The future of AI in video streaming and multimedia is transforming through several key developments you'll want to track. You'll see advanced personalization algorithms that predict viewing preferences and customize content recommendations in real-time. Machine learning is revolutionizing content creation workflows by automating editing, captioning, and quality enhancement processes.
Technical innovations include flexible bitrate streaming and smart CDNs that optimize delivery while reducing buffering times. Security measures and ethical considerations remain vital as these technologies evolve.
From automated content moderation to immersive AR/VR experiences, exploring these emerging trends will show how AI continues reshaping the digital entertainment landscape.
Key Takeaways AI-driven personalization will evolve through advanced recommendation systems and real-time content modification to create hyper-customized viewing experiences. Integration of AR/VR technologies with AI will enable more immersive streaming experiences and interactive content consumption. Automated content creation tools will revolutionize production workflows, reducing costs while maintaining quality through AI-powered editing and optimization. Smart CDNs and AI-powered bandwidth management will ensure seamless streaming experiences with minimal buffering across diverse network conditions. Multilingual capabilities will expand through natural language processing, breaking down language barriers and enabling global content accessibility. Current State of AI in Video Streaming AI-powered streaming platforms analyze viewer behavior and preferences to create engaging, personalized experiences that bring friends together through shared entertainment moments Video streaming technology has evolved from basic content delivery to advanced AI-powered systems that can analyze viewing patterns, predict user preferences, and optimize streaming quality in real-time. With video streaming now accounting for approximately 65.93% of current Internet traffic, the technology's significance in digital communication cannot be overstated (Jesús-Azabal, 2024 ).
AI algorithms are now essential for content recommendation engines, flexible bitrate streaming, and automated content moderation across major streaming platforms like Netflix, Amazon Prime, and Disney+.
There reshaping how you can enhance your streaming service's user experience through personalized content delivery, improved video quality, and reduced buffering times — all while managing bandwidth more efficiently.
Evolution of Streaming Technology Modern streaming platforms have evolved beyond simple video delivery into advanced AI-driven ecosystems. Live streaming services have experienced remarkable growth, with global engagement and sales performance increasing by 266% through these platforms (Widodo, 2023 ).
Today's platforms incorporate intricate content personalization algorithms that analyze viewing patterns and preferences to enhance viewer engagement. These AI-driven technologies enable you to deliver targeted recommendations and dynamically adjust streaming quality based on network conditions.
As you develop your streaming platform, you can implement flexible bitrate streaming, which automatically adjusts video quality to match available bandwidth.
You should consider integrating machine learning models that predict user behavior and preload content to reduce buffering times. By utilizing edge computing and content delivery networks , you'll optimize video delivery while maintaining high quality.
These technological advancements help you create a more responsive and personalized streaming experience for your users.
Impact on Entertainment Industry Today's AI-driven streaming platforms have fundamentally reshaped the entertainment industry's development landscape.
Artificial intelligence is fueling advanced content delivery systems that analyze viewer patterns and optimize streaming quality in real-time. These systems enable you to implement personalized recommendations through machine learning algorithms that track user preferences and viewing history.
To enhance your platform's user experience, you can integrate AI-fueled features like automated content tagging, smart thumbnail generation, and predictive buffering.
The streaming industry now relies heavily on these technologies to deliver engaging content while reducing development costs . You can utilize AI tools to automatically generate metadata, optimize video compression, and create flexible bitrate solutions.
These implementations help you maintain competitive advantage by offering seamless playback and contextually relevant content suggestions to your users .
Key Market Drivers and Innovations Content recommendation engines are becoming more advanced, analyzing user behavior patterns to deliver increasingly accurate suggestions. By implementing personalized content recommendations, you can reduce viewer churn and increase engagement on your platform.
Your development team should focus on utilizing knowledge into viewer behavior to optimize the streaming experience. Consider implementing adaptive bitrate streaming technology that automatically adjusts video quality based on network conditions and device capabilities. This guarantees smooth playback while maximizing bandwidth efficiency.
You can also integrate AI-powered analytics to track viewing patterns, helping you make data-driven decisions about content acquisition and user interface improvements. These technological advancements will help you stay competitive in today's streaming market.
AI-Powered Personalization and User Experience Your video streaming platform can enhance user satisfaction through AI-powered features that go beyond basic recommendation algorithms.
