methods, and Integration. Extract of Ishikawa diagram.
In the Human category, user motivation, acceptance, and skills are key themes. SME representatives noted challenges due to limited IT staff and the lack of user access, which often lowers engagement. Licensing restrictions further complicated tool adoption. Cost-related issues centre on licensing fees, training, and onboarding. One participant emphasized the underestimated installation effort, while several noted that per-user costs often exceed SME budgets, limiting tool rollout. Under Organization/Management, the lack of clear tool ownership and missing alignment with business processes was discussed. The need for internal contact and vendor support, while it was stressed that implementations often lack strategic planning and relevant use cases, were highlighted. In the Environment category, external dependencies like compliance and partner systems are seen as constraints. IT was pointed out that tool selection is often shaped by external stakeholders, reducing control and increasing effort. The Methods dimension focuses on rollout strategies and onboarding. Academic participants called for a structured, stepwise introduction process and highlighted that preparatory steps, such as stakeholder involvement, are often neglected but essential for success. Integration issues include infrastructure, system compatibility, and data migration. SME representative noted that technical limitations, such as insufficient computing power, often prevent effective tool use, even if the tool itself is suitable.
The workshop revealed differing emphases: while industry participants focused on hands-on challenges like usability, costs, and limited resources, academic participants stressed systemic factors like organizational culture and structured implementation methods. Despite these differences, a shared concern was the complexity and unpredictability of the overall effort. The final output, a categorized Ishikawa diagram, served as a structured foundation for the Delphi study.
Qualitative Expert Interviews
To further contextualize the factors influencing IT tool stack deployment effort in SMEs, a series of ten, 1 hour long semi-structured expert interviews was conducted. The ten interview participants represented a mix of SME employees, consultants, and IT specialists. All interviewees were anonymized and coded I1 through I10. The sample included individuals working in small manufacturing firms, digital consultancies, mid-sized service providers, and university-based technology groups. Professional roles ranged from software developers and team leaders to senior consultants and company founders. Several participants held dual functions, such as managing directors who were also deeply involved in technical tool evaluations or consultants who participated in client implementations. This diversity provided a broad perspective on implementation realities.
The interviews were guided by a semi-structured format consisting primarily of open-ended questions. This format was chosen to allow participants to describe experiences, perceptions, and challenges in their own terms while maintaining consistency across interviews. The structure focused on implementation processes, perceived barriers, technical integration, user involvement, and organizational preconditions. In this study, all interviews were transcribed, coded, and thematically grouped using a combination of inductive and deductive strategies. The resulting data pool included approximately 270 coded text segments. The coded interview segments were grouped and consolidated into five overarching categories of implementation effort factors: Integration and Implementation, Human Factors, Environmental Influences, Methodological Approaches and Cost-Related Considerations.
From these categories, 29 discrete influencing factors were derived (see Figure 3): Influencing factors on IT Tool Stack Introduction.
Integration and implementation
A recurring theme across interviews was the technical and organizational effort involved in integrating IT tools into existing system landscapes. Participants emphasized that the more complex and powerful a tool is, the greater the implementation burden becomes. As one expert noted, “The bigger and more complex the tool, the more effort it takes” (I10). A key challenge was the integration into legacy systems and diverse infrastructures, where compatibility and dependencies significantly shaped the required resources. One participant pointed out, “Compatibility with existing infrastructure, whether hardware or software, is critical. Whether it works out-of-the-box, needs to be customized, or requires a specific workaround determines how much effort is needed” (I1). Another frequently underestimated factor was data migration. While tools themselves may be ready for deployment, the transfer and structuring of legacy data often turned out to be a bottleneck. One interviewee stressed, “Migration topics are often neglected at the start of such projects, but they catch up with you later; once the new system is in place and you realize the legacy data isn’t there in the right structure” (I5). Beyond the technical aspects, implementation support played a crucial role. Participants described long feedback loops with vendors and misunderstood requirements, which slowed down the process. As one interviewee put it, “We go back and forth, reporting an issue, the developer fixes it, we test it, realize it’s still not right, and go back again. If we could make this loop more efficient, that would really help” (I7). These insights reflect the layered and interdependent nature of implementation effort, where technical readiness, support structures, and data integrity must align to avoid disruption and inefficiencies.
