A Decision Support Framework for Business Development Performance Evaluation Using Analytic Hierarchy Process

1. Introduction

Organizational growth requires structured and strategic business development (BD) [1,2]. Companies must identify, evaluate, and pursue opportunities in competitive and dynamic markets [3,4]. Business development extends beyond sales and short-term growth [5,6]. It includes screening ideas, assessing fit, conducting due diligence, negotiating terms, and managing alliances [7,8]. These activities require alignment of strategy and execution and coordination among internal functions [9,10].

Despite its importance, business development lacks a consistent definition in academic research [11,12,13]. Some studies equate it with client acquisition and revenue generation [14,15], while others treat it as a broader function involving innovation, partnerships, and long-term value creation [16,17,18]. This inconsistency creates confusion in both academic and practical settings. The scope of business development varies across industries, firm sizes, strategic orientations, and organizational structures [19,20]. These differences raise questions about which indicators to measure and which evaluation methods to apply.

Evaluating business development performance involves distinct challenges [10,21]. Business development moves across extended timelines and relies on external actors and unstable conditions [22,23]. Strategic intent, partner behavior, and execution quality define results [24,25,26]. Some initiatives yield no immediate financial gain but still improve strategic position or organizational learning [27,28,29]. Conventional metrics focus on cost, output volume, or short-term return, missing broader sources of value in business development activities [30,31].

Research in this area remains fragmented. Many studies focus on specific components such as opportunity identification, partner selection, or alliance formation [32,33,34]. Others rely on conceptual models without defined indicators. Case studies provide insight but offer limited support for comparison or generalization. Without structured tools, managers rely on informal methods or subjective judgment, which weakens consistency and limits long-term improvement.

This study closes these gaps with a structured method for evaluating business development performance. The model captures process complexity and supports variation in strategic priorities. Analytic Hierarchy Process structures the evaluation criteria, and Python ensures consistency in execution [35,36]. Simulation and a real-world case from DuPont demonstrate the model’s applicability.

The model organizes business development into six phases: screening, identification, evaluation, due diligence, negotiation, and alliance management [37]. Each phase includes three indicators selected through a literature review of strategic alliances, innovation processes, and organizational performance. This structure captures how firms progress from initial contact to long-term partnership management. By evaluating performance at each phase, the model enables comparison across projects and time periods.

To validate the model, the study simulates scenarios aligned with distinct strategic orientations. These include a growth-oriented scenario focused on early-phase activity, an efficiency-oriented scenario centered on analysis and control, and a balanced scenario that gives equal weight to all phases [38,39]. Simulations use the Saaty nine-point scale to score indicators. The Analytic Hierarchy Process (AHP) framework calculates both phase-level and overall performance scores [40,41], showing how each strategic priority shapes outcomes. Application to a real-world case confirms the framework’s practical relevance.

Stability tests confirm the model’s reliability. Adjusting phase weights yields consistent results and preserves internal logic [42,43]. This flexibility allows managers to align the model with strategic goals without disrupting its structure. The framework supports use cases such as annual reviews, project evaluations, milestone tracking, and cross-project comparison.

This research contributes by integrating theory, structure, and application in a unified model. It provides a comprehensive framework for evaluating a function that receives limited attention in academic literature. By linking business development activities to measurable outcomes, the model supports informed decision-making and strategic alignment. Python implementation ensures transparency and scalability. The framework addresses a clear gap and offers practical value for both researchers and practitioners.

2. Literature Review

2.1. Business Development as a Strategic Function

Business development serves as a strategic mechanism through which firms achieve long-term growth and competitive advantage [24,44]. It integrates diverse functions such as innovation, alliance formation, and market expansion, enabling organizations to identify and exploit new opportunities [45,46]. Unlike operational functions focused on efficiency, business development operates with ambiguity, bridging gaps between strategic planning and execution [47,48].

Several scholars emphasize business development’s role in navigating uncertainty and facilitating cross-functional collaboration [10,49]. Its strategic nature allows firms to respond to external shifts, build partnerships, and adapt their value propositions [25,50]. Effective evaluation and alignment require viewing business development as a dynamic, strategic process rather than a transactional activity [51].

2.2. Gaps in Performance Evaluation

Despite the increasing importance of business development in strategic management, the academic literature lacks robust evaluation frameworks [52,53]. Many existing approaches prioritize financial or output-based metrics, which fail to capture business development’s multidimensional contributions, such as relationship building, strategic fit, and long-term value creation [54,55].

As a result, many managers assess initiatives using criteria that misalign with strategic objectives [51]. This misalignment creates ambiguity in performance tracking and undermines learning [56]. A critical gap remains in models that evaluate business development across its phases using structured criteria that reflect both qualitative and strategic dimensions.

