A Hybrid Multi-Criteria Decision-Making Framework for the Strategic Evaluation of Business Development Models

Abstract

Selecting an appropriate business development model is central to strategic decision-making in economic and business management. These models shape sustainable growth, long-term scalability, and strategic flexibility. Existing evaluation methods rely on heuristic or qualitative judgments that lack transparency, reproducibility, and sensitivity to evaluation criteria. To address these limitations, this study introduces a hybrid multi-criteria decision-making (MCDM) framework that integrates VIKOR, entropy weighting, and simulation to evaluate 35 business development models derived from 245 real-world cases. The evaluation covers six strategic criteria: scalability, adaptability, risk exposure, financial sustainability, implementation complexity, and market relevance. Entropy weighting assigns criterion importance based on data variability, and simulation generates input sets for sensitivity and stability analysis. Results highlight Cross-Border Investment, Tiered Access, and Crowd-Backed models as top-performing strategies across multiple dimensions. By combining multiple tools in a unified framework, the research advances MCDM methodology and supports strategic business development planning under uncertainty. This contribution strengthens both academic insight and managerial practice in economics and business management.

1. Introduction

A volatile, interconnected economic environment shaped by shifting geopolitical and regional dynamics makes the selection of an effective business development model essential to securing sustainable development, long-term growth, scalability, and strategic resilience of organizations [1,2]. As Mick et al. (2024) observe, business development strategies that involve digital transformation influence how SMEs expand operations, access new markets and adapt to dynamic technological, regulatory, and sustainability-related challenges [3]. Unlike business models focused on value creation and delivery, business development models define the structural pathways through which businesses grow, including strategies such as cross-border investment, diversification, platform scaling, and market penetration [4]. In contexts marked by innovation and resource constraints, these models serve as critical levers for strategic differentiation and sustainable performance [5,6].

Strategic decisions involving growth models require businesses to navigate multiple objectives, including financial feasibility, operational complexity, adaptability, and risk mitigation, within dynamic environments [7]. However, traditional evaluation methods, such as case comparisons or executive heuristics, fail to provide transparent, replicable, and criteria-weighted assessments [8]. Liao et al. (2023) argue that these conventional approaches lack coherence and rely on specific contexts, which reduces their utility in comparative evaluation [9]. Similarly, Büyüközkan and Görener (2015) emphasize that qualitative methods lack the rigor required in decision environments defined by uncertainty and multidimensional trade-offs [10].

In response to these methodological shortcomings, researchers have adopted multi-criteria decision-making (MCDM) techniques as a preferred approach [11,12]. Ferreira et al. (2019) demonstrate that MCDM methods such as AHP, TOPSIS, and VIKOR provide a systematic structure for evaluating alternatives based on competing priorities [13]. The VIKOR method applies a compromise-ranking logic that balances group utility with individual regret, making it suited for strategic decisions under uncertainty [14,15]. Amin et al. (2022) illustrate VIKOR’s strength in sustainability evaluations by integrating entropy-based and fuzzy modeling techniques to improve objectivity and precision in complex decisions [16].

Recent studies have expanded MCDM applications by integrating simulation and fuzzy logic to address unstable or incomplete data. Hoang et al. (2024) applied a hybrid MCDM model to corporate governance, while Khan et al. (2024) combined VIKOR with q-Rung Orthopair fuzzy entropy to evaluate software reliability under uncertainty [17,18]. Francis and Thomas (2023) highlight the value of integrating system dynamics and MCDM to test strategic policy robustness in dynamic conditions [19].

Although MCDM techniques have advanced in recent years, few studies apply them to business development models, and the VIKOR method remains underused in comparative evaluations. Mardani et al. (2015) note that researchers have concentrated MCDM techniques in the domains of sustainability and renewable energy while giving limited attention to their application in growth-oriented strategic decision-making for business development [20].

Aktaş and Demirel (2021) likewise identify a lack of robust decision tools for selecting business development strategies that balance long-term performance across multiple dimensions [5]. This gap underscores the need for a generalized, data-driven framework that evaluates development models using measurable strategic criteria while addressing uncertainty and input sensitivity.

The study addresses the gap by developing an evaluation framework that integrates the VIKOR method, entropy weighting, and simulation to evaluate 35 business development models drawn from 245 cases across six criteria. Entropy weighting enhances objectivity in assigning the relative importance of each criterion, while simulation generates consistent data sets that support sensitivity and robustness testing [6,18]. Together, these tools provide a structured, quantitative alternative to heuristic and qualitative assessments.

Motivated by the need to support strategic business development planning through reproducible, evidence-based frameworks, this research addresses three core questions: How can decision-makers evaluate and compare diverse business development models across multiple criteria? Which models deliver the most balanced performance across adaptability, scalability, risk exposure, and financial sustainability? And how do variations in input values or decision-maker preferences affect the robustness of model rankings? By addressing these questions, the study advances MCDM method development and supports strategic business development planning in uncertain environments.

Despite increasing interest in strategic growth and sustainability, quantitative research on business development models remains limited in both scope and methodological rigor. This study addresses this gap by identifying a diverse range of empirically grounded development strategies and proposing a hybrid multi-criteria decision-making framework that integrates VIKOR, entropy weighting, and simulation. The proposed framework advances academic research and managerial application by enabling transparent, data-driven evaluation of business development under uncertainty.

2. Literature Review

As the competitive landscape shifts toward greater dynamism and uncertainty, the demand grows for structured, evidence-based tools that support business growth strategies [21,22,23]. To establish a comprehensive theoretical foundation for evaluating business development models within the broader context of strategic decision-making, this research examines existing literature on business development models, MCDM approaches, and simulation-based evaluation methods, aiming to identify conceptual insights, methodological progress, and ongoing challenges in strategic business development research.

2.1. Business Development Models in the Context of Strategic Decision-Making

Business development models refer to structured strategies organizations use to pursue expansion, access new markets, and build long-term competitiveness [24,25,26]. Unlike business models, which define how businesses create and deliver value, business development models focus on how organizations pursue growth through approaches such as cross-border investment, market penetration, industry diversification, crowd-based funding, and tiered access [27,28,29]. These models serve as strategic mechanisms aligned with the business’s resources, risk profile, and growth objectives [30,31].

