The ball of wax we call HR analytics

2019 ◽  
Vol 18 (1) ◽  
pp. 21-25
Author(s):  
Julie Fernandez
Keyword(s):  
Artificial Intelligence ◽  
Performance Indicators ◽  
Design Methodology ◽  
Human Judgment ◽  
Content Type ◽  
Slowing Down ◽  
Advanced Technologies ◽  
Center Of Expertise ◽  
Apply Regression Analysis ◽  
New Hires

Purpose The debate surrounding automating analytics processes continues as technology becomes more prominent and advanced in the workplace. Specifically, when it comes to HR analytics, it is important to recognize that human judgment as it is used in recruiting today is flawed. One tool that can provide further analysis and measurement beyond performance indicators and predictors is machine learning. Through automation, HR professionals may someday be able to compare characteristics, apply regression analysis to identify the influence of a characteristic and make adjustments based on new hires, retention and promotion results. Design/methodology/approach With more and more companies using artificial intelligence, it is difficult to see how it will revolutionize the HR process. As humans already have biases, will they transfer over to these artificial intelligence machines? Human judgment is already flawed in the recruiting process, so it is crucial to take a look into how it plays a role when AI is becoming built into the process as well. Findings Advancements in automation and HR technology are not slowing down anytime soon. As HR departments become increasingly reliant on advanced technologies and the numbers they produce, they also will experience the need for new skillsets required to deploy and use them. The HR process is rapidly changing, and as people, we must adapt now to see how AI is going to affect it. With a growing need for a center of expertise (COE) for HR data and technology, we will need to use this to focus resources on workforce analytics to drive business insights and recommendations. Originality/value This paper discusses the importance of understanding the implications of advanced analytics on recruiting and people management.

scholarly journals Teaching innovations in Asian higher education: perspectives of educators

2018 ◽  
Vol 13 (2) ◽  
pp. 179-190 ◽  
Author(s):  
T.M. Wong
Keyword(s):  
Higher Education ◽  
Design Methodology ◽  
Higher Education Institutions ◽  
The Other ◽  
Asian Countries ◽  
Structured Interviews ◽  
Content Type ◽  
Advanced Technologies ◽  
The Many

Purpose The purpose of this paper is to identify the teaching innovations that have been implemented in higher education institutions in Asia and the perspectives of educators on them. Design/methodology/approach Semi-structured interviews were conducted with 28 educators who were affiliated with 23 higher education institutions in ten Asian countries/regions. The interviews covered information about the teaching innovations of the participants’ institutions, the characteristics of the innovative practices and the participants’ views on them. The relationships between the characteristics of institutions and their teaching innovations were also examined. Findings The results showed that the teaching innovations included two main categories, namely, those which involved the use of advanced technologies and those which did not. The innovations that involved the use of advanced technologies were mainly from larger institutions, while the other category was mainly from smaller ones and had been practised for less than 1.5 years. Differences were also identified between the two categories in terms of the aims and importance of innovations, innovative features, the evaluation of innovations and improvements needed for them. Originality/value The results highlighted that technology is only one of the many aspects of teaching innovations, which is different from the view prevailing in the literature. They also suggested that differences in the scale of institutions (in terms of number of students) possibly influences the kind of teaching innovations adopted.

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.

AI and libraries: trends and projections

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adetoun A. Oyelude
Keyword(s):  
Language Processing ◽  
Service Provision ◽  
Design Methodology ◽  
Human Robot Interaction ◽  
Human Intelligence ◽  
Library Service ◽  
Robot Interaction ◽  
Content Type ◽  
New Horizons ◽  
Advanced Technologies

Purpose This paper aims to focus on the trends and projection for future use of artificial intelligence (AI) in libraries. AI technologies is the latest among the technologies being used in libraries. The technology has systems that have natural language processing, machine learning and pattern recognition capabilities that make service provision easier for libraries. Design/methodology/approach Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which AI is used and can be futuristically used in libraries. Findings This paper found that uses of AI in libraries entailed enhanced services such as content indexing, document matching, content mapping content summarization and many others. AI possibilities were also found to include improving the technology of gripping, localizing and human–robot interaction and also having artificial superintelligence, the hypothetical AI that surpasses human intelligence and abilities. Originality/value It is concluded that advanced technologies that AI are, will help librarians to open up new horizons and solve challenges that crop up in library service delivery.

