scholarly journals A Longitudinal Look at Strategy, Intellectual Capital and Profit Pools

2015 ◽  
Vol 5 (2) ◽  
Author(s):  
G. Scott Erickson ◽  
Helen N. N. Rothberg
Keyword(s):  
Intellectual Capital ◽  
Additional Data ◽  
High Growth ◽  
Competitive Intelligence ◽  
Profit Margins ◽  
Intelligence Activity ◽  
Depth Study ◽  
Knowledge Strategies ◽  
Industry Sectors ◽  
Key Industry

Explores the link between the disparate fields of knowledge management, intellectual capital, competitive intelligence, and strategy.  Using an existing profit pool study of the digital economy, looks at the key industry sectors involved and their revenue levels and profit margins.  These data include results from both 2002 and 2010.  The profit pool observations are then compared with additional data on intangible assets (knowledge and related assets) and competitive intelligence activity in each sector.  Explores but generally dismisses the idea that sector revenue and/or profitability might be linked to high levels of intangibles.  Similarly, demonstrates that the link between sector revenue and/or profitability and competitive intelligence activity may be generally weak (though pronounced in some specific high-growth circumstances).  Alternatively, does provide some guidance for more in-depth study, identifying the knowledge strategies necessary for success across sectors as well as what competitive intelligence attitude may be needed to move from one sector into another.  

Measuring and Managing Intellectual Capital for both Development and Protection

2011 ◽  
pp. 254-267
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Knowledge Management ◽  
Strategic Management ◽  
Intellectual Capital ◽  
Additional Data ◽  
Competitive Intelligence ◽  
Strategic Decisions ◽  
Knowledge Assets ◽  
The Core ◽  
Intelligence Activity ◽  
Core Metrics

This chapter considers the strategic management of intellectual capital, balancing the need to develop knowledge assets with the need to protect them. In making more strategic decisions, metrics on the level of intellectual capital and degree of knowledge management necessary to compete in an industry are required, as are those on the threat from competitive intelligence activity. The authors develop the case for appropriate metrics that accomplish these purposes, noting both potential and limitations. The authors also consider alternatives, additional data that could contribute to the usefulness and understanding of the core metrics, and provide suggestions for further research.

scholarly journals Benchmarking Competitive Intelligence Activity

2012 ◽  
Vol 2 (3) ◽  
Author(s):  
Helen N. Rothberg ◽  
G. Scott Erickson
Keyword(s):  
Intellectual Capital ◽  
Research Program ◽  
Knowledge Development ◽  
Competitive Intelligence ◽  
Scholarly Work ◽  
Strategic Choices ◽  
Financial Reports ◽  
Comprehensive Database ◽  
Knowledge Protection ◽  
Intelligence Activity

This paper reports on results drawn from a comprehensive database formed from public financial reports and a proprietary benchmarking survey conducted by a major competitive intelligence consulting firm.  Our overall aim is to identify different circumstances in which knowledge development and knowledge protection have greater or lesser importance.  Very little work has been done on a industry-wide (or wider) basis concerning intellectual capital and/or competitive intelligence activities in firms and how that may vary according to circumstances.  The wider study and database are designed to better address such questions.   In this study, we look at one piece of this overall research program, specifically how competitive intelligence activity varies in distinctive environments.  Based on these results, as practitioners better understand their environments, they can make better decisions on the level and aggressiveness of their own CI operations as well as on protection and counterintelligence efforts.  The results will also begin to move scholarly work in the field into these new areas of macro studies and strategic choices.

scholarly journals USE OF “KNOWLEDGE HOUSE”, DWS, DMS AND DSS METHODOLOGY BY COMPLETING A COMPETITORS' ANALYSIS IN THE RAILWAY SECTOR