human factors
Human-related aspects were identified as major contributors to implementation effort. Interviewees emphasized that employee motivation, openness to change, and digital skills significantly influenced tool adoption. One participant summarized: “Change management is a constant issue” (I1). Tool usability was a recurring theme. “It must be intuitive and simple - big buttons, not overloaded” (I7), while another noted, “How user-friendly it is, and how fast people can learn it, is key” (I2). Change resistance often stemmed from psychological barriers. “Some don’t want to work with outdated tools, while others fear learning something new late in their careers” (I9). Self-motivation and perceived benefit strongly shaped engagement. Leadership support and communication were also critical. “Management must clearly communicate purpose and stand behind the tool” (I6), and users need encouragement: “We’ll get through this together” (I9). Staff availability was another key factor. “If core people aren’t available, projects slow down, cost more or fail” (I4). Overall, enabling users through clear support and communication proved essential for successful implementation.
Environmental Influences
Though external to the core implementation team, environmental factors, such as market demands, legal regulations, and customer requirements, played a substantial role in shaping tool adoption decisions. Multiple interviewees noted that client systems or value chain partners often dictated which tools could be used, thereby constraining internal choices and increasing adaptation effort. “The customers have a certain power and tell us what we have to use” (I1). Another added, “If the customer uses something different, that naturally increases our effort” (I1). Dependencies on external infrastructure also introduced complications. One participant explained, “If we need firewall settings that can only be provided externally, even quick tool rollouts can become a problem” (I10). Consultant and vendor quality varied considerably. Some expressed frustration with inefficient support cycles: “We report an issue, it goes to the developer, comes back with misunderstandings, and must be revised again. That loop, if not streamlined, is really critical” (I7). Others cautioned, “They just want to sell you something” (I9). Participants also noted the value of tool-related communities. “You’re not alone with challenges” (I9), one interviewee said, while another emphasized how perceived tool complexity can shape attitudes before adoption even begins: “Even without experience, people think SAP is complex” (I6).
Methodological approaches
Several interviewees attributed high implementation effort to missing structure and ad hoc decisions. “Sometimes someone has a cool idea and just rolls it out - no one knows how it works, no one uses it” (I2). In contrast, well-planned rollouts and structured approaches were seen as effective. “Rollout plans are essential” (I8), and enterprise-wide introductions require integration into formal rollout tools (I1). Clear requirement gathering was considered crucial. “We need to know the real requirements” (I7), and aligning user needs with organizational goals helped ensure relevance (I6). Cross-functional involvement with process experts supported better decisions (I4). Pilot users were widely seen as helpful for internal support and adoption. “Find pioneers who enjoy the tool and can act as change agents” (I6), while structured training programs helped maintain continuity: “The vendor trained key users who answered questions and supported new colleagues” (I8). Finally, testing environments were noted as effort-saving: “Testing beforehand avoids a lot of later headaches” (I3). When lacking formal methods, implementation became more error-prone, slower, and less accepted.
Cost-related considerations
Cost was a consistently cited driver of implementation effort. Beyond obvious licensing and software investments, interviewees highlighted hidden expenses such as personnel time, training, and consulting, often underestimated in early planning. “We underestimated initial effort, like search and information costs, those are already personnel hours before the rollout even starts” (I1). Several participants pointed to the complexity of cost models. “Total Cost of Ownership includes acquisition, operations, and qualification of both IT and end users” (I5), while others emphasized the unpredictability of license models: “Depending on the license model, the resulting cost situation varies drastically” (I8). Rollout costs were also discussed, especially in relation to technical dependencies. “Costs arise especially when dependencies exist between software tools; this can complicate and significantly increase rollout costs” (I1). Such financial uncertainties often delayed or downsized projects in budget-sensitive SME environments.
From the qualitative coding and subsequent consolidation, several individual factors stood out as particularly influential across the interviews. These included: tool usability, lack of systematic internal release and approval processes, insufficient employee involvement during tool selection and introduction, low methodological support, cost issues, depth of functionality and system integration, complexity of tool integration, low user motivation, unclear or missing requirements gathering, and absence of structured implementation procedures. While these factors provide rich insight into the causes of implementation effort, not all of them lend themselves to a direct comparison between IT tools especially those related to human or organizational behaviour.
