scholarly journals Coordinating Human-Robot Teams with Dynamic and Stochastic Task Proficiencies

2022 ◽  
Vol 11 (1) ◽  
pp. 1-42
Author(s):  
Ruisen Liu ◽  
Manisha Natarajan ◽  
Matthew C. Gombolay
Keyword(s):  
Learning Curve ◽  
Task Performance ◽  
Prior Distribution ◽  
Human Performance ◽  
User Study ◽  
Data Driven ◽  
Temporal Constraints ◽  
High Quality ◽  
Content Type ◽  
Human Robot Collaboration

As robots become ubiquitous in the workforce, it is essential that human-robot collaboration be both intuitive and adaptive. A robot’s ability to coordinate team activities improves based on its ability to infer and reason about the dynamic (i.e., the “learning curve”) and stochastic task performance of its human counterparts. We introduce a novel resource coordination algorithm that enables robots to schedule team activities by (1) actively characterizing the task performance of their human teammates and (2) ensuring the schedule is robust to temporal constraints given this characterization. We first validate our modeling assumptions via user study. From this user study, we create a data-driven prior distribution over human task performance for our virtual and physical evaluations of human-robot teaming. Second, we show that our methods are scalable and produce high-quality schedules. Third, we conduct a between-subjects experiment (n = 90) to assess the effects on a human-robot team of a robot scheduler actively exploring the humans’ task proficiency. Our results indicate that human-robot working alliance ( p\lt 0.001 ) and human performance ( p=0.00359 ) are maximized when the robot dedicates more time to exploring the capabilities of human teammates.

Day-level job crafting and service-oriented task performance

2020 ◽  
Vol 25 (4) ◽  
pp. 355-371 ◽  
Author(s):  
Inge L. Hulshof ◽  
Evangelia Demerouti ◽  
Pascale M. Le Blanc
Keyword(s):  
Task Performance ◽  
Work Engagement ◽  
Design Methodology ◽  
Meaningful Work ◽  
Job Crafting ◽  
High Quality ◽  
Content Type ◽  
Service Oriented ◽  
Multilevel Sem

PurposeThis study examines whether job crafting is related to service-oriented task performance (i.e. performance aimed at providing high-quality services) through meaningful work and work engagement.Design/methodology/approachData were collected from 156 employees of a Dutch unemployment agency (4 days, 531 observations). Multilevel SEM was used to analyze the data.FindingsResults showed that job crafting was related to service-oriented task performance via meaningful work and work engagement. Specifically, seeking resources and seeking challenges were positively related to service-oriented task performance via meaningful work and work engagement, whereas reducing demands was negatively related to service-oriented task performance via meaningful work and work engagement.Originality/valueThe study concludes that seeking resources and seeking challenges are beneficial for service-oriented task performance.

Role of burnout on job level, job satisfaction, and task performance

2017 ◽  
Vol 38 (5) ◽  
pp. 630-645 ◽  
Author(s):  
Won Ho Kim ◽  
Young-An Ra ◽  
Jong Gyu Park ◽  
Bora Kwon
Keyword(s):  
Job Satisfaction ◽  
Task Performance ◽  
Organizational Context ◽  
Content Type ◽  
Job Level ◽  
Mediating Role ◽  
Final Sample ◽  
The Relationship ◽  
And Task

Purpose The purpose of this paper is to examine the mediating role of burnout (i.e. exhaustion, cynicism, professional inefficacy) in the relationship between job level and job satisfaction as well as between job level and task performance. Design/methodology/approach The final sample included 342 Korean workers from selected companies. The authors employed the Hayes (2013) PROCESS tool for analyzing the data. Findings The results showed that all three subscales of burnout (i.e. exhaustion, cynicism, professional inefficacy) mediate the relationship between job level and job satisfaction. However, only two mediators (i.e. cynicism, professional inefficacy) indicated the mediating effects on the association between job level and task performance. Originality/value This research presented the role of burnout on the relationships between job level, job satisfaction, and task performance especially in South Korean organizational context. In addition to role of burnout, findings should prove helpful in improving job satisfaction and task performance. The authors provide implications and limitations of the findings.

scholarly journals Beneficial and harmful explanatory machine learning

2021 ◽  
Author(s):  
Lun Ai ◽  
Stephen H. Muggleton ◽  
Céline Hocquette ◽  
Mark Gromowski ◽  
Ute Schmid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Beneficial Effect ◽  
Empirical Evidence ◽  
Task Performance ◽  
Human Performance ◽  
Human Learning ◽  
Science Literature ◽  
Definition Of ◽  
Self Learning

AbstractGiven the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the beneficial effect of a machine learned logic theory for a classification task, yet no existing work to our knowledge has examined the potential harmfulness of machine’s involvement for human comprehension during learning. This paper investigates the explanatory effects of a machine learned theory in the context of simple two person games and proposes a framework for identifying the harmfulness of machine explanations based on the Cognitive Science literature. The approach involves a cognitive window consisting of two quantifiable bounds and it is supported by empirical evidence collected from human trials. Our quantitative and qualitative results indicate that human learning aided by a symbolic machine learned theory which satisfies a cognitive window has achieved significantly higher performance than human self learning. Results also demonstrate that human learning aided by a symbolic machine learned theory that fails to satisfy this window leads to significantly worse performance than unaided human learning.

