Online communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can ...occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community-resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing-induced by online communities-has implications for theorizing about the more general case of knowledge collaboration in organizations.
Our era is one of increasingly pervasive digital technologies, which penetrate deeply into the very core of the products, services, and operations of many organizations and radically change the ...nature of product and service innovations. The fundamental properties of digital technology are reprogrammability and data homogenization. Together, they provide an environment of open and flexible affordances that are used in creating innovations characterized by
convergence
and
generativity
. An analysis of convergence and generativity observed in innovations with pervasive digital technologies reveals three traits: (1) the importance of digital technology platforms, (2) the emergence of distributed innovations, and (3) the prevalence of combinatorial innovation. Each of the six articles in this special issue relates to one or more of these three traits. In this essay, we explore the organizational research implications of these three digital innovation traits and identify research opportunities for organization science scholars. Examples from the articles in this special issue on organizing for innovation in the digitized world are used to demonstrate the kind of organizational scholarship that can faithfully reflect and inform innovation in a world of pervasive digital technologies.
Theorizing about the sharing-protecting tension has been mostly at the organizational level. We draw on advances in self-regulation theory and hot cognition microfoundations of organizational ...capabilities to articulate three capabilities that dyads of interacting individuals in interorganizational collaborations must possess to regulate the sharingprotecting tension. Besides the recursive influences within dyads, the three capabilities involve recursive multilevel influences between individuals and their home organizations. The interactive self-regulatory theory helps explain how interacting individuals are able to dynamically adjust their sharing and protecting behaviors to the complexity, emergence, and adaptation required in interorganizational collaborations.
This paper provides an overview of the main perspectives and themes emerging in research on open innovation (OI). The paper is the result of a collaborative process among several OI scholars - having ...a common basis in the recurrent Professional Development Workshop on 'Researching Open Innovation' at the Annual Meeting of the Academy of Management. In this paper, we present opportunities for future research on OI, organised at different levels of analysis. We discuss some of the contingencies at these different levels, and argue that future research needs to study OI - originally an organisational-level phenomenon - across multiple levels of analysis. While our integrative framework allows comparing, contrasting and integrating various perspectives at different levels of analysis, further theorising will be needed to advance OI research. On this basis, we propose some new research categories as well as questions for future research - particularly those that span across research domains that have so far developed in isolation.
There is substantial research on the effects of formal control structures (i.e., incentives, identities, organization, norms) on knowledge sharing leading to innovative outcomes in online ...communities. However, there is little research on how knowledge-sharing trajectories in temporary online crowds create innovative outcomes without these structures. Such research is particularly of interest in the context of temporary online crowds solicited with crowdsourcing in which there is only minimal structure for knowledge sharing. We identify eight types of crowdsourcing with different knowledge-sharing patterns. The focus of this study is on the one type of crowdsourcing—collaborative innovation challenges—in which there is the least restriction on knowledge sharing in the crowd. A content analysis was conducted of all time-stamped posts made in five different collaborative innovation challenges to identify different knowledge-sharing trajectories used. We found that a paradox-framed trajectory was more likely to be followed by innovative outcomes compared to three other knowledge-sharing trajectories. A paradox-framed trajectory is one in which a novel solution emerges when different participants post in the following sequence: (1) contributing a paradox associated with the problem objective, (2) sharing assumptions to validate the paradox, and (3) sharing initial ideas for resolving the paradox in a manner that meets the problem statement. Based on the findings, a theory of paradox-framed trajectories in temporary online crowds is presented along with implications for knowledge creation theories in general and online knowledge-creating communities in particular.
The adaptation process for new technology is not yet well understood. This study analyzes how an inter-organizational virtual team, tasked with creating a highly innovative product over a 10 month ...period, adapted the use of a collaborative technology and successfully achieved its challenging objectives. The study of such a virtual team is especially useful for extending our understanding of the adaptation process as virtual teams have more malleable structures than typical organizational units and controlled group experiments. Data were obtained from observations of weekly virtual meetings, electronic log files, interviews, and weekly questionnaires administered to team members. We found that the team initially experienced significant misalignments among the pre-existing organizational environment, group, and technology structures. To resolve these misalignments, the team modified the organizational environment and group structures, leaving the technology structure intact. However, as the team proceeded, a series of events unfolded that caused the team to reevaluate and further modify its structures. This final set of modifications involved reverting back to the pre-existing organizational environment, while new technology and group structures emerged as different from both the pre-existing and the initial ones. A new model of the adaptation process-one that integrates these findings and those of several previous models-is proposed.
The tools for crowdsourcing have been a research focus for quite some time. However, even today, crowdsourcing platforms have remained rather technologically rudimentary as simply idea dropboxes and ...discussion forums. Our research has indicated that technical modifications to current crowdsourcing platforms are needed for crowds to generate more novel and useful solutions. In this article, we briefly describe basic technology requirements to foster collective co-production. Then, we suggest further digital innovations involving the use of AI technologies that are needed to maximize the potential of crowdsourcing.
Digital Innovation Management Nambisan, Satish; Lyytinen, Kalle; Majchrzak, Ann ...
