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The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

Knowledge search is considered a broad concept and semi-intentional behavior. The path and boundary conditions through which search strategies affect intra-organizational knowledge creation remain elusive. Drawing on recombinant search theory and knowledge-based view, the authors seek to identify knowledge complexity as an important intermediate variable between knowledge search and innovation performance, such as research and development (R&D) output and R&D output quality. A second goal of this study is to examine the moderating roles of government support and technological turbulence.

The authors employed a longitudinal panel of 609 global pharmaceutical firms and obtained the firms' patent records from 1980 to 2015 for the analysis. The authors used generalized estimating equations (GEE) to evaluate the models and tested the consistency via panel fixed-effects estimations.

The authors' findings show that organizational routine-guided search has a negative effect on knowledge complexity, while routine-changing search exerts a positive impact on knowledge complexity. Governmental support and technological turbulence moderate these relationships. Notably, knowledge complexity has an inverted U-shaped relationship with innovation performance.

The authors' research context, the pharmaceutical industry, may constrain the generalizability of our findings. In addition, potential types of routine-guided and routine-changing search behaviors were not considered.

Despite these limitations, this study offers important implications. First, knowledge complexity transmits the effects of knowledge search on innovation performance. Practitioners should balance routine-guided and routine-changing search processes to build and manage complex knowledge. Second, a moderate level of knowledge complexity is the key to good R&D output and R&D output quality.

The study identifies knowledge complexity as one important intermediate variable between knowledge search behaviors and intra-organizational knowledge creation.

Why is it that highly trained and seasoned executives fail? On the surface, this doesn’t make sense because they are very successful; yet research in the organization sciences provides no shortage of evidence to prove just that. From the classic Mann Gulch fire disaster of Weick’s famous collapse of sensemaking study, to studies of myopia of learning, escalation of commitment, threat-rigidity, dominant logic, the architecture of simplicity, the Icarus Paradox, to core competencies turning into core rigidities, and navigating new competitive markets using “old” cognitive maps, and many more such examples point to a ubiquitous phenomenon where highly trained and experienced professionals find themselves “stuck” in the heat of battle, unable to move and progress. On the one hand, for some, there is a desperate need for change, but are unable to do so, due to their trained incapacities. On the other hand, some simply cannot see the need for change, and continue with their “business as usual” mentality. For both, their visions of the world shrink, they have a tendency to cling onto their past habitual practices and oversimplify the complexity of the situation. In moments like these: DROP YOUR TOOLS and UNLEARN! This book chapter introduces a framework (grounded in clinical psychology) that has had consistent success in helping seasoned executives and key decision-makers open up the alternatives whenever they find themselves stuck with complexity.

This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number.

The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities.

The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system’s intentions.

Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics.

The purpose of this study is to propose a methodological approach for modeling catastrophic consequences caused by black swan events, based on complexity science, and framed on Feyerabend’s anarchistic theory of knowledge. An empirical application is presented to illustrate the proposed approach.

Thom’s nonlinear differential equations of morphogenesis are used to develop a theoretical model of the impact of catastrophes on international business (IB). The model is then estimated using real-world data on the performance of multinational airlines during the SARS-CoV-2 (COVID-19) pandemic.

The catastrophe model exhibits a remarkable capability to simultaneously capture complex linear and nonlinear relationships. Through empirical estimations and simulations, this approach enables the analysis of IB phenomena under normal conditions, as well as during black swan events.

To the best of the author’s knowledge, this study is the first attempt to estimate the impact of black swan events in IB using a catastrophe model grounded in complexity theory. The proposed model successfully integrates the abrupt and profound effects of catastrophes on multinational corporations, offering a critical perspective on the theoretical and practical use of complexity science in IB.

The aim of this study was to appraise the delivery of information communication technology (ICT) projects and identify key determinants for stakeholder integration.

Given that empirically, little was known about stakeholder integration in the ICT sector and its influence or effect on project delivery; qualitative method was used. Forty-seven semi-structured interviews were carried out to derive senior project practitioners and policymakers' constructs of stakeholder integration and infrastructure performance improvement of ICT projects. The verification and validation of the proposed assessment tool were achieved through the use of focus group discussion.

As established in this research study, there is a need for project delivery teams to evaluate the level of stakeholder integration, the formulation of a project business case, the project processes and issues of compliance and regulation in ICT projects. What is evident in the findings of the study is that the management model adopted for the stakeholders in the Kenyan ICT sector ought to make communication the fulcrum of their engagement.

The inferences made herein are critical in contributing to knowledge regarding the ICT infrastructure project management terrain in developing countries. There is evidence in the study to conclude that the concept of stakeholder management and integration has implications for the sustainability of ICT projects. One of the issues that predominantly featured in the research was the input of stakeholder integration in terms of project sustainability.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.