For this reason, a subset of five technical factors was identified to enable a structured comparison of IT tools for the demonstrator system:
The expert interviews revealed also several key best practices that support successful IT tool stack deployment in SMEs, emphasizing a people-centered and incremental approach. Collaborative peer exchange emerged as a strong enabler: regular, informal discussions boosted both motivation and learning. As one interviewee put it, “We all proceeded at our own pace, but met regularly to share our experiences. That brought a massive motivation and learning boost” (I4).
Early involvement of stakeholders, especially those familiar with operational needs, was also considered essential. Without their input, tools risk being rolled out without practical relevance. “We need contacts who can name the real requirements. Otherwise, you roll out a solution nobody needs” (I6). Pilot users play a crucial role in bridging the gap between vendors and end-users. These internal experts supported colleagues and maintained continuity across updates: “The vendor trained key users who answered questions, onboarded others, and provided continuity” (I7).
A gradual rollout was preferred over rapid deployment, reducing resistance and allowing users to adapt at their own pace. One participant noted, “The rollout was portioned so people could digest it - nothing would break if they did not use the tool right away” (I3). Most of all, clear communication and shared understanding across teams were seen as vital. Visualizing progress and aligning expectations reduced friction, especially in cross-functional settings. By contrast, passive documentation formats such as handouts or FAQs were often ineffective. “We made How-Tos, but nobody really read them… Helpful internally, but not effective for support” (I5). These findings underscore the importance of interactive, supportive, and user-driven strategies in SME IT implementations.
In sum, the qualitative interviews revealed a nuanced, multi-dimensional picture of effort drivers in IT tool stack deployments within SMEs. The diversity of professional roles and company contexts enriched the dataset and underlined the interplay between technical, organizational, and human factors. The five identified categories, Integration and Implementation, Human, Environment, Method, and Cost, serve as the analytical framework for the following Delphi survey. Their derivation from empirical expert input enhances their practical relevance and lays the groundwork for a structured and prioritized understanding of implementation effort in SME digital transformation projects.
Delphi survey outcomes
To validate and refine the previously identified implementation effort factors, a Delphi survey was conducted involving 44 industry professionals. The objective of this phase was twofold: first, to assess the relative importance of key technical factors influencing IT tool stack deployment, and second, to capture structured evaluations of perceived effort levels across a range of influencing factors and tool categories. The participant sample included a diverse range of professional roles and industry sectors. A large majority of respondents (37 out of 44) were non-executive employees, while seven held managerial or executive positions. The gender distribution was predominantly female, with 29 women and 15 men participating. Most respondents reported relatively early-stage professional experience in IT tool stack deployment contexts, with 36 indicating 1-5 years of experience, seven reporting 6-10 years, and only two indicating more than a decade of experience. Sectoral, the participants represented a cross-section of industries. The largest groups came from metal processing, electronics, and automotive sectors, followed by participants from the chemical and plastics industries, construction, and a variety of other sectors. This industrial diversity provided a robust empirical basis for comparative analysis.
The central element of the Delphi survey involved assessing the relative weight of five previously identified technical effort (see Figure 4) factors. Respondents were asked to distribute weights across these factors based on their practical relevance and impact on implementation effort. Tool integration emerged as the most significant factor, receiving approximately one-third of the total weighting. Participants frequently cited the challenges (see Figure 4) associated with integrating new software solutions into existing IT infrastructures, including limitations in interface functionality and system compatibility. Tool usability was the second most critical factor, highlighting the importance of intuitive, user-friendly systems that support employee adoption. Cost considerations followed in third place, though with a noticeably lower weighting, suggesting that financial aspects are often secondary to functional and technical feasibility. Methodological support, such as the availability of training materials, documentation, and vendor guidance, was also seen as a notable contributor to effort, albeit less prominently. Finally, the functional depth of the tool and its embeddedness within the broader IT landscape were rated as the least influential among the five, though still relevant. Importance of technical factors and implementation challenges.