2016 ◽  
Vol 17 (6) ◽  
pp. 1022-1051
Author(s):  
Vaidas GAIDELYS ◽  
Stasys DAILYDKA
Keyword(s):  
Intellectual Capital ◽  
Positive Impact ◽  
Competitive Intelligence ◽  
Sources Of Information ◽  
Strategic Decisions ◽  
Foreign Companies ◽  
Information Accessibility ◽  
Use Of Knowledge ◽  
Selection Of

In completing a competitors’ analysis in the railway sector by using the “Knowledge House” method, there is frequently a problem of data and information accessibility. The quality of primary information has direct influence on the quality of analytical conclusions. One more condition for the qualitative application of this method is the intellectual capital and experience of the analyst. One should note that in this regard we face another problem, that of selection of proper personnel, on the qualification of whom depends the accuracy of the evaluation and final results, on the basis of which strategic decisions are taken. The main aim of the paper is to assess the opportunities for applications of competitive intelligence methods in the railway sector. The study is using “Knowledge House”, DWS, DMS, DSS methodologies. Having analysed the scientific works the direct scientific sources of information, which are oriented to the application of the methods of competitive intelligence to the railway sector, have not been identified. The paper is absolutely original in that until now the competitive intelligence techniques have not been applied for the railway sector companies. Considering the fact that foreign companies, which compete for freighting at the international level, are regarded as the main competitors of the railway sector, the use of the methods of the competitive intelligence becomes more important while fighting for the part of the market. The competitive intelligence methods and their application to the railway sector companies are little studied. In accordance with application of the relevant methods in other sectors, it can be assumed that these innovative approaches could have a positive impact on the competitiveness of companies in the railway sector and their income.

scholarly journals Management control systems, organization and impact on performances: Evidences from Italy

2017 ◽  
Vol 14 (2) ◽  
pp. 250-257
Author(s):  
Elisa Truant
Keyword(s):  
Intellectual Capital ◽  
Management Control ◽  
Management Systems ◽  
Incentive Systems ◽  
Multiple Sources ◽  
Management Tools ◽  
Accounting Systems ◽  
Corporate Managers ◽  
Key Factor ◽  
Depth Study

The value creation is the primary goal of each organization and intellectual capital is certainly a key factor for long-term success. The intellectual capital variables have to be managed and measured within advanced management systems, in order to facilitate the communication and translation of strategy’s tangible and intangible elements into operational terms. This study focuses on a sample of medium-sized Italian firms and is based on multiple sources of evidence: the in-depth study of internal documents and interviews with corporate managers holding key positions within the organization. The research aims at investigating if managers identified, measured and monitored intellectual capital variables within advanced management accounting systems, over a period of 5 years. Because the strategy and the organizational structure are highly interdependent, this study also focuses on evaluation and incentive systems implemented within selected companies. Then, it was decided to analyze whether the use of managerial and organizational tools influence firms’ performances. This research contributes to extend existing literature on intellectual capital and management systems: the results revealed that companies able to manage and monitor intellectual capital within advanced management tools, as well as implement evaluation and incentive systems, achieved higher and more stable performances. The main limit of this study is strictly related to the choice of these variables: in fact, company’s performances are influenced by a significant number of factors, endogenous and exogenous to the organization. Future researches can involve a greater number of companies and organizational variables, in order to validate or confute the actual findings.

The intellectual capital needs of a transitioning economy

2015 ◽  
Vol 16 (3) ◽  
pp. 466-489 ◽  
Author(s):  
Allan O'Connor ◽  
Kai Du ◽  
Göran Roos
Keyword(s):  
Strategic Management ◽  
Intellectual Capital ◽  
Value Chain ◽  
Value Added ◽  
Intangible Asset ◽  
Firm Level ◽  
Content Type ◽  
Economic Productivity ◽  
Industrial Sectors ◽  
Industry Sectors

