Global brain-reflective accounting practices

2019 ◽  
Vol 20 (6) ◽  
pp. 733-762 ◽  
Author(s):  
Khaldoon Al-Htaybat ◽  
Khaled Hutaibat ◽  
Larissa von Alberti-Alhtaybat
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Intellectual Capital ◽  
Value Creation ◽  
Data Analytics ◽  
Business Models ◽  
New Technologies ◽  
Big Data Analytics ◽  
Content Type ◽  
Global Brain

Purpose The purpose of this paper is to explore the intersection of accounting practices and new technologies in the age of agility as a form of intellectual capital, through sharing the conceptualization and real implications of accounting and accountability ideas in exploring and deploying new technologies, such as big data analytics, blockchain and augmented accounting practices and expounding how they constitute new forms of intellectual capital to support value creation and realise Sustainable Development Goals (SDGs). Design/methodology/approach The adopted methodology is cyber-ethnography, which investigates online practices through observation and discourse analysis, reflecting on new business models and practices, and how accounting relates to these developments. The global brain sets the conceptual context, which reflects the distributed network intelligence that is created through the internet. Findings The main findings focus on various developments of accounting practice that reflect, utilise or support digital companies and new technologies, including augmentation, big data analytics and blockchain technology, as new forms of intellectual capital, that is knowledge and skills within organisations, that have the potential to support value creation and realise SDGs. These relate to and originate from the global brain, which constitutes the umbrella of tech-related intellectual capital. Originality/value This paper determines new developments in accounting practices in relation to new technologies, due to the continuous expansion and influence of the intelligence of the collective network, the global brain, as forms of intellectual capital, contributing to value creation, sustainable development and the realisation of SDGs.

scholarly journals Big data analytics and sustainable textile manufacturing

2020 ◽  
Vol 58 (8) ◽  
pp. 1699-1714 ◽  
Author(s):  
Dieu Hack-Polay ◽  
Mahfuzur Rahman ◽  
Md Morsaline Billah ◽  
Hesham Z. Al-Sabbahy
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Environmental Sustainability ◽  
Textile Industry ◽  
Data Analytics ◽  
New Technologies ◽  
Developing World ◽  
Big Data Analytics ◽  
Content Type ◽  
Member Exchange

PurposeThe purpose of this article is to discuss issues associated with the application big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.Design/methodology/approachThe leader–member exchange theoretical framework to consider the nature of the relationships between owners and followers to identify the potential issues that affect decision-making was used. However, decisions to adopt such environmentally friendly biotechnologies are hampered by the lack of awareness amongst owners, intergenerational conflict and cultural impediments.FindingsThe article found that the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors. The article exposes two key new technologies that could help the industry reduce its carbon footprint.Originality/valueThe study suggests more awareness raising amongst plant owners and greater empowerment of new generations in decision-making in the industry. This study, therefore, bears significant implications for environmental sustainability in the developing world where the textile industry is one of the major polluting industries affecting water quality and human health.

Big data analytics for investigating Taiwan Line sticker social media marketing

2019 ◽  
Vol 32 (2) ◽  
pp. 589-606 ◽  
Author(s):  
Shu-Hsien Liao ◽  
Szu-Yu Hsu
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Model ◽  
Data Analytics ◽  
Business Models ◽  
Model Development ◽  
Big Data Analytics ◽  
Social Media Marketing ◽  
Content Type ◽  
Purchase Behaviors

Purpose Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social media marketing (SMM) in its business model concerns. Design/methodology/approach This study examines the experience of various Taiwanese Line stickers users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into five sections with 30 items in terms of the database design. All questions use nominal and order scales. This study develops a big data analytics approach, including cluster analysis and association rules, based on a big data structure and a relational database. Findings The authors divide Taiwan Line sticker users into three clusters by their profiles and then find each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM and business models. Originality/value This is the first study to offer a big data analytics to investigate and analyze the varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model development.

