Does intellectual capital efficiency affect financial performance? The case of Italian listed firms

Guerrini, Andrea; Romano, Giulia; Leardini, Chiara

doi:10.1504/ijlic.2014.060805

Cited by 11 publications

(8 citation statements)

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“…These findings are supported by Guerrini et al (2014), Kehelwalatenna (2016), Maji and Goswami (2016), and Yalama (2013) and among others.…”

Section: Discussionsupporting

confidence: 75%

“…The more debt a company has, the riskier its stock is since debtholders have the first claim to a company's assets. In line with previous VAIC literature, firm leverage is measured using the ratio of total debt to book value of total assets (Dzenopoljac et al, 2016;Guerrini et al, 2014;Maji & Goswami, 2016;Mondal & Ghosh, 2012;Yalama, 2013). Figure 2 represents the overall conceptual framework developed in this research.…”

Section: Ato = Total Revenue/total Assetsmentioning

confidence: 99%

“…There are (1) Figure 1 represents the schematic diagram of the construction of VAIC. To be consistent with previous research (Alhassan & Asare, 2016;Chen et al, 2014;Guerrini et al, 2014;Yalama, 2013), bank size (measured as the log of total assets value) is included in regression as a control variable to minimize its interaction with dependent variables.…”

Section: Ato = Total Revenue/total Assetsmentioning

confidence: 99%

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Intellectual Capital (IC) Determinants: Impact on Productivity of Islamic Banks

Aziz

Hashim

2017

Binus Business Review

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This research aimed to investigate the relationship between the Intellectual Capital (IC) efficiency empirically. It consisted of human capital, structural capital, capital employed, and relational capital with the impact on the productivity of Islamic banks in Malaysia. The Pulic's Value-Added Intellectual Coefficient (VAIC) method with the extended and modified version introduced by former scholars was used to measure IC, whereas bank productivity was measured through Assets Turnover Ratio (ATO). Three internal factors that might have determinants effect on VAIC, namely bank size, bank risks, and leverage were further tested to find their relationship. Structural stability tests and dynamic regression models for panel data were also used for the data of 16 Islamic banks in Malaysia from 2009 to 2016. The panel-corrected standard errors estimation technique was used to estimate a panel regression model with bank productivity and VAIC as the dependent variables. The regression analysis suggests that Malaysian Islamic banks are depending heavily on the capital employed component of intellectual capital, followed by human capital, structural capital, and relational capital. The results also suggest that bank's risks and leverage play a major role in determining intellectual capital. The findings may serve as a useful input for Islamic bankers to indicate whether the contribution of intellectual capital and its components needs further improvement which it has produced the best results, and internal factor might affect IC.

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“…These findings are supported by Guerrini et al (2014), Kehelwalatenna (2016), Maji and Goswami (2016), and Yalama (2013) and among others.…”

Section: Discussionsupporting

confidence: 75%

Section: Ato = Total Revenue/total Assetsmentioning

confidence: 99%

Section: Ato = Total Revenue/total Assetsmentioning

confidence: 99%

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Intellectual Capital (IC) Determinants: Impact on Productivity of Islamic Banks

Aziz

Hashim

2017

Binus Business Review

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“…Specifically, this paper’s strategic research view uses the knowledge-based theory of the firm, which posits that knowledge is the primary resource of the firm (Ansoff, 1990) and that development of knowledge about customers, markets and new product technologies determines the firm’s future sustainable competitive advantage (Barney, 2001). In the past 30 years, there has been a rapid transition from making things to learning about people, processes and behavior (Romano et al , 2014), “in which wealth is created by developing and managing knowledge and intangible assets – commonly known as IC” (Andriessen, 2004; Ricceri and Guthrie, 2009; Dumay and Garanina, 2013a, p. 12).…”

Section: Introductionmentioning

confidence: 99%

Developing a knowledge management strategy for data analytics and intellectual capital