2.3. Phase-Based Frameworks in the Literature

Several scholars propose business development models structured around sequential phases. While Lorenzi (2013) emphasizes process structure, Forsman (2008) takes a project-based view, and Hollander (2002) introduces multi-dimensional performance factors [32,37,57]. Park et al. (2017) extend the discussion by linking business development to measurable outcomes like commercialization success [58].

While these models offer valuable insight into business development’s structure, they fall short of providing actionable performance metrics [59]. Their descriptive nature limits their utility for empirical assessment or management control [60]. Therefore, researchers must integrate phase-based models with quantifiable indicators to improve decision-making and strengthen accountability [61].

2.4. Selecting Performance Indicators

The identification of performance indicators forms a critical part of business development evaluation [62,63]. Indicators must reflect strategic intent, operational feasibility, and adaptability [64,65]. Drawing from strategic management and business development literature, this study assigns three indicators to each phase, balancing process efficiency, outcome quality, and strategic alignment.

Selected indicators include opportunity alignment, resource compatibility, risk mitigation, and knowledge transfer [66,67]. These draw from established frameworks such as the Balanced Scorecard, value creation theory, and alliance capability models. Their inclusion grounds the evaluation in both theory and practice, enhancing robustness and clarity.

2.5. Analytic Hierarchy Process (AHP)

Analytic Hierarchy Process structures complex decisions through ranked comparisons of qualitative and quantitative criteria, allowing consistent and transparent evaluation across business development phases [68,69]. This method uses pairwise comparison and prioritization to support weight assignment across multiple evaluation criteria, enhancing decision clarity and consistency [70,71]. Scholars have applied AHP in various business contexts, including logistics, project evaluation, and human resource assessment, confirming its adaptability and robustness for performance measurement [72,73].

AHP matches the structure of business development models and supports systematic prioritization and aggregation of performance data [74,75]. It strengthens evaluation frameworks by linking theoretical models to observable measures and by supporting structured planning and scoring. [76,77].

2.6. Simulation for Scenario Testing and Case Validation

Simulation allows organizations to test how different strategic orientations influence business development performance [78]. By adjusting weights and priorities, they can model outcomes under growth-focused, efficiency-driven, or balanced strategies. This process supports proactive planning and clarifies trade-offs in resource allocation and timing.

Scenario-based simulation aligns the model with practical decision-making and enhances its applicability. Its use in a case study confirms feasibility in real-world settings.

Sensitivity analysis reveals how assumptions shape decision outcomes and enables stakeholders to assess stability across scenarios [79]. This predictive capability complements the AHP framework, making the overall model both analytical and practical. Such dual functionality supports the needs of both researchers and decision-makers seeking data-driven tools.

2.7. Comparison with Conventional Evaluation

Performance evaluations focus on short-term financial outcomes or output metrics. These measures suit stable operations but fail to capture the strategic role and long-term value of business development. Business development needs broader criteria that address uncertainty, external interaction, and learning [67].

Conventional metrics ignore the sequential and cumulative nature of development activities. Each phase builds on the previous one, and failure in any stage can compromise the entire effort. Effective evaluation requires precision, continuity, and strategic alignment. Standard methods lack these qualities.

Table 1 summarizes key differences between business development evaluation and conventional performance assessment.

These contrasts highlight the need for distinct evaluation frameworks tailored to business development contexts.

2.8. Challenges in Business Development Evaluation

Evaluating business development involves challenges such as intangible outcomes, delayed impact, and reliance on external actors. Evaluation criteria must reflect non-financial benefits such as strategic positioning, innovation potential, and partnership quality [55,56].

AHP combined with simulation addresses these challenges through structured prioritization and scenario-based testing. This hybrid approach helps organizations manage complexity while maintaining consistency and adaptability in evaluation.

Table 2 outlines each challenge and its implications in greater detail.

Prior studies propose conceptual models and partial evaluation methods for business development, but they lack integrated frameworks that connect strategic phases with measurable indicators. This study addresses that gap by introducing a structured model enhanced with simulation, supporting both empirical analysis and practical application.

3. Materials and Methods

3.1. Research Design

This study uses a structured research design based on established theory to build and test a performance evaluation model for business development. It treats business development as a dynamic process with multiple phases that connect strategy and execution. The model builds on Lorenzi’s (2013) work and defines six phases [37]: screening, identification, evaluation, due diligence, negotiation, and alliance management. Three key performance indicators (KPIs) define each phase. Strategic management and innovation literature reinforce this structure.

AHP offers a structured way to evaluate business development. It breaks the problem into a hierarchy and assigns weights to KPIs based on their relevance. Simulation complements AHP by supporting scenario-based analysis and aligning evaluation with strategic needs.