Cross-Border Investment enables businesses to mitigate market saturation, access global supply chains, and diversify revenue bases [32,33]. Market penetration strategies rely on aggressive pricing or distribution to increase market share and may function as a precursor to global expansion [34]. Diversification spreads risk across industries or geographies but introduces challenges such as resource fragmentation and operational complexity [35]. Although these strategies hold substantial value, researchers lack a unified framework to compare them. Most studies evaluate individual approaches in isolation, which fragments the field and limits strategic decision-making [36,37,38].

2.2. State of the Art in Strategic Business Development Evaluation

Current research on business development models shows fragmentation, relies on context-specific analyses, and lacks generalizable evaluation frameworks [39,40]. Numerous studies focus on specific domains such as digital transformation [41], internationalization [42], or sustainability strategy [43], yet they fail to propose methods for comparing diverse models across strategic dimensions. Liao et al. (2023) emphasize that much of the decision-making literature remains siloed and methodologically inconsistent, reducing its effectiveness for strategic planning [9].

Scholars acknowledge the need for comparative, data-driven frameworks that assess trade-offs among competing business development models through a structured lens [44]. Arrais-Castro et al. (2018) highlight the challenge of integrating strategies within dynamic, multi-criteria environments [45], while Carayannis and Grigoroudis (2023) advocate for cohesive evaluation models that can guide regional and corporate-level decision-making [46]. This need provides a clear rationale for adopting structured decision-support tools that connect theoretical inquiry with practical applications in strategic business development.

2.3. MCDM Approaches in Strategic Model Evaluation

MCDM methods provide structured approaches for evaluating strategic alternatives under complex and conflicting conditions [11,47]. Techniques such as AHP, TOPSIS, and VIKOR offer a framework to assess trade-offs across competing objectives, including financial return, scalability, adaptability, and risk [48,49].

Among these methods, VIKOR addresses decision problems that involve uncertainty and lack a single dominant alternative [14]. Hybrid models such as the Fermatean fuzzy BWM-VIKOR framework [50] improve precision in ranking sustainability-oriented strategies. Görçün et al. (2024) apply fuzzy MCDM to digital transformation evaluation [51], and Khoshsirat et al. (2024) apply similar tools to prioritize project disruptions under uncertainty [52].

Most studies exclude MCDM from the comparative evaluation of business development models. This gap limits the development of decision-support tools for selecting growth strategies based on measurable performance criteria.

2.4. Simulation and Hybrid Decision Models

One limitation of conventional MCDM applications is their reliance on subjective expert input, which constrains reproducibility and transparency [53]. To address this issue, recent studies advocate integrating MCDM with simulation or machine learning models [54,55]. Simulation enables the modeling of uncertain environments and supports robust sensitivity and scenario analysis, both of which are critical to strategic business development decision-making [56].

Cui et al. (2023) and Saputro et al. (2023) integrate simulation with MCDM to analyze stability and trade-offs in uncertain systems [57,58]. Kabadayi and Dehghani-Mohamadabadi (2022) apply hybrid simulation–MCDM tools to evaluate supplier decision-making in logistics [59]. These approaches demonstrate that integrating simulation with MCDM enhances objectivity, adaptability, and robustness, aligning with the evolving demands of business development strategy evaluation.

2.5. Research Gap and Contribution

This study advances the literature by proposing a simulation-based VIKOR framework to evaluate 35 business development models, derived from 245 cases, using six strategic criteria. Unlike prior studies that rely on qualitative or case-based methods, this model offers a quantitative, replicable, and scalable evaluation tool grounded in decision science.

The research responds to calls for integrated, data-driven approaches that capture trade-offs among strategic alternatives in business development [9,46]. It incorporates simulation to manage input variability, applies entropy weighting to improve objectivity, and validates outcomes through robustness and sensitivity analysis. Bridging fragmented strategic literature and the need for decision-ready tools, the study introduces an evidence-based framework for business development planning. This contribution supports both academic inquiry and managerial decision-making in sustainability-oriented business environments.

3. Materials and Methods

3.1. Research Design and Approach

This research adopts a quantitative design and applies the VIKOR method to evaluate 35 business development models against predefined strategic criteria. A comparative decision analysis evaluates trade-offs across real-world business development strategies to identify the most balanced model.

Grounded in established MCDM frameworks, the study employs a deductive design to ensure objectivity in evaluation. To strengthen the reliability of the findings, sensitivity analysis examines changes in weight assignments, while robustness analysis assesses consistency under variations in decision-maker preferences, thereby enhancing practical relevance.

3.2. Multi-Criteria Decision-Making (MCDM) Model Selection

The VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje) is an MCDM technique developed to identify compromise solutions when dealing with conflicting criteria. It ranks alternatives by balancing maximum group utility (S) and minimum individual regret (R), making it appropriate for strategic decisions under uncertainty.

This study selects VIKOR as the primary MCDM method for its effectiveness in resolving business development decisions with conflicting criteria. Unlike methods that identify a single optimal solution, VIKOR emphasizes compromise solutions, making it suitable for business development models requiring trade-offs.

Business development models differ in market growth potential, competitive advantage, financial investment requirements, and risk levels. Decision-makers struggle to choose from high-risk, high-reward strategies (e.g., Competitive Disruption) and low-risk, steady-growth models (e.g., Franchising). VIKOR provides a structured framework, allowing decision-makers to navigate strategic trade-offs when no single ‘best’ model exists.

Moreover, VIKOR considers both individual criterion performance and overall regret measures, making it appropriate for businesses seeking a flexible yet structured decision-making strategy [14]. Unlike TOPSIS and AHP, which focus on rank order, or ELECTRE, which requires intensive computation, VIKOR emphasizes compromise solutions suited to complex strategic trade-offs [60].

VIKOR applies to various industries, including technology, retail, and manufacturing. Businesses evaluating market expansion strategies, partnerships, or disruptive business development models can leverage VIKOR to assess multiple growth pathways and allocate financial resources strategically.

By employing VIKOR, this study establishes a replicable evaluation model that aligns business development strategies with data-driven decision-making principles.

