Helen N. Rothberg

Big data systems: knowledge transfer or intelligence insights?

Purpose This paper aims to bring together the existing theory from knowledge management (KM), competitive intelligence (CI) and big data analytics to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge and intelligence). By doing so, the interactions of the intangibles are better understood and recommendations can be made for the appropriate structure of big data systems in different circumstances. Metrics are also applied to illustrate how one can identify and understand what these different circumstances might look like. Design/methodology/approach The approach is chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work are applied to illustrate some concepts. Findings Theory suggests that the KM theory is particularly useful in guiding big data system installations that focus primarily on the transfer of data/information. For big data systems focused on analytical insights, the CI theory might be a better match, as the system structures are actually quite similar. Practical implications Though the guidelines are general, practitioners should be able to evaluate their own situations and perhaps make better decisions about the direction of their big data systems. One can make the case that all the disciplines have something to add to improving how intangibles are deployed and applied and that improving coordination between KM and analytics/intelligence functions will help all intangibles systems to work more effectively. Originality/value To the authors’ knowledge, very few scholars work in this area, at the intersection of multiple types of intangible assets. The metrics are unique, especially in their scale and attachment to theory, allowing insights that provide more clarity to scholars and practical direction to industry.

Intellectual capital, competitive intelligence and the Economic Espionage Act

This article analyses the growth and practice of intellectual capital and competitive intelligence, two industry practices that have received increasing attention over the last decade. Related in their conceptual structure, the two fields also interact in terms of the benefits and costs of leveraging organisational knowledge in the face of competitive intelligence efforts. This interaction poses the key question of how widely intellectual capital should be dispersed. In the USA, at least, the Economic Espionage Act provides some guidance. After a discussion of the key provisions of the Act, the paper passes to practical suggestions for using intellectual capital resources as widely as possible while still protecting the firm from competitive intelligence threats. Compliance standards, risk assessment and management, collaborator certification and signalling are all considered in turn.

Healthcare and hospitality: intangible dynamics for evaluating industry sectors

ABSTRACT In order to assess potentially profitable exchanges across the healthcare and hospitality industries, this paper reviews intangibles theory, ranging from big data through knowledge assets (explicit and tacit) to intelligence, establishing the potential value from each and best practices for managing. Metrics are offered for the assessment of firms and industries according to these intangibles. Based on the metrics, intangible practices can be identified in all the industry sectors across healthcare as well as from hospitality sectors. Similarities are identified between hospitality and specific healthcare sectors (retail pharmacies have most in common with hospitality firms, both possessing strong data and explicit knowledge capabilities). Implications for strategy, competition, and intangibles management systems can also be drawn (chiefly process and customer relationship data and knowledge leading to operational and marketing excellence).

A strategic approach to knowledge development and protection

A standard prescription for industry is increased investment in and application of intangible assets, including knowledge management, competitive intelligence, and, now, big data. This paper examines whether a more measured, strategic approach might be appropriate, particularly whether knowledge development and protection needs vary by type of industry. Overall, knowledge decisions should be more strategic, there are some patterns in type of industry (services) and type of knowledge. In developing this case for a more strategic approach to developing and protecting knowledge assets, this paper applies concepts from the fields of knowledge management, intellectual capital, and competitive intelligence to healthcare industries. The literature is scanned to develop a categorization of the competitive conditions facing firms in these industries. This categorization (a Strategic Protection Factor framework) identifies the degree of knowledge development in an industry as well as the level of competitive intelligence activity and resulting knowledge protection concerns. Assigning data reflecting these variables, knowledge development and knowledge protection, the paper assesses the competitive circumstances surrounding 10 healthcare industries and provides broad strategic recommendations. The results show that knowledge development and knowledge protection conditions vary dramatically, even within a specific realm such as healthcare. Similarly, when looking at service and non-service healthcare industries, the same conclusion is apparent, with widely varying competitive conditions. The results call for a much more strategic, circumstance-specific approach to decisions concerning knowledge in organizations as well as more scholarly work on the differing conditions within which knowledge management takes place as well as where and why they exist.

Assessing Knowledge Management Needs: A Strategic Approach to Developing Knowledge

Most knowledge management work encourages practicing managers to pursue ever more knowledge development and sharing. There is an assumption that more knowledge always leads to greater competitive advantage and superior financial performance. Even though this may be the case, it is far from proven, and the application and use of knowledge management techniques may actually be more strategic, with optimal levels of effort and spending determined by differing circumstances. This study investigates these topics by assessing whether knowledge strategies might vary by industry, asset structure, and other potential variables.

Alternative Metrics for Assessing Knowledge Assets

This paper analyzes two different approaches to assessing the knowledge assets of multiple firms in a given industry. Typically, in evaluating knowledge management (KM) results by measuring intellectual capital (IC), studies in the field have looked specifically at individual firms or a small cluster of organisations. Measuring a large number of firms has proven more of a challenge even though the discipline has some established metrics to do so, principally those using some variation of Tobins q. In moving such methodologies forward, we look at two variations on Tobins q for assessing KM requirements in an industry and the relative level of KM success in member firms. We contrast different industries with apparently different KM circumstances, allowing some deeper insights into the strengths and weaknesses of our two metrics. These results will be of interest to those studying KM and IC as they provide guidance in evaluating performance (as well as the need to invest in order to keep up with industry top performers). For the same reason, the results and methodology will inform practitioners taking a strategic approach to knowledge investment, giving them a way to assess relative standing within and across industries. KM strategies can and should differ, and these metrics provide guidance for such decisions.

Predicting Strategic Actions Across Industry Sectors: The Role of Intangible Dynamics

Abstract This chapter examines firm strengths and weaknesses from the standpoint of intangible assets. These are compared within and across industry sectors in order to better understand who might be a potential collaborator (or competitor) in different contexts. Establishing the conceptual basis of a range of intangibles, including data, explicit knowledge, tacit knowledge, and intelligence, the chapter moves to metrics for assessing industry averages and individual firm capabilities. Finally, several sectors in healthcare are examined, specifically identifying what kinds of collaborators would best fit with a technology-driven start-up like Theranos.

SPF 15: From the Inside

We have talked a bit about large market share firms in SPF 15 industries. The toy industry does not show up on the lists covered in the last chapter (too few firms/observations in our database) but there are few competitive arenas with a more long-standing, powerful market presence than Mattel and Barbie. The firms that did represent this industry fell squarely into the SPF 15 quadrant, with substantive knowledge value and no recognizable CI activity (though we will talk about how this might be changing). So we feel comfortable using the long, detailed legal skirmish between Mattel and MGA Entertainment as an illustrative example of conditions in these types of industries.

SPF 5: From the Top

The hotel industry is not technically in SPF 5, rating a KM value of 1.05 and a CI value of 1, but it is clearly very close. And the presence of a single firm, with a higher KM value, actually pushes the entire industry above the 1.00 cutoff—the rest of the firms fall well within the SPF 5 values. Given a dearth of interesting examples from SPF 5, since knowledge often is not worth fighting over or writing about, this “close enough” industry can help illustrate some of the circumstances found in SPF 5.

SPF Review: Our Journey

Several years ago, we authored our first book, From Knowledge to Intelligence, Creating Competitive Advantage in the Next Economy. In that book, we first proposed a framework, captured in the term Strategic Protection Factor (SPF), accomplishing several things related to the knowledge management (KM) and competitive intelligence (CI) fields.