3.2. Business Development Process Framework

The business development process follows a structured sequence of six interdependent phases, each serving a distinct purpose and contributing to strategic goals. Screening serves as the initial filter, identifying opportunities aligned with strategic priorities. Identification evaluates prospective partners or projects for alignment with organizational needs. Evaluation dives deeper into the feasibility and potential impact of the selected initiatives, incorporating both qualitative insights and quantitative assessments.

Due diligence serves as a critical validation step. It assesses risk, verifies assumptions, and ensures access to all necessary information before commitments. Negotiation establishes the terms of engagement, aligning stakeholder expectations and securing mutually beneficial agreements. Alliance management focuses on maintaining the relationship post-implementation, ensuring that collaborations deliver on their intended value. This phased framework is essential for aligning evaluation with the realities of strategic business development.

3.3. Assumptions of the Framework

This model relies on the idea that business development follows a structured sequence that supports systematic evaluation. Each phase adds distinct value to strategic outcomes, and the model uses defined KPIs to track performance. These indicators emphasize strategic fit, relationship strength, and execution quality. The model assumes organizations possess sufficient insight to evaluate each phase reliably.

AHP plays a central role by capturing stakeholder priorities and aligning them with strategic choices. The framework treats the relative weight of each phase and indicator as measurable and stable. Decision-makers use pairwise comparisons to gain insight into trade-offs and alignment. These assumptions shape a model built for strategic relevance and practical use.

3.4. Construction of Performance Indicators

A structured method develops performance indicators with relevance, clarity, and strategic alignment. Each phase of business development defines its purpose and expected outcomes. The method draws insight from literature in strategic management, innovation, and performance evaluation. Foundational concepts include the Balanced Scorecard, stakeholder theory, and resource-based views.

The study defines key indicators for every phase using literature and case analysis. These indicators capture both qualitative and quantitative aspects such as strategic alignment, partner engagement, knowledge exchange, and execution quality. Simulations test the indicators across varied scenarios to examine their responsiveness. This process ensures theoretical grounding and enables practical use.

The method supports internal validity by linking each indicator to the core function of its related phase. Each KPI stems from literature and aligns with the intended outcome of its activity. The design prevents overlap among indicators and keeps measurement consistent with the phase-specific goal.

External validity comes from using accepted frameworks with broad recognition, rather than case-specific data or expert opinion. The model offers flexibility to fit various business contexts. Organizations in different industries can apply or adjust the indicators based on their strategic focus. This structure supports transferability without loss of conceptual integrity.

Table 3 presents the six business development phases alongside their corresponding evaluation indicators. Each indicator highlights a distinct aspect of performance aligned with strategic goals, operational clarity, and organizational capacity. The descriptions draw on a synthesis of strategic management literature and case analysis to ensure both theoretical grounding and practical relevance. Representative sources that informed indicator development appear within the table.

3.5. Evaluation Method: Analytic Hierarchy Process

The Analytic Hierarchy Process serves as the core methodology for evaluating business development performance. AHP facilitates structured decision-making by organizing complex problems into a hierarchy consisting of a goal, criteria (phases), and sub-criteria (indicators). The model enables pairwise comparisons among criteria to derive relative weights, thereby reflecting the perceived importance of each element. This process ensures consistency in evaluation and supports data-driven decision-making.

AHP uses fixed weights drawn from theoretical and empirical sources instead of relying on expert judgment. This structure improves objectivity and supports consistent replication across scenarios. Scenario testing compares strategic orientations such as growth, efficiency, and balanced focus to reveal their impact on performance. Combining AHP with simulation strengthens the model’s logic and supports practical use. Citing real cases shows its feasibility in applied settings.

3.6. Data Sources and Simulation Logic

This study uses secondary data drawn from literature, case examples, and simulated scenarios to test the evaluation model. Simulations allow for controlled examination of the model under different strategic conditions. Each scenario reflects practical challenges such as partner risk, market shifts, and conflicts in strategic goals. Python handles score calculation, simulation runs, and visual output to ensure consistency across trials.

Simulation reveals how assumptions and strategic choices influence business development outcomes. A variety of test cases highlight the model’s range and show when specific KPIs or phases carry greater weight. This method avoids restrictions tied to confidential corporate data while producing results that apply to broader organizational contexts.

The model uses scenarios derived from both literature and practice. A DuPont case from published research illustrates typical decisions across phases and confirms the feasibility of structured evaluation [116,117].

3.7. Methodological Limitations

The model provides a structured way to assess business development, but several limitations remain. Simulated data fails to reflect the complexity of real decisions and the influence of unforeseen factors on business outcomes. Fixed weights offer consistency but risk oversimplifying the shifting priorities common in strategic work.