MIS quarterly,
03/2017, Volume:
41, Issue:
1
Journal Article
Peer reviewed
Open access
Rapid and pervasive digitization of innovation processes and outcomes has upended extant theories on innovation management by calling into question fundamental assumptions about the definitional ...boundaries for innovation, agency for innovation, and the relationship between innovation processes and outcomes. There is a critical need for novel theorizing on digital innovation management that does not rely on such assumptions and draws on the rich and rapidly emerging research on digital technologies. We offer suggestions for such theorizing in the form of four new theorizing logics, or elements, that are likely to be valuable in constructing more accurate explanations of innovation processes and outcomes in an increasingly digital world. These logics can open new avenues for researchers to contribute to this important area. Our suggestions in this paper, coupled with the six research notes included in the special issue on digital innovation management, seek to offer a broader foundation for reinventing innovation management research in a digital world.
Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to ...flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.
Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. Machine learning is the most common form of AI and largely relies on supervised learning—when the machine (i.e., AI) is trained with labels applied by humans. Deep learning and adversarial learning involve training on unlabeled data, or when the machine (via its algorithms) clusters data to reveal underlying patterns.
AI is simply a tool. Entrepreneurship is also simply a tool. How they are combined and used will determine their impact on humanity. While researchers have independently developed a greater understanding of entrepreneurship and AI, these two streams of research have primarily run in parallel. To indicate the scope of current and future AI, we provide examples of AI (at different levels of development) for four sectors—customer service, financial, healthcare, and tertiary education. Indeed, experts from industry research and consulting firms suggest many AI-related business opportunities for entrepreneurs to pursue.
Further, we elaborate on several of these opportunities, including opportunities to (1) capitalize on the “feeling economy,” (2) redistribute occupational skills in the economy, (3) develop and use new governance mechanisms, (4) keep humans in the loop (i.e., humans as part of the decision making process), (5) expand the role of humans in developing AI systems, and (6) expand the purposes of AI as a tool. After discussing the range of business opportunities that experts suggest will prevail in the economy with AI, we discuss how entrepreneurs can use AI as a tool to help them increase their chances of entrepreneurial success. We focus on four up-and-coming areas for entrepreneurship research: a more interaction-based perspective of (potential) entrepreneurial opportunities, a more activities-based micro-foundation approach to entrepreneurial action, a more cognitively hot perspective of entrepreneurial decision making and action, and a more compassionate and prosocial role of entrepreneurial action. As we discuss each topic, we also suggest opportunities to design an AI system (i.e., entrepreneurs as potential AI designers) to help entrepreneurs (i.e., entrepreneurs as AI users).
AI is an exciting development in the technology world. How it transforms markets and societies depends in large part on entrepreneurs. Entrepreneurs can use AI to augment their decisions and actions in pursuing potential opportunities for productive gains. Thus, we discuss entrepreneurs' most critical tasks in developing and managing AI and explore some of the dark-side aspects of AI. Scholars also have a role to play in how entrepreneurs use AI, but this role requires the hard work of theory building, theory elaboration, theory testing, and empirical theorizing. We offer some AI topics that we hope future entrepreneurship research will explore. We hope this paper encourages scholars to consider research at the nexus of AI and entrepreneurship.
Purpose
The purpose of this study is to offer implications and future research directions related to new organizational forms like crowds. Organizations are increasingly relying on online crowds to ...innovate through mechanisms such as crowdsourcing, open innovation, innovation challenges and tournaments. To leverage the "wisdom of crowds", crowdsourcing platforms that enable heterogeneous knowledge sharing in crowds lead to novel solution generation by individuals in the crowd. Based on the associative variety memory model of creativity, the authors hypothesize that when a crowd contributes a heterogeneous knowledge in form of a variety of knowledge associations, individual crowd members tend to generate solutions that are more novel. In contrast to the brainstorming view that focuses on ideas as knowledge, the authors propose, test, find and elaborate on implications of crowd sharing of heterogeneous knowledge for the generation of innovation, i.e. novel ideas. The authors coded and analyzed all the posts in 20 innovation challenges leveraging online temporary crowds that were structured to foster knowledge sharing as part of the idea generation process. The analysis shows a positive relationship between the variety of knowledge associations contributed by the crowd and the generation of novel solutions by individuals in the crowd. Further, the variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared. The authors offer four implications and several future directions for research on the new organizational form of online crowds.
Design/methodology/approach
The authors coded and analyzed all the posts in 20 innovation challenges. They also designed and ran these challenges in collaboration with corporate sponsors. The ideas in the challenge were rated by senior executive at each company using a creative forecasting method.
Findings
The variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared.
Research limitations/implications
The authors offer four implications and several future directions for research on the new organizational form of online crowds.
Practical implications
The authors propose several ways in which companies running innovation challenges can moderate and encourage crowd to generate a variety of knowledge.
Originality/value
The authors believe that we are the first empirical paper to emphasize and show that associative variety of knowledge sharing in crowds has impact on novel idea generation by crowds. This view is counter to "electronic brainstorming" view where crowd is asked to just generate these ideas and often just submit their ideas to the sponsor. Their view also goes beyond knowledge refinement of ideas by crowds to more of knowledge integration by crowds.
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