Understanding intellectual capital disclosure in online media Big Data

2018 ◽  
Vol 26 (3) ◽  
pp. 499-530 ◽  
Author(s):  
Valentina Ndou ◽  
Giustina Secundo ◽  
John Dumay ◽  
Elvin Gjevori
Keyword(s):  
Social Media ◽  
Big Data ◽  
Intellectual Capital ◽  
Collective Intelligence ◽  
Online Media ◽  
Online Data ◽  
Content Type ◽  
Future Goals ◽  
Research Stream ◽  
Media Channels

PurposeIntellectual capital disclosure (ICD) in universities is gaining increasing attention, especially through the adoption of innovative technologies. Online media, as a relevant source of Big Data, is shifting ICD. The purpose of this paper is to explore how Big Data generated through online media, such as websites and platforms like Facebook, can be used as rich sources of data and viable disclosure channels for ICD in a university.Design/methodology/approachThis is an exploratory case study, following the methodology in Yin (2014), that examines how online media data contributes to closing the ICD gap. The IC disclosed through different online media channels by a private university in Albania is analysed using Secundo et al.’s (2016) collective intelligence framework. The online data sources include the university’s website, Facebook page, periodic reports and statements outlining future goals.FindingsWhat the authors discover in this research is that IC is an important part of how universities operate, and IC is communicated through social media, although unintentionally. However, this only serves to highlight the importance of IC, and if researchers want to discover IC and understand how it works in an organisation, they need to include social media and a prime resource for developing that understanding.Research limitations/implicationsMost importantly, the findings add to a growing consensus that ICD researchers, and researchers in other management and accounting disciplines, who traditionally rely on annual corporate social responsibility and other periodic reports, they need to change their medium of analysis because these reports no longer can be relied on to understand IC and its impact on an organisation.Originality/valueOnline media tools and the advent of Big Data have created new opportunities for universities to disclose their IC information to stakeholders in a timely manner and to gain relevant insights into their impact on the society. The originality of the paper resides in the contribution of Big Data to the ICD research stream.

Intellectual capital disclosure on Twitter – empirical evidence from the world’s largest companies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Łukasz Bryl ◽  
Justyna Fijałkowska ◽  
Dominika Hadro
Keyword(s):  
Social Media ◽  
Intellectual Capital ◽  
Recent Literature ◽  
Future Research ◽  
Corporate Disclosure ◽  
Content Type ◽  
Management Philosophy ◽  
Business Units ◽  
Structural Capital ◽  
Practical Implications

Purpose This study aims to examine intellectual capital disclosure (ICD) on Twitter by 60 of the world’s largest companies and explains the main themes communicated to stakeholders. The second objective is to determine which topics provoke most stakeholders’ reactions. Design/methodology/approach The authors perform content analysis on more than 42,000 tweets to examine ICD practices along with the reactions of stakeholders in the form of retweets and “favorites” toward the information disclosed. Findings Intellectual capital (IC) is an important theme in corporate disclosure practices, as more than one-third of the published tweets refer to IC. The world’s largest companies focus on relational capital information, followed by human and structural capital. The main IC themes disclosed were management philosophy, corporate reputation and business partnering. Tweets related to IC are of greater interest to stakeholders than other tweets and provoke more reactions. There is no complete consistency between the topics most intensively disclosed by companies and those that elicit the most vivid responses from the addressees. Practical implications This study offers an understanding of the world’s largest companies’ practices that refer to ICD via social media and has implications for organizations in the creation and use of communication channels when developing a dialogue with stakeholders on topics regarding IC that may lead to better management of IC performance. Originality/value This paper is a response to the call for studies on ICD via social media, which is strongly highlighted in the recent literature concerning future research on IC and until now was almost absent in the field of business units. This research provides in-depth insights into the use of Twitter to disclose IC elements and indicates which fields and topics of this disclosure provoke stakeholders’ reactions, which is a novelty in ICD studies.