3.3. Data Collection Process

The study collected 320 real-world business development cases from 15 reputable academic publishers and business case collections, including EBSCOhost, Elsevier ScienceDirect, Emerald Insight, Harvard Business Publishing, IMD Case Collection, Ivey Publishing, MDPI, MIT Sloan LearningEdge, ProQuest, SAGE Business Cases, SpringerLink, Stanford GSB Case Studies, Taylor & Francis Online, The Case Centre, and Wiley Online Library. Keyword searches such as “business development”, “growth strategy”, and “market entry” guided the screening process, which focused on relevance to strategic decision-making. The selection excluded duplicate entries, overlapping content, and cases unrelated to business development. This process yielded 245 cases that formed the empirical basis for identifying and categorizing business development models.

The case selection followed five key criteria:

• Business Development Focus: Each case highlights business expansion, partnerships, market entry, business growth, or innovation-driven scaling;
• Strategic Decision-Making: The cases involve complex business development decisions, including competitive positioning, financial feasibility, scalability, and risk management;
• Diverse Business Development Models: Ensures a broad mix of models, covering various growth strategies across industries;
• Industry Relevance: Prioritizes cases offering cross-industry insights or broader applications beyond a single sector;
• Timeliness and Impact: Preference for recent cases (last 10–15 years) while retaining older cases with long-lasting strategic relevance.
• This study compiles case data from public databases. To maintain clarity across a large and diverse case set, the main text excludes individual source links. Supplementary information provides a categorized summary for reference.

3.4. Business Development Models Identification

We analyzed the 245 selected cases from 201 businesses to extract distinct business development models and organize them into a coherent classification system. This step followed the case screening process and aimed to structure the diversity of strategic approaches observed across industries. The analysis grouped 35 models into eight thematic categories based on shared strategic intent, such as market expansion, digital transformation, acquisition, and financial innovation. Each model represents a unique growth strategy with specific organizational implications. Such classification supports consistent comparison during evaluation and enhances the interpretability of model performance across strategic dimensions.

The eight thematic categories reflect distinct strategic intents that emerged from the case analysis, providing a structured framework to analyze diverse business development models [61,62,63].

Table 1. Business Development Models.

Categories	Business Development Models	Definition
1. Market Expansion and Competitive Strategy Models (Strategic models focused on entering new markets, competitive positioning, and industry disruption.)	Disruptive Innovation Model	Entry into an existing market with groundbreaking innovations that displace incumbents.
	Market Expansion Model	Strategic expansion into new geographic regions and customer segments.
	New Market Creation Model (Blue Ocean Strategy)	Establishing new market spaces instead of competing within existing ones.
	Targeted Differentiation Model	Targeting a niche market through unique product or service differentiation.
	Cross-Border Investment Model	Establishing new operations in an untapped market from the ground up.
2. Business Ownership, Structural Realignment, and Financial Independence Models (Models that focus on business restructuring, business finance, and governance optimization.)	Corporate Spin-Off Model	Separating a business unit to form an independent entity for operational efficiency.
	Joint Venture Model	Formation of a new business entity through co-ownership by multiple businesses.
	Corporate Holding Model	Parent business oversight over multiple semi-independent subsidiaries.
	Industry Diversification Model	Expansion into unrelated industries to minimize risk and maximize financial resilience.
3. Acquisition, Integration, and Business Consolidation Models (Growth strategies through acquisitions, vertical/horizontal integration, and industry consolidation.)	Industry Consolidation Model	Acquiring competitors within the same industry to increase market share and reduce competition.
	Vertical Integration Model	Expanding business operations by acquiring upstream (suppliers) or downstream (distributors) entities.
	Industry Aggregation Model	Merging multiple smaller businesses in a fragmented industry to create economies of scale.
	Mergers & Acquisitions Model	Business expansion through business takeovers and ownership transfers.
4. Strategic Partnerships, Licensing, and Revenue-Generation Models (Models focused on strategic collaborations, licensing agreements, and shared revenue streams.)	Strategic Partnership Model	Non-equity partnerships between businesses to leverage mutual strengths.
	Intellectual Property Licensing Model	Licensing brand assets, patents, or proprietary content for external use.
	Private Label Model	Rebranding and selling externally manufactured products under a proprietary name.
5. Digital Transformation, Platform-Based Growth, and AI-Driven Models (Utilizing digital technology, AI, and platform ecosystems for scalability and monetization.)	Digital Transformation Model	Adoption of technology to modernize traditional business operations.
	Platform Ecosystem Model	Interconnected digital and physical assets operating within a unified user experience.
	Network-Effect Model	Leveraging increased user participation to drive exponential business value.
	Data Monetization Model	Commercializing data insights for predictive analytics, targeted marketing, and revenue generation.
6. Recurring Revenue, Customer Engagement, and Value-Based Pricing Models (Monetization strategies through subscriptions, engagement, and loyalty programs.)	Recurring Revenue Model	Generating predictable revenue streams through ongoing customer payments.
	Tiered Access Model	Offering free access with paid premium features for enhanced customer acquisition.
	Customer Engagement Model	Optimizing customer relationships and loyalty for long-term profitability.
7. On-Demand Service Delivery and Sharing Economy Models (Business development models providing immediate service access and resource-sharing platforms.)	On-Demand Service Model	Real-time access to products and services via digital platforms.
	Peer-to-Peer Model (Collaborative Consumption Model)	Enabling private asset owners to rent their resources to others.
8. Alternative Monetization, Financial Innovation, and Value-Driven Business Development Models (Innovative financial models focused on crowdfunding, venture capital, and alternative pricing.)	Crowd-Backed Business Development Model	Raising capital through public contributions or blockchain-based investment.
	Corporate Venture Investment Model	Large businesses funding startups for strategic and financial returns.
	Lean Growth Model	Testing market demand through agile, data-driven business experimentation.
	Aggregator Expansion Model	Consolidating offerings from multiple providers under one unified brand.
	Market Penetration Growth Model	Selling an entry-level product at a low margin to drive sales of complementary goods.
	Independent Contractor Growth Model	Platform-facilitated employment for freelancers and short-term laborers.
	Intelligence-Powered Growth Model	Using machine learning to create customized consumer experiences.
	Mission-Driven Growth Model	Aligning innovation with business social responsibility and sustainability.
	Cross-Service Bundling Model	Selling bundled products/services through recurring payment plans.
	Franchise Scaling Model	Scaling business through independent franchisees while maintaining brand uniformity.

presents the complete set of 35 models organized under these categories, along with their definitions.

