Toward Business Models for a Meta-Platform: Exploring Value Creation in the Case of Data Marketplaces

2023 | HICSS | Citations: 0

Authors: Abbas, Antragama Ewa; Ofe, Hosea; Zuiderwijk, Anneke; De Reuver, Mark

Abstract: Investigating meta-platforms has been a continuing concern within information sy ... Expand

Abstract: Investigating meta-platforms has been a continuing concern within information system literature due to the increasingly complex constellations of platforms in ecologies of ecosystems. A meta-platform is a platform built on top of two or more platforms, hence connecting their respective ecosystems. One promising case to benefit from meta-platforms is data marketplaces: a particular type of platform that facilitates responsible (personal and non-personal) data sharing among companies. Given that business models for meta-platforms are largely unexplored in this emerging case, how they can create value for data marketplaces remain speculative. As a starting point toward business model investigations, this paper explores value creation of a meta-platform in the case of data marketplaces. We interviewed fourteen data-sharing consultants and six meta-platform experts. We identify three potential value creation archetypes of a meta-platform. The discovery aggregator archetype emphasizes searching and dispatching value, while the brokerage one focuses on promoting and supporting value. Finally, the one-stop-shop archetype creates value by standardizing, regulating, sharing, and experimenting. This study is among the first that explore value creation archetypes for a meta-platform, thus identifying core value as a base for further business model investigations. Collapse

Topics: value creation business model digital platform information technology infrastructure decision making

Methods: qualitative interview theory development personal interview longitudinal research

A Taxonomy of Algorithmic Control Systems

2023 | International Conference on Information Systems | Citations: 0

Authors: Alizadeh, Armin; Hirsch, Felix; Jiang, Jennifer; Wiener, Martin; Benlian, Alexander

Abstract: Algorithmic control (AC) uses digital technologies and advanced algorithms to c ... Expand

Abstract: Algorithmic control (AC) uses digital technologies and advanced algorithms to control workers and is rapidly becoming a central component of modern work environments.While previous research has explored the implications of AC by examining its specific forms, mechanisms, or functions, this research argues for a broader understanding of AC as socio-technical systems, explicitly considering the technological and organizational characteristics of AC. The overarching goal of this study is to identify and conceptualize the core dimensions and respective characteristics of AC systems. To achieve this goal, we develop a taxonomy based on a review of prior literature and an analysis of 21 empirical examples. Furthermore, we demonstrate the application and usefulness of the derived taxonomy by applying it to three real-world AC systems. By adopting a holistic system perspective and developing a validated taxonomy, our study contributes to the theoretical understanding of AC systems and sets the stage for deeper exploration in future research. Collapse

Topics: organizational control organizational context computer hardware information technology capability Uber

Methods: literature study case study theory development computational algorithm

Theories: socio technical theory

Strategic Investments for Platform Launch and Ecosystem Growth: A Dynamic Analysis

2023 | Journal of Management Information Systems | Citations: 0

Authors: Anderson Jr., Edward G.; Parker, Geoffrey G.; Tan, Burcu

Abstract: Multi-sided platforms must make decisions on both pricing and engineering invest ... Expand

Abstract: Multi-sided platforms must make decisions on both pricing and engineering investment and must continually adjust them as the platform scales over its lifecycle. Engineering investments can be allocated to features that improve a platform's standalone value, social features to take advantage of same-side network effects, or integration tools and boundary resources to facilitate third-party content creation. Guidance in the academic or practitioner literature is not granular. Moreover, relevant normative economic models that consider externalities are rarely dynamic. Hence, there is a gap in knowledge about how to best balance tradeoffs between different strategic decisions throughout the entire platform lifecycle. To begin to address this gap, we explore normative strategies for coordinating pricing and engineering investment decisions on a continuous basis under different ecosystem conditions. We build a simulation model informed by economics and marketing theory and perform extensive sensitivity analyses on key parameters in different ecosystem scenarios over a multi-period lifecycle. We find that pricing and investment strategies must continuously change to perform optimally. In particular, strategies that are most effective at launch often differ from those that are most effective during scaling as well as those most effective at maturity. We also find that the optimal strategy depends strongly on the monetization model and market aversion to price changes. Lastly, we specifically examine four different industry segments: mobile platforms, social media, the sharing economy, and business-to-business. The results provide evidence that the trajectory of platform pricing and investment strategies should greatly differ depending on industrial context. Collapse