In addition to weighting technical dimensions, participants were asked to rank a broader set of influencing factors according to their perceived impact on implementation effort. Tool usability was ranked highest, confirming its central role in shaping both employee engagement and overall project success. Organizational aspects such as internal approval processes and the degree of employee involvement during tool selection and rollout followed closely. Participants repeatedly emphasized that neglecting to involve end-users early in the process often led to misalignment, increased resistance, and ultimately, greater effort. A lack of methodological support and unclear or missing process structures were also identified as significant effort drivers. Interestingly, while cost remained a factor of concern, it was consistently ranked lower than usability and organizational readiness. Other relevant factors included the complexity of technical integration, incomplete requirements gathering, and limited employee motivation, each of which added additional layers of difficulty during implementation.
Respondents were also asked to estimate the average effort associated with each of the identified factors on a five-point Likert scale. The results further reinforced the importance of usability and integration challenges. Tool usability received the highest average effort score, followed closely by the complexity of integrating new systems into existing infrastructures. A lack of systematic implementation approaches and cumbersome internal approval processes were also perceived as highly effort intensive. Several respondents highlighted the frequent absence of formalized roll-out strategies or phased approaches, which contributed to reactive project management and increased operational strain. Methodological deficiencies, concerns over data security, and resource-intensive training requirements were also among the most effort-demanding factors, reflecting a broad consensus around the need for structured implementation processes and learning environments.
Beyond individual factors, the Delphi study also explored the perceived implementation effort of different categories of IT tools. The findings revealed significant variation across tool types. ERP, PLM and CRM systems were consistently associated with high implementation effort, particularly due to their complexity, integration requirements, and long-term strategic impact. Similarly, industry-specific applications, often highly customized and domain-specific, were frequently rated as resource-intensive and demanding. In contrast, collaboration and communication tools were generally perceived as more manageable, especially when they were modular and user-friendly. Project management and data or knowledge management tools were most often rated in the mid-to lower-effort range, indicating that these systems, while not trivial, posed fewer integration and training challenges than more complex enterprise solutions.
Overall, the Delphi survey confirmed the multifactorial nature of implementation effort and supported the interview findings by quantitatively prioritizing usability, integration, and organizational alignment as the dominant effort drivers. Moreover, the findings highlighted a clear differentiation between tool categories, with enterprise-level and highly integrated tools requiring significantly more planning, support, and stakeholder management than lightweight, task-specific applications. The results reinforce the need for SMEs to adopt structured, user-oriented approaches that consider both technical feasibility and organizational readiness in order to manage implementation effort effectively.
Reliability analysis
The reliability of the AHP instrument was assessed using Cronbach’s Alpha. 42 The overall coefficient of 0.725 exceeds the established minimum standard of 0.70, indicating acceptable internal consistency. Corrected item–total correlations ranged between 0.215 and 0.524, with several items showing strong contributions (CITC > 0.40) and the remainder moderate but acceptable values. No item required elimination, as removing any would only marginally affect the overall reliability (α = 0.676–0.729).
These results confirm the construct validity of the instrument, demonstrate a homogeneous item structure, and ensure sufficient measurement precision. The findings justify the aggregation of the pairwise comparison data for subsequent statistical analyses of IT tool selection criteria.
MCDM model initiation
Based on the outcomes of the Delphi study, a MCDM framework was developed using the Analytic Hierarchy Process (AHP). This method was selected for its ability to structure complex decision problems and derive relative weights from expert judgments. The Delphi-derived factor weights form the basis of the decision hierarchy, allowing IT tool stacks to be systematically assessed against the prioritized criteria. The model shall generate a ranking of alternatives that reflects both SME resource constraints and practical implementation needs.
In doing so, the MCDM model bridges expert insights with quantitative evaluation, offering SMEs a transparent and evidence-based approach to IT tool selection. This marks a transition from identifying relevant factors to providing actionable decision support, laying the foundation for practical applications in real-world SME contexts.
Discussion
This study revealed that IT tool stack deployment effort in SMEs is shaped by a complex interplay of technical, human, organizational, and methodological factors. Among the most critical drivers identified were tool usability, the complexity of integration with existing systems, and the degree of employee involvement. These findings highlight that successful implementation is not solely dependent on software functionality or cost, but also on the alignment between technical infrastructure and organizational readiness.
A particularly salient insight is the interconnectedness of the factor categories. For example, insufficient methodological support, such as lack of training materials or onboarding processes, can exacerbate technical challenges, while poor usability may increase resistance among employees and delay adoption. The results underline the need for integrated implementation strategies that simultaneously address infrastructure, people, and process dimensions.
