SlideShare a Scribd company logo
1 of 20
3rd presentation of IC reporting
Method of Identifying IC-related information –content
          analysis (Guthrie & Petty, 2000)




          Manually VS Computer-based
Process of content analysis
             “manually”
Develop the categories of IC
Coding (put IC-related information into the
 classification based on the selected criterion )
Analysis (the amount of items )
Limitation of Manual Type
                    Manually

   Labor-intensive/the volume of text is limited

   Different coding methods applications

   Coding , Identification and Categorization are
   highly subjective (sensitivity, personal bias)

   Difficult to replicate studies


   Top-down
Computer-based Tool
     Electronic search (Bontis, 2002)
Process:
 Electronic database (11000 Canadian Annual report)
 Terminology was compiled based on several intellectual
  capital books and articles
 Each of these terms was searched individually in the
  database.
Limitation of Electronic search (Bontis, 2002)
                    Computer-based
        Difficult to recognize the synonyms and
        words with multiple meanings
        Context of the keywords is ignored
        Inadequate to investigate the IC disclose
        Top-down
The development of Computer-based
              tools
• Dictionary keyword-based tools(Lee &
  Guthrie, 2010)
• Concept-based tools(Lee & Guthrie, 2010)
• Classification assistant tools(Lee & Guthrie,
  2010) Nvivo
• Electronic taxonomies tools(Lee & Guthrie,
  2010)
• Intelligent Taxonomy “Factiva” (Lee & Guthrie,
  2010)
Intelligent Taxonomy “Factiva”
Mapping
between accepted IC terms (Guthrie and Petty2000)
      and the Factiva (Lee & Guthrie, 2010)
           intelligent taxonomy terms
Contribution VS Limitation
           Factiva (Lee & Guthrie, 2010)
            Contribution                                   Limitation
Identify the IC-related information that   The mapping is not accurate enough
are not mentioned in the academic          (e.g Recruitment )
articles
The contents that are mined are not just   The content that are mined are not
limited to the Annual Report               transparent enough (e.g marketing )
Neutral and negative news are also         Some contents are difficult to be put into
identified                                 practice

Bottom-up method is used
The development of the content
                  analysis
 Manually            Automatically                       Intelligent

 Annual Reports          Sustainability reports (Cinquini,et.al, 2012)

“Positive”        “Positive” ”Neutral” ”Negative” (Lee & Guthrie, 2010)

Top- down           Bottom-up (Lee & Guthrie, 2010)
Content analysis
           advantages VS disadvantages
            advantages                                   disadvantages
Capture the IC trends in reporting           Few explanation of the nature of IC
                                             information allocated to IC categories
Compare the different level of IC disclose   Insufficient for discovering potential IC
among the different organizations            elements that are not already
                                             declared/revealed in the literature
A technique for analyzing textual            Insufficient for the purposes of identifying
repositories                                 underlying relationships of IC
To clear the process of how IC is            The contents that are analyzed are quite
categorized by different researchers         limited

Benchmarking
How to overcome the limitation of content
                   analysis

Semantic technology
Semantic technology understands the real meaning of words based on
theories of human comprehension



Gives Structure to Unstructured Data
Integrates Data from Multiple Sources
Able to Deeply Mine Data
How to use Semantic Technology to
            identify IC

  The first thing is to develop and refine the
             ontology
The “top-down” ontology is
developed based on the
literature review

      (Lammi,2012)




Good enough?
The 1st barrier of developing ontology
               “What is IC?”

 Information about employees =?=human capital
 Information about organization=?=structural capital
 Information about customers and suppliers =?=relational capital
The 2nd barrier of developing ontology



   How to map the IC-related information
       with the classification of IC ?
              (see the excel)
The further research
• How to develop the IC ontology that helps to
  identify IC-related information effectively?
• Which intelligent approach can be used to fill
  the research gap?
Literature Review
•   An Yi, & Davey, H. (2010). Intellectual capital disclosure in Chinese (mainland ). Journal of
    intellectual capital 11(3), 326-347.
•   Bontis, N. (2002). Intellectual Capital Disclosure in Canadian Corporations. Journal of Human
    Resource Costing & Accounting.
•   Laurence Lock Lee, & Guthrie, J. (2010). Visualising and measuring intellectual capital in capital
    markets: a research method. Journal of Intellectual Capital, 11(1), 4-22.
•   Lino Cinquini, Emilio Passetti, Andrea Tenucci, & Frey, M. (2012). Anlyzing inellectual capital
    information in sustainability reports: some empirical evidence Journal of Intellectual Capital, 13(4).
•   Lammi, A. (2012). Intellectual Capital Strategy-Integrating Strategic Management and Intellectual
    Capital Ontology
•   J.Guthrie, R. P., K. Yongvanich, F.Ricceri. (2004). Using content analysis as a research method to
    inquire into intellectual capital reporting. Journal of Intellectual Capital, 5(2), 282-293.
•   Viven Beattie , & Thomson, S. J. (2006). Lifting the Lid on the use of Content analysis to investigate
    intellectual capital disclosure.
•   Mouritsen, J. (2009). Classification, measurement and the ontology of intellectual capital entities
    Journal of Human Resource Costing & Accounting, 13(2), 154-162.
•   Mouritsen, J. (2006). Problematising intellectual capital research: ostensive versus performative IC.
    Accounting, Auditing& Accountability Journal 19(6), 820-841.
Thank you very much!

