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Clough and Wu (2020) provide an interesting and thought-provoking response to our article (Gregory, Henfridsson, Kaganer, & Kyriakou, 2020) on the role of Artificial Intelligence (AI) and data network effects for the creation of user value. We welcome the debate around data network effects as a new category of network effects. In this response note, we build upon the points raised by Clough and Wu (2020) to outline three clarifications to our theory of data network effects concerning: (1) conditions when data network effects accrue, (2) the importance of theorizing shared data, and (3) the model’s ability to explain the cumulative effect of data-driven learning on value creation and value capture.\ud \ud

ObjectiveThe objective of this research was to explore the lived experiences of long-term care facilities’ staff during the COVID-19 pandemic and examine if and how the pandemic played a role in their decision to leave their jobs.DesignQualitative study using thematic analysis of semistructured interviews. Interview transcripts were analysed using coding techniques based in grounded theory.ParticipantsA total of 29 staff with various roles across 21 long-term care facilities in 12 states were interviewed.ResultsThe pandemic influenced the staff’s decision to leave their jobs in five different ways, namely: (1) It significantly increased the workload; (2) Created more physical and emotional hazards for staff; (3) Constrained the facilities and their staff financially; (4) Deteriorated morale and job satisfaction among the staff and (5) Increased concerns with upper management’s commitment to both general and COVID-19-specific procedures.ConclusionsStaff at long-term care facilities discussed a wide variety of reasons for their decision to quit their jobs during the pandemic. Our findings may inform efforts to reduce the rate of turnover in these facilities.

Some of the world’s most profitable firms own platforms that exhibit network effects. A platform exhibits network effects if the more people that use it, the more valuable that it becomes to each user. Theorizing about the value perceived by users of a platform that exhibits network effects has traditionally focused on direct and indirect network effects. In this paper, we theorize about a third type of network effects—data network effects—that has emerged from advances in artificial intelligence (AI) and the growing availability of data. A platform exhibits data network effects if the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user. We argue that there is a positive direct relationship between the AI capability of a platform and the value perceived in the platform by its users—a relationship that is moderated by platform legitimation, data stewardship and user-centric design.\ud \ud

We present a model of phenomenon-driven theorizing that may guide researchers in bridging art and science as they engage in pure theory development. The model seeks to leverage activities related to discovery and prescience (conceiving the theory), imagination and logic (constructing the theory), as well as storytelling and scripting (communicating the theory). We illustrate the model through a reflective inquiry into our recent work on data network effects. The model represents a balanced take on theory development and is useful for tempering the natural tendency in academia to drift toward the science of theorizing at the expense of the art of theorizing.

Digital ventures typically face significant growth expectations. A common response is to extend the current operations into new areas through repurposing its digital core (e.g., search engine, data mining technique, platform, or voice interface). Grounded in prior literature, we surmise that the high versatility of the digital venture’s digital core facilitates such an extension by reducing cost and increasing speed. However, we know little about the process by which digital ventures draw on their digital core to extend current operations. To this end, we use Penrose’s work to analyze a two-year in-depth case study of a Chinese digital venture’s extension of its initial operations based on its credit rating technology. Our findings suggest that digital venture extension is facilitated by templating, which is a digitally enabled process of generating and using generic solutions across business areas. Through our grounded analysis, we unpack templating by tracing three processes contributing to digital venture extension: concepting, generalizing, and porting. Synthesizing our findings, we contribute to the emerging digital innovation and entrepreneurship literature by developing a process model of digital venture extension.\ud \ud

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.

The generation of platform content is essential for platform growth and competition. However, the overwhelming number of platform complementors makes it impossible for platform operators to engage in extensive communication with each complementor about which content contributions are desired. Therefore, platform operators need to find a way to signal strategic interests to platform complementors. In this paper, we employ a mixed-methods design using data from the geodata platform OpenStreetMap to develop and test two distinct types of platform signals as a means of implementing a platform operator’s strategy: (1) opportunity signals, which aim to stimulate activity in new areas of the platform, and (2) endorsement signals, which aim to increase activity in existing areas of the platform. In particular, we examine how platform signals influence the generation of platform content in terms of the volume and diversity of information on the platform. We contribute important insights to the platform governance literature by developing and empirically testing a signaling perspective on the generation of platform content and discussing its implications for guiding platform complementors in content creation.

Outsourcing work relies on the supplier’s interpretation of the work delegated by the client. Existing streams of outsourcing literature tend to assume that the supplier should use the same convention as the client to make sense of the work package. In this research, we use a semiotic lens to challenge this assumption by viewing such sensemaking as a process of decoding symbolic representations. This complementary view involves innovative use of digital technology for re-representing the outsourced work through new conventions. We studied a Chinese business process outsourcing supplier in-depth to learn how such re-representation is achieved through the creation of special-purpose languages. Our research contributes to the Information Systems outsourcing literature by providing a semiotic view on the design of outsourcing work supported by digital technologies. Three re-representation practices (i.e., dissociating the signifiers, signifying through new conventions, and embedding new conventions in the digital infrastructure) constitute the core of this view. The results are highly significant for outsourcing theory and practice, not least since they suggest that the use of semiotics and visuals for re-representation may enable suppliers to reformulate outsourcing work and the expertise needed to deliver services.

Digital infrastructures enable delivery of information services in functional areas such as health, payment, and transportation by providing a socio-technical foundation for partnership governance, resource reuse, and system integration. To effectively serve new purposes and emerging possibilities, however, a key question concerns how an infrastructure can be extended to cater for future services in its functional area? In this paper, we approach such digital infrastructure growth as a challenge related to the alignment of new partners whose capabilities spur innovative services that attract more users. We advance an initial typology that covers four growth tactics (i.e., adding services, inventing processes, opening identifiers, and providing interfaces) with potential to set extension of infrastructures in motion. We then explore the proposed typology by investigating the ways in which its particular tactics successfully extended the scope of a digital infrastructure for public transportation. Our insights invite IS scholars to engage more deeply in the development of growth tactics, which achieve infrastructure extensions that make service delivery durable.

Avital, Michel. "Digital "x": In Need of New Theories or Do Prior Theories Suffice?" Proceedings - Academy of Management. 2019.1 (2019): 1-1. Web.

Identity, a holistic organization definition of what they do and who they are, is pivotal to help make sense and explain actions in organizational practices. In digital venture context, identity pr...

Increasingly, firms embark on digital journeys that involve changing business models, strategies, and processes that straddle industries and geographies, and create complex ecosystems. In this symp...