How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based ...decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.
► Knowledge management as IS implementations that enable knowledge processes. ► Knowledge management builds protective capabilities in firms. ► Knowledge management evolves through the increasing use ...of social software. ► New strategic research agenda on knowledge management by social software.
Knowledge management is commonly understood as IS implementations that enable processes of knowledge creation, sharing, and capture. Knowledge management at the firm level is changing rapidly. Previous approaches included centrally managed, proprietary knowledge repositories, often involving structured and controlled search and access. Today the trend is toward knowledge management by social software, which provides open and inexpensive alternatives to traditional implementations. While social software carries great promise for knowledge management, this also raises fundamental questions about the very essence and value of firm knowledge, the possibility for knowledge protection, firm boundaries, and the sources of competitive advantage. I draft a strategic research agenda consisting of five fundamental issues that should reinvigorate research in knowledge management.
Nonaka's paper 1994. A dynamic theory of organizational knowledge creation. Organ. Sci. 5(1) 14-37 contributed to the concepts of "tacit knowledge" and "knowledge conversion" in organization science. ...We present work that shaped the development of organizational knowledge creation theory and identify two premises upon which more than 15 years of extensive academic work has been conducted: (1) tacit and explicit knowledge can be conceptually distinguished along a continuum; (2) knowledge conversion explains, theoretically and empirically, the interaction between tacit and explicit knowledge. Recently, scholars have raised several issues regarding the understanding of tacit knowledge as well as the interaction between tacit and explicit knowledge in the theory. The purpose of this article is to introduce and comment on the debate about organizational knowledge creation theory. We aim to help scholars make sense of this debate by synthesizing six fundamental questions on organizational knowledge creation theory. Next, we seek to elaborate and advance the theory by responding to questions and incorporating new research. Finally, we discuss implications of our endeavor for organization science.
Laursen and Salter (2006) examined the impact of a firm's search strategy for external knowledge on innovative performance. Based on organizational learning and open innovation literature, we extend ...the model hypothesizing that the search itself is impacted by firm context. That is, both 'constraints on the application of firm resources' and the 'abundance of external knowledge' have a direct impact on innovative performance and a firm's search strategy in terms of breadth and depth. Based on a survey of Swiss-based firms, we find that constraints decrease and external knowledge increases innovative performance. Although constraints lead to a broader but shallower search, external knowledge is associated with the breadth and the depth of the search in a U-shaped relationship.
Research summary
Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, we employ hybrid inductive methods to study discussions around ...initiation and change of software licenses—a fundamental and potentially contentious governance issue. First, we apply machine learning algorithms to identify robust patterns in data: resolution is more likely in larger discussion groups and in projects without a license compared to those with a license. Second, we analyze textual data to explain the causal mechanisms underpinning these patterns. The resulting theory highlights the group process (reflective agency switches disputes from bargaining to problem solving) and group property (preference alignment over attributes) that are both necessary for the resolution of governance disputes, contributing to the literature on community governance.
Managerial summary
Online communities play an increasingly important role in how companies innovate across organizational boundaries and attract talent across geographic locations. However, online communities are no Utopia; disputes abound even (more) when we collaborate virtually. In particular, governance disputes can threaten the functioning and existence of online communities. Our study suggests that governance disputes in online communities either unfold as bargaining over which solution is better or searching for a satisfactory solution. The latter is more likely to reach a resolution, when there is common ground. Companies interested in leveraging the power of online communities should (a) identify or train certain participants to transform endless bargaining into collective problem solving and (b) foster shared knowledge and value basis among participants through recruitment and strong organizational culture.
Organizational knowledge creation integrates context, knowledge assets, and knowledge creation processes throughout the organization. Using organizational knowledge creation theory as an organizing ...framework, we conduct a literature review that shows prior work has focused on the role of central, upper‐echelon, leadership in knowledge creation processes, without devoting much attention to context and knowledge assets. To remedy these weaknesses, we develop a new framework for situational leadership in organizational knowledge creation. The framework is based on a continuum that ranges from centralized to distributed leadership at three layers of activity: a core layer of local knowledge creation; a conditional layer that provides the resources and context for knowledge creation; and a structural layer that forms the overall frame and direction for knowledge creation in the organization. We discuss the implications of this framework for theory and practice.
Prior information systems research highlights the vital role of information technology (IT) for innovation in firms. At the same time, innovation literature has shown that accessing and integrating ...knowledge from sources that reside outside the firm, such as customers, competitors, universities, or consultants, is critical to firms’ innovative success. In this paper, we draw on the knowledge-based view of the firm to investigate how search in external knowledge sources and information technology for knowledge absorption jointly influence process innovation performance. Our model is tested on a nine-year panel (2003–2011) of Swiss firms from a wide range of manufacturing industries. Using instrumental variables, and disaggregating by type of IT, we find that data access systems and network connectivity hold very different potential for the effective absorption of external knowledge, and the subsequent realized economic gains from process innovation. Against the backdrop of today’s digital transformation, our findings demonstrate how firms should coordinate strategies for sourcing external knowledge with specific IT investments in order to improve their innovation performance.
Open source software (OSS) is a social and economic phenomenon that raises fundamental questions about the motivations of contributors to information systems development. Some developers are unpaid ...volunteers who seek to solve their own technical problems, while others create OSS as part of their employment contract. For the past 10 years, a substantial amount of academic work has theorized about and empirically examined developer motivations. We review this work and suggest considering motivation in terms of the values of the social practice in which developers participate. Based on the social philosophy of Alasdair Maclntyre, we construct a theoretical framework that expands our assumptions about individual motivation to include the idea of a long-term, value-informed quest beyond short-term rewards. This motivation-practice framework depicts how the social practice and its supporting institutions mediate between individual motivation and outcome. The framework contains three theoretical conjectures that seek to explain how collectively elaborated standards of excellence prompt developers to produce high-quality software, change institutions, and sustain OSS development. From the framework, we derive six concrete propositions and suggest a new research agenda on motivation in OSS.
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and ...automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for
predictions without explanations
that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate
algorithm supported induction
, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.
Technologies are changing at a rapid pace and in unpredictable ways. The scale of their impact is also far-reaching. Technologies such as artificial intelligence, data analytics, robotics, digital ...platforms, social media, blockchain, and 3-D printing affect many parts of the organization simultaneously, enabling new interdependencies within and between units and with actors that many organizations have typically considered to be outside their boundaries. Consequently, today’s emerging technologies have the potential to fundamentally shape all aspects of organizing. This article introduces the special issue “Emerging Technologies and Organizing.” We treat these new technologies as “emerging” because their uses and effects are still varied and have yet to stabilize around a recognizable set of patterns and because the technologies themselves are, by design, always changing and adapting. To theorize the relationship between emerging technologies and organizing, we draw on relational thinking in philosophy and sociology to develop a relational perspective on emerging technologies. Our goal in doing so is to create a new way for organizational scholars to incorporate the ever-increasing role of technology in their theorizing of key organizational processes and phenomena. By developing a relational perspective that treats emerging technologies not as stable entities, but as a set of evolving relations, we provide a novel way for organizational scholars to account for the role of technology in their topics of interest. We sketch the outlines of this relational perspective on emerging technologies and discuss the implications it has for what organizational scholars study and how we study it.
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