Positioning big data analytics capabilities towards financial service agility

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abeeku Sam Edu
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Business Models ◽  
Empirical Test ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Financial Service ◽  
Content Type ◽  
It Capability

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Big Data analytics for prediction: parallel processing of the big learning base with the possibility of improving the final result of the prediction

2018 ◽  
Vol 46 (3) ◽  
pp. 147-160 ◽  
Author(s):  
Laouni Djafri ◽  
Djamel Amar Bensaber ◽  
Reda Adjoudj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Sampling Method ◽  
New Technologies ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Sampling Strategy ◽  
Original Data ◽  
Data Set ◽  
Content Type

Purpose This paper aims to solve the problems of big data analytics for prediction including volume, veracity and velocity by improving the prediction result to an acceptable level and in the shortest possible time. Design/methodology/approach This paper is divided into two parts. The first one is to improve the result of the prediction. In this part, two ideas are proposed: the double pruning enhanced random forest algorithm and extracting a shared learning base from the stratified random sampling method to obtain a representative learning base of all original data. The second part proposes to design a distributed architecture supported by new technologies solutions, which in turn works in a coherent and efficient way with the sampling strategy under the supervision of the Map-Reduce algorithm. Findings The representative learning base obtained by the integration of two learning bases, the partial base and the shared base, presents an excellent representation of the original data set and gives very good results of the Big Data predictive analytics. Furthermore, these results were supported by the improved random forests supervised learning method, which played a key role in this context. Originality/value All companies are concerned, especially those with large amounts of information and want to screen them to improve their knowledge for the customer and optimize their campaigns.

Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prakash Agrawal ◽  
Rakesh Narain
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
New Technologies ◽  
Technology Development ◽  
Matrix Multiplication ◽  
Big Data Analytics ◽  
Structural Modelling ◽  
Content Type ◽  
Interpretive Structural Modelling

PurposeOver the years, technology development has rationalized supply chain processes. The demand economy is disrupting every sector causing the supply chain to be more innovative than ever before. The digitalization of the supply chain fulfils this demand. Several technologies such as blockchain, big data analytics, 3D printing, Internet of things (IoT), artificial intelligence (AI), augmented reality (AR), etc. have been innovated in recent years, which expedite the digitalization of the supply chain. The paper aims to analyse the applicability of these technological enablers in the digital transformation of the supply chain and to present an interpretive structural modelling (ISM) model, which presents a sequence in which enablers can be implemented in a sequential manner.Design/methodology/approachThis paper employed the ISM approach to propose a various levelled model for the enablers of the digital supply chain. The enablers are also classified graphically based on their driving and dependence powers using matrix multiplication cross-impact applied to classification (MICMAC) analysis.FindingsThe study indicates that the enablers “big data analytics”, “IoT”, “blockchain” and “AI” are the most powerful enablers for the digitalization of the supply chain and actualizing these enablers should be a topmost concern for organizations, which want to exploit new opportunities created by these technologies.Practical implicationsThis study presents a systematic approach to adopt new technologies for performing various supply chain activities and assists the policymakers better organize their assets and execution endeavours towards digitalization of the supply chain.Originality/valueThis is one of the initial research studies, which has analysed the enablers for the digitalization supply chain using the ISM approach.

scholarly journals Big Data in fashion: transforming the retail sector

2019 ◽  
Vol 41 (4) ◽  
pp. 21-27 ◽  
Author(s):  
Emmanuel Sirimal Silva ◽  
Hossein Hassani ◽  
Dag Øivind Madsen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Academic Research ◽  
Big Data Analytics ◽  
Retail Sector ◽  
Consumer Experience ◽  
Trend Prediction ◽  
Content Type ◽  
Fashion Retail