AHP brings clarity through hierarchy and comparison but treats phases and indicators as independent. In practice, business development includes overlap, mutual influence, and shifts shaped by context. Model development can expand to include real-time inputs, cross-industry testing, and flexible weighting. Despite limitations, the current version offers a practical base for evaluating strategic business development.

3.8. Summary

The method builds and tests a model for evaluating business development through a structured design. A six-phase framework connects to performance indicators, and the model applies AHP and simulation to evaluate outcomes. Each phase addresses a distinct strategic role, and indicators reflect insights from literature and scenario analysis aligned with real-world priorities.

Key assumptions and limitations shape the model. Simulated data and fixed weights introduce constraints but offer a foundation for early validation. Structured logic, scenario testing, and strategic focus support a practical approach to measuring business development and enable future refinement.

4. Developing a Model to Evaluate Business Development Performance

A performance evaluation model reflects business development workflows. Its structure progresses from indicators to an overall assessment. Six phases structure the model: Screening, Identification, Evaluation, Due Diligence, Negotiation, and Alliance Management. Each phase includes three indicators that guide measurement and enable comparison.

Analytic Hierarchy Process organizes decision criteria and runs strategic scenario simulations. This framework connects business development activities with strategic goals. Users define parameters that align with specific organizational needs. A final case example illustrates the model and confirms its practical feasibility.

4.1. Structuring Performance Evaluation by Process Phase

Performance evaluation begins with the structure of the business development process because each phase shapes outcomes in a distinct way. Strategic activities unfold through a sequence of decisions, assessments, and engagements rather than as a single event. Evaluating performance by stage clarifies how execution quality changes and shows where teams create or miss value. This structure reflects how organizations manage uncertainty and allocate effort across time.

Six phases organize the business development process. Screening identifies initial opportunities. Identification examines potential partners. Evaluation tests feasibility. Due Diligence confirms assumptions. Negotiation sets commitments. Alliance Management sustains outcomes. Each phase fulfills a different purpose and demands different capabilities.

The model assigns three indicators to each phase. These indicators capture aspects such as strategic fit, risk control, and coordination quality. Scoring follows a ten-point scale and provides a consistent format for measuring contribution and comparing projects.

Indicator scores reflect the strength or weakness of execution in each area. High resource assessment scores suggest strong planning and clear internal coordination. Low agreement quality scores reveal unresolved expectations or vague commitments.

Stage-level evaluation reveals patterns across multiple initiatives. It shows whether recurring gaps affect certain phases or whether strategic emphasis matches actual practice. This method helps organizations diagnose performance gaps and improve execution when financial results remain incomplete or unpredictable.

Evaluating business development by process stage links actions to strategic goals. It turns abstract intentions into observable performance and supports informed decisions during project reviews or planning sessions.

4.2. Structuring Evaluation with Analytic Hierarchy Process

The Analytic Hierarchy Process structures the evaluation model into three levels that connect strategic objectives with specific performance indicators. This layered approach supports clear scoring, consistent comparisons, and alignment with process phases.

The top level sets the evaluation goal. In this case, the goal involves measuring business development performance across multiple phases and dimensions.

The second level identifies six core criteria. These criteria reflect the phases of business development: Screening, Identification, Evaluation, Due Diligence, Negotiation, and Alliance Management. Each phase addresses a different stage of execution and requires unique capabilities.

The third level defines specific indicators for each phase. These indicators translate strategic tasks into observable performance. Evaluators use them to compare progress across initiatives or scenarios.

This structure helps decision-makers simplify complex evaluations. By breaking the process into smaller parts, the model improves clarity and traceability. Each score supports diagnostic insight and strategic alignment.

The three-level hierarchy ensures full coverage of business development functions and allows consistent weighting and aggregation. The model connects long-term goals with process-level observations and supports structured judgment throughout the evaluation.

Figure 1 illustrates the AHP model’s three-level structure for evaluating business development performance. Six process phases form the middle level and guide execution across the workflow. Specific indicators anchor the bottom level and link measurement to each phase. This arrangement supports consistent weighting, traceable scoring, and alignment between strategic goals and operational results.

4.3. Phase Weight Assignment with AHP

4.3.1. Determining the Importance Sequence of Phases

The sequence of phase importance reflects logic and business needs, ranked from most to least important as follows:

• Due Diligence;
• Evaluation;
• Alliance Management;
• Negotiation;
• Identification
• Screening.

Due Diligence: Represents a critical checkpoint in many cases where all prior findings converge. Mistakes here can have severe consequences, so it gets the highest importance.

Evaluation: Plays a key role in assessing strategic fit and potential success. It holds strong influence but shows weaker decisiveness than Due Diligence.

