Consumer electronics show 2019, from connected devices and big data to artificial intelligence: implications for libraries

2019 ◽  
Vol 36 (5) ◽  
pp. 11-14
Author(s):  
Martin A. Kesselman ◽  
Wilson Esquivel
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Design Methodology ◽  
Consumer Electronics ◽  
Content Type

Purpose Report of the CES 2019 Conference Design/methodology/approach Attendance at the conference Findings Implications for libraries Originality/value Original thoughts

Autonomous vehicles: a review of the ethical, social and economic implications of the AI revolution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shweta Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Design Methodology ◽  
Technological Advancement ◽  
Economic Implications ◽  
Content Type ◽  
The Public ◽  
Autonomous Cars ◽  
The Subject ◽  
Practical Implications

PurposeThere are ethical, legal, social and economic arguments surrounding the subject of autonomous vehicles. This paper aims to discuss some of the arguments to communicate one of the current issues in the rising field of artificial intelligence.Design/methodology/approachMaking use of widely available literature that the author has read and summarised showcasing her viewpoints, the author shows that technology is progressing every day. Artificial intelligence and machine learning are at the forefront of technological advancement today. The manufacture and innovation of new machines have revolutionised our lives and resulted in a world where we are becoming increasingly dependent on artificial intelligence.FindingsTechnology might appear to be getting out of hand, but it can be effectively used to transform lives and convenience.Research limitations/implicationsFrom robotics to autonomous vehicles, countless technologies have and will continue to make the lives of individuals much easier. But, with these advancements also comes something called “future shock”.Practical implicationsFuture shock is the state of being unable to keep up with rapid social or technological change. As a result, the topic of artificial intelligence, and thus autonomous cars, is highly debated.Social implicationsThe study will be of interest to researchers, academics and the public in general. It will encourage further thinking.Originality/valueThis is an original piece of writing informed by reading several current pieces. The study has not been submitted elsewhere.

A bibliographic study on big data: concepts, trends and challenges

2017 ◽  
Vol 23 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Deepa Mishra ◽  
Zongwei Luo ◽  
Shan Jiang ◽  
Thanos Papadopoulos ◽  
Rameshwar Dubey
Keyword(s):  
Big Data ◽  
Citation Analysis ◽  
Design Methodology ◽  
Future Directions ◽  
Content Type ◽  
Current Trends ◽  
Bibliographic Study

Purpose The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field. Design/methodology/approach To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals. Findings The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data. Research limitations/implications This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

Big data analysis of trends in the second-hand apparel market: a comparison between 2014 and 2019

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanghee Kim ◽  
Hongjoo Woo
Keyword(s):  
Big Data ◽  
Design Methodology ◽  
Semantic Network ◽  
Distribution Channels ◽  
Subject Area ◽  
Future Research ◽  
Semantic Network Analysis ◽  
Content Type ◽  
The Past ◽  
Network Patterns

Purpose According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years. Design/methodology/approach This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis. Findings The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels. Originality/value This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.

Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlos Flavián ◽  
Alfredo Pérez-Rueda ◽  
Daniel Belanche ◽  
Luis V. Casaló
Keyword(s):  
Artificial Intelligence ◽  
Technology Adoption ◽  
Design Methodology ◽  
Technology Readiness ◽  
Intention To Use ◽  
Content Type ◽  
Financial Investment ◽  
Managerial Implications ◽  
Barriers To Technology

PurposeThe automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by artificial intelligence (AI) suggests that this technology-based service will become increasingly popular. This study examines how customers' technology readiness and service awareness affect their intention to use analytical AI investment services.Design/methodology/approachThe automation of services is rapidly growing, led by sectors such as banking and financial investment. The growing number of investments managed by AI suggests that this technology-based service will become increasingly popular. This study examines how customers' technology readiness and service awareness affect their intention to use analytical AI investment services.FindingsThe results indicated that customers' technological optimism increases, and insecurity decreases, their intention to use robo-advisors. Surprisingly, feelings of technological discomfort positively influenced robo-advisor adoption. This interesting finding challenges previous insights into technology adoption and value co-creation as analytical AI puts customers into a very passive role and reduces barriers to technology adoption. The research also analyzes how consumers become aware of robo-advisors, and how this influences their acceptance.Originality/valueThis is the first study to analyze the role of customers' technology readiness in the adoption of analytical AI. The authors link the findings to previous technology adoption and automated services' literature and provide specific managerial implications and avenues for further research.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Preface

2021 ◽  
Vol 2050 (1) ◽  
pp. 011001
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Industrial Applications ◽  
Program Committee ◽  
Technical Session ◽  
Technical Program ◽  
Content Type ◽  
The World ◽  
Travel Restrictions ◽  
Virtual Conference