Topics: price management digital platform social media decision making business-to-business

Methods: simulation sensitivity analysis system dynamics optimization model conjoint analysis

Theories: control theory marketing theory

How to react to a shock? Effects of Airbnb hosts’ choices and market segmentation at the time of Covid-19

2023 | Information & Management | Citations: 0

Authors: Buzzacchi, Luigi; Milone, Francesco Luigi; Paolucci, Emilio; Raguseo, Elisabetta

Abstract: We investigate the way service providers who operate on an online peer-to-peer ( ... Expand

Abstract: We investigate the way service providers who operate on an online peer-to-peer (P2P) platform readapted their marketing choices to face the Covid-19 pandemic. Through an empirical investigation on a large dataset of Airbnb properties in Rome, observed from January 2018 to December 2020, we provide a threefold contribution by investigating how Airbnb hosts reacted to the Covid-19 pandemic shock, in terms of marketing choices, such as price adjustments and flexible cancellation policies; the direct effects of these choices on their economic returns; and how service providers on Airbnb reacted to address the new needs of their customers during the Covid-19 pandemic. The findings provide useful insights for researchers and practitioners and show that the adoption of combined marketing choices led to more than proportional effects on performances as it allowed Airbnb hosts to exploit profitable market segmentation mechanisms. Collapse

Topics: pandemic Airbnb marketing management price management peer-to-peer model

Methods: econometric modeling descriptive statistic instrumental variables estimation robustness check longitudinal research

Introduction to the Minitrack on Crowd-based Platforms

2023 | HICSS | Citations: 0

Authors: Chen, Pei-Yu; Huang, Nina; Gu, Bin

Abstract: ... Expand

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Topics: crowdsourcing crowdfunding platform worker social media online labor market

Methods: difference in differences natural language processing longitudinal research natural experiment machine learning

Theories: signaling theory theory of affordance lemon market theory

Economic Impacts of Platform-Endorsed Quality Certification: Evidence from Airbnb

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Dewan, Sanjeev; Kim, Jooho; Nian, Tingting

Abstract: We contribute to the emerging literature on quality certification by digital pl ... Expand

Abstract: We contribute to the emerging literature on quality certification by digital platforms by studying the launch of the Airbnb Plus service, wherein the platform inspects properties and provides a badge that presumably signals the quality of the property and the reliability of the host. Our identification strategy relies on the fact that the Airbnb Plus service was launched in different cities at different times, and listings within the cities received the certification at different times. Using a staggered difference-in-differences estimation strategy in conjunction with suitable matching methods, we found that the Airbnb Plus certification increased the weekly booking rate of Plus listings by about 6.8% on average (direct effect). We also found some evidence that non-Plus listings saw a temporary decline in booking rate when one or more nearby properties received a Plus certification (externality effect). The net impact of the Airbnb Plus service on the platform itself was an annual increase in revenue of about $37,500 for the average 2-kilometer zone in a U.S. city that included one or more Plus listings, as compared to matched zones without any Plus listings (local platform effect). We performed additional analyses, including a randomized experiment, to demonstrate the robustness of our findings. Overall, our results suggest that platform-endorsed quality certification has significant economic impacts-not just on the listings that receive the certification but on other listings on the platform as well as on the platform itself. Collapse

Topics: Airbnb website sharing platform peer-to-peer model Google+

Methods: experimental group experiment propensity score method product review robustness check

Disruption and Legitimacy: Big Data in Society

2023 | Information Systems Frontiers | Citations: 1

Authors: Ferreira, Carlos; Merendino, Alessandro; Meadows, Maureen

Abstract: The growing availability of data and the emergence of business analytics ecosyst ... Expand