Alliance Management: Supports long-term value realization and goal alignment, contributing to sustained success.

Negotiation: Shapes formal agreements and resource commitments. It holds greater influence than screening at the initial phase but carries lower criticality than evaluation and due diligence.

Identification: Helps structure the opportunity list and improves evaluation focus but carries moderate strategic weight.

Screening: Functions as an initial filter. It holds strategic relevance but makes a smaller contribution to outcomes than subsequent phases.

Our preliminary judgment of each phase’s impact and importance in meeting business goals guided this sequence. This is our assumption based on the standardized AHP method. In actual applications, decision makers can adjust it with flexibility to match enterprise strategy and priorities.

4.3.2. Constructing the Pairwise Comparison Matrix

Next, using the standardization steps of the AHP method, we constructed a pairwise comparison matrix based on the Saaty scale (1-9 scale) according to the importance sequence mentioned above. The logic for constructing the matrix is as follows:

• When phase i is more important than phase j, the value at position a_ij in the matrix is an integer from 1 to 9, with larger values indicating greater differences in importance;
• When phase i is less important than phase j, the value at position a_ij in the matrix is 1/a_ji;
• When phases i and j are equally important, the value at position a_ij in the matrix is 1.

For example:

• Due Diligence is slightly more important than Evaluation, so a₁₂=2;
• Due Diligence is extremely more important than Screening, so a₁₆=9.

Table 4 presents the comparison matrix built using the Saaty scale.

4.3.3. Standardization Process

To calculate the relative weight of each phase, we performed standardization on the pairwise comparison matrix, with the following steps:

1.

Column Sum Standardization:

• Calculate the sum of each column in the comparison matrix;
• Divide each element in the matrix by the sum of its column to obtain the standardized matrix.

2.

Row Average Method:

• Calculate the average of each row in the standardized matrix to obtain the relative weight of each phase (Priority Vector);
• Normalize the values of each column (divide each element by the sum of its column) to obtain the normalized matrix, as shown in Table 5.

4.3.4. Calculate the Weight Vector

We calculated the weight of each business development phase by averaging the values in each row of the normalized matrix. This step produced a priority vector, which reflects the relative importance of each phase in the overall evaluation model.

Table 6 shows the calculated weights. Due Diligence received the highest weight (0.4044609), followed by Evaluation (0.27862927) and Alliance Management (0.14102955). Negotiation (0.09799976), Identification (0.0505145), and Screening (0.027) followed in descending order.

These weights represent the strategic influence of each phase. Higher weights signal greater impact on performance outcomes. In subsequent stages, we used these values to adjust simulation scores and compute composite results.

4.3.5. Consistency Check

To ensure the logical consistency of the pairwise comparison matrix, we conducted a consistency check, which includes:

• Calculating the weighted sum vector:

Multiply the original comparison matrix by the weight vector to obtain the weighted sum vector.

$$\mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{S}\mathrm{u}\mathrm{m} \mathrm{V}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}=\left[\begin{array}{c}1, 2, 4, 5, 7, 9\\ 1/2 , 1, 3, 4, 6, 8\\ 1/4, 1/3, 1, 2, 4, 6\\ 1/5, 1/4, 1/2, 1, 3, 5\\ 1/7, 1/6, 1/4, 1/3, 1, 3\\ 1/9, 1/8, 1/6, 1/5, 1/3, 1\end{array}\right] \mathrm{x}\left[\begin{array}{c}0.40446090\\ 0.27862927\\ 0.14102955\\ 0.09799976\\ 0.05051450\\ 0.02736603\end{array}\right]$$

2.

Calculating the maximum eigenvalue λ_max​;

3.

Calculating the consistency index (CI):

$$\begin{array}{c}CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}\#\end{array}$$

(1)

Where:

λ_max is the largest eigenvalue.

n is the dimension of the matrix (here 6).

4.

Calculating the consistency ratio (CR):

$$\begin{array}{c}CR={\displaystyle \frac{CI}{RI}}\end{array}$$

(2)

Where RI is the Random Index, for n = 6, RI = 1.24.

5.

When CR<0.1, the matrix is considered to have good logical consistency.

Table 7 shows the consistency test results.

4.4. Simulation Design for Model Testing

To validate the AHP-based evaluation framework under varying strategic configurations, we designed a simulation that emulates diverse business development scenarios. Simulation allows controlled testing of the model’s ability to combine qualitative judgments and quantitative indicators and to examine its sensitivity to structured uncertainty. This approach supports a multi-layered examination of applicability, discriminative capacity, and parameter robustness.

4.4.1. Scenario Setting

The simulation tested three strategic configurations. Each one reflected a typical business development approach and allowed direct evaluation of the model’s ability to distinguish and quantify performance under different goals.