Purpose – Developed economies with high-cost environments face industrial transitions from scale-based manufacturing (MAN) to knowledge, technology and intangible asset-based sectors. The purpose of this paper is to examine the changes in employment and value-adding profiles of transitioning industry sectors in Australia and discuss the implications for policy that influences the intellectual capital (IC) profile of industrial sectors in transition. Design/methodology/approach – The approach borrowed concepts from the firm-level strategic management literature and applied them to a macro level of industry analysis. In this paper the authors examine the transitions in the Australian economy which, due to a rising cost base, is experiencing a decline in its value chain-oriented MAN sector. The authors contrast four industry sectors with the MAN sector and examine the different value creation models. Findings – The findings clearly show how the contribution to employment and value added (termed Economic Value Contribution ) of the different sectors vary. The authors extend these findings to a discussion on policy and the dimensions of IC that may have a role to play in facilitating transitions within an economy. The main conclusion is that a more rapid transition and higher value may be created if innovation and entrepreneurship are facilitated by targeted policies in transitioning sector. Research limitations/implications – This work is based on a single country analysis of selected industry sectors. Further work needs to be done across many more countries to contrast the findings across nations/regions that differ in industrial complexity and to refine the analytical framework to improve construct validity and increase analytical power. Practical implications – This work has implications for policy-makers facing the challenges of a transitioning economy, whether national or regional. Governments that are hands-on with respect to interventions to salvage and/or extend the life of sectors are at risk of missing opportunities to build the capacities and capabilities of emerging sectors while those governments that are hands-off, deferring to market mechanisms, risk transitions that are too little and/or too late to maintain a national or regional competitiveness. Originality/value – To the authors knowledge, this is the first attempt to integrate the specific firm-level strategic management perspectives, used in this paper, with the macro-policy level to examine industry sectors with the twin metrics of economic productivity and employment in transitioning economies.

Intellectual capital and performance

2018 ◽  
Vol 19 (2) ◽  
pp. 407-452 ◽  
Author(s):  
Eugénia Pedro ◽  
João Leitão ◽  
Helena Alves
Keyword(s):  
Intellectual Capital ◽  
Research Question ◽  
Empirical Studies ◽  
Content Type ◽  
Wide Range ◽  
Depth Study ◽  
Global Vision ◽  
Customer Capital ◽  
And Performance ◽  
The Relationship

Purpose The purpose of this paper is to determine the predominant classification of intellectual capital (IC), in terms of components, using the literature of reference on the relationship between IC and performance and considering multi-dimensional analysis axes (MAAs): organisational, regional and national. Design/methodology/approach A systematic literature review (SLR) is presented focussing on empirical studies on IC published in the period 1960-2016. A protocol for action is defined and a research question is raised, gathering data from the databases of: Web of Science, Scopus and Google Scholar. A social network analysis is also provided to determine the type of networks embracing groups, IC individual components and performance type. Findings Of the 777 papers included in the SLR, 189 deal with the relationship between IC and performance. The paper highlights the greater development of empirical studies starting from 2004; the organisational MAA is the most studied. The most frequently used groups of components in studies dealing with IC’s influence on performance corresponds to a triad of human capital; structural (organisational or process) capital; and relational (social or customer) capital, which determine positively the performance of organisations/regions/countries, but their influence is not linear and depends on various factors associated with the context and surrounding environment. Practical implications This study has wide-ranging implications for politicians/governments, managers and academics, providing empirical evidence about the relationships between the components of IC and performance, by MAAs, and a global vision and better understanding of how those IC components have developed and how they are related to performance. Originality/value Due to the high number of references covering a wide range of disciplines and the various dimensions (e.g. organisational, regional and national) that form IC, it becomes fundamental to carry out an SRL and systematise its MAAs to deepen knowledge about what has been discovered/developed in this domain, in terms of empirical studies, in order to situate the topic in a wider theoretical-practical context. The paper is exceptionally wide-ranging, covering the period 1960-2016. It is one of the first clarifying studies on systemisation of the literature on IC, by MAA, and an in-depth study of IC’s impact on the performance of organisations/regions and countries which may serve as a guideline for future studies using the taxonomy proposed.