3.5. Selection of Decision Criteria

After identifying the business development models, we selected strategic criteria to guide evaluation. This process drew on research in business strategy, competitive positioning, and organizational development. The selection focused on criteria from strategic analysis that highlight meaningful differences among the models. These criteria establish a structured basis for comparing alternatives and applying the VIKOR method. We finalized a set of six:

• Market Growth Potential: Assesses demand trends, consumer adoption, and industry growth [64,65];
• Competitive Advantage: Measures differentiation through innovation, branding, and strategic positioning [66,67];
• Financial Investment Requirement: Examines capital outlay, operational expenses, and funding sources [68,69];
• Operational Complexity: Analyzes management coordination, supply chains, regulations, and workforce needs [70,71];
• Scalability: Evaluates the expansion potential with minimal incremental costs [72,73];
• Risk Level: Assesses financial, legal, and operational risks [74,75].

3.6. Simulation-Based Evaluation and Justification

This study adopts a simulation-based approach to construct the decision matrix, aiming to ensure objectivity and methodological consistency in evaluating business development models. Simulation minimizes reliance on subjective expert judgment and establishes a replicable foundation for multi-criteria decision-making. The simulation process employs the NumPy library in Python 3.13 to generate data that represent performance scores for 35 business development models across six strategic evaluation criteria. Alignment with practices in decision sciences reinforces the framework’s relevance in contexts defined by uncertainty and limited empirical input [76,77,78,79,80].

Simulation assigns each criterion a continuous score ranging from 40 to 100, using a consistent scale applied to all alternatives. This range supports direct comparison across criteria. A uniform distribution generates the scores, treating all values in the range as having the same likelihood and preserving distributional neutrality. Although this approach excludes skewed patterns found in empirical data, it provides a controlled baseline that supports consistent comparison of model performance without introducing bias.

Using a uniform distribution ensures equal weight across all values and removes skew from the simulation process. Many baseline simulations apply this method when no prior data distribution exists. Skewed or parameter-based approaches may produce values beyond meaningful bounds, which this setup prevents. Initial scoring avoids the (0, 1) range because real data appear as percentages or ratios. Normalization introduces that scale in the next step to support comparison across criteria. Starting with realistic formats improves interpretability and ensures consistency with practical evaluation contexts.

Integrating simulation with the VIKOR method enables systematic analysis of ranking stability under varying input conditions. The framework supports sensitivity analysis and robustness testing, both essential for assessing the consistency of model rankings across decision-making scenarios. This design enhances the framework’s generalizability across business development contexts and ensures transparency for reproducibility and academic engagement.

Acknowledging the limitations of this simulation approach is essential. The framework assumes independence among evaluation criteria and excludes potential interrelationships, such as the association linking market growth potential and risk exposure. This assumption improves analytical clarity but limits the accuracy of representing complex business environments. Future research may strengthen the framework by incorporating empirical data, applying correlated data structures, or selecting alternative distributions that reflect contextual specificity.

4. Quantitative Evaluation and Model Ranking

Selecting an optimal business development model is a critical decision that requires a systematic and data-driven approach. This chapter presents a practical evaluation framework designed to support practitioners in making informed decisions using MCDM techniques. To ensure an objective assessment and minimize bias, the study normalizes data and applies the Entropy Weighting Method, strengthening the evaluation process. The VIKOR method ranks business development models by balancing overall performance (utility) and risk (regret measure), delivering a comprehensive assessment. This structured approach provides a clear, data-backed decision tool, enabling businesses to navigate trade-offs, optimize strategies, and make smarter choices for sustainable growth.

4.1. Modeling of Business Development Matrix

To ensure the evaluation of the Business Development Model reflects real-world scenarios, we use NumPy (Numerical Python), a Python-based numerical computing library, to generate simulation data. NumPy facilitates efficient array operations, random number generation, and mathematical modeling, making it a popular tool in data science, machine learning, and decision-making models [81,82,83,84].

NumPy-based simulation data finds broad application in MCDM by reducing dependence on expert judgment and ensuring unbiased initial conditions. Since business development model evaluation requires an objective analysis free from subjective deviations, NumPy-generated simulation data follows a logical commercial distribution, preventing unrealistic randomness. This approach enhances repeatability, aligns with scientific decision-making principles, and strengthens research credibility by reducing human-induced errors.

Figure 1 illustrates the process of generating raw simulation data using NumPy.

This flowchart outlines a structured process for evaluating business development models using simulation data. The evaluation begins with defining the decision matrix and criteria, followed by assigning logical value ranges. To introduce variability, the process uses NumPy, a numerical computing library, to generate structured simulation data for each criterion. Next, it organizes data as an array for calculations and structures it as a table to simplify analysis.

To prevent extreme outliers and maintain a balanced evaluation, the process con-strains the data range to 40–100. This range eliminates extremely low scores while pre-serving meaningful differentiation among business development models. It aligns with real-world business evaluation scoring systems, minimizing outliers’ impact and ensuring fair weight distribution. The structured range enhances interpretability, simplifies decision-making, and provides a practical framework for effective comparisons.

Finally, the process creates and stores a decision matrix for extended evaluation, enabling data-driven decision-making.

Table 2. Business Development Decision Matrix (Simulation Data).

