Platform Providers Research Articles

Fragment of Interest: Personalized Video Fragment Recommendation with Inter-Fragment & Intra-Fragment Contextual Effect

In today’s fast-paced digital landscape, the attention span of users consuming video content is alarmingly brief, often as short as 15 seconds for music or entertainment videos and 6 minutes for lecture videos. This presents a significant challenge for video producers and platform providers as they seek to engage users with longer content. One promising solution involves recommending specific fragments within longer videos that align with individual user profiles. In this paper, we address this challenge by introducing a novel framework for video fragment recommendations, guided by three key insights. First, we implement a Self-Attention Block that captures the inter-fragment contextual effect, enhancing the relevance of recommendations. Second, we incorporate video-level preferences to ensure that the fragment recommendations are consistent with users’ overall interests. Third, we propose a Self-Attentive Herding Effect (SAHE) module to model the intra-fragment contextual effect, specifically the herding effect of time-sync comments within a fragment. To evaluate the effectiveness of our proposed method, we conduct extensive experiments comparing our model against the state-of-the-art approaches in terms of NDCG@K and Recall@K. Our results demonstrate that the model effectively leverages inter-fragment, intra-fragment contextual effects, and video-level preferences, outperforming existing methods. Additionally, we carry out empirical experiments to analyze the key components and parameters of the proposed model, providing further insights into its performance.

Actor opportunism, social capital and coping strategies in the sharing economy: drivers, outcomes and marketing implications

Purpose This paper aims to provide a conceptual understanding of the drivers and outcomes of actor opportunism in the context of the three key actors of the sharing economy – the service provider, the platform and the consumer. Design/methodology/approach The research uses a conceptual approach by drawing on literature from within and outside of marketing. Findings The current research introduces a conceptual framework of opportunism in the sharing economy with seven underlying propositions. The framework posits a U-shaped moderating effect of social capital for the relationship between opportunism and its drivers, actor vulnerability and asset specificity. Furthermore, a 2 × 2 matrix consisting of two types of opportunistic behaviors (active and passive) and two coping strategies by other actors (defensive and nondefensive) suggests that passive opportunism tends to lead to value codestruction independently of the coping strategies employed by other actors. Counterintuitively, the combination of active opportunism and defensive coping strategy presents an opportunity for value cocreation due to its potential to break up older structures and generate new ones. Research limitations/implications While our research provides a higher-level understanding of opportunism pertaining to platform, consumers and service providers in the sharing economy, future research could situate our framework within specific regulatory environments, incorporate the role of competitors and examine individual interaction effects between type of opportunism and coping strategies. Practical implications The framework enables service providers, platforms and consumers to identify drivers of opportunistic behaviors of their partners and discern instances in which opportunistic behaviors lead to value codestruction for all actors. Originality/value This research transcends prior work on the bright and dark sides of the sharing economy by identifying its dynamic nature and examining the contributing role of opportunism.

Impacts of AI on Music Consumption and Fairness

The algorithmic systems utilized by music streaming services have the potential to positively influence individual choices by promoting new artists, but they are also often accused of perpetuating biases. In this research note, we aim to explore the impact of these platforms’ AI-based algorithms on fairness in music consumption. To address this question, we adopt a multidimensional approach that considers the legal, economic, and algorithmic dimensions of fairness. This approach is applied to our EU Horizon Europe Fair MusE project, which advocates for a fairer music ecosystem. However, it should be noted that we propose a tool to score output (playlists) based on fairness models instead of directly altering the algorithms. Data from end users, data brokers, and open-source databases will inform the model, while the processing of the data is aimed at providing users with insights into algorithmic biases and empower them to influence the output. Acknowledging this aspect, this research note serves as a prelude to highlight the need for increased transparency and explainability of algorithms. Furthermore, we seek to inform policy interventions that promote fairness, particularly regarding data sharing between creators and platform providers. Such interventions would foster trust among stakeholders and benefit both users and businesses.