Forecasting daily attraction demand using big data from search engines and social media

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fengjun Tian ◽  
Yang Yang ◽  
Zhenxing Mao ◽  
Wenyue Tang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Search Engine ◽  
Elastic Net ◽  
Percentage Error ◽  
Mean Square ◽  
Social Media Data ◽  
Content Type ◽  
Forecasting Error ◽  
Media Data

Purpose This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media. Design/methodology/approach Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy. Findings Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error. Practical implications Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions. Originality/value This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Cross-Cultural Aspects of Collective Intelligence Online

2019 ◽  
pp. 1-21
Author(s):  
Lesley S. J. Farmer
Keyword(s):  
Social Media ◽  
Intellectual Capital ◽  
Collective Intelligence ◽  
Group Interaction ◽  
Cross Cultural ◽  
Collective Knowledge ◽  
Interactive Technologies ◽  
Online Technology ◽  
Interactive Mode ◽  
Cultural Aspects

Today's wisdom society depends on intellectual capital, that is, collective knowledge and informational assets. Increasingly, the global scene reflects a more interactive mode relative to information, particularly because of social media. As heterogeneous groups bring different expertise and perspectives, their gathered and organized knowledge can lead to more informed decisions and resultant actions. This collective intelligence has been transformed with the advent of easily accessible interactive technologies. Adding to the complexity, cross-cultural aspects impact the processes leading to collective intelligence as culture impacts individual and group interaction. This chapter explores the intersection of collective intelligence, online technology, and cross-cultural aspects. The chapter also shares research-based conditions to optimize that intersection.

scholarly journals Tackling social media data analysis

2019 ◽  
Vol 22 (2) ◽  
pp. 94-113 ◽  
Author(s):  
Violetta Wilk ◽  
Geoffrey N. Soutar ◽  
Paul Harrigan
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Exploratory Research ◽  
Online Data ◽  
Social Media Data ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Brand Advocacy ◽  
Qualitative Data Analysis Software ◽  
Media Data

PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

CID: Categorical Influencer Detection on microtext-based social media

2020 ◽  
Vol 44 (5) ◽  
pp. 1027-1055
Author(s):  
Thanh-Tho Quan ◽  
Duc-Trung Mai ◽  
Thanh-Duy Tran
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Learning Approaches ◽  
Content Type ◽  
Modeling Process ◽  
Variational Autoencoder ◽  
Media Channels

PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

A knowledge management super-strategy fueled by big data

2019 ◽  
Vol 35 (1) ◽  
pp. 22-23
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Intellectual Capital ◽  
Management Strategy ◽  
Design Methodology ◽  
Business Value ◽  
Content Type ◽  
Pertinent Information ◽  
Conceptual Paper ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This conceptual paper proposes a model for growing company competitive advantage into the future by integrating a knowledge management strategy with progressive insights from Big Data and artificial intelligence. The ultimate strategic aim here is to create and codify intellectual capital that adds business value. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

scholarly journals Transforming the stakeholders’ Big Data for intellectual capital management

2018 ◽  
Vol 26 (3) ◽  
pp. 420-442 ◽  
Author(s):  
Lorna Uden ◽  
Pasquale Del Vecchio
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Activity Theory ◽  
Intellectual Capital ◽  
Value Creation ◽  
Organizational Values ◽  
Service Science ◽  
Future Research ◽  
Content Type ◽  
Service Ecosystems

PurposeThis paper aims to define a conceptual framework for transforming Big Data into organizational value by focussing on the perspectives of service science and activity theory. In coherence with the agenda on evolutionary research on intellectual capital (IC), the study also provides momentum for researchers and scholars to explore emerging trends and implications of Big Data for IC management.Design/methodology/approachThe paper adopts a qualitative and integrated research method based on a constructive review of existing literature related to IC management, Big Data, service science and activity theory to identify features and processes of a conceptual framework emerging at the intersection of previously identified research topics.FindingsThe proposed framework harnesses the power of Big Data, collectively created by the engagement of multiple stakeholders based on the concepts of service ecosystems, by using activity theory. The transformation of Big Data for IC management addresses the process of value creation based on a set of critical dimensions useful to identify goals, main actors and stakeholders, processes and motivations.Research limitations/implicationsThe paper indicates how organizational values can be created from Big Data through the co-creation of value in service ecosystems. Activity theory is used as theoretical lens to support IC ecosystem development. This research is exploratory; the framework offers opportunities for refinement and can be used to spearhead directions for future research.Practical implicationsThe paper proposes a framework for transforming Big Data into organizational values for IC management in the context of entrepreneurial universities as pivotal contexts of observation that can be replicated in different fields. The framework provides guidelines that can be used to help organizations intending to embark on the emerging paradigm of Big Data for IC management for their competitive advantages.Originality/valueThe paper’s originality is in bringing together research from Big Data, value co-creation from service ecosystems and activity theory to address the complex issues involved in IC management. A further element of originality offered involves integrating such multidisciplinary perspectives as a lens for shaping the complex process of value creation from Big Data in relationship to IC management. The concept of how IC ecosystems can be designed is also introduced.