Scenario A presents a growth-oriented strategy. It focuses on market expansion, acquiring new partners, and building long-term opportunities. The highest scores appear in phases involving identification and alliance management. This scenario tests the model’s treatment of aggressive priorities that focus on external growth.

Scenario B uses an efficiency-centered strategy. It emphasizes process discipline, thorough evaluation, and risk control. The highest scores occur in the evaluation and due diligence phases. This case tests the model’s handling of internal control and quality-driven behavior.

Scenario C uses a balanced strategy. It treats all business development phases with equal importance. The scores remain consistent across phases. This case shows how the model responds to strategies that assign equal weight to all phases.

We assigned performance scores to 18 key indicators for each case. The model processed these inputs through the AHP hierarchy to compute composite performance outcomes.

This setup checks whether the model detects alignment of strategic intent with performance structure. A higher composite score suggests stronger alignment between scenario emphasis and phase importance in the AHP model.

4.4.2. Aggregating Performance Outputs

The model aggregated each scenario’s performance by applying AHP-derived weights to the KPI inputs and summing them through the hierarchy. This process maintained consistent treatment of indicator-phase relationships and allowed direct comparison across scenarios.

Scenario A outperformed others in Alliance Management and Identification, showing that strategies focused on partner engagement and initial screening align with phases that receive greater weight in the model. The model recognized this alignment by assigning a higher composite score, confirming its ability to capture expansion-focused priorities.

Scenario B led in Evaluation and Due Diligence, phases that hold substantial weight in the AHP structure. This outcome reflects the model’s sensitivity to performance in quality-driven areas and supports its relevance for efficiency-oriented strategies.

Scenario C produced consistent mid-level scores across all phases. The model responded predictably to this uniform input, reinforcing its capacity to reflect balanced execution without bias toward any specific strategic focus.

These results confirm that the model quantifies performance levels and incorporates strategic alignment into its evaluations. It rewards strategic emphasis that matches the weighted structure of business development and embeds strategic logic in its evaluation outcomes. Table 8 presents the results of the empirical simulation.

4.4.3. Sensitivity and Robustness Analysis

We tested model stability by adjusting Due Diligence weight from 0.32 to 0.48. This phase holds the highest weight and shapes key risk outcomes in business development.

Each variation required proportional rescaling of the remaining weights to maintain a normalized structure. Following each adjustment, we recalculated total scenario scores to track changes in rank order and overall performance.

Scenario B consistently produced the highest score, indicating strong robustness underweight variation. In contrast, Scenario A showed greater variability, suggesting moderate sensitivity to changes in non-priority phases. The results for Scenario C remained stable, reflecting its uniform scoring structure.

These findings demonstrate the model’s resilience and strategic fidelity:

• It remains robust under moderate judgmental uncertainty in weight setting;
• Scenarios with strong alignment to high-weight phases, such as Scenario B, show increased stability;
• The framework supports practical use in environments where expert weight assignments differ across decision-makers or contexts;
• It preserves structural validity and interpretive consistency under input variation.

Figure 2 presents the results of the sensitivity and robustness analysis.

4.5. Empirical Case

This study applies the evaluation model to a case from DuPont based on secondary literature. The case outlines DuPont’s business development efforts in innovation and alliance formation. Each phase in the model uses selected indicators to assess performance.

Appendix A presents the full case. Although the case draws from published sources, it demonstrates how the model handles real-world examples with incomplete data. The model identifies which phases drive outcomes and highlights performance variation across strategic priorities.

4.5.1. Implications of Empirical Case

This illustration supports the model’s relevance. It shows how structured evaluation clarifies complex business development efforts. Managers can use similar applications to diagnose strengths and weaknesses across phases, link strategy with execution, and improve decision-making.

The DuPont case provides meaningful insights by demonstrating how the proposed framework reflects actual business development practice. It shows:

• Alignment with Real-World Processes: DuPont’s approach aligns with the six phases and chosen indicators, which supports the model’s relevance;
• Support for Structured Evaluation: It illustrates how the framework captures strategic focus, coordination, and performance, reinforcing the model’s utility;
• Theoretical and Practical Relevance: By linking academic structure with corporate action, it strengthens the connection between research and application.

This alignment confirms the model’s potential as a reliable tool for both researchers and practitioners.

4.5.2. The Role of Performance Evaluation Model

• Enhancing Decision-Making Systematically;

Structured Indicator Design: The framework offers a multi-layered indicator system based on the Analytic Hierarchy Process, helping DuPont systematically track performance across different business development phases, such as screening, identification, evaluation, due diligence, negotiation, and alliance management.