You'll find opportunities to implement real-time content modification systems that adjust video quality based on network conditions and user preferences, while also incorporating interactive elements like smart chapter selection and scene skipping.
Through machine learning models, you can develop accessibility features such as automated captioning, audio descriptions, and personalized playback controls that make your content more inclusive for all viewers.
Advanced Content Recommendation Systems Modern streaming platforms thrive on advanced recommendation systems that use AI to analyze user behavior, viewing patterns, and content metadata. These systems deliver personalized experiences by processing data-driven observations to enhance user engagement and satisfaction.
To further enhance user experience and optimize content recommendations, you can:
Implement collaborative filtering algorithms that analyze viewing histories across user segments to generate smarter recommendations based on similar viewer preferences. Develop content tagging systems using machine learning to automatically categorize and match content attributes with user interests. Create hybrid recommendation models that combine both content-based and user-based filtering for more accurate suggestions. Deploy A/B testing frameworks to continuously evaluate and refine recommendation algorithms based on user interaction metrics. You'll benefit from integrating these advanced content recommendation systems to increase viewer retention and maximize the value of your content library while delivering more engaging streaming experiences.
Real-Time Content Adaptation Streaming platforms can now dynamically modify content delivery based on real-time user behavior and network conditions. For instance, our experience with Worldcast Live , a concert streaming platform we developed, demonstrated the critical importance of real-time adaptation. We achieved sub-second latency (0.4-0.5 seconds) using a custom WebRTC and Kurento solution, making it possible to stream HD concerts to 10,000 viewers simultaneously with pristine audio quality.
By implementing content-aware encoding and AI-driven solutions, you'll enhance streaming quality for each viewer's specific circumstances, from device capabilities to bandwidth limitations. For example, Worldcast Live automatically adjusts video quality based on viewers' internet connection speeds while maintaining HD audio quality comparable to local playback.
The system analyzes viewing patterns and automatically modifies video bitrates, resolution, and audio quality to maintain ideal playback even during network fluctuations.
Consider implementing flexible streaming technologies that monitor device performance and viewer engagement metrics to make split-second adjustments.
Interactive Viewing Experiences Through advanced AI algorithms, interactive viewing experiences have evolved beyond basic playback controls. You can now utilize AI-powered solutions to create engaging experiences that respond to your viewers' content preferences and behaviors in real-time.
By analyzing user-generated content and interaction patterns, your platform can deliver personalized, dynamic content that keeps audiences actively involved.
To enhance viewer engagement further, you can also:
Implement branching narratives where viewers' choices influence story progression Deploy AI-driven interactive overlays that display relevant information based on viewer interests Create smart recommendation systems that suggest interactive content segments during playback Develop real-time content modification features that adjust to viewer engagement levels These interactive features transform passive viewing into an active experience, notably increasing user engagement and retention rates. Your streaming platform can continuously learn from user interactions to refine and improve content delivery.
Accessibility Enhancements Enhancing accessibility in video streaming platforms has become essential for reaching diverse audiences and ensuring inclusive user experiences.
Implement AI-enabled solutions that can automatically generate accurate closed captions, real-time translations, and audio descriptions for your content. Consider integrating smart features that adjust to viewer preferences and physical needs. You can develop systems that automatically modify contrast, font sizes, and playback speeds based on user settings. These accessibility enhancements create more immersive experiences for viewers with different abilities. Your development team should focus on implementing AI-powered tools that can detect and enhance low-quality audio, provide multiple language options, and create alternative navigation methods.
These improvements not only comply with accessibility standards but also expand your platform's reach to previously underserved audiences.
Next-Generation Content Creation AI-powered content creation tools enable social media creators to effortlessly produce and edit engaging video content directly from their smartphones while maintaining authentic, spontaneous moments Modern AI tools can transform video production workflows by automating time-consuming tasks like video editing, caption generation, and quality enhancement.
You'll find notable advantages in utilizing AI-driven content analytics to predict viewer engagement patterns and optimize production investments before committing resources.
These technologies enable your team to create more content at lower costs while maintaining quality standards, as AI assists with everything from script analysis to post-production optimization.
AI in Production Workflows As video streaming platforms evolve, AI-powered production workflows are revolutionizing content creation pipelines. Users can streamline content development process by integrating AI tools that enhance efficiency and quality control.
These systems help manage video content creation while maintaining consistent digital watermarks and interactive features.
