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Prior research has emphasized the importance of boundary spanners in facilitating the transfer of knowledge between organizational units. The successful transfer of knowledge between organizational units is critical for a number of organizational processes and performance outcomes. The empirical evidence on the success of boundary spanners is mixed, however. Research findings indicate boundary spanners can either facilitate or inhibit the flow of knowledge between organizational units. We develop and test a theoretical argument emphasizing the importance of the broader network context in which boundary spanning occurs. In particular, we consider how tie strength, network cohesion, and network range affect the level of knowledge acquired in cross-unit knowledge transfer relationships. An analysis of knowledge transfer relationships among several hundred scientists indicates that each network feature had a positive effect on the level of knowledge acquired in cross-unit knowledge transfer relationships. Our findings illustrate how network features contribute to the flow of knowledge between organizational units and, therefore, how network context contributes to heterogeneity in boundary-spanning outcomes.

The causal inference literature has provided a clear formal definition of confounding expressed in terms of counterfactual independence. The literature has not, however, come to any consensus on a formal definition of a confounder, as it has given priority to the concept of confounding over that of a confounder. We consider a number of candidate definitions arising from various more informal statements made in the literature. We consider the properties satisfied by each candidate definition, principally focusing on (i) whether under the candidate definition control for all \"confounders\" suffices to control for \"confounding\" and (ii) whether each confounder in some context helps eliminate or reduce confounding bias. Several of the candidate definitions do not have these two properties. Only one candidate definition of those considered satisfies both properties. We propose that a \"confounder\" be defined as a pre-exposure covariate C for which there exists a set of other covariates X such that effect of the exposure on the outcome is unconfounded conditional on (X, C) but such that for no proper subset of (X, C) is the effect of the exposure on the outcome unconfounded given the subset. We also provide a conditional analogue of the above definition; and we propose a variable that helps reduce bias but not eliminate bias be referred to as a \"surrogate confounder.\" These definitions are closely related to those given by Robins and Morgenstern [Comput. Math. Appl. 14 (1987) 869-916]. The implications that hold among the various candidate definitions are discussed.

Applying a new theoretical and empirical approach to intrafirm knowledge transfers, this paper provides some initial insight to the little-researched phenomenon of why some subsidiaries are isolated from knowledge-transfer activities within the multinational corporation (MNC). Knowledge transfer is framed as a problemistic search process initiated by the recipient unit. We show that knowledge flows from units that are perceived to be highly capable to units that perceive themselves to be highly capable. Knowledge flows are also associated with existing levels of communication and reciprocity. Taken together, these findings suggest that knowledge transfers in MNCs typically occur between highly capable members of an \"in crowd,\" and the isolated minority rarely, if ever, engages in knowledge-sharing activities. Finally, we show that the isolated minority underperforms other subsidiaries, suggesting the possibility of a \"liability of internal isolation.\"

This paper investigates the mechanisms through which organizations develop dynamic capabilities, defined as routinized activities directed to the development and adaptation of operating routines. It addresses the role of (1) experience accumulation, (2) knowledge articulation, and (3) knowledge codification processes in the evolution of dynamic, as well as operational, routines. The argument is made that dynamic capabilities are shaped by the coevolution of these learning mechanisms. At any point in time, firms adopt a mix of learning behaviors constituted by a semiautomatic accumulation of experience and by deliberate investments in knowledge articulation and codification activities. The relative effectiveness of these capability-building mechanisms is analyzed here as contingent upon selected features of the task to be learned, such as its frequency, homogeneity, and degree of causal ambiguity. Testable hypotheses about these effects are derived. Somewhat counterintuitive implications of the analysis include the relatively superior effectiveness of highly deliberate learning processes such as knowledge codification at lower levels of frequency and homogeneity of the organizational task, in contrast with common managerial practice.

Is \"the field of management's devotion to theory too much of a good thing?\" [Hambrick, D. C. 2007. The field of management's devotion to theory: Too much of a good thing? Acad. Management J. 50 (6) 1346-1352]. In his paper, Hambrick criticizes the practice employed by many journals in the management field that requires that papers submitted for publication make a strong theoretical contribution. I argue that part of the problem is caused by the misunderstanding and misuse of the term \"theory.\" To clarify the status of theory, I review three modes of research formulation in the organizational sciences: theories, models, and conceptual frameworks. Language plays an important role in scientific research. I therefore discuss two research languages that are used in research in management that appear to be the farthest apart: mathematics, which is the language of precision; and narratives, which is the language that provides rich data. I provide a discussion of the use of mathematics in theory development and the use of narratives in research development. The two languages and three modes of research formulation are needed for contribution to knowledge, which should be the main goal of research in organization science.

Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research.

We examine the role of social relationships in facilitating knowledge flows by estimating the flow premium captured by a mobile inventor's previous location. Once an inventor has moved, they are gone—but are they forgotten? We find that knowledge flows to an inventor's prior location are approximately 50% greater than if they had never lived there, suggesting that social relationships, not just physical proximity, are important for determining flow patterns. Furthermore, we find that a large portion of this social effect is mediated by institutional links; however, this is not the result of corporate knowledge management systems but rather of personal relationships formed through co-location within an institutional context that endure over time, space, and organizational boundaries. Moreover, we find the effect is nearly twice as large for knowledge flows across as compared to within fields, suggesting that co-location may substitute for communities of practice in determining flow patterns.

Prior research suggests that equity joint ventures (JVs) are particularly effective vehicles for accessing complex technology. Different schools of thought have emphasized different reasons why joint ventures might support greater knowledge transfer than \"bare\" license agreements: incentive alignment, organizational embeddedness, and enhanced administrative controls. We probe and refine these theoretical perspectives, drawing out implications of the different theories for the extent and speed of alliance-related knowledge transfer, as well as for knowledge \"leakage\" in areas not directly related to alliance activities. Using a proprietary data set derived from regulatory filings with the Japanese government we test these implications in our empirical analysis of U.S.-Japan agreements. The picture that emerges from the analysis is one of particularly intense but contained knowledge transfer in equity joint ventures, relative to bare license agreements: knowledge transfers directly related to the alliance activity are enhanced in the JV, and the speed of integration into the Japanese firm's subsequent innovations also increases. In marked contrast, leakage of unrelated technology is significantly reduced. These findings suggest that administrative structures that reduce technology leakage are a key feature of the equity joint venture, a result that is inconsistent with a \"pure\" knowledge-based perspective on alliances.

Universities are expected to be places where knowledge is shared freely among academicians. However, the reality shows that knowledge sharing is barely present within universities these days. As Malaysia shifts towards building a knowledge-based society, academic institutions, particularly the public universities, now face ever-growing faculty demands for sharing quality resources and expertise. As a result, knowledge sharing in academia has become a rising concern. The purpose of this study, then, is to uncover the factors that propel knowledge sharing among academicians in higher learning institutions of Malaysia. Using the Theory of Reasoned Action as the basis for this study's research framework, data was gathered from 447 academicians in 10 public universities scattered throughout the country. Findings from the Partial Least Squares analysis revealed that extrinsic motivation, reciprocal relationships, sense of self-worth and subjective norm are vital determinants of an academician's attitude towards knowledge sharing. In turn, this attitude that is formed will largely determine whether the academician engages in knowledge sharing behaviors or not. Besides having a positive effect on attitude, sense of self-worth also exhibited a positive impact on the subjective norm to share knowledge. Consequently, this subjective norm will have an impact upon knowledge sharing behavior. In addition, perceived behavioral control and organizational climate were discovered to have a direct influence on knowledge sharing behaviors. Implications, limitations as well as suggestions for future research are accordingly discussed in this paper. (HRK / Abstract übernommen).

There is a growing incentive for sociologists to demonstrate the use-value of their research. Research 'impact' is a driver of research funding and a measure of academic standing. Academic debate on this issue has intensified since Burawoy's (2004) call for a 'public' sociology. However, the academy is no longer the sole or primary producer of knowledge and empirical sociologists need to contend with the 'huge swathes' of social data that now exist (Savage and Burrows, 2007). This article furthers these debates by considering power struggles between competing forms of knowledge. Using a case study, it specifically considers the power struggle between normative and empirical knowledge, and how providers of knowledge assert legitimacy for their truth claims. The article concludes that the idea of 'impact' and 'use-value' is extremely complex and depends on the policy context of knowledge power struggles, and on how policy makers want to view the world.