Multi-Criteria Decision Support: DuPont can use the framework’s indicators (e.g., strategic fit, risk identification, value creation) to compare multiple criteria and ensure that decisions in each phase align with the company’s overall strategic goals.

2.

Increasing Transparency in Performance Management;

Quantifiable Performance Metrics: The framework converts intangible metrics such as partnership compatibility and negotiation flexibility into specific indicators. These indicators support evaluation with reduced subjectivity.

Visualized Results: Simulations and data analysis produce performance visuals across strategic scenarios. Managers use these results to identify strengths and locate weaknesses in business development.

3.

Strengthening Strategic Execution Flexibility;

Adaptability to Different Strategic Goals: The framework supports weight adjustments, allowing DuPont to adapt indicator allocation based on specific business development strategies, such as growth-focused, efficiency-focused, or balanced approaches.

Cross-Functional Coordination: The framework facilitates internal collaboration, helping DuPont integrate resources across departments and improve the overall effectiveness of strategy execution.

4.

Supporting Long-Term Value Creation;

Evaluating Non-Financial Outcomes: The framework goes beyond short-term financial returns to evaluate the long-term value of business development efforts, such as market positioning, knowledge accumulation, or the expansion of collaboration networks. This is critical for DuPont’s innovation and sustainability goals.

Tracking Milestone Progress: The framework provides clear tools for tracking milestones during business development processes, ensuring that projects progress as planned.

5.

Improving Strategic Comparisons;

Multi-Scenario Simulations: Through simulations of different strategic scenarios (e.g., rapid growth or efficiency prioritization), DuPont can compare performance outcomes across various approaches, helping management choose the optimal strategy.

Annual Reviews and Strategic Insights: DuPont applies performance results from the framework during annual reviews and project evaluations. These reviews support adjustments to future business development strategy.

6.

Increasing Practical Value of the Model.

The DuPont case demonstrates the framework’s effectiveness within a large multinational corporation. This application confirms its value across practical business development settings.

Connecting Theory and Practice: By applying the framework to the DuPont case, it bridges academic theory with practical usage, offering a structured management tool for business development.

The performance evaluation framework brings substantial benefits to the DuPont case, including systematic decision-making, enhanced transparency, strategic flexibility, long-term value creation, effective strategic comparisons, and practical application. DuPont strengthens business development processes through structured evaluation. This application highlights the framework’s potential for broader use in professional settings.

4.6. Practical Applications and Implementation Contexts

The AHP-based evaluation framework developed in this research offers flexible and structured tools for assessing business development performance across organizational levels and timeframes.

Organizations can use this model to evaluate individual business development personnel by translating role-specific indicators into weighted performance scores that reflect strategic emphasis and actual outcomes. It supports team-level assessment by aggregating indicators from members to identify strengths and gaps in collaboration.

The framework accommodates multiple evaluation formats:

• Annual reviews help track long-term contributions and alignment with strategic goals;
• Project-based evaluations capture outcomes linked to specific initiatives;
• Milestone assessments support phase-based reviews, such as post–due diligence or post–negotiation checkpoints.

By combining quantitative data with expert weighting, the model allows both standardized benchmarking and contextual customization. It supports internal diagnostics, cross-project comparisons, and external validation within partnerships or governance reviews.

The evaluation framework serves as a structured instrument for diagnosis and strategic decision support. It helps organizations align business development activities with overarching goals and offers a consistent foundation for performance measurement.

5. Discussion

Performance theory provides a basis to understand evaluation results. Strategic choices influence business development outcomes in distinct ways. The model identifies patterns that align operational activities with overarching goals. Insights from this analysis strengthen coordination between decisions and development planning.

5.1. Interpretation Based on Performance Theory

Performance theory emphasizes the need for goal clarity, measurable indicators, and continuous feedback to guide organizational behavior. The evaluation model reflects these principles by structuring business development in six phases and linking each with performance indicators to turn strategic intent into operational practice. This structure supports goal alignment and promotes accountability.

Simulated scenarios demonstrate how strategic priorities shape business development outcomes. A growth-oriented approach excels in opportunity recognition and external collaboration. An efficiency-focused strategy shows strength in internal assessment and risk control. This balanced configuration achieves moderate results across all phases. These differences confirm that effective execution requires aligning strategic focus with operational delivery.

The model enables clear visibility into performance at multiple levels. Decision-makers can identify which phases contribute most to overall success or signal the need for corrective action. This supports continuous performance improvement and adaptive learning. Early testing of strategies improves decision quality and lowers the risk of misalignment.

Structured evaluation systems, as illustrated in the model, reinforce organizational control by offering repeatable, transparent, and comparable assessments. When integrated into planning and review cycles, such systems encourage learning, resource prioritization, and cross-functional coordination. This reflects performance theory’s view that organizations improve when data-driven feedback loops guide decisions.