More Related Content

Similar to Manual vs automatic vs intelligent

PatternLanguageOfData
PatternLanguageOfDataPatternLanguageOfData
PatternLanguageOfData
kimErwin
 
Clustering of Deep WebPages: A Comparative Study
Clustering of Deep WebPages: A Comparative StudyClustering of Deep WebPages: A Comparative Study
Clustering of Deep WebPages: A Comparative Study
ijcsit
 
2015-06-02-SCIA-Presentation-Infocodex-Final
2015-06-02-SCIA-Presentation-Infocodex-Final2015-06-02-SCIA-Presentation-Infocodex-Final
2015-06-02-SCIA-Presentation-Infocodex-Final
Beat Meyer
 
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docxAnnotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
justine1simpson78276
 
16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx
RAJU852744
 
16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx
herminaprocter
 
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docxTHEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
susannr
 

Similar to Manual vs automatic vs intelligent (20)

Text, Content, and Social Analytics: BI for the New World
Text, Content, and Social Analytics: BI for the New WorldText, Content, and Social Analytics: BI for the New World
Text, Content, and Social Analytics: BI for the New World
 
Beyond the Facts: Data as Digital-Semantic Artifacts
Beyond the Facts: Data as Digital-Semantic ArtifactsBeyond the Facts: Data as Digital-Semantic Artifacts
Beyond the Facts: Data as Digital-Semantic Artifacts
 
PatternLanguageOfData
PatternLanguageOfDataPatternLanguageOfData
PatternLanguageOfData
 
Clustering of Deep WebPages: A Comparative Study
Clustering of Deep WebPages: A Comparative StudyClustering of Deep WebPages: A Comparative Study
Clustering of Deep WebPages: A Comparative Study
 
Seminar DevOPS Mohamed Nejjar SS23 03757306.pdf
Seminar DevOPS Mohamed Nejjar SS23 03757306.pdfSeminar DevOPS Mohamed Nejjar SS23 03757306.pdf
Seminar DevOPS Mohamed Nejjar SS23 03757306.pdf
 
Workflows and Metadata Quality
Workflows and Metadata QualityWorkflows and Metadata Quality
Workflows and Metadata Quality
 
Data literacy
Data literacyData literacy
Data literacy
 
2015-06-02-SCIA-Presentation-Infocodex-Final
2015-06-02-SCIA-Presentation-Infocodex-Final2015-06-02-SCIA-Presentation-Infocodex-Final
2015-06-02-SCIA-Presentation-Infocodex-Final
 
Ibrahim ramadan paper
Ibrahim ramadan paperIbrahim ramadan paper
Ibrahim ramadan paper
 
Mapping the content ecosystem
Mapping the content ecosystemMapping the content ecosystem
Mapping the content ecosystem
 
Summary of Research on Text Information and Enterprise Delisting
Summary of Research on Text Information and Enterprise DelistingSummary of Research on Text Information and Enterprise Delisting
Summary of Research on Text Information and Enterprise Delisting
 
UKSG 2024 -From algorithms to empowerment:teaching algorithmic literacy (AL) ...
UKSG 2024 -From algorithms to empowerment:teaching algorithmic literacy (AL) ...UKSG 2024 -From algorithms to empowerment:teaching algorithmic literacy (AL) ...
UKSG 2024 -From algorithms to empowerment:teaching algorithmic literacy (AL) ...
 
A Review Of Digital Literacy Assessment Instruments
A Review Of Digital Literacy Assessment InstrumentsA Review Of Digital Literacy Assessment Instruments
A Review Of Digital Literacy Assessment Instruments
 
Decision Support for E-Governance: A Text Mining Approach
Decision Support for E-Governance: A Text Mining ApproachDecision Support for E-Governance: A Text Mining Approach
Decision Support for E-Governance: A Text Mining Approach
 
China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC...
China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC...China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC...
China HCI Symposium 2010 March: Augmented Social Cognition Research from PARC...
 