Exploring the impact of paid over-the-top service and mobile network profiles in watching TV content on mobile devices

PurposeThe television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.Design/methodology/approachThis study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.FindingsThe study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.Originality/valueThis research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

A systematic review of blockchain for energy applications

The increasing penetration of distributed energy resources and the growing electrification of end-use consumption complicate energy management. Current strategies, which rely on centralized systems for peer-to-peer interactions, face issues of scalability, security, traceability, single points of failure, and privacy. Blockchain, with its decentralized nature, offers immutability, transparency, automation, and scalability as potential solutions. However, practical implementation remains challenging. This study systematically reviews 156 studies published since 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore these limitations. Results highlight that selecting the appropriate blockchain platform and consensus mechanism is crucial. Observations show that 42% of studies proposing new consensus methods and 46% developing new platforms or using simulations struggle with practical implementation. In contrast, leveraging existing standard blockchain platforms and consensus mechanisms enhances practicality and scalability. Additionally, seamless interoperability with Internet of Things (IoT) remains a significant challenge due to the high costs associated with the few platforms that offer this feature. Standardization of blockchain methodology, interoperability, performance measurement, and governance remains a major issue despite several parallel efforts by multiple stakeholders, including blockchain platform providers. Consolidation of these efforts into a common framework, together with the utilization of existing blockchain components, is key to resolving current limitations and fostering wider adoption. Among the various blockchain components suited for different applications, this study provides key criteria for selection, guiding the development of practical and scalable blockchain-based energy applications.

The impact of budget allocation by research service and platform providers on R&D outcomes: Reassessing the value of investment in research infrastructure in a knowledge-based society

This study aims to empirically analyze the impact of budget allocation by the Korea Institute of Science and Technology Information (KISTI) on national research competitiveness, thereby reassessing the value of investing in research infrastructure within a knowledge-based society. In the 21st century, research and development (R&D) have emerged as a pivotal element of national competitiveness, underlining the increasing importance of investments aimed at constructing and enhancing research infrastructure. However, empirical studies examining the causal relationship between research infrastructure investment and national research competitiveness are still notably scarce. Accordingly, this research endeavors to systematically delineate the effect of research infrastructure investment, with a focus on KISTI’s budget allocation, on enhancing national R&D outcomes. To achieve this, the structural relationship between KISTI’s budget, national R&D budget, and various academic and industrial performance indicators was analyzed using multiple regression and simple regression analysis. In particular, by demonstrating the mechanism through which the budget management of research support organizations like KISTI contributes to strengthening national research competitiveness, this study aims to shed new light on the strategic value of research infrastructure investment in a knowledge-based society. Furthermore, these findings are expected to provide valuable evidence for the formulation of national R&D policies in Korea and the strategic planning of budget operations for research support organizations. Through strategic investment of limited budgets, this could enhance the efficiency of national R&D investments and contribute to strengthening the capacity for scientific and technological innovation required in a knowledge-based society.

Legal Protection of Children's Personal Data in the Digital Era

In the rapidly evolving digital era, the use of digital technology has become an integral part of everyday life, including for children. However, this development also raises concerns about the protection of children's personal data in the digital environment, which is vulnerable to misuse and exploitation. The purpose of this study is to determine and analyze the legal protection of children's personal data in Indonesia in the context of the digital era and explore the negative impact of the abuse of children's privacy rights in the digital environment, using a normative juridical research method with a statutory approach. The results show that in Indonesia there are several laws that protect children's personal data, such as the Child Protection Act, the Electronic Information and Transaction Act, the Personal Data Protection Act, the Minister of Communication and Information Technology Regulation, and the Government Regulation on Compensation for Children Who Are Victims of Criminal Acts. The negative impacts of the misuse of children's privacy include the risk of cybercrime, impaired child development, and emotional trauma. Therefore, efforts to improve the protection of children's personal data in the digital era are still needed by the government, technology platform providers, and parents to create a safe online environment that supports children's growth and development. The findings of this study have significant implications for policymakers, legal practitioners, and stakeholders involved in child protection in the digital space. Strengthening the legal framework and ensuring its effective implementation are important steps to protect children's personal data from misuse.