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.

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.

Exploring linkages of performance with metacognition

2019 ◽  
Vol 38 (3) ◽  
pp. 195-207 ◽  
Author(s):  
Paul R. Lyons ◽  
Randall Paul Bandura
Keyword(s):  
Performance Measures ◽  
Performance Indicators ◽  
Design Methodology ◽  
Learning And Development ◽  
Content Type ◽  
Self Evaluation ◽  
Secondary Purpose ◽  
Core Self ◽  
And Training ◽  
Practical Implications

PurposeIn this exploratory, correlational study the authors set out to demonstrate the relationships as well as inter-correlations among direct and indirect performance measures, along with measures of knowledge of cognition, and evaluation of cognition. The information helps inform manager learning and development. The purpose of this paper is twofold: first, primary purpose, to identify linkages of performance with individual’s efforts to improve their learning processes via metacognition; and second, secondary purpose, primarily for the benefit of practitioners, is the provision of detailed information regarding performance measures and practical measures of metacognition.Design/methodology/approachThe study made use of correlation analysis among performance measures and measures of metacognitive effort. The design is not intended to support cause and effect relationships, nor demonstrate the technical, predictive value of measures.FindingsA majority of associations among indirect performance measures with one another and with nearly all of the measures of knowledge of cognition, and evaluation of cognition were positive and significant (mostly at the 0.01 level). Findings offer broad support for the linkage of self-efficacy (SE), and core self-evaluation (CSE) with performance.Practical implicationsRelationships identified in this study may help practitioners alter and improve their practices/methods of identifying individuals who possess attributes that are highly related to performance and learning. The new knowledge may influence decisions about recruitment, selection and training.Originality/valueLittle research has focused on relationships among indirect performance indicators such as SE, CSE and established measures of metacognition. The present study helps to identify important relationships.

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.

Development of a model for maintenance performance measurement

2019 ◽  
Vol 26 (1) ◽  
pp. 69-86
Author(s):  
Sungtae Ku ◽  
Changeun Kim
Keyword(s):  
Performance Indicators ◽  
Design Methodology ◽  
Quantitative Measurement ◽  
Maintenance Management ◽  
Case Study Method ◽  
Content Type ◽  
Energy Company ◽  
Practical Implications ◽  
Maintenance Performance

Purpose The purpose of this paper is to develop a model that can measure the equipment maintenance performance of the energy company K-company. Design/methodology/approach The case study method was adopted for the investigation of maintenance performance indicators (MPIs). The development of a model for measuring maintenance performance suggested new ways to apply the methodologies of existing papers to evaluate the level of maintenance. Findings Maintenance indicators, which are managed differently for each plant, were assessed for their performance relevance, applicability and data reliability and then standardized into five key MPIs. The MPI model, which enables comprehensive and quantitative measurement of maintenance performance using the five selected MPIs, was presented, and the criteria for assessing the maintenance level were presented in five stages. Practical implications The authors selected MPIs that match the characteristics of the company and proposed a model that can comprehensively and quantitatively evaluate maintenance performance. The model also standardizes maintenance indicators that are individually managed and provides a basis for comparing and indexing the level of maintenance indicators at each plant. Originality/value The criterion for selecting the key MPIs considering the characteristics of the company and a model that can comprehensively and quantitatively evaluate maintenance performance were presented. In addition, a standard for evaluating the level of maintenance at the global level of maintenance management was suggested.

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