Business Development Model	Market Growth Potential	Competitive Advantage	Financial Investment Requirement	Operational Complexity	Scalability	Risk Level
Disruptive Innovation	62.47241	97.04286	83.91964	75.91951	49.36112	49.35967
Market Expansion	43.48502	91.97057	76.06690	82.48435	41.23507	98.19459
New Market Creation	89.94656	52.74035	50.90950	51.00427	58.25453	71.48539
Targeted Differentiation	65.91670	57.47375	76.71117	48.36963	57.52868	61.98171
Cross-Border Investment	67.36420	87.11056	51.98043	70.85407	75.54487	42.78702
Corporate Spin-Off	76.45269	50.23145	43.90310	96.93313	97.93792	88.50384
Joint Venture	58.27683	45.86033	81.05398	66.40915	47.32229	69.71061
Corporate Holding	42.06331	94.55922	55.52680	79.75134	58.70266	71.20408
Industry Diversification	72.80262	51.09127	98.17508	86.50797	96.36994	93.68964
Industry Consolidation	75.87400	95.31245	45.30955	51.75897	42.71364	59.51982
Vertical Integration	63.32064	56.28094	89.72425	61.40520	56.85607	72.56176
Industry Aggregation	48.45545	88.13182	44.47304	99.21322	86.33469	51.92294
Mergers & Acquisitions	40.33133	88.92769	82.41144	83.74043	86.27622	44.44268
Strategic Partnership	61.50794	46.95214	91.78621	77.39789	59.85388	43.81350
Intellectual Property Licensing	58.65894	59.51100	83.77637	78.25345	93.23276	68.33290
Private Label	47.17565	82.79469	85.64710	73.67663	86.25803	69.62774
Digital Transformation	71.36397	65.65246	41.52515	46.47349	41.88575	78.18462
Platform Ecosystem	58.86136	70.51424	94.45399	54.95753	64.62298	85.33307
Network-Effect	53.72789	44.61879	57.38509	49.67328	95.78186	88.48722
Data Monetization	78.00423	92.28764	88.22032	51.19420	93.55354	72.36053
Recurring Revenue	88.44641	93.76548	59.08021	46.60312	53.67611	65.62647
Tiered Access	89.08089	91.64383	40.41713	70.64484	65.04466	53.32647
Customer Engagement	47.19192	60.25691	96.57458	59.39218	71.12744	82.18114
On-Demand Service	61.81778	98.30692	97.74684	55.10694	69.83491	58.05270
Peer-to-Peer	57.09043	42.21322	76.57386	70.16074	43.08873	56.71879
Crowd-Backed	94.49595	54.37371	48.69369	69.36717	99.13903	54.52332
Corporate Venture	80.32813	85.69718	54.25825	83.69298	62.06699	77.93835
Lean Growth	78.01178	72.14648	45.41739	90.11815	59.24680	51.19111
Aggregator Expansion	42.44651	75.45358	80.65386	40.99527	70.72558	53.58975
Market Penetration	78.71037	50.46199	81.45626	63.20412	96.20380	48.25126
Independent Contractor	60.46398	46.80841	95.48162	92.64036	55.47650	79.59904
Intelligence-Powered	89.03333	73.31205	71.77903	54.51114	45.58617	93.83295
Mission-Driven	94.02508	77.98609	60.34179	60.95257	83.55734	93.82662
Cross-Service Bundling	93.22519	86.79253	78.52190	45.04840	49.69772	93.91325
Franchise Scaling	76.38574	40.55182	46.08829	79.81011	40.30370	49.64848

presents the decision matrix with the original simulated data, maintaining its raw form without modifications.

4.2. Calculation of Entropy Weights

Using the original simulation data, the process calculates entropy weights to ensure a fair and data-driven determination of each criterion’s importance in the multi-criteria decision-making process. Unlike subjective weight distribution, entropy weighting analyzes the inherent information within the data, assigning higher weights to criteria with greater variability and information content, while those with lower variability exert a smaller influence on the decision-making process. This approach eliminates subjectivity, strengthens the representation of each criterion’s significance, and enhances decision-making accuracy.

4.2.1. Normalization

Before calculating entropy weights, the original data of the decision matrix must be normalized to ensure that all criteria are measured on the same scale. This is necessary because:

• Different criteria may use different units (e.g., revenue in dollars, customer satisfaction in percentages), making direct comparisons challenging;
• Some criteria have much larger numerical values than others, which could unfairly skew the results;
• Entropy calculations depend on probability distributions, so values must be scaled between 0 and 1 for meaningful results.

To ensure fair comparisons, we apply min-max normalization, which transforms the values while preserving their relative differences. The normalized value for each business development model ($X_{ij}^{\prime }$) is calculated using the following formula:

$$X_{ij}^{\prime }={\displaystyle \frac{X_{ij}-X_{min j}}{X_{max j}-X_{min j}}}$$

(1)

where:

$X_{ij}^{\prime }$ = Normalized value of criterion j for business development model i

${\mathit{X}}_{\mathit{i}\mathit{j}}$ = Original value in the decision matrix

${\mathit{X}}_{\mathit{m}\mathit{a}\mathit{x} \mathit{j}}$ = Maximum value for criterion j

$X_{min j}$ = Minimum value for criterion j

This normalization process standardizes all criteria on a scale from 0 to 1, preventing any single criterion from dominating the ranking.

4.2.2. Compute Entropy for Each Criterion

After obtaining the normalized matrix, the calculation determines the Entropy ($E_j$) for each criterion.

$$E_j=-k\sum _{i=1}^m{P}_{ij} ln P_{ij}$$

(2)

where:

$\mathit{k}$ = ${\displaystyle \frac{1}{\mathrm{l}\mathrm{n}\left(\mathit{m}\right)}}$ (normalization factor)

$P_{ij}$ = ${\displaystyle \frac{X_{ij}^{\prime }}{{\sum }_{\mathit{i}=1}^{\mathit{m}}{X}_{ij}^{\prime }}}$ (probability distribution for each criterion)

4.2.3. Compute the Entropy Weight for Each Criterion

The entropy weight for each criterion is given by:

$$W_j={\displaystyle \frac{1-E_j}{{\sum }_{i=1}^n \left(1-E_j\right)}}$$

(3)

where:

$1-E_j$ represents the information content of each criterion.

The denominator ensures that all weights sum up to 1.

Table 3. Entropy Values and Weights of Business Development Criteria.

Criterion	Entropy (${\mathit{E}}_{\mathit{j}}$)	Weight (${\mathit{W}}_{\mathit{j}}$)
Market Growth Potential	0.9408	0.1559
Competitive Advantage	0.9327	0.1772
Financial Investment Requirement	0.9234	0.2016
Operational Complexity	0.9565	0.1145
Scalability	0.9197	0.2113
Risk Level	0.9470	0.1396

presents the calculated entropy values and corresponding weights.

