Organizations are increasingly adopting algorithms to automate mundane tasks in knowledge work, which prior research has shown to liberate human members to interact and innovate. However, mundane tasks entail a different kind of human interaction essential to the innovation process that algorithms may have displaced. This study investigates how the adoption of algorithms automating mundane tasks reshapes human interaction patterns and impacts idea generation for innovation. We argue that while algorithms automating mundane tasks allow human members to redirect interactions toward broader knowledge exchange which expands recombinant potential, they can deprive members of interactions over deeper details of the work domain’s evolving requirements and constraints. Using a stacked difference-in-differences design across 23,274 matched GitHub project-stacks, we find that the adoption of Continuous Integration (CI) bots increases the volume of new idea suggestions by project members, though this increase is disproportionately driven by practically infeasible suggestions—consistent with members broadening their knowledge while losing depth in their understanding. Textual analysis further shows that an increase in generative exchange coupled with a reduction in interaction over details partially explains these outcomes. Our findings challenge the prevailing notion that algorithms automating mundane tasks uniformly benefit innovation, stressing instead that mundane tasks involve human interaction essential for innovation which, when replaced by algorithms, can disrupt the balance between knowledge breadth and depth and between idea generation’s volume and feasibility.

A digital transaction-platform intermediates using algorithm while often allowing third-party human-intermediaries to co-exist on the platform, prompting questions of what strategic benefits they bring. Conventional research typically focuses on participants’ value-add to the platform, implicitly assuming that algorithm remains static in participants’ presence. We instead recognize that algorithm learns and changes dynamically. We draw on the organizational learning literature to depict intermediation as a learning process and illustrate that human-intermediaries add learning benefits to the algorithm. Using Instagram data and Sentiment Analysis, we first show that while algorithm on average generates wider user-reach for advertising brands (complementors), influencer (human-intermediary) elicits more positive user-sentiments. Next, we demonstrate that algorithm learns vicariously from a given influencer to elicit more positive user-sentiments over time, especially when the influencer provides richer or less ambiguous information on her past intermediations. Further, we find that algorithm’s vicarious learning from the influencer alone appears insufficient; rather, it generates improvements interactively with algorithm’s own experiential learning over time, similar to the vicarious-experiential interaction effect in human-learning. Findings stress that without recognizing such algorithm learning, we may have previously underestimated value-add of human participants on platforms or in organizations that employ algorithm for similar tasks. Findings also join recent research in stressing that a platform is inclusive of participants not just for promoting generative joint-value creation but can also be for enhancing its own value capture.

Complementor participation is critical to platform growth. Prior research asserts that increasing it, however, can sometimes result in competitive crowding which discourages further participation. Departing from this assertion, we highlight that the platform orchestrator often offers algorithmic intermediation to help a complementor reach new users. We then draw on the competitive search literature to propose that competitive crowding may not reduce participation but rather changes its nature – induces the complementor to seek the adoption of algorithmic intermediation. Using Instagram data, LDA topic-modeling and a natural experiment, we demonstrate that this effect varies with the algorithm's, competitors’ and the complementor’s user reach abilities. Findings suggest that the tradeoff between competitive crowding and network effect is more nuanced and highlight how competition works as a value-appropriation tool for the platform.

The products with network effects face uncertainty not only with respect to the standalone value of the product but also whether the product could generate network effects, and these dual sources of uncertainty may shape managers' decision-making throughout the product life-cycle, especially during the early stages of product development. Specifically, during the early stages of product development, there is a lot of uncertainty about its market acceptance, and managers often seek signals to resolve that uncertainty by relying on user feedback. However, the tension arises when the user feedback conveys differential signals about the underlying quality of the product, which helps in resolving uncertainty regarding the standalone value of the product, and the potential installed base of the product, which help resolve uncertainty regarding its potential to create network effects. This paper aims to abductively explores such decesion-making processes of managers, especially how network effects impact managers' decision-making when uncertainty regarding the standalone value of the product and its potential to generate network effects are not fully resolved.

Given the strategic importance and inertial forces of collaboration networks, extant literature highlights strategies that enable existing firms to overcome the inertial forces and become more connected players. However, research thus far has overlooked how firms that are totally new to domains can become more connected players. This study redresses this tension by examining the entry strategies of firms: collaborative entry and standalone entry. We argue that standalone entrants take longer to succeed in initial activities in domains, as they lack essential resources. However, once they succeed in such activities, they become more valued as prospective partners, since their capabilities become evident. We demonstrate such dynamics in the context of corporate incumbents’ investments in ventures. Overall, this study provides implications for research on network evolution.