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Effective leadership is critical in driving innovation and success in organisations, particularly in today’s rapidly changing environment. However, achieving effective leadership at all levels of the organisation can be challenging. This chapter argues that understanding how the brain functions is essential for innovation leaders to achieve positive results and higher rates of success in their projects. By analysing relevant research on neuroscientific functioning patterns and developing interventions based on these foundations, this chapter establishes that the brain’s self-organising ability and cognitive processing systems offer valuable insights for effective innovation leadership. Based on neuroscientific evidence this chapter concludes that effective innovation leadership should focus on inviting others to engaged co-creation, rather than directing others to perform specific tasks as if they were ‘a prolonged arm’. Additionally, effective innovation leadership integrates insights from information processing in the brain by providing behavioural-oriented impulses that activate the brain, enabling individuals to maintain focus, restore motivation or emotional stability, enhance mood and confidence, and increase cognitive flexibility. Evidence-based interventions range from structured breaks to powernapping and walking. The importance of self-leadership is stressed throughout the chapter. By deriving solutions from an understanding of how the brain functions, interventions that may have been known for a long time can become evidence-based and optimised for use in organisations. Future research could explore the intersection of neuro- and behavioural science with leadership to further innovate organisational principles.

This conceptual article presents a schematic for use with extended cybernetic recursion in living systems and applies it to the issue of hyper vigilance as a demonstration of its utility.

The test-operate-test-exit (TOTE) schematic of Miller et al. (1960) is critically evaluated along with other schematics, including those of ordered cybernetics, and a new schematic is proposed, a recursive test-operate-test (rTOT), which emphasizes teleological purpose and hierarchical structure. The background psychophysiology of trauma is reviewed and then rTOT is applied to hyper vigilance, a cardinal component of post-traumatic stress disorder (PTSD).

Once the schematic was developed, it was applied to the behavior of hyper vigilance. Other applications are suggested.

As demonstrated, the rTOT schematic has potentially wide application because of its pragmatic and detailed structure.

The rTOT requires careful consideration of teleological purposes for its application and is simple enough, but also complex enough, for relevant utilization. Its compact nature and adjustable hierarchy scope are good mini-max complexity solutions for cybernetic, information modeling schematics.

The revealed teleological purpose of the trauma adaptation of hyper vigilance presents significant alternative formulation options for prevention and intervention.

While the rTOT schematic is derived from previous schematics, it is original in its emphasis on information processing, the teleological aspects of extended recursion and on the provision of a hierarchical structure for those recursions. It is considerably more compact than other schematics associated with the ordered cybernetics literature. The explication of the adaptation model for post-trauma consequences is significantly enhanced by the rTOT application.

Improving a startup’s ability to obtain funding is critical to the survival of the organization. Although existing studies have observed various biases in investment decisions, few have studied the neural mechanisms behind such behavioral observations. We propose to apply cutting-edge neuroscientific techniques to uncover the neural processes engaged during pitches by entrepreneurs to investors and to use this new knowledge to identify strategy artifacts promoting pitch success. We hypothesize that pitches are dynamically shaped by covert cognitive, emotional, and social processes, which are in turn influenced by tactical approach (story-telling vs dry facts), physical context (online vs in-person), and demographics (gender, ethnicity). The role of inter-brain synchrony (i.e., correlation of cortical activity between brains) – within the startup team or between the entrepreneurs and investors – in pitch outcomes remains unknown. By uncovering the covert processes that mediate pitch outcomes, we provide an evidence-based, scientific approach to improving pitch success.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

Prior research on relations between mental health and pain has focused on negative mental health aspects (e.g. depression), while the literature is scarce in terms of positive aspects, such as mental well-being. This study aims to investigate prospective associations of mental well-being at baseline with pain and functional impairment due to pain in the following year.

Data stem from a Danish nationally representative survey of 5,000 adults (aged 15+) conducted in 2019 and 2020, which was linked to register data. The Short Warwick–Edinburgh Mental Well-being Scale was used to assess mental well-being, both continuously (range 7–35) and categorically (low, moderate, high). Logistic regressions were conducted to assess associations between mental well-being in 2019 and pain and functional impairment due to pain (among those reporting any pain) in 2020.

In the fully adjusted models, each point increase in mental well-being was inversely associated with pain (OR = 0.97, 95%CI 0.95–0.99) and functional impairment due to pain (OR = 0.96, 95%CI 0.94–0.99). As compared to low mental well-being, moderate mental well-being was associated with an OR of 0.76 (95%CI 0.58–0.99) for pain and an OR of 0.63 (95%CI 0.46–0.87) of functional impairment due to pain, while high mental well-being was associated with an OR of 0.56 (95%CI 0.40–0.77) for pain and an OR of 0.53 (95%CI 0.34–0.79) for functional impairment due to pain.

Higher mental well-being levels may be protective against pain and functional impairment due to pain. Wider mental health promotion may be considered to prevent pain and associated functional impairments in the general population.

The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises.

Against the background of the “three red lines” regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk.

The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises.

It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems.

Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises.

The purpose of this theoretical chapter is to rework a promising but limited theory of the foundations of reciprocity. Reciprocity is often attributed to an “internalized norm of reciprocity” – a deeply felt moral obligation to help those who have helped us in the past. Leifer's theory of local action develops a radically different and compelling foundation for reciprocity – one in which the impetus for reciprocity is a thinly veiled battle for status. We rework the theory to offer a new one that addresses its limitations. The key idea is that the impetus for reciprocity is the desire to signal that one intends to create joint value rather than to capture it from the counterparty.

Our analytical approach rests on close examination of a puzzling and underrecognized feature of social exchange: people who initiate social exchange routinely deny giving anything of value (“it was nothing”) while the receiver inflates their indebtedness to the giver (“this is too much!”). We refer to this negotiation strategy as reverse bargaining and use it as a window into the logic of social exchange.

We develop a more general theory of how people manage the threat of opportunism in social exchange that subsumes local action theory. The key insight is that people who initiate social exchange and seek reciprocity must balance two competing objectives: to ensure that the person receiving a benefit recognizes a debt she must repay; and to mitigate the receiver's suspicion that the giver's ulterior motive is to capture value from the receiver.