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Uncovering the role of weak ties in implicit networks of influence: a network analysis on recommendation algorithms' neighborhood.

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Titel: Uncovering the role of weak ties in implicit networks of influence: a network analysis on recommendation algorithms' neighborhood.
Autoren: Baccelloni, Angelo1 (AUTHOR) angelo.baccelloni@johncabot.edu, Mazzú, Marco Francesco2 (AUTHOR) mmazzu@luiss.it, Ricotta, Francesco3 (AUTHOR) francesco.ricotta@uniroma1.it, Mattiacci, Alberto4 (AUTHOR) alberto.mattiacci@uniroma1.it
Quelle: European Journal of Marketing. 2025, Vol. 59 Issue 12, p2725-2762. 38p.
Schlagwörter: *RECOMMENDER systems, *INTERPERSONAL relations, *ELECTRONIC commerce, *WORD of mouth advertising, SOCIAL influence, INFORMATION dissemination, IMPLICIT learning
Abstract: Purpose: In the contemporary postmodern context, consumers are often portrayed as liberated from social ties, fostering an environment conducive to individualism. Algorithmic artifacts, such as recommendation algorithms (RAs), are contributing to this paradigm by functioning as anti-link tools: they establish implicit social links among individuals with similar preferences, giving rise to clusters termed neighborhoods. These neighborhoods facilitate the provision of personalized suggestions based on shared interests, paradoxically fostering social connections amid the backdrop of individualism. RAs actively generate implicit networks of influence characterized by users sharing analogous preferences, thereby enhancing the predictability of user behaviors. Despite extensive research on explicit networks of influence and the impact of RAs on decision-making, there remains a scarcity of evidence on how users influence others within implicitly generated networks and the roles they play in shaping information flow across such networks. The purpose of this paper is to address this gap by examining how user interactions contribute to influence dynamics and information dissemination within implicit networks. Design/methodology/approach: This study, drawing on the strength of weak ties theory, analyzes with a social network analysis a real-world network of 37,427 users and 1,300 products facilitated by RAs on an e-commerce platform. Findings: The results contribute to literature on word-of-mouth (WOM) by clarifying the inherent characteristics and interconnections within implicit influence networks driven by recommendation agents (RAs). The findings identify the key users responsible for accelerating recommendations diffusion within these networks and reveal significant implications for scholars and marketers seeking to comprehend the effects of product recommendations in e-commerce contexts and refine their targeting strategies. Research limitations/implications: The results contribute to the existing literature by highlighting the inherent characteristics and connections of implicit networks of influence facilitated by recommendation agents (RAs), identify the key users who facilitate the flow of the information inside the networks. Practical implications: The paper shed light on substantial implications for WOM scholars and marketers aiming to understand the effects of product recommendations in an e-commerce setting and targeting processes. Originality/value: To the best of authors' knowledge, this study represents the first investigation into the implicit networks of influence facilitated by Ras. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
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Abstract:Purpose: In the contemporary postmodern context, consumers are often portrayed as liberated from social ties, fostering an environment conducive to individualism. Algorithmic artifacts, such as recommendation algorithms (RAs), are contributing to this paradigm by functioning as anti-link tools: they establish implicit social links among individuals with similar preferences, giving rise to clusters termed neighborhoods. These neighborhoods facilitate the provision of personalized suggestions based on shared interests, paradoxically fostering social connections amid the backdrop of individualism. RAs actively generate implicit networks of influence characterized by users sharing analogous preferences, thereby enhancing the predictability of user behaviors. Despite extensive research on explicit networks of influence and the impact of RAs on decision-making, there remains a scarcity of evidence on how users influence others within implicitly generated networks and the roles they play in shaping information flow across such networks. The purpose of this paper is to address this gap by examining how user interactions contribute to influence dynamics and information dissemination within implicit networks. Design/methodology/approach: This study, drawing on the strength of weak ties theory, analyzes with a social network analysis a real-world network of 37,427 users and 1,300 products facilitated by RAs on an e-commerce platform. Findings: The results contribute to literature on word-of-mouth (WOM) by clarifying the inherent characteristics and interconnections within implicit influence networks driven by recommendation agents (RAs). The findings identify the key users responsible for accelerating recommendations diffusion within these networks and reveal significant implications for scholars and marketers seeking to comprehend the effects of product recommendations in e-commerce contexts and refine their targeting strategies. Research limitations/implications: The results contribute to the existing literature by highlighting the inherent characteristics and connections of implicit networks of influence facilitated by recommendation agents (RAs), identify the key users who facilitate the flow of the information inside the networks. Practical implications: The paper shed light on substantial implications for WOM scholars and marketers aiming to understand the effects of product recommendations in an e-commerce setting and targeting processes. Originality/value: To the best of authors' knowledge, this study represents the first investigation into the implicit networks of influence facilitated by Ras. [ABSTRACT FROM AUTHOR]
ISSN:03090566
DOI:10.1108/EJM-02-2024-0136