scholarly journals CRM in the digital age: implementation of CRM in three contemporary B2B firms

2015 ◽  
Vol 17 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Heini Sisko Maarit Lipiäinen
Keyword(s):  
Social Media ◽  
Customer Relationships ◽  
Customer Relationship ◽  
Research Approach ◽  
Empirical Examination ◽  
Content Type ◽  
The Future ◽  
Collaborative Working ◽  
Media Systems ◽  
Media Channels

Purpose – The purpose of this study was to contribute to the current discussion on digitization in companies’ marketing from a customer relationship management (CRM) perspective by examining the role and objectives of CRM and the exploitation of social media to serve the objectives of CRM in contemporary business-to-business (B2B) companies. Design/methodology/approach – The data are collected through semi-structured themed interviews with key marketing/sales managers from three B2B firms. Findings – CRM seems to be moving closer to the company’s core activity and becoming everybody’s business to a greater extent than ever before, but its main goal, to enhance customer relationships, will not necessarily change. Understanding the customer is vital and requires different functions to cooperate closely to ensure the firm has the best possible understanding of its customers. Public social media tools played almost no part in CRM, but closed social media systems might have potential in the future. Research limitations/implications – The chosen research approach limits the generalization of the results. Practical implications – It seems likely that firms will benefit from a collaborative working style over the traditional silo approaches. For B2B firms, public social media does not seems to be the most suitable source to serve CRM but private social media channels might have potential in the future. Originality/value – The lack of empirical examination of the change from company ecosystem to customer ecosystem from a CRM perspective, and the lack of research on social media for CRM in the B2B context, determines the purpose of this study. Furthermore, digitization is a rather new and unstructured phenomenon and many companies are still considering how to reconcile to it.

New ICTs for Knowledge Management in Organizations

2016 ◽  
Vol 20 (3) ◽  
pp. 417-422 ◽  
Author(s):  
Pedro Soto-Acosta ◽  
Juan-Gabriel Cegarra-Navarro
Keyword(s):  
Knowledge Management ◽  
Information And Communication Technologies ◽  
Intellectual Capital ◽  
Collective Intelligence ◽  
Organizational Level ◽  
Special Issue ◽  
Content Type ◽  
Academic Knowledge ◽  
The Impact

Purpose The purpose of this special issue is to point out the possibilities of new information and communication technologies (ICTs) for knowledge management (KM) in organizations, offering different perspectives on and approaches for the role of new ICTs for KM, as well as measuring the impact and diffusion of new ICTs for KM within organizations. Design/methodology/approach The selection of the papers included in this special issue is largely based on the work of the conference “7th European Conference on Intellectual Capital - ECIC” (April 2015, Cartagena, Spain), where the special issue editors organized a track on “New ICTs for Knowledge Management in Organizations”. The conference gathered leading scholars in the fields of intellectual capital and KM, dealing with the acquisition, creation and sharing of collective intelligence and how to utilize increased academic knowledge and networking in promoting economic and organizational innovations and changes. Findings The collection of papers covered in this special issue identifies challenging problems on the role of new ICTs for KM and their role in the design and implementation of innovative products, services or processes in organizations. Research limitations/implications The special issue tries to offer some new relevant advances for the academic and practice communities in the growing body of research analyzing new ICTs for KM. However, the theoretical and empirical advances showed represent only a partial view, which corresponds to the impact of new ICTs for KM at the organizational level of analysis. Practical implications The nature of new ICTs, such as social networking tools, wikis, internal blogging and the way they are used, suggest that nowadays they may differ from traditional organizational systems in two critical ways: the voluntary (typically not mandatory) use and their lack of activity or process orientation. Originality/value The special issue explores the phenomena by integrating different perspectives and approaches, including qualitative and quantitative empirical. This integration overcomes some limitations about the understanding of the issues under investigation.

Developing a knowledge management strategy for data analytics and intellectual capital

2018 ◽  
Vol 26 (3) ◽  
pp. 400-419 ◽  
Author(s):  
Harold D. Harlow
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Intellectual Capital ◽  
Data Analytics ◽  
Management Strategy ◽  
Management Tool ◽  
Social Implications ◽  
Knowledge Management Strategy ◽  
Content Type ◽  
Strategic Intent

PurposeThis 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/approachA 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.FindingsA 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/implicationsThe model presented needs to be empirically tested over a sample of companies and periods to determine if performance improves using this model.Practical implicationsUse 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 implicationsThe 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/valueThe 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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