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docxAnnotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
Annotation_M1.docxSubject Information SystemsJoshi, G. (2.docx
 
16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx
 
16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx16     Decision Support and Business Intelligence Systems (9th E.docx
16     Decision Support and Business Intelligence Systems (9th E.docx
 
Chapter 1 Introduction to Information Storage and Retrieval.pdf
Chapter 1 Introduction to Information Storage and Retrieval.pdfChapter 1 Introduction to Information Storage and Retrieval.pdf
Chapter 1 Introduction to Information Storage and Retrieval.pdf
 
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docxTHEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
THEORY & REVIEWTHEORIZING THE DIGITAL OBJECT1Philip Fa.docx
 

Recently uploaded

Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
dlhescort
 
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
amitlee9823
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usage
Matteo Carbone
 
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
Anamikakaur10
 
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
amitlee9823
 

Recently uploaded (20)

Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
 
Organizational Transformation Lead with Culture
Organizational Transformation Lead with CultureOrganizational Transformation Lead with Culture
Organizational Transformation Lead with Culture
 
VVVIP Call Girls In Greater Kailash ➡️ Delhi ➡️ 9999965857 🚀 No Advance 24HRS...
VVVIP Call Girls In Greater Kailash ➡️ Delhi ➡️ 9999965857 🚀 No Advance 24HRS...VVVIP Call Girls In Greater Kailash ➡️ Delhi ➡️ 9999965857 🚀 No Advance 24HRS...
VVVIP Call Girls In Greater Kailash ➡️ Delhi ➡️ 9999965857 🚀 No Advance 24HRS...
 
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
Call Girls Jp Nagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bang...
 
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxB.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
 
Mysore Call Girls 8617370543 WhatsApp Number 24x7 Best Services
Mysore Call Girls 8617370543 WhatsApp Number 24x7 Best ServicesMysore Call Girls 8617370543 WhatsApp Number 24x7 Best Services
Mysore Call Girls 8617370543 WhatsApp Number 24x7 Best Services
 
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
Enhancing and Restoring Safety & Quality Cultures - Dave Litwiller - May 2024...
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usage
 
Falcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investorsFalcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investors
 
(Anamika) VIP Call Girls Napur Call Now 8617697112 Napur Escorts 24x7
(Anamika) VIP Call Girls Napur Call Now 8617697112 Napur Escorts 24x7(Anamika) VIP Call Girls Napur Call Now 8617697112 Napur Escorts 24x7
(Anamika) VIP Call Girls Napur Call Now 8617697112 Napur Escorts 24x7
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Century
 
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
 
Call Girls Ludhiana Just Call 98765-12871 Top Class Call Girl Service Available
Call Girls Ludhiana Just Call 98765-12871 Top Class Call Girl Service AvailableCall Girls Ludhiana Just Call 98765-12871 Top Class Call Girl Service Available
Call Girls Ludhiana Just Call 98765-12871 Top Class Call Girl Service Available
 
Falcon Invoice Discounting platform in india
Falcon Invoice Discounting platform in indiaFalcon Invoice Discounting platform in india
Falcon Invoice Discounting platform in india
 
Monthly Social Media Update April 2024 pptx.pptx
Monthly Social Media Update April 2024 pptx.pptxMonthly Social Media Update April 2024 pptx.pptx
Monthly Social Media Update April 2024 pptx.pptx
 
RSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataRSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors Data
 
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
Call Now ☎️🔝 9332606886🔝 Call Girls ❤ Service In Bhilwara Female Escorts Serv...
 
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
Nelamangala Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...
 
Uneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration PresentationUneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration Presentation
 
Business Model Canvas (BMC)- A new venture concept
Business Model Canvas (BMC)-  A new venture conceptBusiness Model Canvas (BMC)-  A new venture concept
Business Model Canvas (BMC)- A new venture concept
 