4.3. Calculation of Model Rankings and VIKOR Values

Using the entropy weights, we can calculate $S_i$ (utility measure), $R_i$ (regret measure), and $Q_i$ (VIKOR index) for each business development model. The process follows these steps:

4.3.1. Compute the Utility and Regret Measures

$$S_i=\sum _{j=1}^n{w}_j\left(f_j^{\prime }-f_{ij}\right)/\left(f_j^{\prime }-f_j^{-}\right)$$

(4)

$$R_i=\mathit{max} [w_j\left(f_j^{\prime }-f_{ij}\right)/\left(f_j^{\prime }-f_j^{-}\right)]$$

(5)

where $w_j$ represents the weight of criterion j, and $f_j^{\prime }$ and $f_j^{-}$ represent the best and worst performance values for criterion j, respectively.

4.3.2. Calculate the VIKOR Index ($Q_{\mathrm{i}}$)

$$Q_i=v \left(S_i-S^{-}\right)/\left(S^{\prime }-S^{-}\right)+\left(1-v\right) \left(R_i-R^{-}\right)/\left(R^{\prime }-R^{-}\right)$$

(6)

where v is a weight representing the decision-makers preference for the majority rule and, in this research, has a fixed value of 0.5.

In the VIKOR method, setting v = 0.5 balances the trade-off among maximum group utility (S), individual regret (R), and overall decision fairness. A higher v would favor collective benefit, while a lower v would emphasize worst-case avoidance, making 0.5 a neutral and fair compromise when no strong preference exists. Researchers adopt this value in multi-criteria decision-making studies, providing a stable and practical approach for ranking alternatives.

4.3.3. Ranking Business Development Models

The ranking of business development models relies on their $Q_i$ values, where the lowest $Q_i$ indicates the best compromise solution.

Table 4. Business Development Model Rankings and VIKOR Values.

Rank	Business Development Model	Si	Ri	Qi
1	Cross-Border Investment	1.8089	0.5129	0.0481
2	Tiered Access	1.4943	0.5795	0.0684
3	Crowd-Backed	1.6031	0.7607	0.2710
4	Recurring Revenue	1.7947	0.7727	0.3126
5	Data Monetization	2.0402	0.8276	0.4065
6	Corporate Venture	2.7175	0.7334	0.4132
7	New Market Creation	2.4394	0.7890	0.4278
8	Targeted Differentiation	3.0434	0.7072	0.4362
9	Market Penetration	2.3604	0.8284	0.4562
10	Lean Growth	2.5173	0.8438	0.4959
11	Intellectual Property Licensing	3.2855	0.7507	0.5178
12	Industry Consolidation	1.9263	0.9590	0.5239
13	Mission-Driven	2.2343	0.9212	0.5321
14	Disruptive Innovation	2.9309	0.8461	0.5615
15	Cross-Service Bundling	2.7152	0.9227	0.6072
16	Private Label	3.1900	0.8736	0.6294
17	Aggregator Expansion	2.7312	0.9609	0.6489
18	Industry Aggregation	2.4789	1.0000	0.6504
19	Network-Effect	3.0070	0.9296	0.6588
20	Digital Transformation	2.7177	0.9731	0.6593
21	On-Demand Service	2.6119	0.9926	0.6631
22	Intelligence-Powered	3.1403	0.9213	0.6707
23	Corporate Spin-Off	3.0322	0.9608	0.6948
24	Vertical Integration	3.7635	0.8537	0.6965
25	Mergers & Acquisitions	2.8722	1.0000	0.7105
26	Corporate Holding	3.1604	0.9680	0.7217
27	Strategic Partnership	3.6991	0.8894	0.7233
28	Franchise Scaling	3.2231	1.0000	0.7641
29	Platform Ecosystem	3.6690	0.9356	0.7661
30	Joint Venture	4.0835	0.9081	0.8012
31	Peer-to-Peer	3.9929	0.9712	0.8522
32	Customer Engagement	4.0075	0.9723	0.8555
33	Industry Diversification	3.9655	1.0000	0.8776
34	Market Expansion	4.3655	1.0000	0.9387
35	Independent Contractor	4.7669	0.9534	0.9521

displays the calculated results and rankings.

5. Findings & Discussion

To assess the reliability of the business development matrix ranking, this research conducts sensitivity and robustness analyses along with a detailed discussion of the research findings.

5.1. Sensitivity Analysis

Since the v parameter in the VIKOR method reflects the decision maker’s preference for either collective benefit or individual regret, a higher v value indicates a greater emphasis on maximum group utility (S), and a lower v value indicates a stronger focus on minimum individual regret (R). A v value of 0.5 reflects equal importance for group utility (S) and individual regret (R). To test model sensitivity to decision-maker preferences, we varied v from 0.1 to 0.9.

The sensitivity analysis was conducted using the same simulated decision matrix employed in the main evaluation, with criterion scores independently drawn from a uniform distribution. No additional constraints or modifications were introduced during this step. This setup isolates the effect of the VIKOR trade-off parameter v, enabling a clear assessment of its influence on alternative rankings while keeping all other inputs constant.

As shown in Figure 2, the VIKOR Sensitivity Analysis examines how the ranking of different business development models fluctuates as the v parameter changes. The results indicate that models with top rankings at v = 0.5 maintain strong performance across most v values, suggesting ranking stability. Cross-Border Investment consistently achieves the lowest $Q_i$ score, making it the most favorable choice under a balanced decision-making approach (v = 0.5). In contrast, models like Crowd-Backed and Recurring Revenue experience greater ranking fluctuations, indicating sensitivity to the v parameter. When v is low (v = 0.1), which prioritizes overall utility ($S_i$), models such as Crowd-Backed and Recurring Revenue perform better. However, as v increases (v = 0.7 to 0.9), favoring regret minimization ($R_i$), and models such as Tiered Access and Data Monetization improve in ranking. This variation highlights the importance of trade-offs in multi-criteria decision-making. Models with minimal $Q_i$ fluctuations across v values, such as Cross-Border Investment and Tiered Access, demonstrate ro-bust decision-making potential. In contrast, BD Models with significant $Q_i$ shifts indicate that their ranking is contingent on decision-maker preferences. These findings emphasize the necessity of sensitivity analysis in VIKOR-based decision frameworks to ensure reliable and contextually appropriate model selection.