5.2. Strategy and Organizational Coordination

Strong coordination depends on clear strategy, effective communication, and alignment of daily operations. The evaluation model helps examine whether these conditions are present. It simulates strategic options and compares their results to assess the effectiveness of translating intentions into outcomes.

Different scenarios express varied priorities. A growth strategy strengthens phases tied to exploration and external partnerships. An efficiency strategy focuses on internal checks and decision validation. This configuration yields uniform results across functions.

These results show that strategy must shape practice. Leaders need to embed their goals in daily operations. This requires consistent direction across roles, regular review of outcomes, and shared understanding across teams.

Comparing strategies under a common framework highlights gaps in execution. Managers can use this view to align plans with processes. Shared evaluation tools improve communication, guide resource use, and bring different departments into closer cooperation. The model helps unify performance goals and strategic learning.

5.3. Evaluation and Strategic Planning

Strategic planning benefits from structured tools that connect vision with specific actions. Evaluation supports this connection by showing whether selected strategies meet expectations. The model gives managers a way to examine which focus areas help or hinder execution. Scenario comparisons show how priorities shape measurable outcomes.

Growth strategies show strength in identifying and pursuing external opportunities. Efficiency strategies emphasize internal checks and structured decision processes. Balanced strategies maintain steady results without concentrated focus. These outcomes show the need to match strategic intent with actual capabilities.

Effective planning depends on feedback that arrives on schedule and in a form that supports action. The model helps leaders evaluate outcomes and adjust decisions before implementation begins. Managers can adjust plans using specific data from each phase.

Clear evaluation builds accountability. Linking performance to specific choices makes learning and change easier. This model encourages routine checks on strategy and steady alignment between changing goals and available resources.

5.4. Evaluation and Short-Term Accountability

Business development requires long-term commitment, and its outcomes emerge over time. Still, regular operations require accountability. The model identifies mechanisms within the business development process that allow regular monitoring without losing sight of long-term goals. Structured evaluation helps prevent drift by reinforcing strategic focus through phase-specific indicators.

Much of the existing literature examines business development success or failure in hindsight. Few studies offer ways to evaluate short-term performance in ongoing processes. This research contributes a new perspective by providing a structured method to assess immediate outcomes while maintaining alignment with strategic objectives.

By bridging long-term goals with short-term feedback, the model supports decision-makers in maintaining strategic discipline. It creates opportunities for performance discussion in practice and academia, opening paths for expanded management practices and deeper inquiry.

5.5. Research Outlook

The evaluation model uses simulated scenarios and provides a structured basis for analysis. Future research should apply this framework to actual business development cases to validate its utility and refine its assumptions. Empirical testing can reveal whether the indicators and weights hold across different organizational contexts or require adjustment.

Expanding the model with adaptive components, such as real-time data inputs or iterative feedback loops, could improve responsiveness. Researchers might explore alternative decision-making tools that enhance precision or address complexity in dynamic environments. Exploring hybrid models that combine AHP with methods like machine learning or system dynamics could provide greater predictive capability.

Continued development of this framework promises to strengthen its relevance. Applied consistently, it offers a foundation for aligning strategic planning with operational measurement in business development. This creates opportunities for coherent decision-making, stronger resource allocation, and lasting performance gains.

6. Conclusions

This study presents a structured model for evaluating business development performance using the Analytic Hierarchy Process and scenario simulation. Business development involves multiple phases that evolve across extended timelines and may lack clear evaluation criteria. The model organizes these activities into defined phases with concrete indicators, supporting structured and consistent assessment.

Each simulation scenario highlights distinct strategic behaviors. A growth-focused strategy emphasizes external engagement. An efficiency-focused strategy centers on internal control. This balanced approach maintains attention across all phases. These patterns show how strategic focus shapes outcomes and reinforce the need for alignment between strategic planning and operational execution.

The model enables ongoing evaluation within long-term processes. It identifies phase-level performance trends and offers a consistent structure for monitoring business development. Managers can detect issues early, adjust plans accordingly, and ensure that decisions support broader goals.

This study addresses a gap in existing literature by presenting a method for evaluating short-term performance in ongoing development efforts. The model supports immediate feedback, operational learning, and resource coordination. It allows decision-makers to assess current activities without waiting for long-term results.

Future research should apply the model to real business cases. Testing the indicators and weights in different organizations can improve accuracy and generalizability. Additional exploration of data integration or model enhancements may increase its responsiveness and decision support value.

This work contributes a structured and practical approach to business development evaluation. It connects strategic planning with operational practice and offers a tool for consistent assessment, informed decision-making, and long-term performance improvement.