Abstract: The growing availability of data and the emergence of business analytics ecosystems offer possibilities for companies developing innovative business models. However, the disruptive impact of these business models on society is not always judged favourably. This paper explores the growing tensions in the relationship between disruptive Big Data companies and society through the lens of legitimacy – a judgement about the fit and propriety of an entity, such as a company, to society. The study is based on four instrumental cases where Big Data organisations were faced with challenges to their legitimacy. The findings elaborate how digital transformations require companies to understand and manage how much to disrupt and how much to conform to social norms and values. Big Data businesses face a dynamic and paradoxical tension between the potential costs and benefits of their disruptive business models. The topic of legitimacy management is also addressed, drawing out implications for practice. Collapse

Topics: big data business model Airbnb Facebook disruptive innovation

Methods: case study literature sample qualitative content analysis archival research triangulation

NOTION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE - AN EMPIRICAL INVESTIGATION FROM A USER'S PERSPECTIVE

2023 | European Conference On Information Systems | Citations: 0

Authors: Haque, Bahalul; Islam, Najmul; Mikalef, Patrick

Abstract: ... Expand

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Topics: recommender system explainable artificial intelligence user requirement artificial intelligence website

Methods: qualitative interview qualitative content analysis focus group group interview computational algorithm

Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Hou, Jian-Ren; Zhang, Jie; Zhang, Kunpeng

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such a ... Expand

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such as crowdfunding platforms. However, overwhelmingly, crowdfunding campaigns are not meeting their goals. Therefore, it is imperative to examine how the success of charity fundraising campaigns can be improved. In this paper, we focus on the design of project images on a crowdfunding website, which portray the themes and content of the projects. Employing the stimulus-organism-response (S-O-R) model, we investigate the relationships between image attributes (S) and image emotions (O), and between image emotions (O) and campaign outcomes (R). We developed and trained a deep neural network model to identify the emotions conveyed in the images, and then implemented it to analyze project images from a popular crowdfunding platform. We applied the obtained image emotions together with the objective image attributes and the project outcome metrics to explore from a design perspective, what image attributes evoke image emotions, and how image emotions are related to the success of charity fundraising projects. Our results confirm these relationships and further suggest that the roles of image emotions on the success of crowdfunding campaigns vary with project characteristics such as the project budget and category. In addition, the image emotions of competing projects on the crowdfunding platform were found to reduce the project's performance. In an extended study, we conducted an online randomized controlled experiment by manipulating image attributes to reexamine the causal relationships and verify the mediating roles of positive and negative empathies between image emotions and campaign outcomes. This research contributes to the charity fundraising literature from a novel perspective of emotions in project images. It presents new and unique findings regarding the mediation roles of positive and negative empathies and the limitations of the emotion of sadness in certain types of charity fundraising. In addition, our findings provide useful insights for practitioners seeking to design successful online charity campaigns. Collapse

Topics: crowdsourcing crowdfunding project productivity Google+ website

Methods: experiment deep learning computational algorithm machine learning descriptive statistic

Privacy Policy and Hosts’ Concerns on Accommodation Sharing Platforms

2023 | HICSS | Citations: 0

Authors: Hu, Jiang; Zhang, Jie; Liang, Huigang

Abstract: Accommodation-sharing services are gaining great popularity via online community ... Expand

Abstract: Accommodation-sharing services are gaining great popularity via online community platforms in recent years. Meanwhile, users’ privacy concerns over social interactions and online transactions on these platforms are escalating. This study investigates whether and how privacy policy can properly mitigate hosts’ privacy concerns, enhance perceived benefits, and subsequently encourage their information disclosure on the accommodation sharing platforms (ASPs). Through a scenario-based survey and a controlled experiment, we find that the hosts are more concerned about the other users’ misappropriating the private information that the hosts disclose on the platform than the platforms’ privacy invasion behaviors. However, this major concern is not significantly mitigated by the current privacy policy. Moreover, privacy policy engenders two types of perceived benefits, among which the perceived social benefit has a stronger effect than economic benefit on the hosts’ intentions to disclose information on ASPs. Collapse

Topics: information privacy concern privacy data breach decision making electronic commerce

Methods: experiment survey parametric test factorial experiment two-way ANOVA

Theories: privacy calculus theory