Harlow

2018

MEDAR

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Purpose This paper aims to build on current analytics and Big Data definitions and strategies from the literature to develop an overall strategic model connecting knowledge management strategy (KMS) for intellectual capital (IC) acquisition and business use. It also extends the IC research stages to a fifth stage of IC research including IC strategic intent. Design/methodology/approach A literature review highlights the connections among strategic intent, firm strategy, KMS and a data analytics strategy aligned with firm and KMS strategic intent. An extended model of the interrelationships is developed from the prior research. Findings A model framework was developed from the literature that connects Big Data to achieve the goals of a firm KMS and demonstrates how Big Data analytics (BDA) needs to shift from being a tactical tool to a strategic knowledge management tool directed by the overall strategy and strategic intent of the firm. Research limitations/implications The model presented needs to be empirically tested over a sample of companies and periods to determine if performance improves using this model. Practical implications Use of this model proposes that strategic intent will be enhanced and improve the capture of intellectual property derived from advanced analytics and increase sustainable advantages at firm. Social implications The social implications of lack of strong privacy laws coupled with the possible elimination of millions of knowledge worker jobs creates a pressing need for more research into and identification of firm’s and government’s Big Data strategic use for both good and perhaps evil. Originality/value The research in this paper extends current models of IC development and adds strategic intent and collective intelligence as the fifth stage of IC research and presents an overall KMS/BDA model.

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“…Nevertheless, this approach could not reveal hidden values inherent in human and organizational structure (Fernandez, 2003;Ordóñez de Pablos, 2005). Because of this issue, several studies attempted to disclose these intangible assets, and found that the management of IC could bring several advantages to organizations, such as enhanced wealth for companies (Guerrini et al, 2014), supplying of a new resource-based view (RBV) (Bontis, 1996), and so on. Therefore, in recent decades, several methods were developed to manage the intangible assets, such as invisible balance sheet, intangible assets monitor, BSC, economic value added (EVA TM ), IC-index, technology broker, Skandia navigator, and so on.…”

Section: Introductionmentioning

confidence: 99%

An efficient resource allocation in strategic management using a novel hybrid method

Wudhikarn

2016

Management Decision

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Purpose – The purpose of this paper is to suggest a novel hybrid method by integrating a decision sciences approach with balanced scorecard (BSC) in order to scientifically enable the efficient strategic management of an organization under limited resources. The proposed research model endeavors to improve critical basis deficiencies of the original BSC as well as formerly improved forms of BSC by appropriately integrating three disparate methods: BSC, analytic network process (ANP), and zero-one goal programming (ZOGP). Design/methodology/approach – The designed approach is separated into three major parts. At first, the traditional BSC, concentrating on both financial and intellectual capital, was adopted as the strategic management framework, and then priorities as well as the importance of tactical drivers derived from BSC application were consecutively identified by the application of ANP. Finally, the study further applied the obtained results of integrated BSC and ANP to ZOGP in order to scientifically identify the optimal strategic investment under simulated constraints of the considered organization. Findings – An application of BSC, ANP, and ZOGP with a case study of an academic institution provided an improved strategic management approach for optimally and scientifically utilizing the limited resources of the organization. The suggested results indicated that only 11 of the 23 strategic projects should be executed. Moreover, the selected tactical tasks would efficiently use less than 36 percent of the strategic expenses of the traditional management approach. Originality/value – Based on the intensive literature reviews, the proposed method could be determined as a novel hybrid approach. It newly conveyed the practical management approach by innovatively including the proper decision sciences method to BSC. This improvement scientifically considered on the resource allocation process that has never been studied before in formerly improved BSC.

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Does intellectual capital efficiency affect financial performance? The case of Italian listed firms

Cited by 11 publications

References 0 publications

Intellectual Capital (IC) Determinants: Impact on Productivity of Islamic Banks

Intellectual Capital (IC) Determinants: Impact on Productivity of Islamic Banks

Developing a knowledge management strategy for data analytics and intellectual capital

An efficient resource allocation in strategic management using a novel hybrid method

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