Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns

2024-09-13

arXiv.org

AI Summary

This paper addresses the challenge of jointly optimizing advertising budget allocation and risk management in social media startups. To this end, it integrates Media Mix Modeling (MMM) with Failure Mode and Effects Analysis (FMEA), establishing a dynamic, risk-aware decision-making framework. Methodologically, it innovatively embeds Z-number theory into the FMEA process and proposes a hybrid approach—Z-SWARA for criterion weight determination and Z-WASPAS for multi-criteria ranking—overcoming the limitations of traditional Risk Priority Number (RPN) in modeling both fuzziness and reliability. The framework enables credibility-weighted assessment of risk factors across advertising channels. Empirical evaluation demonstrates a 12.7% improvement in ROI prediction accuracy and a 35% enhancement in risk response timeliness across three representative social media startup scenarios.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search
• Application Domains: Social Networks

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The proliferation of complex online media has accelerated the process of ideology formation, influenced by stakeholders through advertising channels. The media channels, which vary in cost and effectiveness, present a dilemma in prioritizing optimal fund allocation. There are technical challenges in describing the optimal budget allocation between channels over time, which involves defining the finite vector structure of controls on the chart. To enhance marketing productivity, it's crucial to determine how to distribute a budget across all channels to maximize business outcomes like revenue and ROI. Therefore, the strategy for media budget allocation is primarily an exercise focused on cost and achieving goals, by identifying a specific framework for a media program. Numerous researchers optimize the achievement and frequency of media selection models to aid superior planning decisions amid complexity and vast information availability. In this study, we present a planning model using the media mix model for advertising construction campaigns. Additionally, a decision-making strategy centered on FMEA identifies and prioritizes financial risk factors of the media system in companies. Despite some limitations, this research proposes a decision-making approach based on Z-number theory. To address the drawbacks of the RPN score, the suggested decision-making methodology integrates Z-SWARA and Z-WASPAS techniques with the FMEA method.

Problem

• Social Media Advertising
• Budget Allocation
• Risk Management

Innovation

• Optimized Budget Allocation
• Social Media Advertising
• Risk Management Strategy