To further enhance video content management, consider the following steps:
Implement AI-driven content exploration algorithms to automatically tag and categorize video assets. Deploy machine learning models to analyze viewer engagement and optimize content delivery. Use AI-generated content suggestions to enhance production scheduling. Integrate automated quality control systems to detect technical issues before distribution. Automated Content Generation Video platforms today employ automated content generation to revolutionize production pipelines. You can implement AI-powered video streaming platforms to automatically create personalized thumbnails, trailers, and highlight reels from your existing content.
These systems analyze viewer behavior and deliver targeted content to users based on their preferences and watching patterns. To maximize engagement, consider integrating programmatic advertising that dynamically adjusts based on user interactions.
Your platform can gather significant knowledge about content performance through AI analytics, helping you optimize future content creation. Automated systems can now generate lower-thirds, transitions, and even basic video edits, streamlining your production workflow.
For technical implementation, focus on developing modular components that can scale with your platform's growth while maintaining consistent quality across automatically generated content.
Predictive Analytics for Content Success Predictive analytics takes your streaming platform to the next stage by forecasting potential content success before production commences.
By analyzing user preferences and viewing patterns, you'll make data-driven decisions that enhance audience retention and deliver more relevant content to your viewers.
To further enhance content strategy, consider the following steps:
Implement machine learning algorithms to analyze historical viewing data and identify content patterns that resonate with your target audience. Deploy sentiment analysis tools to evaluate viewer feedback and social media reactions, helping content creators adjust their strategy. Employ A/B testing frameworks to experiment with different content formats and optimize engagement metrics. Integrate predictive modeling systems to forecast viewer demographics and content preferences, enabling better resource allocation for future productions. These analytics-driven approaches help minimize production risks while maximizing the potential for creating successful, engaging content.
Cost-Effective Production Solutions Modern production workflows can use AI to dramatically reduce content creation costs while maintaining high quality. By implementing generative AI tools, you'll streamline video editing, automate post-production tasks, and optimize video content delivery processes.
To guarantee cost-effective production solutions, consider integrating AI-powered automation for routine tasks like transcoding, metadata tagging, and quality control. These systems can protect security in content ownership through blockchain verification while reducing manual oversight.
You can enhance your seamless streaming experience by implementing flexible bitrate technologies and automated quality checks.
For maximum efficiency, use AI tools that handle multiple production stages simultaneously - from initial content creation to final distribution. This approach not only cuts operational costs but also maintains consistent quality standards across your video streaming platform.
Technical Infrastructure Advancements Advanced streaming infrastructure and smart CDN technologies enable seamless video delivery to users of all ages, ensuring high-quality playback even for less tech-savvy viewers accessing content from home devices Modern video streaming platforms need strong technical infrastructure to handle massive data loads and user demands efficiently.
Implementing smart Content Delivery Networks (CDNs) alongside blockchain-based rights management systems can greatly enhance your platform's performance and content protection capabilities.
Your development roadmap should prioritize energy-efficient streaming solutions and advanced security measures to meet growing environmental concerns while safeguarding user privacy.
Smart Content Delivery Networks Through advanced machine learning algorithms, Smart Content Delivery Networks (CDNs) now optimize video streaming routes in real-time, reducing buffering by up to 30% (Sangeetha, 2024 ). Our experience with Worldcast Live's Multiple Venue Streaming (MVS) feature demonstrated how crucial smart CDN implementation is for maintaining synchronization across multiple websites simultaneously streaming the same content.
These technology advances enable your platform to deliver a seamless video streaming experience by analyzing network conditions and user behavior patterns to predict and prevent potential streaming issues.
For instance, when implementing Worldcast Live's picture-in-picture functionality for multiple camera angles, we ensured the CDN could handle multiple synchronized HD streams without compromising the sub-second latency.
Blockchain for Rights Management Blockchain technology revolutionizes how streaming platforms handle digital rights management.
You'll benefit from implementing smart contracts that automatically track content usage and distribute royalties to content owners in real-time, reducing payment delays and disputes.
To strengthen your digital streaming platform's security features, consider integrating blockchain-based authentication protocols. These protocols create immutable records of content ownership and licensing agreements, making it notably harder for bad actors to commit copyright infringement. You can implement decentralized content verification systems that automatically validate viewing rights before granting access to premium content. For enhanced transparency, deploy blockchain ledgers to track content distribution across multiple platforms.
