Given its sociotechnical focus, information systems research has the potential to generate diverse forms of impact and advance society's grand challenges. But producing research impact beyond academia remains a difficult endeavor. In this editorial, we discuss the multifaceted nature of IS research impact and offer a framework and guidelines for researchers to plan, deliver, measure, and communicate research impact across academia, business, and society, while considering the research context.

Within the Hawaii International Conference on System Sciences (HICSS), we organize for the ninth time a mini-track on trust and information technologies. The minitrack welcomes papers that theoretically and or empirically advance our understanding of trust and digital technologies in organizations, inter-organizational relationships, and ecosystems.

Algorithmic governance is not enough on its own to manage and protect the interests of multiple stakeholders. Therefore, platforms are increasingly seeking to involve stakeholders in their algorithmic governance, synthesizing the domains of algorithmic governance and stakeholder governance. We denote this synthesis as algorithmic stakeholder governance. In this paper, we examine algorithmic stakeholder governance in the context of content platforms focusing on the algorithmically mediated interactions among three key stakeholders-creators, consumers, and advertisers. We explore this topic by conducting an in-depth study of the YouTube platform. Using grounded theory techniques, we generate a model that explains the process by which platform stakeholder interactions are algorithmically governed to address key stakeholder conflicts related to free speech, information diversity, and content safety. Our model suggests that algorithmic stakeholder governance provides a forum for resolving these stakeholder conflicts and theorizes the interplay between the platform's approach to algorithmic stakeholder governance and stakeholder participation. Our research contributes primarily to the literature on platform governance and algorithmic governance on platforms more specifically, by shifting the focus from centralized control and viewing users as objects of governance towards a more decentralized and balanced perspective where platform users are viewed as active stakeholders.

Algorithms are increasingly seen as capable of autonomously initiating and managing interactions with humans—for example, through delegating the rights and responsibilities for successful outcomes of shared tasks without human intervention. While research into such interactions primarily focuses on dyadic configurations, complex settings where multiple agents work together have become a nexus of more nuanced interactions that go beyond the dyad. This paper explores such interactions through the lens of delegation by investigating how many algorithms delegate to many humans in a multi-agent setting. Analyzing patent data and interviews with drivers and passengers, we unpack delegation in the context of the ride-hailing application Uber. We theorize distributed delegation as a construct capturing collective hybrid appraisal, collective hybrid distribution, and collective hybrid coordination, in which a collective of algorithms delegates by drawing on inputs from multiple human agents. Our findings highlight that distributed delegation is collective, hybrid, and relational by nature, and demonstrate the extent to which human inputs are necessary for collectives of algorithms to exercise the capacity to delegate. Distributed delegation as a continuum of algorithmic and human involvement poses a challenge for recent theories suggesting the unprecedented autonomy of algorithms from humans.

The success of peer-to-peer (P2P) lending platforms has proven inconsistent and uneven over time and geography. This paper aims to strengthen our understanding of the market evolution through an analysis of risks on P2P lending platforms, which can be significantly affected by the way the platform design, regulatory structural building, nature of the transaction, and interdependencies between organizational components. We extend the social-technical model and create a systematic framework to map and analyze the financial risks from a hybrid financial and organizational approach. By implementing textual and statistical analysis on a dataset from Renrendai platform, we found that risks are generated not only from the stakeholders but also due to the weaknesses of interdependencies between organizational components and platform design. We also utilize our models to investigate why some P2P platforms such as LendingClub and Upstart (US), Renrendai (China), and Zopa (UK) have succeeded or failed from both finance and IS perspectives, and further propose potential risk-mitigation strategies for P2P lending platforms.

While the amount of information available has exponentially increased, our cognitive abilities to process information have not improved. Hence, social media platforms employ algorithmic filtering to keep information load at manageable levels. Algorithmic filtering leads to algorithmic distortion, creating phenomena such as filter bubbles and echo chambers resulting in information blindness. In this paper, we investigate whether nudging presents a valuable approach to tackle this problem. Through a natural experiment and a quantitative study on clickstream data, we investigate the impact of nudging on information diversity. We also answer a secondary question on how to design nudges to enhance their effectiveness.

Digital platforms facilitate the coordination, match making, and value creation for large groups of individuals. In consumer-to-consumer (C2C) online sharing platforms specifically, trust between these individuals is a central concept in determining which individuals will eventually engage in a transaction. The majority of today’s online platforms draw on various types of cues for group coordination and trust building among users. Current research widely accepts the capacity of such cues but largely ignores their changing effectiveness over the course of a user’s lifetime on the platform. To address this gap, we conduct a laboratory experiment, studying the interplay of cognitive and affective trust cues over the course a multi-period trust experiment for the coordination of groups. We find that the trust-building capacity of affective trust cues is time-dependent and follows an inverted u-shape form, suggesting a dynamic complementarity of cognitive and affective trust cues.