Manual vs automatic vs intelligent

  • 1. 3rd presentation of IC reporting
  • 2. Method of Identifying IC-related information –content analysis (Guthrie & Petty, 2000) Manually VS Computer-based
  • 3. Process of content analysis “manually” Develop the categories of IC Coding (put IC-related information into the classification based on the selected criterion ) Analysis (the amount of items )
  • 4. Limitation of Manual Type Manually Labor-intensive/the volume of text is limited Different coding methods applications Coding , Identification and Categorization are highly subjective (sensitivity, personal bias) Difficult to replicate studies Top-down
  • 5. Computer-based Tool Electronic search (Bontis, 2002) Process:  Electronic database (11000 Canadian Annual report)  Terminology was compiled based on several intellectual capital books and articles  Each of these terms was searched individually in the database.
  • 6. Limitation of Electronic search (Bontis, 2002) Computer-based Difficult to recognize the synonyms and words with multiple meanings Context of the keywords is ignored Inadequate to investigate the IC disclose Top-down
  • 7. The development of Computer-based tools • Dictionary keyword-based tools(Lee & Guthrie, 2010) • Concept-based tools(Lee & Guthrie, 2010) • Classification assistant tools(Lee & Guthrie, 2010) Nvivo • Electronic taxonomies tools(Lee & Guthrie, 2010) • Intelligent Taxonomy “Factiva” (Lee & Guthrie, 2010)
  • 9. Mapping between accepted IC terms (Guthrie and Petty2000) and the Factiva (Lee & Guthrie, 2010) intelligent taxonomy terms
  • 10. Contribution VS Limitation Factiva (Lee & Guthrie, 2010) Contribution Limitation Identify the IC-related information that The mapping is not accurate enough are not mentioned in the academic (e.g Recruitment ) articles The contents that are mined are not just The content that are mined are not limited to the Annual Report transparent enough (e.g marketing ) Neutral and negative news are also Some contents are difficult to be put into identified practice Bottom-up method is used
  • 11. The development of the content analysis Manually Automatically Intelligent Annual Reports Sustainability reports (Cinquini,et.al, 2012) “Positive” “Positive” ”Neutral” ”Negative” (Lee & Guthrie, 2010) Top- down Bottom-up (Lee & Guthrie, 2010)
  • 12. Content analysis advantages VS disadvantages advantages disadvantages Capture the IC trends in reporting Few explanation of the nature of IC information allocated to IC categories Compare the different level of IC disclose Insufficient for discovering potential IC among the different organizations elements that are not already declared/revealed in the literature A technique for analyzing textual Insufficient for the purposes of identifying repositories underlying relationships of IC To clear the process of how IC is The contents that are analyzed are quite categorized by different researchers limited Benchmarking
  • 13. How to overcome the limitation of content analysis Semantic technology Semantic technology understands the real meaning of words based on theories of human comprehension Gives Structure to Unstructured Data Integrates Data from Multiple Sources Able to Deeply Mine Data
  • 14. How to use Semantic Technology to identify IC The first thing is to develop and refine the ontology
  • 15. The “top-down” ontology is developed based on the literature review (Lammi,2012) Good enough?
  • 16. The 1st barrier of developing ontology “What is IC?”  Information about employees =?=human capital  Information about organization=?=structural capital  Information about customers and suppliers =?=relational capital
  • 17. The 2nd barrier of developing ontology How to map the IC-related information with the classification of IC ? (see the excel)
  • 18. The further research • How to develop the IC ontology that helps to identify IC-related information effectively? • Which intelligent approach can be used to fill the research gap?
  • 19. Literature Review • An Yi, & Davey, H. (2010). Intellectual capital disclosure in Chinese (mainland ). Journal of intellectual capital 11(3), 326-347. • Bontis, N. (2002). Intellectual Capital Disclosure in Canadian Corporations. Journal of Human Resource Costing & Accounting. • Laurence Lock Lee, & Guthrie, J. (2010). Visualising and measuring intellectual capital in capital markets: a research method. Journal of Intellectual Capital, 11(1), 4-22. • Lino Cinquini, Emilio Passetti, Andrea Tenucci, & Frey, M. (2012). Anlyzing inellectual capital information in sustainability reports: some empirical evidence Journal of Intellectual Capital, 13(4). • Lammi, A. (2012). Intellectual Capital Strategy-Integrating Strategic Management and Intellectual Capital Ontology • J.Guthrie, R. P., K. Yongvanich, F.Ricceri. (2004). Using content analysis as a research method to inquire into intellectual capital reporting. Journal of Intellectual Capital, 5(2), 282-293. • Viven Beattie , & Thomson, S. J. (2006). Lifting the Lid on the use of Content analysis to investigate intellectual capital disclosure. • Mouritsen, J. (2009). Classification, measurement and the ontology of intellectual capital entities Journal of Human Resource Costing & Accounting, 13(2), 154-162. • Mouritsen, J. (2006). Problematising intellectual capital research: ostensive versus performative IC. Accounting, Auditing& Accountability Journal 19(6), 820-841.
  • 20. Thank you very much!

Editor's Notes

  1. Artificial intelligence technologies have yet to deliver thecapacity for the textual understanding levels that humans are capable of.
  2. websites, press releases, information memorandums, prospectuses