This allows you to monitor usage patterns, manage territorial rights, and guarantee proper revenue attribution while maintaining an auditable trail of all content transactions.
Energy-Efficient Streaming Solutions Streaming platforms' substantial energy consumption demands innovative optimization approaches. As a content platform provider, you'll need to implement energy-efficient streaming solutions that balance video quality with resource usage.
Modern optimization techniques help reduce energy consumption while maintaining seamless mobile content delivery across varying internet speeds.
To further enhance video streaming performance, consider the following strategies:
Use flexible bitrate streaming that automatically adjusts video quality based on network conditions and device capabilities. Implement efficient content caching strategies at edge locations to reduce server load and transmission costs. Use AI-driven compression algorithms that maintain quality while reducing file sizes and bandwidth requirements. Integrate smart CDN routing to optimize content delivery paths and minimize energy expenditure during peak streaming periods. These technical improvements will help you reduce operational costs while supporting sustainable streaming practices that benefit both your platform and users.
Security and Privacy Measures Modern video streaming platforms must prioritize strong security and privacy measures to protect both content and user data.
You'll need to implement end-to-end encryption, secure authentication protocols, and real-time threat detection systems to safeguard your streaming services against potential breaches. To stay competitive, consider integrating AI-powered security features that can detect unusual viewing patterns and potential account sharing.
These systems complement human creativity while maintaining user privacy through advanced anonymization techniques. The future of video streaming depends on balancing accessibility with sturdy protection mechanisms. You can enhance your platform's security by regularly updating your encryption standards, implementing multi-factor authentication, and using blockchain technology for content rights management.
Regular security audits and compliance checks will help maintain user trust and protect your significant content assets.
Implementation and Future Outlook AI-powered streaming platforms enhance the collective viewing experience by delivering personalized content recommendations and optimized video quality for groups watching together To integrate AI into your video streaming platform, you'll need to evaluate both your product's technical readiness and your users' needs through thorough testing and gradual feature rollouts. For instance, when our team developed Vodeo , an iOS-based movie streaming platform for Janson Media Group, we implemented several AI-driven features incrementally to ensure optimal performance and user satisfaction.
Your development roadmap should prioritize key AI implementations like personalized recommendations, automated content moderation, and flexible bitrate optimization, while ensuring compliance with data privacy regulations and ethical AI guidelines.
As you look ahead, consider partnering with AI solution providers (like us) who can help you scale these features .
Industry Adoption Strategies Video service providers seeking to implement AI capabilities must prioritize a phased adoption approach. During the Vodeo project, we experienced firsthand how crucial this strategy is. We implemented basic features like content categorization and adaptive video quality first, before moving on to more complex systems.
For example, Vodeo's implementation of automatic video quality adjustment (480p to 1080p) based on connection speed demonstrates how AI can enhance user experience without overwhelming system resources. The platform's content management system, which allows for efficient movie uploading through Amazon S3 server fragmentation, showcases how technical infrastructure can be optimized using AI-driven solutions.
When planning your AI adoption timeline, consider these proven steps:
Start with basic AI implementations like automated content tagging and recommendation systems Integrate advanced machine learning models for real-time video quality optimization Develop multilingual capabilities using natural language processing Implement predictive analytics for content demand forecasting Emerging Technologies Integration While software development teams explore cutting-edge solutions, several emerging technologies stand ready for integration into streaming platforms.
Natural language processing enhances content revelation through improved search functions and personalized recommendations. Consider implementing AI-powered video quality enhancement algorithms to deliver consistently high-quality video content across varying bandwidth conditions.
You should evaluate integrating real-time analytics for viewer behavior tracking and automated content moderation systems.
These tools will help you create better experiences for viewers while optimizing resource usage. Look into implementing machine learning models that can predict viewing patterns and automatically adjust streaming parameters.
You can also explore blockchain-based solutions for content rights management and peer-to-peer streaming capabilities to improve delivery efficiency.
Ethical Considerations As streaming platforms integrate more AI capabilities, ethical implementation becomes a foundational aspect of development strategy.
You'll need to address key ethical considerations while enhancing your viewing experience through AI integration. Implementing real-time content analysis and personalization requires careful attention to user privacy and data protection protocols.
