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ABSTRACTR&D projects in high‐tech organizations bring together diverse knowledge domains to quickly develop new products and processes. The fast‐paced context of high‐tech organizations makes it challenging to create new knowledge and solve complex problems. Managing these R&D projects requires understanding both the mechanisms and the type of knowledge created to achieve project objectives. This research conducts a two‐phased multimethod study to understand knowledge creation in high‐tech R&D projects. The first phase uses qualitative data to develop a theory on knowledge creation in R&D projects. The second phase involves a survey that collects data from R&D projects to test the theory. Results from the case study find that R&D projects benefit from two types of knowledge – objective and intuitive. The case analyses show that intuitive and objective knowledge creation in high‐tech organizations occurs by creating not only diverse but also psychological safe project teams. The large‐scale survey finds that team diversity positively influences objective knowledge creation while psychological safety affects intuitive knowledge creation. Surprisingly, the results show that team diversity negatively affects intuitive knowledge creation. A post hoc analysis takes a more granular look at diversity and shows that different kinds of diversity have different effects on knowledge creation. This helps to better explain how to manage innovation across boundaries. Finally, the analysis shows that both objective and intuitive knowledge influence R&D project performance. Taken together, these results help explain how to manage innovation across functional boundaries to create knowledge and enhance R&D project performance.

ABSTRACTManufacturing firms in emerging economies face research and development (R&D) challenges that are quite different from firms in developed economies. Firms in emerging economies complement their internal R&D capabilities by accessing two forms of external knowledge: domestic knowledge from local partners and foreign knowledge from multinationals. Accessing these knowledge sources requires distinct organizational capabilities, and they may differently moderate the relationship between internal R&D investments on exploration and exploitation and innovation outcomes. Exploitation R&D investments refer to investments made on refining products and processes, while exploration R&D investments refer to investments made on designing new products and processes. We examine this issue by combining two waves of surveys from 155 manufacturing firms in Thailand. Results show that relative exploration (i.e., the ratio of R&D investments made toward exploration over sum of exploration and exploitation) exhibits an inverted U‐shaped relationship with the number of process and product innovations developed by the firm. This optimal mix, corresponding to the tipping point of an inverted U‐shaped curve, shifts toward a higher level of relative exploration (i.e., higher exploration and lower exploitation) when manufacturers have more access to domestic knowledge, and shifts toward a lower level of relative exploration (i.e., lower exploration and higher exploitation) when manufacturers have more access to foreign knowledge. Our findings are robust with similar effects on a firm's revenue generated from innovations. Taken together, these results demonstrate how external knowledge flows can moderate, in contrasting ways, the relationship between R&D portfolio mix and innovation outcomes. These findings also imply that inappropriately prioritizing R&D mix can lead to suboptimal innovation outcomes, offering further insights to both practicing managers and academics interested in emerging economy contexts.

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. The authors interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them.

PurposeThis research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs. The authors also investigate the moderation effect of competition in local hospital markets on these relationships.Design/methodology/approachA six-year panel data is assembled from five separate sources to obtain information of 2,524 USA hospitals. Fixed-effect (FE) models are used to test the proposed hypotheses.FindingsFirst, nurse staffing is initially associated with improved care quality until nurse staffing reaches a turning point, beyond which nurse staffing is associated with worse care quality. Second, a similar pattern applies to the relationship between nurse staffing and operating costs, although the turning point is at a much lower nurse staffing level. Third, market competition moderates the relationship between nurse staffing and care quality so that the turning point of nurse staffing will be higher when the degree of competition is higher. This shift of turning point is also observed in the relationship between nurse staffing and operating costs.Practical implicationsThe study identifies three ranges of nurse staffing in which hospitals will likely experience simultaneous improvements, a tradeoff or simultaneous decline of care quality and operating costs when investing in more nursing capacity. Hospitals should adjust nurse staffing levels to the right directions to achieve better care or reduce operating costs.Originality/valueNurses constitute the largest provider group in hospitals and profoundly impact care quality and operating costs among all health care professionals. Optimizing the level of nurse staffing, therefore, can significantly impact the care quality and operating costs of hospitals.

AbstractThe benefits of additive manufacturing (AM) extend beyond the attributes of physical products and production processes they enable. Experience with AM can augment the way design is approached and can increase opportunities to pivot toward less familiar design tasks. We begin this qualitative study with a natural experiment made possible by an exogenous shock: the COVID‐19 pandemic. Through a three‐stage case study approach using a grounded theory‐building method, we contrast AM usage among a set of firms, half of which pivoted their resources away from their traditional production and toward a response to this shock. We engage in an abductive reasoning approach to consider common threads in AM capabilities that facilitated this pivoting. Our analyses suggest that the advanced use of generative design (GD), a category of computational technologies enabling novel and optimized design, is a critical attribute of these firms that ended up pivoting to make COVID‐related products. Specifically, firms with experience applying this capability demonstrated a unique ability to pivot during this shock and emphasized their valuation of AM‐enabled agility. We revisited these firms 2 years after initial contact and found that GD was associated with higher levels of innovation and was largely viewed by designers as a mechanism driving double‐loop learning. Overall, our study provides insights into the symbiosis between human and artificially intelligent GD, and the role of such symbiosis in advancing AM capabilities.