Big Data, Knowledge, and Business Intelligence

2018 ◽  
pp. 943-950
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Competitive Advantage ◽  
Intellectual Capital ◽  
Business Intelligence ◽  
Competitive Intelligence ◽  
Business Analytics ◽  
The Past ◽  
The People ◽  
Tools And Techniques

Knowledge management (KM), intellectual capital (IC), and competitive intelligence are distinct yet related fields that have endured and grown over the past two decades. KM and IC have always differentiated between the terms and concepts of data, information, knowledge, and wisdom/intelligence, suggesting value only comes from the more developed end of the range (knowledge and intelligence). But the advent of big data/business analytics has created new interest in the potential of data and information, by themselves, to create competitive advantage. This new attention provides opportunities for some exchange with more established theory. Big data gives direction for reinvigorating the more mature fields, providing new sources of inputs and new potential for analysis and use. Alternatively, big data/business analytics applications will undoubtedly run into common questions from KM/IC on appropriate tools and techniques for different environments, the best methods for handling the people issues of system adoption and use, and data/intelligence security.

How does cluster location and intellectual capital impact entrepreneurial success within high-growth firms?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yama Temouri ◽  
Vijay Pereira ◽  
Glenn W. Muschert ◽  
Vikash Ramiah ◽  
Michael Babula
Keyword(s):  
Knowledge Management ◽  
Intellectual Capital ◽  
High Growth ◽  
Probit Model ◽  
Positive Influence ◽  
Research Model ◽  
Entrepreneurial Success ◽  
Content Type ◽  
Cluster Membership ◽  
Growth Firms

PurposeThe purpose of this paper is to examine the role of intellectual capital and knowledge management in the entrepreneurial success of firms through a research model which is subsequently tested empirically.Design/methodology/approachThe paper utilises the knowledge-based perspective to formulate three sets of hypotheses which the authors subsequently test in the empirical analysis on data derived from the Orbis database, which includes over 1-million data points from approximately 240,000 firms across 174 geographic subdivisions of economic regions in 14 European countries, from 2010 to 2013. The analysis utilises probit model regressions on the likelihood of becoming a high-growth firms (HGF), in the presence of a number of control factors including firm age, firm size, tangible assets, foreign ownership, competitiveness (via Herfindahl index), return on assets, industry sector and country location.FindingsFindings from our analysis suggest that investments in intangible assets and generating patents from research and development (R&D) efforts is positively related to the likelihood of becoming a HGF. In addition, cluster membership seems to be a positive influence on becoming a HGF, however the moderating impact of intangible investments and patents is less clear in clusters.Research limitations/implicationsThe authors highlight the mixed effects from cluster membership and the beneficial impact from intellectual capital and knowledge management in achieving high growth firm status.Originality/valueThe authors derive and test our research model, which outlines the interrelationship of the various factors leading to firms becoming high-growth firms. The results suggest that there may be further fruitful ground for future investigation in the intersections of knowledge management and intellectual capital concepts within entrepreneurial contexts.

Screening Model in Enterprise Competitive Intelligence Activity

2010 ◽  
Vol 121-122 ◽  
pp. 360-363
Author(s):  
Hai Dong Yu ◽  
Fang Liu ◽  
Yun Feng Luo
Keyword(s):  
Service Providers ◽  
Quality Standard ◽  
Competitive Intelligence ◽  
Signaling Game ◽  
Game Model ◽  
Bayesian Nash Equilibrium ◽  
Screening Model ◽  
Intelligence Activity ◽  
True Type ◽  
Standard Term

The paper researched the screening model in enterprise competitive intelligence activity based on game theory. It studied the service provider’s decision in competitive intelligence(CI) project and proved it could be satisfied with Bayesian Nash equilibrium. It also revealed the heterogeneity between the service providers through a signaling game model in which signal set was the combine of CI quality standard term. The result shows that a quality standard about CI should be designed in contract which provides a signal for service provider to self-certify its own true type and is in favor of screening for enterprise.

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