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Why is it so difficult to make change happen? Many companies fail because they do not have the right internal structure and mindset to succeed. The author illustrates how to manage change effectively by examining how product‐based companies must transform to launch true service‐driven ventures. Using three case studies, he documents common pitfalls and barriers, then outlines the steps necessary for developing a well‐conceived service strategy. He introduces “business logics” as a key for identifying characteristics that define business types and the performance measures that support their success. The author then explains how a knowledge‐driven business model identifies the critical behaviors, competencies, market conditions, finances and other resources needed to make change happen. He advises organizations to test their strategies using simulations to ensure employees are prepared to internalize behavior and culture changes.

Why is it so difficult to make change happen? Many companies fail because they do not have the right internal structure and mindset to succeed. The author illustrates how to manage change effectively by examining how product‐based companies must transform to launch true service‐driven ventures. Using three case studies, he documents common pitfalls and barriers, then outlines the steps necessary for developing a well‐conceived service strategy. He introduces “business logics” as a key for identifying characteristics that define business types and the performance measures that support their success. The author then explains how a knowledge‐driven business model identifies the critical behaviors, competencies, market conditions, finances and other resources needed to make change happen. He advises organizations to test their strategies using simulations to ensure employees are prepared to internalize behavior and culture changes.

You may be surprised to discover that millions of dollars in revenue are sitting, undiscovered, inside your own organization. Follow the authors as they walk you through different areas of your business to discover the untapped potential of “forgotten” intangible assets that may already exist, including patents, trademarks, licensing arrangements, employee know‐how, infringement protection plans and much more. Learn how to manage these assets to their fullest potential by creating an Intellectual Asset Management Portfolio (I‐AMP). The authors reveal their own five‐step process and then present three case studies (an energy company, a high‐tech manufacturer and a telecom company), which illustrate the remarkable increases in revenue generated by this program. In one case, $1 billion was shifted from the expenditure to the revenue side of the ledger!

Several causes may induce firm managers to analyze the actual technology condition of patent portfolios, among which is the need of exploiting patents strategically. In this paper, the question of how to support intellectual property (IP) managers of large high technology companies in their strategic decision-making process of evaluating patents is examined. The purpose of this paper is to provide a decision support framework that suggests the suitable exploitation strategy for patents.

The paper proposes an audit framework able to point out whether patents are aligned to the overall business strategy, to select those that are not aligned, and to identify the most appropriate exploitation strategy for each patent of the portfolio. The framework is structured into two phases: in the first one, patents are selected through the analysis of four dimensions that characterize the value of patents effectively; in the second one, a questionnaire is distributed to IP managers in order to support their decision on patents. The paper illustrates case-based applications of the framework.

Results of applications show that the framework is able to suggest IP managers the suitable exploitation strategy on four possible alternatives (maintaining, licensing, selling, abandoning) for each patent of their portfolios.

The framework is an innovative and valuable tool to IP managers, and besides its structural formulation, it is appreciable in terms of application expedition and efficiency of performance.

This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.

Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).

This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.

From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.

This paper aims to analyse the open data business models (ODBMs) as a source of knowledge and innovation to generate economic and social value. A framework for understanding ODBMs is presented. First, the knowledge structure of the ODBMs literature is identified. Second, a conceptual model for analysing the ODBMs is proposed. And finally, the future trends in ODBMs research are discussed.

In this paper, co-word analysis is performed to identify the topics related with ODBMs.

The ODBMs structure of knowledge is articulated in five themes: business model, smart city, business ecosystem, decision-making and innovation. Based on these results, a five-step model for analysing ODBMs is proposed. Finally, a discussion of the future trends of ODBMs focussed on a knowledge management perspective, open data ecosystems and business intelligence is presented.

The paper presents a picture of what, where, for whom and for what ODBMs have been studied and develops a new model to explain the value creation process of ODBMs. Taking a step further, applying the principles and models of knowledge management and business intelligence to ODBMs is also recommended in order to transfer and transform open data into valuable knowledge that can be used for developing apps. In that context, the importance of encouraging collaboration between different agents in the so-called open data ecosystem is presented.