On-demand mobility-as-a-Service platform assignment games with guaranteed stable outcomes

Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators, forming a mobility ecosystem in which multiple operators compete or cooperate to serve customers under a governing platform provider. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services. The matching problem is formulated as a convex multicommodity flow network design problem under congestion that captures the cost of accessing MOD services. The local stability conditions reflect a generalization of Wardrop's principles that include operators’ decisions. Due to the presence of congestion, the problem may result in non-stable designs, and a subsidy mechanism from the platform is proposed to guarantee local stability. A new exact solution algorithm to the matching problem is proposed based on a branch and bound framework with a Frank-Wolfe algorithm integrated with Lagrangian relaxation and subgradient optimization, which guarantees the optimality of the matching problem but not stability. A heuristic which integrates stability conditions and subsidy design is proposed, which reaches either an optimal MaaS platform equilibrium solution with global stability, or a feasible locally stable solution that may require subsidy. For the heuristic, a worst-case bound and condition for obtaining an exact solution are both identified. Two sets of reproducible numerical experiments are conducted. The first, on a toy network, verifies the model and algorithm, and illustrates the differences local and global stability. The second, on an expanded Sioux Falls network with 82 nodes and 748 links, derives generalizable insights about the model for coopetitive interdependencies between operators sharing the platform, handling congestion effects in MOD services, effects of local stability on investment impacts, and illustrating inequities that may arise under heterogeneous populations.

Opportunities for self-preferencing in international online marketplaces

PurposeLarge digital platforms face intense scrutiny over self-preferencing, which involves a platform provider favoring its own offers over those of competitors. In online marketplaces, also called retail or e-commerce platforms, much of the academic and regulatory debate focuses on determining whether the marketplace provider gives preference to its own private labels, such as “Amazon Basics” or Walmart’s “Great Value” products. However, we outline, both conceptually and empirically, that self-preferencing can also occur through other dimensions of vertical integration – namely, retailing and fulfillment.Design/methodology/approachThis article contributes by conceptualizing three dimensions of vertical integration in online marketplaces – private labels, retailing and fulfillment. Then, two studies empirically assess (1) which of the 20 most-visited global online marketplaces vertically integrates which dimension and (2) which share of 600 m available offers is vertically integrated to which degree in eleven international Amazon marketplaces.FindingsThe majority of the leading marketplaces vertically integrate all three dimensions, implying ample opportunities for self-preferencing. Across international Amazon marketplaces, only 0.02% of available offers consist of an Amazon private-label product. However, Amazon is a retailer for around 31% and fulfills around 38% of all available offers in its marketplaces. Hence, self-preferencing on Amazon can occur most frequently through retailing and fulfillment but comparatively infrequently through private-label offers. Still, these shares differ substantially by country – every second offer is vertically integrated in the USA, but only one in ten in India.Originality/valueMost of the self-preferencing debate often focuses on private-label products. Instead, we present large-scale empirical results showing that self-preferencing on Amazon could occur most often through retailing and fulfillment because these channels affect much larger shares of offers. We also measure the variation of these shares across countries and relate them to regulatory environments.

Digital Platforms as a Fertile Ground for the Economic Sustainability of Startups: Assaying Scenarios, Actions, Plans, and Players

This study examines the transformative role of digital platforms in fostering sustainable entrepreneurship within emerging economies. We argue that platforms transcend mere communication channels, acting as catalysts for innovation and collaboration among startups, thereby driving economic, social, and environmental progress. Our framework emphasizes platform-enabled startups, navigating the unique challenges and opportunities presented by these dynamic markets. We adopt a dual lens, using a mixed-methods approach to analyze digital development trends through the prism of platforms in emerging economies. This reframes the discourse on technology-driven development, acknowledging the unidirectional flow of platform adoption from developed nations. The research emphasizes the need for prioritizing sustainability standards in these regions. Furthermore, we delve into the interplay between platforms and sustainable entrepreneurship with the following three objectives: (1) deciphering the drivers of platform–startup interaction for sustainability goals, (2) formulating policies to maximize platform benefits while mitigating risks, and (3) developing actionable strategies for stakeholders to cultivate a thriving ecosystem of sustainable platform-based ventures. The findings of this study reveal six key uncertainties that will shape the future trajectories of sustainable entrepreneurship within digital ecosystems, particularly in developing nations. These uncertainties encompass the following: environmental and social standards, ongoing education and development, mobile application development and utilization, global market access, and competitiveness and value creation. Several alternative future scenarios have been constructed based on these uncertainties, including advancements in digital technologies, dynamic market conditions and evolving consumer behaviors, a heightened emphasis on sustainability and corporate social responsibility, and a paradigm shift towards collaborative business models. A comprehensive framework of supportive policies and interventions has been proposed to facilitate the realization of these scenarios. Moreover, the analysis underscores the pivotal roles of digital platform providers and startups as key stakeholders in this evolving landscape.