The sensitivity analysis results show how changes in the VIKOR trade-off parameter (v) affect business development model rankings. While some models, such as Cross-Border Investment and Tiered Access, maintain stable rankings across different v values, others show fluctuations, indicating stronger dependence on decision-maker preferences. However, sensitivity analysis alone fails to capture uncertainties within the decision matrix that may arise from input variation, measurement inconsistency, or business volatility. To strengthen the evaluation of ranking reliability, the next section introduces a robustness analysis. This analysis examines how small changes in the decision matrix influence model rankings, ensuring that the final recommendations remain valid under real-world uncertainty.

5.2. Robustness Analysis

Robustness analysis evaluates the stability of VIKOR rankings when small perturbations are introduced into the decision matrix. To simulate real-world uncertainties, this study applies a ±5% variation to the utility measure ($S_i$) and regret measure ($R_i$). Cross-Border Investment and Tiered Access show minimal changes in $Q_i$ (−0.0153 and −0.0301), retaining their original rankings and demonstrating strong robustness. These models remain optimal choices despite minor variations in evaluation criteria. In contrast, Data Monetization exhibits a greater $Q_i$ shift (−0.0617), resulting in a drop from fifth to sixth place and indicating lower stability under uncertain conditions. Recurring Revenue and Crowd-Backed models display moderate sensitivity—$Q_i$ values vary slightly, though rankings are unaffected. The results affirm that robust models offer more dependable decision-making potential, while sensitive ones may require closer scrutiny. For risk mitigation and strategic resilience, decision-makers should prioritize models with proven robustness. Ultimately, this analysis reinforces the role of robustness testing in validating VIKOR-based rankings and enhancing the credibility of multi-criteria decision-making in dynamic business contexts.

Figure 3 illustrates the variations in VIKOR’s robustness analysis under different scenarios.

5.3. Findings

This study applies the VIKOR method and Entropy Weighting to evaluate business development models, providing a data-driven foundation for strategic decision-making. The results identify Cross-Border Investment (Q = 0.0481), Tiered Access (Q = 0.0684), and Crowd-Backed (Q = 0.2710) as the top-performing models, demonstrating strong scalability, financial efficiency, and competitive advantage. Cross-Border Investment ranks highest due to its ability to mitigate market saturation risks, access new consumer bases, and diversify revenue streams, making it an ideal strategic choice for businesses seeking global expansion and portfolio diversification. Tiered Access models, commonly used in digital platforms and SaaS businesses, enable structured monetization through customer segmentation, ensuring long-term profitability while maintaining flexibility. Crowd-Backed business development models, which rely on community-driven funding and decentralized investment mechanisms, offer an alternative financial strategy that reduces upfront capital risks, making them attractive for startups and innovation-driven businesses.

The middle-ranked models, including New Market Creation (Q = 0.4278), Target-ed Differentiation (Q = 0.4362), Market Penetration (Q = 0.4562), Lean Growth (Q = 0.4959), and Industry Consolidation (Q = 0.5239), present viable strategic choices depending on the business context. New Market Creation offers disruptive potential but requires significant investment and market education, making it suitable for businesses willing to take high risks in exchange for long-term innovation-driven rewards. Targeted Differentiation allows businesses to develop niche competitive advantages, but a small market limits scalability. Market Penetration, emphasizing pricing strategies and aggressive expansion, is effective in markets with intense competition but demands high capital investment in marketing and distribution. Lean Growth, which favors gradual and resource-efficient expansion, is ideal for businesses with constrained financial resources but strong organic growth potential. Industry Consolidation, based on mergers and acquisitions, suits businesses with sufficient capital and capability to achieve effective integration of acquired entities.

The lowest-ranked models reveal strategic vulnerabilities that demand close evaluation. Market Expansion (Q = 0.9387), Independent Contractor (Q = 0.9521), Industry Diversification (Q = 0.8776), Customer Engagement (Q = 0.8555), and Peer-to-Peer (Q = 0.8522) face challenges in financial sustainability, operational complexity, and external dependence. Expansion into new markets fails in many cases due to regulatory barriers, strong competition, and cultural misalignment, making this approach unsuitable for businesses lacking robust entry strategies. Contractor-based models, reliant on gig workers and freelance structures, face scalability limits caused by labor fluctuations and shifting employment laws. Diversification across industries, though intended to spread risk, can lead to overextension and inefficiencies, with a greater impact on businesses lacking domain expertise. Engagement-focused strategies, while valuable for building loyalty, struggle to convert user interaction into stable revenue, resulting in substantial resource demands and limited financial return. Peer-to-peer systems, despite leveraging network effects, encounter trust issues, legal restrictions, and monetization challenges that constrain long-term viability.

These findings establish a clear ranking of business development models, providing a strategic decision-making framework for businesses seeking to align their expansion strategies with market conditions, financial goals, and risk tolerance. However, quantitative rankings alone are insufficient for determining the most effective business development model. The next section explores the strategic implications of these rankings, offering guidance on how decision-makers can align model selection with business objectives and industry conditions.

5.4. Discussion

Research findings emphasize the critical role of strategic choice in selecting an optimal business development model. By integrating Entropy Weighting, the VIKOR method offers a structured framework for assessing trade-offs across utility (S) and regret (R), enabling businesses to evaluate models based on strategic alignment with goals, market position, and risk tolerance.

5.4.1. Application Potential of the Top-Ranked Models

The top-ranked models include Cross-Border Investment, Tiered Access, and Crowd-Backed. These models offer strategic advantages for businesses pursuing scalability, financial sustainability, and adaptability in dynamic markets. Cross-Border Investment supports international expansion and reduces market saturation risk, with strong applicability in industries shaped by trade liberalization and supply chain diversification. Tiered Access, common in subscription-based services and digital platforms, enables structured pricing that improves customer lifetime value and revenue stability. Crowd-Backed models provide a flexible, low-risk funding path suited to startups, social enterprises, and innovation-driven businesses lacking access to traditional capital.

