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In the evolving landscape of higher education, identifying key drivers of organizational performance is crucial. This study investigates the impact of intellectual capital on knowledge management and innovation and how these factors collectively enhance organizational performance. Conducted among top leaders at 21 state universities in Indonesia, this quantitative research used structured questionnaires validated by prior studies. The high response rate strengthens the reliability of findings analyzed using SPSS for descriptive statistics and Structural Equation Modeling (SEM) with SmartPLS 3.0 for hypothesis testing. The results indicate intellectual capital significantly influences knowledge management and innovation, which in turn positively affect organizational performance. Intellectual capital enhances both knowledge management and organizational performance, while effective knowledge management fosters innovation, further contributing to improved performance. The novelty of this research lies in its comprehensive integration of intellectual capital, knowledge management, and innovation within the context of higher education, providing empirical evidence of their interconnectedness and collective impact on organizational performance, which has been largely unexplored in previous studies. This study's practical implications encourage strategic investments in intellectual capital and knowledge management, ultimately contributing to improved organizational performance and providing valuable insights for policy and decision-making in the higher education sector. [ABSTRACT FROM AUTHOR]

This study investigates the impact of European Key Enabling Technologies (KETs) on regional innovation. KETs are a group of six technologies that share features with General-Purpose Technologies and are anticipated to contribute to innovation, Europe’s industrial competitiveness, and tackling grand societal challenges. As previous studies show innovation-driving effects of KETs, we are interested in the role of KETs in the regional technological space and its effect on innovation within a region. We analyse data from 141 German Labor Market Regions (LMRs) over a 15-year period and find varying and even negative impacts of individual KETs on regional knowledge creation. These findings emphasize KETs’ heterogeneity and the need for tailored regional policies. [ABSTRACT FROM AUTHOR]

Research & Innovation (R&I) policy initiatives employed by the European Union (EU) across its regional economies are important instruments to ensure the scientific and technological progress along with the associated benefits. One relevant aspect in this regard is to encourage and enable collaboration between regional partners to enhance potential learning opportunities and to ensure cohesive long-term development patterns. Furthermore, frequently these initiatives are also targeted at specific technology sectors, such as the EU R&I policy actions towards nanotechnology. Based on an advance theoretical framework and data from the official EU project databases as well as regionalized European Patent Office data, the present study develops a methodological tool through which it is possible to identify effective collaboration settings, while providing policymakers and evaluators with a practical tool that will enable them to predict the possible outcomes of such critical EU-funded R&I projects from the onset. [ABSTRACT FROM AUTHOR]

This paper explores the determinants of knowledge flows, proxied by patent citations, within 31 Chinese administrative provinces. Relying on patent data from the US Patent and Trademark Office during the period 1995-2019, we are able to identify 27118 patent citation pairs (focusing only on USPTO patents with first inventors residing in China). In order to explain the number of citations from a Province to another, in addition to geographic and technological proximity, we use factor analysis in order to capture the effect of regional research structure. In particular, we measure the private versus public research intensity of each Chinese administrative province. Our econometric results show that, as expected, geographical and technological distance between Provinces are negatively correlated with patent citations. In addition, we find that Chinese administrative provinces that show bigger intensity with regard to public and to private research both receive more patent citations and cite more other patents in other Provinces. This effect is particularly significant when the two regions are specialized in private research, which indicates a strong effect of cognitive proximity. Finally, and at odds with prior research, we find that controlling for social proximity between Provinces does not impact the effect of geographical and technological proximity. These results, if confirmed by further studies, might have significant policy implications. [ABSTRACT FROM AUTHOR]

While extensive research studies exist on the influence of AI solutions on organizations as a whole, there is a paucity of comprehensive studies examining the adoption of these solutions in product development and subsequent management processes. This article presents an exploratory investigation of the applications, challenges, and determinants of success associated with artificial intelligence (AI) solutions employed in the product development and management processes. To this end, a qualitative thematic analysis is conducted based on twelve in-depth interviews with experts proficient in AI engineering and product management, representing twelve distinct organizations within the Polish IT sector. This article offers insights into four potential applications and expounds on various factors impacting the challenges and success of deployed AI solutions, generating two additional emergent themes. This article delineates practical implications for organizations and product managers and proposes intriguing directions for future research exploring topical areas of study.

The risk of failure in innovation projects affects organisational behaviour and the performance of firms. This study develops a theory of errors in innovation project-level failure. Error is a set of faults that generates deviations in phases of the R&D process in projects, decreasing the expected results. From a systemic perspective, innovation failure in a project is due to a set of errors that generates a negative outcome. The proposed theory clarifies the general determinants of innovation failure in projects that are due to errors in design, execution and market orientation. Case studies in the drug discovery industry and pharmaceuticals validate the consistency of the proposed theory, giving examples of errors in projects leading to innovation failures. The payoff matrix of success/failure occurrences predicts that the probability of failure in innovation projects can double by setting a difficult goal compared to an easy one. Inductive implications of the proposed theory are that organisations reduce innovation failure in projects with better adaptedness to changes that minimise errors and learn from them. The proposed theory can guide R&D managers, designers, etc. in detecting critical errors in the R&D process to apply strategies of problem solving based on a winning approach by learning from errors that lead to innovation failure in projects to enable the organisation to adapt, take advantage of important opportunities and cope with consequential environmental threats for achieving goals and sustaining the competitive advantage in turbulent markets. [ABSTRACT FROM AUTHOR]

Two questions persist regarding global value chains (GVCs): First, what are the strategies and pathways through which multinational enterprises (MNEs) integrate and navigate GVCs? Second, what are the characteristics of GVCs in which MNEs participate? An analysis of these questions leads to inside–out and outside–in perspectives on GVCs. The literature on MNEs mostly focuses on the quantity and quality of foreign direct investment rather than on the differences in MNEs’ production activities and structure of GVCs. This study examines the effects of MNEs’ participation in simple and complex GVCs on domestic innovation. We find that the positive effects are mainly driven by MNEs’ downstream participation in simple GVCs. The mechanism tests indicate that MNEs’ upstreamness within simple GVCs negatively affects domestic firms’ absorption capacity and internal R&D and that within simple and complex GVCs negatively affects domestic firms’ export resilience.