Boundary Work in Generative AI Platforms: How Platform Providers Manage Complementors’ Changing Role

Boundary Work in Generative AI Platforms: How Platform Providers Manage Complementors’ Changing Role

Software-Defined Wide Area Networks (SD-WANs): A Survey

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described.

Competitive dynamics in a multi-sided mobile payment platform market: an agent-based modeling perspective

PurposeThe purpose of this research is to develop a comprehensive model to better understand competitive dynamics between mobile payment providers in a multi-sided market featuring customers and merchants. This is undertaken by modeling customers performing financial transactions with merchants while two mobile payment systems (MPS) providers deploy different strategies to compete for market share.Design/methodology/approachThe authors developed an agent-based simulation model using the NetLogo environment. The simulation featured two competing platform providers, 1,000 customer agents and 50 merchant agents. Past research, interviews and surveys were conducted to accurately model the behavior of the agents. Each simulation run lasted for 50 time periods. A total of 1,024 experimental conditions were designed to model different competitive environments, and 50 replications were conducted for a total of 51,200 experiments.FindingsThe simulation model provides insight into MPS platform providers’ competitive strategies by simultaneously modelling socioeconomic interactions between customers, merchants and MPS.Research limitations/implicationsFrom a methodological perspective, the paper contributes a comprehensive model that can be used to study competitive dynamics between competing platforms in a multi-sided market. From the perspective of competitive strategies, the results show that pricing alone is not sufficient to influence MPS diffusion. Interactions between pricing, customers’ risk perception, perceived security and ease of use of the platform create unexpected same-side and cross-side network effects, which affect MPS diffusion.Practical implicationsWhile pricing remains a crucial lever for MPS to compete for market share, they should focus on enhancing customers’ and merchants’ trust and reduce their risk perception. This can be done through the improvement of the user experience of their platform, development of educational materials and marketing campaigns that address concerns around security, data breaches and perceived risk.Originality/valueThe paper is a direct response to a recent call for action on studying competition between MPS platforms by simultaneously modelling the socio-economic behavior of heterogeneous consumers and merchants. The proposed agent-based simulation model can be used to provide insights into competitive strategies and as a building block for subsequent research in this area.

Competition policy in a general‐equilibrium platform economy

AbstractThis paper examines the wage and welfare effects of the competition policy in a platform economy. An increase in downstream platform providers widens the skilled‐unskilled wage gap in the short run. Moreover, competition brings about a beneficial price pass‐through effect but a detrimental cost‐pushing effect on upstream manufacturers. This leads to business dynamism in manufacturing firms. By numerical simulations, the number of manufacturing firms increases, thus further widening wage inequality in the long run. The optimal number of platform providers is smaller when the business‐stealing effect is larger, whereas it is larger when the price pass‐through effect is greater.

Legal Regulation on the Use of Online Learning Platforms: Challenges and Solutions in Indonesia