Purpose Big Data is disrupting the fashion retail industry and revolutionising the traditional fashion business models. Nowadays, leading fashion brands and new start-ups are actively engaging with Big Data analytics to enhance their operations and maximise on profitability. In hope of motivating and providing direction to fashion retail managers, industry experts, and academics alike, the purpose of this paper is to consider the most recent and trending applications of Big Data in fashion retailing with the aim of concisely summarising the industry’s current position and status. Design/methodology/approach This conceptual paper provides a brief introduction to the emerging topic of Big Data in fashion retailing by briefly synthesising findings from industry, market and academic research. Findings Most existing fashion brands are yet to fully engage with Big Data. The authors find that the main reasons underlying the application of Big Data analytics in fashion are trend prediction, waste reduction, consumer experience, consumer engagement and marketing, better quality control, less counterfeits and shortening of supply chains. The authors also identify key challenges which must be overcome for the most fashionable industry to be able to capitalize on Big Data to understand and predict fashion consumer behaviour. Research limitations/implications The brief synthesis provides a foundation for future investigations into the use of Big Data in fashion retailing. Originality/value This paper serves as an up-to-date introduction to how Big Data can transform fashion retailing and can act as a sound reference guide for fashion industry managers and professionals grappling with Big Data-related issues.

Dave Snowden on KM and big data/analytics: interview with David J. Pauleen

2017 ◽  
Vol 21 (1) ◽  
pp. 12-17 ◽  
Author(s):  
David J. Pauleen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Theory And Practice ◽  
Content Type ◽  
Face To Face ◽  
The Relationship ◽  
A Company ◽  
Practical Implications ◽  
Scientific Officer

Purpose Dave Snowden has been an important voice in knowledge management over the years. As the founder and chief scientific officer of Cognitive Edge, a company focused on the development of the theory and practice of social complexity, he offers informative views on the relationship between big data/analytics and KM. Design/methodology/approach A face-to-face interview was held with Dave Snowden in May 2015 in Auckland, New Zealand. Findings According to Snowden, analytics in the form of algorithms are imperfect and can only to a small extent capture the reasoning and analytical capabilities of people. For this reason, while big data/analytics can be useful, they are limited and must be used in conjunction with human knowledge and reasoning. Practical implications Snowden offers his views on big data/analytics and how they can be used effectively in real world situations in combination with human reasoning and input, for example in fields from resource management to individual health care. Originality/value Snowden is an innovative thinker. He combines knowledge and experience from many fields and offers original views and understanding of big data/analytics, knowledge and management.

Sustainable life cycle offers through cooperation

2015 ◽  
Vol 4 (1) ◽  
pp. 4-24 ◽  
Author(s):  
Julia Selberherr
Keyword(s):  
Sustainable Development ◽  
Life Cycle ◽  
Business Model ◽  
Value Creation ◽  
Business Models ◽  
Service Providers ◽  
Construction Sector ◽  
Content Type ◽  
New Business ◽  
Societal Level

Purpose – Sustainable buildings bear enormous potential benefits for clients, service providers, and our society. To release this potential a change in business models is required. The purpose of this paper is to develop a new business model with the objective of proactively contributing to sustainable development on the societal level and thereby improving the economic position of the service providers in the construction sector. Design/methodology/approach – The modeling process comprises two steps, the formal structuring and the contextual configuration. In the formal structuring systems theory is used and two levels are analytically separated. The outside view concerns the business model’s interaction with the environment and its impact on sustainability. The inside view focusses on efficient value creation for securing sustainability. The logically deductively developed business model is subsequently theory-led substantiated with Giddens’ structuration theory. Findings – The relevant mechanisms for the development of a new service offer, which creates a perceivable surplus value to the client and contributes to sustainable development on the societal level, are identified. The requirements for an efficient value creation process with the objective of optimizing the service providers’ competitive position are outlined. Research limitations/implications – The model is developed logically deductively based on literature and embedded in a theoretical framework. It has not yet been empirically tested. Practical implications – Guidelines for the practical implementation of more sustainable business models for the provision of life cycle service offers are developed. Social implications – The construction industry’s impact requires it to contribute proactively to a more sustainable development of the society. Originality/value – This paper analyzes the role for the players in the construction sector in proactively contributing to sustainable development on the societal level. One feasible strategy is proposed with a new business model, which aims at cooperatively optimizing buildings and infrastructures and taking the responsibility for the operating phase via guarantees.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

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