When compared to established frameworks such as the TOE or TAM model, this study extends existing theory by explicitly quantifying and prioritizing the drivers of implementation effort. Unlike these models, which focus largely on adoption intentions or contextual enablers, the results offer a structured, SME-specific view of effort-related challenges, enabling better planning and resource allocation.
Theoretical contributions
From a theoretical standpoint, this study contributes to the literature on digital transformation in SMEs by introducing an empirically grounded framework for analyzing implementation effort. The five-category factor model (Integration and Implementation, Human, Environment, Method, and Cost) adds a new layer to the understanding of IT adoption by placing effort estimation at the center of inquiry.
The combination of qualitative and quantitative methods represents a methodological innovation. This hybrid approach proved effective in first exploring the complex set of influencing factors and then refining them through expert validation and prioritization. As such, the study offers a replicable structure for future research on implementation dynamics in other organizational contexts.
Practical implications
The practical relevance of the findings is particularly strong for SMEs seeking to digitalize under conditions of limited resources. The study provides a prioritized catalogue of implementation effort drivers, enabling decision-makers to anticipate potential hurdles and focus their planning efforts where it matters most—such as usability, process integration, and stakeholder alignment.
Consultants and IT solution providers can also benefit from the insights, particularly regarding the importance of support materials, user-centric design, and the need for tailored roll-out strategies. For policymakers and institutions funding digital transformation programs, the results highlight the value of providing targeted assistance in areas such as training, process standardization, and infrastructure development.
Limitations
Despite its contributions, the study is subject to several limitations. The Delphi survey sample, though diverse in industry representation, was relatively small (n = 44) and geographically focused on Europe. This limits the generalizability of the findings across regions with different regulatory, cultural, or digital maturity conditions.
Moreover, while the qualitative interview process was designed for breadth and depth, there is a potential for selection bias in the expert sample. The insights gathered may reflect organizations with above-average digital engagement or more pronounced implementation experiences.
Finally, although the study provides a structured framework and prioritization of effort factors, the results are based on perceived effort rather than empirically measured implementation costs or durations. Future studies could complement this work with longitudinal case studies or implementation metrics to triangulate findings.
Conclusion and outlook
This study provides a comprehensive and structured framework for understanding and evaluating the factors that drive implementation effort in IT tool introduction within SMEs. Through a multi-method approach combining literature analysis, expert workshops, qualitative interviews, and a Delphi survey, five overarching categories and 33 specific effort factors were identified, validated, and prioritized. Among them, tool usability, integration complexity, and organizational readiness emerged as the most significant contributors to effort. These will be the basis for the to be designed MCDM.
The novelty of the results lies in the empirically derived factor hierarchy, which moves beyond existing adoption models by offering a structured, effort-centric view tailored to the realities of SMEs. The use of combined Ishikawa and Delphi methods ensured both breadth and depth, capturing the perspectives of practitioners and systematically consolidating their insights into a coherent model.
Directions for future research
Future research will build upon these findings to develop a practical MCDM model. This model will support SMEs in assessing implementation scenarios based on the identified factors. The goal is to enable effort estimation tools that help SMEs plan implementations in a resource-aware, risk-informed manner.
In this context, the structured qualitative insights from this study will be translated into measurable criteria. For example, “tool usability” may be operationalized through sub-criteria such as training duration, interface complexity, or error rate during initial usage. Similarly, “integration complexity” could be captured through the number of required interfaces or the degree of system customization needed. The factor weights from the Delphi survey provide a first indication of how these elements might be prioritized in the decision model.
This research sets the foundation for further development of digital support systems that make IT implementation more transparent and manageable for SMEs. Future work will focus on pilot applications, refinement of evaluation metrics, and the deployment of the decision-support tool in real-world projects. In doing so, the study not only contributes to implementation theory but also delivers actionable insights for practitioners navigating the challenges of digital transformation.
Footnotes
Acknowledgment
The authors thank the IGF (Grant No. 22534N) for the financial support of the research project.
Ethical considerations
This study was approved by the IPA Ethics Committee on Juni 18, 2025.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Industrielle Gemeinschaftsforschung (22534N).
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The research data can be found on 
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