Background. With the rapid development of information technology, the use of online learning platforms in Indonesia is increasing. However, the legal regulations governing the use of these platforms are still limited and often inadequate. This poses a variety of challenges, including data security, copyright, and access equality issues. Purpose. This study aims to identify the challenges faced in the regulation of the use of online learning platforms in Indonesia and to formulate solutions that can be implemented to overcome these challenges. Method. This research uses a qualitative approach with literature study methods and in-depth interviews. The literature study was conducted to collect secondary data from various literature related to legal regulations and online learning platforms. In-depth interviews were conducted with experts in the fields of law, information technology, and education to gain deeper insights. Results. The results of the study show that the main challenges in regulating the use of online learning platforms in Indonesia include the lack of data security standards, weak copyright protection, and limited access for students in remote areas. In addition, the lack of coordination between relevant government agencies is also an obstacle to the effective implementation of regulations. Conclusion. This study concludes that to overcome the challenges in regulating the use of online learning platforms in Indonesia, comprehensive regulatory updates and better coordination between government agencies are needed. In addition, there is a need to increase awareness about the importance of data security and copyright among users and providers of online learning platforms. The implementation of these solutions is expected to increase effectiveness and fairness in the use of online learning platforms in Indonesia.

Do Perceived Benefits Compensate for Low Provider Trustworthiness in Disclosure Decisions?

Abstract: People regularly receive algorithm-based recommendations (e.g., in social media and online shopping). Today, such algorithmic recommendations cover a wider range of applications, such as finding recipes that match an individual’s needs and food preferences. To give better-fitting recommendations, algorithms incorporate large amounts of personal information about the individual, which raises the issue of privacy concerns. When are people willing to share such information that can then be considered by algorithms? While previous research has indicated that perceived benefits of platform use and provider trustworthiness influence whether people are willing to disclose personal information, these studies often have two major limitations: they are either survey-based – thus, not allowing causal conclusions – or lack realistic usage (i.e., rely on vignettes). Thus, in the present experimental study ( N = 329), we manipulated provider trustworthiness and asked participants about their willingness to disclose information directly after interacting with an algorithm-based system for recipe recommendations. Results indicate that higher perceived benefits and provider trustworthiness are related to more willingness to disclose information. However, these effects are independent of each other. Thus, the present research suggests that provider trustworthiness causally leads to a higher willingness to share information and high benefits do not compensate for low provider trustworthiness.

Exploring Innovation Success Factors and Strategies for B2B Platforms in the Manufacturing Sector in Germany

Objective: Business-to-business (B2B) platforms in the manufacturing sector have a profound impact on the digital transformation of small and medium-sized enterprises (SMEs). However, there is still a lack of research on this topic. The primary objective of this study is to explore the existing landscape of B2B platforms and thoroughly assess the benefits and challenges for companies using them, in order begin to fill the identified research gap. This paper presents a comprehensive investigation of B2B platforms in the German manufacturing sector and their profound influence on the digital transformation of SMEs. Methods: Within the scope of our in-depth analysis of the current state of B2B platforms, a meticulous review of relevant literature is conducted following the approach of Brocke et al. and a comprehensive analysis of both industry-specific and cross-industry best practices on B2B platforms is performed. In addition, the research includes in-depth interviews with platform providers, partners, and academic experts to enhance the depth of understanding. We identify the factors that contribute to their adoption as well as those that hinder their integration, providing the basis for successful innovation. The viability of the concept is assessed through a comprehensive evaluation methodology involving platform operators. Results: By providing an in-depth assessment of the benefits and challenges faced by companies using B2B platforms, this work contributes to filling existing research gaps. It identifies factors that contribute to their adoption and those that hinder their integration, thus laying the foundation for successful innovation. Finally, it proposes forward-looking guidelines that will enable SMEs to capitalize on the transformative capabilities of these platforms in the future. Conclusion: The findings of this study show that SMEs can reap numerous benefits from B2B platforms, such as increades efficiency, optimal use of resources, and access to previously untapped markets. However, certain barriers, including the high costs associated with implementation and a prevailing lack of trust among platform users, serve as obstacles to their widespread adoption. The results of this research provide important insights into the current state of B2B platforms and their potential to drive digital transformation. This research advances academic understanding in this area, and provides valuable knowledge for companies seeking to navigate the landscape of B2B platforms and their role in driving digital transformation as a foundation for innovation.