To build a responsible AI ecosystem, you should:
Develop transparent AI algorithms that explain content recommendations to users, helping them understand how their viewing choices influence suggestions. Implement content filtering systems that respect cultural sensitivities while providing different types of content. Create strong data anonymization processes that protect user privacy while gathering exclusive perspectives. Design AI systems with built-in fairness metrics to prevent bias in content distribution and recommendation engines. When deploying AI features , always prioritize user consent and maintain clear documentation of your ethical guidelines and implementation standards.
Future Growth Predictions Looking ahead to 2025 , streaming platforms will roll out increasingly refined AI implementations that reshape how developers approach video delivery systems. The global market for AI in video streaming is projected to experience substantial growth by 2025, driven by technological advancements and increasing demand for personalized content (Aldoseri, 2024 ).
Focus on integrating flexible bitrate technologies and real-time content optimization to enhance user experience. Major trends you should monitor include AI-powered video compression algorithms and personalized content delivery networks.
The evolution of video advertising will shift toward dynamic ad insertion and viewer behavior analysis, enabling more precise targeting. There's considerable potential for blockchain technology to revolutionize content rights management and create transparent revenue sharing models. Consider implementing upcoming AI trends in multimedia, such as automated quality enhancement and smart buffering systems.
As future tech in streaming evolves, you'll need to prioritize scalable architectures that can handle increasingly complex AI processing demands while maintaining performance.
Practical Applications for Stakeholders Platform developers should integrate AI-powered features like personalized recommendation engines, flexible bitrate streaming, and content categorization APIs to enhance user experience.
You'll find opportunities to utilize computer vision and natural language processing for automated content tagging, thumbnail generation, and real-time speech-to-text capabilities.
Service providers can implement AI algorithms for predictive analytics, quality of service optimization, and automated content moderation to streamline operations while meeting user expectations.
Guidelines for Platform Developers Developers seeking to integrate AI into video streaming platforms should prioritize five core implementation areas: content personalization, flexible bitrate optimization, real-time quality enhancement, automated content moderation, and viewer behavior analytics.
When handling vast amounts of data, focus on features that transform passive viewing experiences into interactive ones, similar to popular streaming platforms.
To build on the previous points, you should also:
Implement adjustable audio settings that modify based on individual device capabilities and viewer preferences. Develop smart scene segmentation algorithms to improve content exploration and chapter marking. Create scalable machine learning models that can process multiple video streams simultaneously. Design modular AI components that can be easily updated without disrupting the main service. These technical implementations will enhance user engagement while maintaining platform stability and performance across different devices and network conditions.
Opportunities for Content Creators Content creators can use AI-powered tools to enhance their video production workflow while complementing the technical capabilities modern streaming platforms offer.
Creators will find opportunities to integrate recommendation engines that analyze viewer behavior and help deliver personalized video content to their target audiences. By implementing AI-driven analytics, creators can track conversion rates and adjust their content strategy based on real-time performance metrics.
Content creators implementing additional features like automated captioning, content moderation, and scene detection to improve the experience for viewers. These tools can help them maintain quality standards while reducing production time.
Content creators can also use AI to optimize video thumbnails, create multi-language versions, and generate metadata automatically. These enhancements will help them scale their content production while maintaining consistency across your video library.
Recommendations for Service Providers Service providers that are looking into AI integration should focus on scalable, cloud-based solutions that enhance their streaming infrastructure. By employing AI algorithms, you'll optimize internet traffic management and deliver personalized experiences to individual users while reducing bandwidth costs.
To enhance the user experience and optimize performance, consider the following steps:
Implement flexible bitrate streaming that automatically adjusts video quality based on each user's connection speed and device capabilities. Deploy AI-powered content piece analysis to improve online advertising targeting and increase revenue through smart ad placement. Make use of machine learning models to predict and pre-cache popular content, reducing server load during peak viewing times. Integrate 3D technology processing capabilities to support emerging formats and maintain compatibility with next-generation content. These technical improvements will help you stay competitive while providing better service quality and user satisfaction across your streaming platform.
Real-World Implementation Example: Vodeo Our experience developing Vodeo for Janson Media Group demonstrates practical implementation of modern streaming technologies.
The platform successfully integrates:
Adaptive streaming quality that automatically adjusts based on network conditions Comprehensive content management system with automated uploading optimization Flexible payment system using internal currency (tickets) for content rental Sophisticated content categorization and discovery features AirPlay and ChromeCast integration for multi-device streaming Vodeo's success in managing independent films and series while maintaining Netflix -like quality standards shows how smaller platforms can effectively compete with major streaming services through smart implementation of AI and streaming technologies.
