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This study aims to investigate the association of FinTech innovation (FinTechINN) and firm performance (FP) by considering the role of knowledge assets (KA) as a causal mechanism underlying the FinTechINN – FP association.

In this study, the authors consider panel data of 1,049 Chinese A-listed firm and construct a structural model for corporate FinTech innovation, knowledge assets and firm performance while considering endogeneity issues in analyses over the period of 2014–2022. The modified value added intellectual capital (VAIC) and research and development (R&D) expenses are used as a proxy measure for knowledge assets, considering governance and corporate performance measures.

According to the findings of this study FinTech innovation (FinTechINN) has a positive significant effect on firm performance. Particularly; the findings disclose that FinTech innovations has a link with knowledge assets, FinTech innovations indirectly affects firm performance, and the association between FinTech innovation and firm performance is partially mediated by knowledge assets (MVAIC and R&D expenses).

Rooted in the dynamic capability and resource-based view, this study pioneers an empirical exploration of the association of FinTech innovation with firm performance. Moreover, it introduces the novel dimension of knowledge assets (on firm-level), acting as a mediating factor with in this relationship.

This study aims to build upon resource orchestration theory to theorize and empirically test a model that demonstrates how knowledge assets and innovation ambidexterity trigger a synergy in favor of firm performance.

Drawing on a survey of 158 Iranian knowledge-intensive companies, this study uses the partial least squares based on structural equation modeling to test the research hypotheses.

The results show that two elements of knowledge assets, namely, structural and relational capital, indirectly affect firm performance through the full mediation of innovation ambidexterity. The findings indicate that human capital has no relationship with both innovation ambidexterity and firm performance.

This study offers fresh insights into the issue of how organizations can create value from an effective orchestration of various strategic resources and capabilities, including knowledge assets and innovation ambidexterity.

This study applies resource orchestration theory to concurrently the areas of knowledge resources and organizational ambidexterity to show how innovation ambidexterity plays a role in translating three various knowledge assets into performance.

This chapter makes a case for extending institutional preservation strategies to the entire landscape of knowledge capital. First, the authors define the three primary types of capital – physical, financial, and knowledge. Knowledge capital is further broken down into three categories – human, structural, and relational. The individual types of knowledge capital are defined, along with their variant economic properties and behaviors. The challenges these variations present for preservation are discussed. The authors also highlight these assets’ significant opportunities for curating new knowledge. Each type of knowledge capital is described, along with the preservation challenges and the curation opportunities.

In this chapter, the authors define the new business term, future-proofing, and apply it to knowledge preservation and curation. The fundamental principles of future-proofing and the challenges and mechanics are discussed. These challenges are discussed in developing future-proofed knowledge preservation, and a curation strategy is identified. The authors identify four challenges to future-proofing a knowledge preservation and curation strategy – availability, visibility, accessibility, and consumability of knowledge assets. Ultimately, the greatest challenge to future-proofing these strategies lies in the channels we use to create, transmit, share, and store knowledge assets. At a minimum, the chapter speaks to the critical importance of future-proofing the preservation of knowledge assets, so there is a possibility of curation at some point in a known or unknown future.

This chapter identifies the five new roles that are critical to establishing and sustaining a knowledge preservation and curation practice. For each role, the authors describe fundamental responsibilities and competencies. Two of the roles support knowledge preservation, including business knowledge analyst and specialized knowledge preservationist. Three of the roles support knowledge curation including business interlocutor/translator, knowledge curator, and knowledge asset developer. Each role faces peculiar challenges in a dynamic and chaotic knowledge economy.

This chapter explores the traditional and evolving practice of preservation. Traditional preservation practices are traced throughout history. Current practice is described as a foundation from which to understand evolving practices. Traditional preservation has historically focused on tangible assets that take a tangible form we can all see and touch. The traditional practices are compared to the evolving practices of knowledge and intangible resources. The chapter examines why, what, how, when, where we preserve, and who preserves to understand the transition in progress today. The authors make the case that shifting the traditional focus from preserving for the past and evidentiary reasons to preserving for the future to support business challenges is crucial. The authors also tie the need to refocus on historical impediments and challenges to knowledge use and reuse in practical business environments.

This chapter defines channels and explains their role in creating and exchanging knowledge assets. Channels are where many of today’s knowledge assets live. Knowledge is increasingly bundled with communication technologies in the chaotic and dynamic channel market. It challenges the preservation and curation of an organization’s knowledge assets. We cannot ignore channels because they are essential to knowledge exchange and flows and engaging in business. At the same time, knowledge preservation and curation strategies mandate that organizations make wise channel choices and manage knowledge assets in a channel-agnostic way. The chapter reviews the evolution and range of channels active today. It explains how channels create complex knowledge environments in scale and scope.

This chapter focuses on organizational preservation and curation capabilities. The authors define capabilities and explain how they pertain to an enterprise architecture. The authors describe preservation as it is currently practiced in relation to information and data. They also explain how preservation should be expanded to cover knowledge assets. Knowledge preservation exists today as a support capability, aligned with information and data management. Curation is described as an emerging but fragmented practice. It is modeled as a new business capability.

In this chapter, the authors make the case that preserving and curating knowledge for the future involves more than changing methods and tactics or extending our current applications and technology to support knowledge capital. It means changing the way we think about the future. It means envisioning multiple futures where various elements may be known or unknown – a four-future quadrant. First, the authors explain what it means to think strategically in multiple known and unknown futures. Next, the chapter presents ideas for strategic thinking about future knowledge preservation and curation. Finally, the authors consider using the four futures to develop a flexible and relevant knowledge preservation and curation strategy.

This study seeks to enhance productivity, motivation and well-being of workers by improving decision-making processes. Using inner knowledge management (IKM), this study aims to identify, manage and transform unconscious beliefs and negative emotions that limit decision-making processes.

The methodology used is empirical research, analyzing multiple cases qualitatively and using the T-test statistical model to analyze the hypotheses. The study tests the relation between different limiting beliefs and negative emotions that influence decision-making processes.

This study shows that IKM is positively related to the productivity, motivation and well-being of workers.

The study is limited to workers who are mentally healthy, who work in teams and who seek professional help to achieve their goals.

The results indicate that there is a lot of potential to be explored applying IKM in companies and organizations. Specifically, this study proves that there are several inner knowledge assets that constrain the workers’ potential and therefore affect the efficiency of businesses.

The results have strong implications for how companies and organizations can create great value for themselves and their workers.

To the best of the authors’ knowledge, this is the first data-based study using an IKM model. The importance of this study opens the door to further exploration of the effects on IKM on productivity, motivation and the general well-being of workers.