A meaning-making perspective on digital ridesharing platforms in underdeveloped markets

PurposeThe digital platform-based sharing economy has become ubiquitous all over the world. In this paper, we explore how market actors’ conflicting interpretations of digital platforms’ business models give form and shape value co-creation and capture practices in contexts marked by weak institutions and underdeveloped markets.Design/methodology/approachIntegrating insights from the broader literature on digital platforms and the contemporary turn to “meaning-making” in social theory, we adopt a problematization method to unpack the collective contest over the interpretation of value co-creation and capture from ridesharing platforms in contexts marked by weak institutions and underdeveloped markets.FindingsCollective contest over the interpretation of digital business models may give rise to competing meanings that may enable (or impede) digital platform providers’ ability to co-create and capture value. We present an integrative framework that delineates how firms caught up in such collective contests in contexts marked by weak institutions and underdeveloped markets may utilise such conditions as marketing resources to reset their organising logic in ways that reconcile the conflicting perspectives.Practical implicationsThe paper presents propositions constituting a contribution to a meaning-making perspective on ridesharing digital platforms by offering insights into how digital business models could potentially be localised and adapted to address and align with the peculiarities of contexts. It goes further to present a theoretical model to extend our understanding of the different sources of contestation of meaning of digital platforms.Originality/valueThe meaning-making perspective on digital platforms extends our understanding of how the collective contest over interpretations of value co-creation and capture may offer a set of contradictory frames that yield possibilities for ridesharing platform providers, and their users, to assimilate the organising logic of digital business models into new categories of understanding.

Coping Responses to the Stress of Using News Platforms’ Recommendation Algorithms

Drawing on the stressor–strain–outcome (SSO) framework, this study examined how information-related stressors (perceived information overload, information quality deterioration, and information narrowing) and privacy-related stressor (perceived privacy risk) in using news platforms’ recommendation algorithms trigger users’ psychological reactance, which leads to their active coping, expressive coping, or avoidance coping with their stress in using such recommendation algorithms. Using a sample of 352 valid research responses, this study found that users’ psychological reactance is positively influenced by perceived information quality deterioration, followed by perceived information overload and perceived privacy risk. Meanwhile, perceived information narrowing has no significant impact on psychological reactance. In turn, psychological reactance exerts a stronger impact on expressive coping and avoidance coping responses than on active coping response. This study enriches the literature on the stress in algorithm usage at the individual level and provides practical insights for the providers of news platforms which have applied recommendation algorithms.

Artificial intelligence in talent acquisition: exploring organisational and operational dimensions

PurposeWith the recent proliferation of AI, organisations are transforming not only their organisational design but also the input and output operational processes of the hiring process. The purpose of this paper is to explore the organisational and operational dimensions resulting from the deployment of AI during talent acquisition process.Design/methodology/approachThe authors conducted semi-structured interviews and meetings with human resources (HRs) professionals, recruiters and AI hiring platform providers in Sweden. Using an inductive data analysis rooted in the principles of grounded theory, the study uncovered four aggregate dimensions critical to understanding the role of AI in talent acquisition.FindingsWith insights from algorithmic management and ambidexterity theory, the study presents a comprehensive theoretical framework that highlights four aggregate dimensions describing AI’s transformative role in talent recruitment. The results provide a cautionary perspective, advising against an excessive emphasis on operational performance driven solely by algorithmic management.Research limitations/implicationsThe study is limited in scope and subject to several constraints. Firstly, the sample size and diversity are restricted, as the findings are based on a limited number of semi-structured interviews and meetings with HRs professionals, recruiters, and AI hiring platform providers. Secondly, the rapid evolution of AI technologies means that the study’s findings may quickly become outdated as new advancements and applications emerge.Practical implicationsThe results provide managers with actionable information that can lead to more precise and strategic management practices, ultimately contributing to improved organizational performance and outcomes. Plus, enhancing their ability to make informed decisions, optimize processes and address challenges effectively.Social implicationsThe results signal both positive and negative impacts on employment opportunities. On the positive side, AI can streamline recruitment processes, making it easier for qualified candidates to be identified and hired quickly. However, AI systems can also perpetuate existing biases present in the data they are trained on, leading to unfair hiring practices where certain groups are systematically disadvantaged.Originality/valueBy examining the balance between transactional efficiency and relational engagement, the research addresses a crucial trade-off that organizations face when implementing AI in recruitment. The originality lies in its critique of the prevailing emphasis on e-recruiting.