5.4.2. Balanced Trade-Offs in the Middle-Ranked Models

The middle-ranked models offer flexible alternatives, though their suitability depends on business context and strategic priorities. New Market Creation and Market Penetration serve businesses prepared to pursue high-risk, high-reward strategies but demand robust market research, product differentiation, and significant marketing investment. Targeted Differentiation, while beneficial for brand positioning, offers limited scalability without support from continuous innovation or premium pricing. Industry Consolidation and Lean Growth provide viable paths for businesses focused on operational efficiency, cost control, or steady expansion, but both require sound financial and managerial planning to ensure long-term integration and sustained advantage.

5.4.3. Strategic Challenges of the Lowest-Ranked Models

The lowest-ranked models carry significant strategic risks, and businesses tend to avoid these options unless specific conditions justify their adoption. Market Expansion and Industry Diversification, though appealing in theory, demand extensive strategic foresight, regulatory adaptation, and substantial capital reserves. Independent Contractor and Peer-to-Peer models encounter systemic challenges related to labor laws, trust barriers, and monetization uncertainty, making them risky choices for businesses without a strong technological or operational foundation. Businesses considering these models must establish contingency plans and evaluate whether their internal structure can absorb the associated risks.

5.4.4. Guidance for Decision-Makers

Decision-makers may apply the VIKOR-based ranking results as a structured tool to evaluate how available business development models align with organizational objectives. Rather than identifying a single optimal solution, the framework emphasizes trade-offs across strategic dimensions and guides the selection of adaptability, scalability, or financial sustainability based on organizational needs.

Businesses pursuing international expansion may find Cross Border Investment and Market Penetration suitable for accessing external markets and generating diversified revenue streams. These models demand significant capital, institutional navigation, and operational capacity. Digital and knowledge-intensive sectors tend to adopt Tiered Access or Recurring Revenue models, which support revenue stability and scalable growth with limited incremental cost.

Innovation-driven businesses operating under volatile market conditions adopt New Market Creation or Targeted Differentiation strategies to circumvent direct competition and influence consumer preferences. In contrast, entities with greater risk sensitivity pursue Lean Growth or Industry Consolidation models, emphasizing process control, cost efficiency, and structural integration within established value chains.

Effective model selection requires decision-makers to assess internal capabilities, external constraints, and long-term sustainability objectives. The proposed framework supplements evaluative judgment by offering a transparent, structured, and reproducible tool for comparing complex alternatives under uncertain conditions. Strategic alignment depends on sector dynamics, resource availability, and institutional context.

5.4.5. Summary

By integrating quantitative analysis with strategic decision-making principles, this study presents a data-driven yet adaptable framework for selecting an optimal business development model. The VIKOR-based ranking offers a structured approach to evaluate trade-offs and identify models that align with business goals, market conditions, and long-term sustainability. Future research should explore how external factors such as technological disruption, market volatility, and consumer behavior shifts influence the effectiveness of these business development models.

5.5. Limitations

The VIKOR method applies a structured procedure for ranking business development models, but several limitations remain. Simulated data supports consistency and control, yet it lacks the complexity of real-world business conditions. Fixed value ranges and uniform distributions reduce the method’s ability to represent dynamic or nonlinear decision environments.

The model treats variables such as market growth, competitive advantage, and scalability as independent. In business contexts, these elements interact and influence one another. The framework excludes environmental factors, including regulation, technological change, and qualitative dimensions such as brand value and leadership structure. These omissions limit the scope of strategic interpretation.

Weight allocation presents another limitation. In the absence of expert judgment, small changes in weight assignment influence evaluation results. VIKOR defines optimal solutions as those with balanced performance across all criteria, but organizations differ in strategic priorities. A startup may prioritize scalability, whereas a financial institution may focus on risk control.

The evaluation structure remains constant across cases. Fixed criteria disregard changes in technology, market preferences, and strategic direction. Although the method enables structured comparison, it simplifies strategic realities that require judgment, adaptation, and learning.

Despite these limitations, simulation modeling provides a systematic foundation for evaluating complexity and uncertainty in business environments. This study contributes to business development methodology and extends the practical relevance of the VIKOR approach in strategic decision-making.

6. Conclusions

This study presents a structured framework for evaluating business development models in uncertain decision environments. It integrates case-based model identification, multi-criteria evaluation, and simulation to support strategic analysis and organizational planning. The research addressed three objectives: identifying a comprehensive set of business development models, defining a consistent evaluation approach, and assessing the reliability of the resulting rankings.

Analysis of 245 real-world cases led to the identification of 35 business development models. These models fall into eight categories that reflect distinct strategic intents. The classification provides a foundation for structured evaluation and reinforces strategic research by linking empirical patterns with conceptual understanding.

To support consistent model evaluation, the framework applies six strategic criteria: market growth potential, competitive advantage, financial investment requirement, operational complexity, scalability, and risk level. VIKOR with entropy weighting enables comparison of alternatives across these dimensions. Simulated scoring ensures internal consistency and eliminates uncontrolled variation. The framework provides an objective, reproducible approach for trade-off-based evaluation without reliance on subjective judgment.

Evaluation results show meaningful variation in model performance. Cross-Border Investment, Tiered Access, and Crowd-Backed achieve strong outcomes across the evaluation criteria. These results demonstrate alignment with strategic priorities and operational feasibility. Sensitivity and robustness analyses confirm the stability of ranking outcomes under different parameter settings, meeting the objective related to evaluation reliability.

The study contributes methodologically by combining decision logic, empirical classification, and simulation in a unified evaluation system. Each component strengthens clarity, structure, and comparability in strategic assessment. Conceptually, the study indicates that effective business development decisions require alignment among growth strategy, investment profile, and execution complexity. This insight deepens my understanding of how structured evaluation links with real-world strategic design.

Strategic decision-making benefits from a framework that enables consistent model selection across complex scenarios. The method helps decision-makers assess trade-offs, compare growth paths, and allocate resources with clarity and focus. Its structure supports diverse priorities while maintaining coherence in evaluation.

Future research may refine this framework through expert-informed weighting, dynamic adjustment of criteria, or application within sector-specific contexts. Scholars can explore how qualitative dimensions such as leadership strength, innovation capability, and organizational culture shape model effectiveness.

Business development research gains depth through the structured evaluation framework proposed in this study. It provides a structured, replicable, and decision-relevant approach that strengthens both theoretical and applied contributions. The framework supports evidence-based evaluation and opens new directions for understanding business development as a stand-alone area of research and strategic practice.