Considering the current situation of COVID-19 and travel restrictions, the 3rd International Conference on Industrial Applications of Big Data and Artificial Intelligence (BDAI 2021) which was planned to be held in Wuhan. China from Sept. 23 to 25, 2021 was changed into virtual conference on Sept. 24, 2021 via Tencent Meeting (Voov) software. BDAI 2021 was organized by China University of Geosciences (Wuhan), sponsored by Hong Kong Society of Mechanical Engineers (HKSME). The Technical Program committee received a total of 38 paper submissions from all over the world, among which 20 papers were accepted, and more than 30 participants attended the conference online, they were from China, Australia, Thailand, Malaysia, India, Japan, UK and more. Four renowned speakers given speeches about their latest research and reports. They are: Prof. Dan Zhang from York University, Canada; Prof. Lefei Zhang from Wuhan University. China: Prof. Deze Zeng from China University of Geosciences (Wuhan), China and Assoc. Prof. Simon James Fong from University of Macau. Macau S.A.R., China. The conference also had 1 technical session and 1 poster sessions. This conference aims to provide a platform for researchers and engineers to share their ideas, recent developments, and successful practices in energy engineering. The participants of the conference were from almost every part of the world, with various background such as academia, industry, and well-known entrepreneurs. Each keynote speech lasted 40 minutes, and authors presentation 15 minutes. Each presentation was included with questions and answers. BDAI 2021 became an effective communication platform for all the participants over the world and unlike some that claim international reach this conference was truly international. The conference proceeding is a compilation of the accepted papers and represent an interesting outcome of the conference. This book covers 3 chapters: 1. Artificial Intelligence: 2. Big Data Technology; 3. Robot System. We would like to acknowledge all of those who supported BDAI 2021. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that BDAI 2021 will be a fomrn for excellent discussions that will put forward new ideas and promote collaborative research and support researchers as they take their work forward. We are sure that the proceedings will serve as an important research source of references and the knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. Dan Zhang, York University, Canada

Software documentation is not enough! Requirements for the documentation of AI

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Florian Königstorfer ◽  
Stefan Thalmann
Keyword(s):  
Artificial Intelligence ◽  
Business Process ◽  
Design Methodology ◽  
Governance Mechanism ◽  
It Industry ◽  
Specific Nature ◽  
Content Type ◽  
Software Documentation ◽  
Disruptive Technologies ◽  
Present Information

Purpose Artificial intelligence (AI) is currently one of the most disruptive technologies and can be applied in many different use cases. However, applying AI in regulated environments is challenging, as it is currently not clear how to achieve and assess the fairness, accountability and transparency (FAT) of AI. Documentation is one promising governance mechanism to ensure that AI is FAT when it is applied in practice. However, due to the nature of AI, documentation standards from software engineering are not suitable to collect the required evidence. Even though FAT AI is called for by lawmakers, academics and practitioners, suitable guidelines on how to document AI are not available. This interview study aims to investigate the requirements for AI documentations. Design/methodology/approach A total of 16 interviews were conducted with senior employees from companies in the banking and IT industry as well as with consultants. The interviews were then analyzed using an informed-inductive coding approach. Findings The authors found five requirements for AI documentation, taking the specific nature of AI into account. The interviews show that documenting AI is not a purely technical task, but also requires engineers to present information on how the AI is understandably integrated into the business process. Originality/value This paper benefits from the unique insights of senior employees into the documentation of AI.

Quantifying human resource management: a literature review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Clotilde Coron
Keyword(s):  
Artificial Intelligence ◽  
Resource Management ◽  
Literature Review ◽  
Human Resource Management ◽  
Human Resource ◽  
Design Methodology ◽  
Theoretical Framework ◽  
Data Sources ◽  
Content Type

PurposeWith a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?Design/methodology/approachThis study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.FindingsThe analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.Originality/valueThis literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

Impact of AI regulation on intention to use robots

2019 ◽  
Vol 8 (2) ◽  
pp. 97-114
Author(s):  
Sheshadri Chatterjee
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Design Methodology ◽  
Regulatory Control ◽  
Control Beliefs ◽  
Intention To Use ◽  
Content Type ◽  
And Control ◽  
The Impact

Purpose The purpose of this paper is to identify the factors influencing the citizens to use robots that would improve the quality of life of the citizens. Design/methodology/approach With the help of different adoption theories and models and with the support of background studies, some hypotheses have been formulated and a conceptual model has been developed with the consideration of the impact of artificial intelligence regulation (IAR) that controls the use of robots as a moderator. The model has been validated and the hypotheses have been tested by statistical analysis with the help of survey works involving consideration of feedbacks from 503 usable respondents. Findings The study reveals that the use of robots by the citizens would appreciably increase if government imposes strict artificial intelligence (AI) regulatory control concerning the use of robots, and in that case, it appears that the use of robots would improve the quality of life of the citizens. Research limitations/implications The duly validated model would help the authority to appropriately nurse and nurture the factors such as ethical dilemma, perceived risks and control beliefs for enhancing the intention of the citizens to use robots for many purposes including domestic usage in the context of appropriate restrictions imposed through AI regulation. Such use of robots would eventually improve the quality of life. Originality/value There are a few studies covering analysis of IAR as a moderator on the linkages of the predictors with the intention of the citizens to use robots. In this context, this study is claimed to have offered a novel contribution.

Working in contexts for which transparency is important

2020 ◽  
Vol 30 (2) ◽  
pp. 143-153
Author(s):  
Jenny Bunn
Keyword(s):  
Artificial Intelligence ◽  
Design Methodology ◽  
Content Type ◽  
Explainable Artificial Intelligence ◽  
Reflective Approach ◽  
The Way

Purpose This paper aims to introduce the topic of explainable artificial intelligence (XAI) and reports on the outcomes of an interdisciplinary workshop exploring it. It reflects on XAI through the frame and concerns of the recordkeeping profession. Design/methodology/approach This paper takes a reflective approach. The origins of XAI are outlined as a way of exploring how it can be viewed and how it is currently taking shape. The workshop and its outcomes are briefly described and reflections on the process of investigating and taking part in conversations about XAI are offered. Findings The article reinforces the value of undertaking interdisciplinary and exploratory conversations with others. It offers new perspectives on XAI and suggests ways in which recordkeeping can productively engage with it, as both a disruptive force on its thinking and a set of newly emerging record forms to be created and managed. Originality/value The value of this paper comes from the way in which the introduction it provides will allow recordkeepers to gain a sense of what XAI is and the different ways in which they are both already engaging and can continue to engage with it.

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