Algorithmic management can create work environment tensions that are detrimental to workplace well-being and productivity. One specific type of tension originates from the fact that algorithms often exhibit limited transparency and are perceived as highly opaque, which impedes workers’ understanding of their inner workings. While algorithmic transparency can facilitate sensemaking, the algorithm’s opaqueness may aggravate sensemaking. By conducting an empirical case study in the context of the Uber platform, we explore how platform workers make sense of the algorithms managing them. Drawing on Weick’s enactment theory, we theorize a new form of sensemaking— algorithm sensemaking—and unpack its three sub-elements: (1) focused enactment, (2) selection modes, and (3) retention sources. The sophisticated, multistep process of algorithm sensemaking allows platform workers to keep up with algorithmic instructions systematically. We add to previous literature by theorizing algorithm sensemaking as a mediator linking workers’ perceptions about tensions in their work environment and their behavioral responses.

Within the Hawaii International Conference onSystem Sciences (HICSS), we organize for the seventh time a mini-track on trust and information technologies.This year, the mini-track on Advances in Trust Research focuses on the context where new digital technologies are developed, brought to markets, implemented, and adopted.The context focus also strives to increase our understanding about trust at different levels of analysis, i.e., individuals, teams, organizations, metaorganizations, and society.

Within the Hawaii International Conference on System Sciences (HICSS ), we organize for the sixth time a mini-track on trust and information technologies. This year, the mini-track highlights distrust along with trust. Through the presentation of six papers and an open discussion, the mini-track participants will have another round of lively debate that involves a variety of modern day information technologies in contexts such as autonomous vehicles and blockchain environments, addressing topics such as (dis)trust and technology adaption, technostress, datafication, and trust transfer.

The motivation for this methodological paper rests on the importance for researchers to be able to foresee societal impacts of the use of emerging technologies, because they may potentially have important ethical implications (cf. biases problems associated with AI/ML). However, we also recognize that it is extremely difficult to a priori identify unintended uses of future technologies, especially before they are unleashed to society. Laws and regulations addressing problematic uses of technologies come generally after these technologies have already made enough damages to people. We therefore propose an innovative methodology to assess the potential future risks of emerging technologies, which concerns the analysis of social science fiction (SSF) such as movies and tv shows. We showcase how this approach can inform researcher on potential future scenarios concerning emerging technologies and society. We contribute to extant IS scholarship by developing a detailed roadmap of how to use SSF in meaningful ways. We substantiate our claims with an example of analysis in the context of the tv show “Black Mirror”.

Within the Conference on System Sciences (HICSS), we organize for the fifth time a mini track on 'Advances in Trust Research', this year addressing 'ArtificialIntelligence in Organizations'.Through the presentation of four papers and an open discussion, the mini-track participants will debate trust and AI in contexts such as AI-mediated work relations, anthropomorphism of AI chatbot, and AI enabled CRM in e-commerce.

Online labor platforms (OLPs) can use algorithms along two dimensions: matching and control. While previous research has paid considerable attention to how OLPs optimize matching and accommodate market needs, OLPs can also employ algorithms to monitor and tightly control platform work. In this paper, we examine the nature of platform work on OLPs, and the role of algorithmic management in organizing how such work is conducted. Using a qualitative study of Uber drivers’ perceptions, supplemented by interviews with Uber executives and engineers, we present a grounded theory that captures the algorithmic management of work on OLPs. In the context of both algorithmic matching and algorithmic control, platform workers experience tensions relating to work execution, compensation, and belonging. We show that these tensions trigger market-like and organization-like response behaviors by platform workers. Our research contributes to the emerging literature on OLPs.

Organizations face a series of tensions related to the implementation process of business analytics. It is therefore essential for organizations to be prepared for the rise of these tensions in order to manage them efficiently and to implement an effective plan of action. We divide the business analytics implementation process in three stages to render the entire process accessible to various stakeholders: (1) data collection and input data, (2) data analysis and computation, and (3) organizational output and data-driven learning. We map the chosen literature to each of the stages, identifying benefits and challenges, and the tensions derived from their coexistence. Instead of discussing the tensions in isolation per current literature, we examine how they interrelate and influence one another, advancing the existing knowledge on business analytics, thus providing a solid base for future empirical research.