This study addresses the fragmented understanding of the relationship between digital transformation (DT) and innovation by proposing a multi-level framework that integrates diverse disciplinary perspectives. This framework provides deep insight into how DT influences innovation. A systematic literature review was conducted, and based on the findings, a research agenda was outlined as a roadmap to guide future studies. This research contributes to the strategic change and innovation literature by providing a multi-level framework explaining how DT-driven structural change affects innovation, considering various contingencies such as market dynamics, technological advancements, and organizational capacities. Additionally, this study contributes to the innovation and strategic information systems domain by demonstrating that DT's role as a strategic asset for achieving innovation should align with firm capabilities, structural characteristics, and environmental and external dynamics.

Project management and project portfolio management (PPM) foster competitiveness by facilitating the implementation of organizational strategy. Although organizations often struggle to develop PPM capabilities, the academic community does not have an in-depth understanding of the conditions for successfully developing these capabilities. In response, we conducted a multiple-case study with 50 interviewees to develop a theoretical model of the PPM capability-building process. This model is built on the notion of organizational sensemaking and identifies aspects that comprehensively explain why it usually takes so long to develop PPM capabilities. We conceptualize the PPM capability-building process as one that is strongly influenced by (1) the effects of structural rearrangements, (2) the appropriate use of external resources during that process, (3) the role of executive support and legitimization, (4) episodes of regression, and (5) the need for internalization and habitualization. In addition, we provide starting points for explaining organizational capability building in more general terms. [ABSTRACT FROM AUTHOR]

This study introduces an innovative automated model, the Scientists and Researchers Classification Model (SRCM), which employs data mining and machine-learning techniques to classify, rank, and evaluate scientists and researchers in university settings. The SRCM is designed to foster an environment conducive to creativity, innovation, and collaboration among academics to augment universities’ research capabilities and competitiveness. The model's development roadmap, depicted in Figure 1, comprises four pivotal stages: preparation, empowerment strategies, university-recognised research ID, and evaluation and re-enhancement. The SRCM implementation is structured across three layers: input, data mining and ranking, and recommendations and assessments. An extensive literature review identifies ten principal procedures further evaluated by experts. This study utilises Interpretive Structural Modelling (ISM) to analyse these procedures’ interactions and hierarchical relationships, revealing a high degree of interdependence and complexity within the SRCM framework. Key procedures with significant influence include determining the input data sources and collecting comprehensive lists of university scientists and researchers. Despite its innovative approach, SRCM faces challenges, such as data quality, ethical considerations, and adaptability to diverse academic contexts. Future developments in data collection methodologies, and addressing privacy issues, will enhance the long-term effectiveness of SRCM in academic environments. This study contributes to the theoretical understanding of academic evaluation systems and offers practical insights for universities that aim to implement sophisticated data-centric classification models. For example, by implementing data-centric models, universities can objectively assess faculty performance for promotion or tenure. These models enable comprehensive evaluations based on publication records, citation counts, and teaching evaluations, fostering a culture of excellence and guiding faculty development initiatives. Despite its limitations, SRCM has emerged as a promising tool for transforming higher education institutions’ academic management and evaluation processes.

The advancement of Artificial Intelligence (AI) is progressing rapidly, compelling companies to integrate it within their operational frameworks to sustain competitiveness, primarily driven by its impact on organizational agility (OA). Nevertheless, the absence of a robust theoretical framework underscores the limited understanding of the relationship between AI and OA. Within this context, the research aims to establish foundational knowledge, delineate the evolutionary trajectory of the topic, and identify prospective avenues for inquiry. To achieve this objective, bibliometric analysis is employed to gain comprehensive insights into the interplay between these variables and discern trends within this research domain. The utilization of the Web of Science (WoS) and Scopus databases up to January 2024 facilitates data collection, while Bibliometrix and Visme are instrumental in crafting a scientific production map. The analysis corroborates the novelty and growth potential of the subject matter, underscoring heightened author interest, particularly evident in 2023, against a backdrop of sparse and temporally dispersed publications until 2017. Notably, the prevalence of conference papers on this topic stands significantly high at 26.98 % in comparison to the total contributions, indicative of the research community's engagement. Furthermore, the findings underscore a robust association between the keywords AI and OA, delineating a burgeoning research domain that converges with the digital transformation of enterprises and the Theory of Standardization Process. The effective integration of AI into corporate operational frameworks marks the zenith of this transformative process, ushering in the genesis and overhaul of organizational routines. This study represents a pioneering endeavour within the literature, as it constitutes the inaugural bibliometric exploration of this subject matter. Moreover, it serves to underpin the establishment of theoretical underpinnings for future research endeavours as it outlines current trends and emerging future research trajectories, concerning the role of AI in OA.

Innovating how organisations run their business is a strategic decision to create more value for customers using or consuming the product and/or service provided. In addition to the incentive of everybody embracing digital transformation, digital technologies, and digital innovation, which frame changes of operating business models today, disruptions, i.e., another incentive that occurs suddenly and impacts globally, all force businesses to adapt and change.