Our Expertise in AI-Powered Video Streaming Solutions As pioneers in multimedia development since 2005, we bring over 19 years of specialized experience in creating advanced video streaming platforms and AI-powered solutions. Our team has successfully implemented cutting-edge streaming technologies across numerous projects, including the development of Worldcast Live's concert streaming platform achieving sub-second latency for 10,000 simultaneous viewers, and Vodeo , an innovative iOS-based movie streaming platform for Janson Media Group.
Our expertise spans the complete spectrum of AI integration in video streaming, from recognition and generation to sophisticated recommendation systems. With a proven track record of 100% project success rating on Upwork and experience working with WebRTC, LiveKit, Kurento, and Wowza, we've developed a deep understanding of how to optimize streaming platforms for maximum performance and user engagement. Our rigorous team selection process, where only 1 in 50 candidates receives an offer, ensures that every solution we develop is crafted by top-tier specialists who understand the intricate technical challenges of video streaming platforms. What sets us apart is our focused expertise in multimedia development and AI implementation.
Unlike general development teams , we specialize exclusively in video streaming, surveillance, and related technologies. This specialized focus allows us to provide insights and solutions based on real-world implementation experience, not just theoretical knowledge. When we discuss AI-powered streaming solutions, we're drawing from our extensive portfolio of successful projects and practical applications across multiple platforms and devices.
Frequently Asked Questions How Can AI Help Reduce Video Buffering and Latency Issues? Implement AI-powered adjustable bitrate algorithms, predictive caching, and smart content delivery networks to analyze usage patterns, optimize streaming quality, and preload content based on user behavior and network conditions.
What Programming Frameworks Are Best Suited for AI Video Processing? TensorFow , PyTorch , and OpenCV are top frameworks for AI video processing. For production, use MediaPipe for real-time analysis and FFMPEG for encoding. They're well-documented and have strong community support.
How Do You Integrate AI Models With Existing Video Streaming Architectures? Use REST APIs to connect AI models to your streaming pipeline, implement inference servers like TensorRT or ONNX Runtime, and guarantee proper load balancing for real-time processing.
Can AI Automatically Detect and Fix Corrupted Video Segments During Streaming? You can implement AI models to detect frame anomalies and artifacts in real-time. Use machine learning to analyze stream quality, automatically request replacement segments, and trigger fallback streams when corruption's detected.
What Computing Resources Are Needed to Implement AI-Powered Video Compression? You'll need GPUs with at least 8GB VRAM, multi-core CPUs, and 32GB+ RAM to run AI compression models. Consider cloud solutions like AWS or Google Cloud if you can't maintain local infrastructure.
To sum up AI's integration into video streaming platforms isn't just transforming today's viewing experience - it's reshaping the industry's future. You'll need to stay informed about emerging technologies like advanced recommendation systems, AI-powered compression, and automated content creation tools.
By strategically implementing these solutions, you can enhance user experiences, optimize technical performance, and maintain a competitive edge in the evolving streaming landscape.
You can find more about our experience in AI development and integration here
Interested in developing your own AI-powered project? Contact us or book a quick call
We offer a free personal consultation to discuss your project goals and vision, recommend the best technology, and prepare a custom architecture plan.
References: Aldoseri, A. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability , 16(5), 1790 p. https://doi.org/10.3390/su16051790
Jesús-Azabal, M. (2024). ML-Enhanced Live Video Streaming in Offline Mobile Ad Hoc Networks: An Applied Approach. Electronics, 13(8), 1569 p. https://doi.org/10.3390/electronics13081569
Sangeetha, K. (2024). An Effective Investigation for Quality of Service Enhancement of Content Delivery Network for HTTP Live Streaming Using H.265. Scalable Computing Practice and Experience , 25(4), pp. 2703-2710. https://doi.org/10.12694/scpe.v25i4.2830
Widodo, M. (2023). Exploring Consumers’ Impulse Buying Behavior on Social Commerce Platforms: The Role of Fear of Missing Out (A Study on Tiktok Livestream-Selling). Proceedings of the 20th International Symposium on Management (INSYMA 2023) , pp. 377-384. https://doi.org/10.2991/978-94-6463-244-6_56
.avif)
Comments