Digital Information and Value

Institutional pressure and open innovation: the moderating effect of digital knowledge and experience-based knowledge

Purpose

When considering the influence of external social, technical and political environments on organizations’ open innovation behavior, especially in emerging markets, institutional theory is especially salient. This study aims to answer the question of how to integrate organizations’ external institutional pressures and internal knowledge structure to mitigate the challenges in the open innovation process.

Design/methodology/approach

This study uses a sample of 2,126 observations from the 2012 World Bank Enterprise Survey. A multivariate regression model is designed to explore the impact of external institutional pressure (i.e. coercive pressure, mimetic pressure and normative pressure) on open innovation, as well as the moderating effect of digital knowledge and experience-based knowledge.

Findings

The results show that institutional pressure has a positive role in promoting open innovation; digital knowledge weakens the positive relationship between institutional pressure and open innovation; experience-based knowledge strengthens the positive relationship between institutional pressure (especially coercive pressure) and open innovation.

Originality/value

This study combines institutional theory and knowledge management to enriches insights into open innovation in emerging markets. Beyond recognizing the inherent multidimensionality of the concept of institutional pressure, this study creates an integrated path for the legitimacy acquiring of enterprises through the knowledge structure design (i.e. digital knowledge and experience-based knowledge). It also deepens the institutional pressure to enable the implementation of digital knowledge to manage open innovation processes.

The Limitations of Decision-Making

In a world faced with technological, health and environmental change and uncertainty, decision-making is challenging. In addition, decision-making itself is becoming a collaborative activity between people and artificial intelligence. This paper analyses decision-making as a form of information processing, using the ideas of information evolution. Information evolution studies the effect of selection pressures and change on information processing and the consequent limitations of that processing. The analysis identifies underlying information evolution factors that affect the quality of information used throughout decision-making and, hence, affect the quality of decisions. These factors imply a set of challenges in which the pressures that drive useful trade-offs in a static environment also hinder decision-making of the required quality in times of change. The analysis indicates the information evolution characteristics of a good decision-making approach and establishes the theoretical basis for tools to demonstrate the information evolution limitations of decision-making.

Information Evolution and Organisations

In a changing digital world, organisations need to be effective information processing entities, in which people, processes, and technology together gather, process, and deliver the information that the organisation needs. However, like other information processing entities, organisations are subject to the limitations of information evolution. These limitations are caused by the combinatorial challenges associated with information processing, and by the trade-offs and shortcuts driven by selection pressures. This paper applies the principles of information evolution to organisations and uses them to derive principles about organisation design and organisation change. This analysis shows that information evolution can illuminate some of the seemingly intractable difficulties of organisations, including the effects of organisational silos and the difficulty of organisational change. The derived principles align with and connect different strands of current organisational thinking. In addition, they provide a framework for creating analytical tools to create more detailed organisational insights.

Artificial Intelligence and the Limitations of Information

Artificial intelligence (AI) and machine learning promise to make major changes to the relationship of people and organizations with technology and information. However, as with any form of information processing, they are subject to the limitations of information linked to the way in which information evolves in information ecosystems. These limitations are caused by the combinatorial challenges associated with information processing, and by the tradeoffs driven by selection pressures. Analysis of the limitations explains some current difficulties with AI and machine learning and identifies the principles required to resolve the limitations when implementing AI and machine learning in organizations. Applying the same type of analysis to artificial general intelligence (AGI) highlights some key theoretical difficulties and gives some indications about the challenges of resolving them.