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The aim of this research is twofold: (1) to investigate when the effect of R&D investment on New Product Development (NPD) performance peaks – the sweet spot and (2) to analyze the influence of firms’ export activities on where that spot is. Drawing on the knowledge-based view (KBV), we argue that export intensity and export experience lead to differential effects on how R&D investments are converted into new products.

We test our conceptual framework using time lagged data and optimal-level analysis. The dataset consists of an unbalanced panel of 608,891 observations and 333,516 firms.

The results support the expected inverted U-shaped relationship between R&D investment and NPD performance. They also show moderating effects of export intensity and experience. Export intensity enhances innovation processes by enabling firms to stretch the points at which R&D investments eventually taper off. In contrast, export experience improves firms’ ability to convert R&D investments into NPD performance. Our results demonstrate that, all else equal, firms with relatively higher export experience can spend less on R&D and still achieve higher levels of NPD performance.

We contribute to the literature by investigating how export activities provide a valuable context for understanding the theoretical mechanisms that help explain the inverted U-shaped relationship between R&D investment and innovation. We show the effects of exporting activities on the precise points where the R&D investment–NPD performance relationship peaks, thereby identifying the optimal point within this nonlinear relationship.

This paper analyses J1 League and J2 League clubs during the period 2011–2020 to anticipate financial distress.

Data were collected for 29 professional football clubs competing in the J1 and J2 League for the financial years ending 2011–2020. Analysis was conducted using Altman’s Z-score methodology and additional statistical tests were conducted to measure differences between groups.

The results show significant cases of financial distress amongst clubs in both divisions and that clubs that have played predominantly in the J1 League are in significantly poorer financial health than clubs that have played predominantly in the J2 League. Overall, the financial situation in Japanese professional football needs to be monitored, a position that could be exacerbated by the economic crisis, caused by the coronavirus disease 2019 (COVID-19).

While the financial situation for a majority of the clubs in the J-League presents an austere picture, comparison with clubs in other leagues across Asia and Europe and understanding the different policies set by these leagues would enable us to understand whether the phenomenon of financial distress is common to other clubs and leagues across different countries and continents.

The paper recommends that J-League visit the existing club licensing criteria and implement equitable cost-control measures, such as implementing a cap on acceptable losses over a specified period or restricting overall expenditures as a percentage of the club’s revenue.

The paper extends the evidence base of measuring financial distress in professional team sports and is also the first paper of its kind to examine this in relation to Asian professional football.

In this chapter, we emphasise how Creative Artificial Intelligence (AI) can and will transform the practice of financial operations (FinOps). To do this, we first place AI in the context of FinOps and how operations need to change, explicitly using Creative AI to be faster, more accurate and more creative when assessing client needs. This is achieved by explaining how traditional approaches fall well short of the mark by highlighting their fundamental limitations and showcasing how AI helps to address those shortcomings. We also provide a detailed discussion of how AI is transforming finance operations when we focus on four discursive areas: (1) risk, (2) fraud detection, (3) predictive analytics and (4) trading algorithms. In all four areas, Creative AI supports many decisions that benefit the clients, improves customer service and guides financial institutions to allocate their resources more effectively. We elaborate throughout this text how AI, in particular by using methods such as natural language processing, generative adversarial networks (GANs) and other related techniques, can be understood as what we have termed ‘Explainable AI’ to address operational issues in the modern financial world creatively. As AI offers great disarming power, we also discuss the threats, limitations and specific pitfalls of AI adoption and use in financial contexts. This includes addressing clearly ethical and regulatory concerns, in addition to the technical ones.

This chapter considers the challenges of applying engineering practices to knowledge. Knowledge cannot be managed like other forms of capital because it is tacit and intangible. Research has identified economic properties and behaviors that set it apart from physical and financial capital. The authors translate the economic typology of human, structural, and relational capital to Blackler’s four forms of characterizations: embrained, embodied, embedded, and encultured. Knowledge elicitation techniques are discussed, and aligned with Blakely’s four forms of characterizations.

Embodied intelligent robots are the iconic productivity of the Industry 4.0 era, and their potential to bring about a productivity surge mainly comes from the driving force of robots on innovation rather than efficiency. However, the dynamic impact of robots on the innovation capability of enterprises has not been empirically tested.

This study integrates panel vector autoregression and threshold effects to investigate this dynamic relationship by a multi-level analysis based on data of Chinese A-share manufacturing listed enterprises.

(1) The short-term momentum of industrial robot applications (IRA) on exploitative innovation (EII) is significant and the long-term momentum on exploratory innovation (ERI) is stronger. (2) EII affected by IRA is the main source of short-term total factor productivity (TFP) growth, while ERI is the driving factor for long-term TFP growth. (3) The impact of IRA on TFP exhibits a double-threshold effect based on ERI and follows a “stepped” incremental pattern. The promoting effect of IRA on TFP will significantly increase only when ERI surpasses certain thresholds.

Industrial robots accelerate the potential productivity growth in the long term, mainly coming from the augmented contribution of ERI, providing reference and inspiration for enterprises to fully utilize the endogenous growth potential of robots and implement innovation strategies. It also provides forward-looking guidance for organisations to undertake adaptive changes for the forthcoming AI economic revolution.

This chapter explains how and why the knowledge economy will increase the demand for knowledge engineering. It defines and traces the evolution of knowledge engineering. It identifies the two components of knowledge engineering – elicitation and representation. It discusses the increased importance of tacit knowledge, specifically know-what and know-how, for organizations and companies. The increased demand for knowledge engineering calls for increased number of knowledge engineers. Knowledge engineering will expand beyond its current homes in systems development and cognitive science. The MASK methodology is an important intermediary between formal knowledge engineering and the methods needed to develop natural language and conceptual modeling for the knowledge economy.

Techno-empowerment refers to giving intelligent technology a decision-making power. It is a growing trend, with algorithms being developed to handle tasks like ordering products or investing in stocks without human consent. Nevertheless, people may feel averse to transfer decision-making autonomy to technology. Unfortunately, little attention was paid in the literature regarding what tasks people exclude from being performed autonomously by non-human intelligent actors. Our chapter presents two qualitative studies: the first one examining what decisions people think autonomous technology (AT) should not make, and the another asking workers which tasks they would not transfer to AT. Results show people oppose AT making decisions when task is perceived as (a) requiring empathy, (b) human experience, (c) intuition, (d) complex, (e) potentially harming human life, (f) having long-term effects, (g) affecting personal space, or (h) leading to loss of control. Workers are not willing to delegate such tasks to AT they perceive as (1) time-consuming, (2) demanding social interaction, (3) providing pleasure, (4) difficult, (5) risky, and (6) responsible. Exclusions are driven by three types of perceived risks: material, contextual, and competitive.

This study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.

The study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.

The results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.

This study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.