Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

El artículo tiene como objetivo evaluar el nivel de desarrollo de la capacidad de absorción realizada (RACAP) en las Pyme colombianas. Se parte de una muestra de 363 Pyme colombianas (Dane,2012), se usa una revisión de literatura con apoyo del análisis de contenido y un modelo de regresión lineal, que permiten mostrar la existencia de una correlación lineal positiva entre la adquisición y asimilación del conocimiento externo en las organizaciones objeto de estudio,. Palabras clave: Capacidades de absorción, capacidades de absorción potencial, medición de las capacidades de absorción ABSTRACT:

Measurement of potential absorption capacity in Colombia's innovative companies [Medición de la capacidad de absorción potencial en las empresas innovadoras de Colombia]

Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the firm-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical–biotechnology R&D alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. © 1998 John Wiley & Sons, Ltd.

Relative absorptive capacity and interorganizational learning

Abstract Artificial intelligence (AI) and other AI-based applications are being integrated into firms’ human resource management (HRM) approaches for managing people in domestic and international organisations. The last decade has seen a growth in AI-based applications proliferating the HRM function, triggering an exciting new stream of research on topics such as the social presence of AI and robotics, effects of AI adoption on individual and business level outcomes, and evaluating AI-enabled HRM practices. Adopting these technologies has resulted in how work is organised in local and international firms, noting opportunities for employees and firms’ resource utilisation, decision-making, and problem-solving. However, despite a growing interest in scholarship, research on AI-based technologies for HRM is limited and fragmented. Further research is needed that analyses the role of AI-assisted applications in HRM functions and human-AI interactions in large multinational enterprises diffusing such innovations. In response to these combined issues—the fragmented nature of research and limited extant literature, we present a systematic review on the theme of this special issue and offer a nuanced understating of what is known, yet to be known, and future research directions to frame a future research agenda for international HRM. We develop a conceptual framework that integrates research on AI applications in HRM and offers a cohesive base for future research endeavours. We also develop a set of testable propositions that serve as directions for future research.

https://www.tandfonline.com/doi/pdf/10.1080/09585192.2022.2035161?needAccess=true

Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda

Although it is transforming the meaning of employment for many people, little is known about the implications of the gig economy for human resource management (HRM) theory and practice. The purpose of this paper is to conceptually explore the notion of HRM in the gig economy, where intermediary platform firms design and implement HRM activities while simultaneously trying to avoid the establishment of employment relationships with gig workers.,To conceptualize HRM in the gig economy, the authors offer a novel ecosystem perspective to develop propositions on the role and implementation of HRM activities in the gig economy.,The authors show that HRM activities in the gig economy are designed to govern platform ecosystems by aligning the multilateral exchanges of three key gig economy actors: gig workers, requesters and intermediary platform firms, for ensuring value co-creation. The authors argue that the implementation of HRM activities in the gig economy is contingent on the involvement and activities of these gig economy actors. This means that they are not mere recipients of HRM but also actively engaged in, and needed for, the execution of HRM activities.,The study contributes to research by proposing a theoretical framework for studying the design of HRM activities, and their implementation, in the gig economy. From this framework, the authors derive directions for future research on HRM in the gig economy.

/pdf/conceptualizing-human-resource-management-in-the-gig-economy-5cgy22n68b.pdf

Conceptualizing human resource management in the gig economy: Toward a platform ecosystem perspective

Crowdsourcing platforms are online organizations that organize work by sourcing tasks to their members. As participation on crowdsourcing platforms is completely voluntary, getting members to actively participate in tasks on an ongoing basis is essential for the survival of these online platforms. Why members would be active on an ongoing basis, however, is currently not well understood. To understand ongoing member activity on crowdsourcing platforms, we build on the group engagement model, which postulates that feelings of pride and respect influence engagement because they foster identification with the group. We argue that, although in general the nature of crowdsourcing platforms limits the effects of identification processes on member behaviour, feelings of pride and respect will still play central roles in such online organizations, because feelings of pride and respect can directly drive members’ cooperative behaviors towards the platform organization. Moreover, we posit that the way in which platform organizations communicate with their members affects these feelings of pride and respect. We test these ideas in a longitudinal, multisource field study and find that feelings of pride drive ongoing member activity on an online crowdsourcing platform directly and that platform management can increase members’ feelings of pride and respect by engaging in specific organizational communication practices.

Feelings of pride and respect as drivers of ongoing member activity on crowdsourcing platforms

Across different disciplinary boundaries, research into algorithmic surveillance (Newlands, 2020), people analytics (Gal et al., 2020; Marler & Boudreau, 2017; Tursunbayeva et al., 2018), human res...

Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM

Crowdsourcing for innovation : How related and unrelated perspectives interact to increase creative performance

/pdf/call-for-papers-for-the-special-issue-on-digitization-and-30lgf0x53q.pdf

Call for papers for the special issue on Digitization and the transformation of human resource management

Individuals today discuss information and form judgements as crowds in online communities and platforms. "Wisdom of the crowd" arguments suggest that, in theory, crowds have the capacity to bring together diverse expertise, pooling distributed knowledge and thereby solving challenging and complex problems. This paper concerns one way that crowds might fall short of this ideal. A large body of research in the social psychology of small groups concerns the shared information bias, a tendency for group members to focus on common knowledge at the expense of rarer information which only one or a few individuals might possess. We investigated whether this well-known bias for small groups also impacts larger crowds of 30 participants working on Amazon’s Mechanical Turk. We found that crowds failed to adequately pool distributed facts; that they were partially biased in how they shared facts; and that individual perception of group decisions was unstable. Nonetheless, we found that aggregating individual reports from the crowd resulted in moderate performance in solving the assigned task.

Distributed knowledge in crowds: Crowd performance on hidden profile tasks

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