Examining User Loyalty in Algorithmic News Recommendations

In an article published in the journal Humanities & Social Sciences Communications, researchers from the Republic of Korea discussed factors affecting user satisfaction and loyalty toward algorithmic news recommendation services (ANRS). They proposed a research model based on loyalty theory, service quality, and personal factors to recommend personalized news based on reading preferences and interests.

Study: Examining factors influencing the user’s loyalty on algorithmic news recommendation service. Image Credit: Gorodenkoff / ShutterstockStudy: Examining factors influencing the user’s loyalty on algorithmic news recommendation service. Image Credit: Gorodenkoff / Shutterstock

Background

Algorithmic recommendation services (ARS) are widely used in various online platforms, such as e-commerce, social media, and entertainment, to provide personalized products or content to users. These services use algorithms to analyze user’s preferences, interests, and behavior, and recommend items. News media have also adopted ARS on their online platforms, such as websites and mobile apps, to provide personalized news to users. These services are called ANRS, and they aim to help users read news relevant to their interests or preferences efficiently and conveniently.

However, ANRS has also some risks and challenges for users, service providers, and society. Users may express privacy concerns regarding the collection and use of personal data, or they may develop biased perspectives due to selective information provided by ANRS. Service providers may face technical difficulties in providing timely and accurate news recommendations, or they may have ethical and social responsibilities for the effects of ANRS on users and society. Therefore, understanding how users evaluate ANRS and the factors influencing their satisfaction and loyalty to the service is crucial.

About the Research

In the present paper, the authors explained the various factors affecting user’s loyalty to the ANRS. They adopted ANRS by NAVER, a dominant online portal in Korea, as their main subject and used loyalty theory as a theoretical lens to examine it. The study proposes a research model that consists of three main constructs: satisfaction, trust, and loyalty, and seven sub-constructs: perceived accuracy, perceived diversity, perceived news value, perceived usability, perceived transparency, pre-existing attitude toward the service provider, and privacy concerns.

The paper hypothesizes that satisfaction and trust positively affect loyalty, and that trust positively affects satisfaction. It also assumes that the sub-constructs positively or negatively affect satisfaction and trust, depending on their nature.

The authors conducted an online survey for users of “My News”, the free mobile ANRS of NAVER, and analyzed the data from 483 responses. They used partial least squares structural equation modeling (PLS-SEM) to test the research model and the hypotheses. Additionally, the study explored the indirect effects of satisfaction and trust on loyalty.

Research Findings

The outcomes showed that the developed model explains 70.3% of the variance in user loyalty, 72.3% of the variance in satisfaction, and 58.9% of the variance in trust in ANRS. Both satisfaction and trust positively affect loyalty to ANRS, and trust positively affects satisfaction. Moreover, perceived accuracy positively affects satisfaction, and perceived news value positively affects trust among technical quality factors.

Among the sub-constructs, perceived accuracy, perceived usability, and pre-existing attitude toward the service provider positively affect satisfaction, and perceived news value, perceived usability, perceived transparency, pre-existing attitude toward the service provider, and privacy concerns positively or negatively affect trust. Additionally, functional quality-related factors, such as usability and transparency, have more influence on satisfaction and trust than technical quality-related factors, such as accuracy, diversity, and news value.

This research suggests the following potential actions for the development and management of ANRS:

  • Identify the key factors influencing users' loyalty to their services and prioritize them accordingly.
  • Provide insights for service developers to design and improve the user interface and user experience of their services, especially in terms of usability and transparency.
  • Inform service providers about the user’s personal characteristics that affect their perceptions of the services, such as pre-existing attitudes and privacy concerns, and suggest ways to address them.

Conclusion

In summary, the study suggested that user’s loyalty to ANRS was influenced by their satisfaction and trust, which were impacted by various service quality and personal factors. The paper also provided some practical implications for service providers and developers to manage and improve ANRS effectively based on user responses and perceptions.

The authors acknowledged some limitations and suggested directions for future research. They focused on one specific ANRS by NAVER, which may limit the generalizability of the findings. Future studies could compare different ANRS provided by various service providers or platforms, including online portals, social media, or news aggregators.

The research employs a cross-sectional survey method, which may not capture the dynamic and longitudinal aspects of user’s perceptions and behaviors. Future research could utilize a longitudinal or experimental approach to examine the changes and effects of user’s perceptions and behaviors over time.

Furthermore, the study does not consider contextual or situational factors affecting user perceptions and behaviors, such as news topics, sources, formats, or consumption situations. New research could explore how these factors influence users’ satisfaction, trust, and loyalty to ANRS.

Journal reference:

Article Revisions

  • Jan 11 2024 - Main article image replaced
Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

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