How to turn managers into data-driven decision makers

2019 ◽  
Vol 25 (3) ◽  
pp. 553-578 ◽  
Author(s):  
Kevin Daniel André Carillo ◽  
Nadine Galy ◽  
Cameron Guthrie ◽  
Anne Vanhems
Keyword(s):  
Measurement Instrument ◽  
Data Driven ◽  
Business Analytics ◽  
Business World ◽  
Content Type ◽  
Multi Stage ◽  
Attitudes Toward Statistics ◽  
Attitude Toward Business ◽  
Professional Contexts ◽  
The Way

Purpose The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers. Design/methodology/approach This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school. Findings The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics. Research limitations/implications As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals. Practical implications The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students. Originality/value By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

Effects of air mattress pressure on sleep quality and physiological responses: comparison of shoulder and hip pressure relief

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yoon Jeong Baek ◽  
Seung-Hyun Kim ◽  
Sayup Kim ◽  
Eui-Sang Yoo ◽  
Joo-Young Lee
Keyword(s):  
Sleep Quality ◽  
Surface Pressure ◽  
Pressure Control ◽  
Body Region ◽  
High Quality ◽  
Pressure Relief ◽  
Content Type ◽  
Quality Sleep ◽  
Improve Sleep Quality

PurposeThe purpose of the present study was to evaluate the effect of air mattress pressure on sleep quality.Design/methodology/approachTen young healthy males participated in all hard surface [AH], shoulder soft [SS] and shoulder and hip soft mattress [SHS] conditions. The surface pressure for SS and SHS were set at their preferred levels.FindingsThe results showed that sleep efficiencies were over 95% for all the three conditions; there were no significant differences in individual sleep variables among the three conditions, but overall sleep quality was better for SS than AH (p = 0.065); heart rates during sleep was greater for AH than the other two conditions (p < 0.1); and a stronger relationship between clothing and bed microclimate humidity were found for SS and SHS than that for AH.Research limitations/implicationsThese results indicated that the both pressure relief air mattresses that were set at their own preferred levels provided high quality sleep with no marked differences.Practical implicationsAir pressure relief mattresses can improve sleep quality of healthy individuals during sleep at night. The results can be used to understand appropriate pressure distribution on surface mattress according to body region, and also to develop algorithms to provide optimum sleep using mattresses with surface pressure control by body region.Originality/valueThe present study found that the shoulder and/or hip pressure relief air mattresses that were set at their own preferred levels provided high quality sleep with no marked differences.

Big data-driven supply chain and performance: a resource-based view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shampy Kamboj ◽  
Shruti Rana
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Small And Medium Enterprises ◽  
Data Driven ◽  
Equation Modeling ◽  
Firm Age ◽  
Content Type ◽  
Sustainable Performance ◽  
Medium Enterprises

PurposeThe main objective of this paper is to study the role of supply chain performance (SCP) as a mediator between big data-driven supply chain (BDDSC) and firm sustainable performance. In addition, the role of firm age as a moderator between BDDSC and SCP as well as between SCP and firm sustainable performance has also been explored.Design/methodology/approachThe 200 managers of medium or senior level positions in micro, small and medium enterprises (MSMEs) located at Delhi-NCR have been contacted. Further, collected data have been confirmed with confirmatory factor analysis (CFA). In this paper, structure equation modeling (SEM) has been employed to empirically check the proposed hypotheses and their relationships.FindingsThe findings confirmed that SCP mediates the link between BDDSC and firm sustainable performance. Additionally, firm age moderates the association between BDDSC and SCP as well as between SCP and firm sustainable performance.Research limitations/implicationsThe role of SCP and firm age between BDDSC and sustainable performance have been examined in the context of MSMEs in Delhi-NCR and thereby limit the generalization of results to other industries and country contexts.Originality/valueThe present study adds to the existing literature via recognizing the blackbox using SCP and firm age to comprehend BDDSC and firm sustainable performance relationship.

Reinforcing the significance of human factor in achieving quality performance in data-driven supply chain management

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanjana Mondal ◽  
Kaushik Samaddar
Keyword(s):  
Supply Chain ◽  
Organizational Performance ◽  
Industry 4.0 ◽  
Management Practices ◽  
Human Factor ◽  
Research Work ◽  
Data Driven ◽  
Theory Approach ◽  
Quality Performance ◽  
Content Type

PurposeThe paper aims to explore the various dimensions of human factor relevant for integrating data-driven supply chain quality management practices (DDSCQMPs) with organizational performance. Keeping the transition phase from “Industry 4.0” to “Industry 5.0” in mind, the paper reinforces the role of the human factor and critically discusses the issues and challenges in the present organizational setup.Design/methodology/approachFollowing the grounded theory approach, the study arranged in-depth interviews and focus group sessions with industry experts from various service-oriented firms in India. Dimensions of human factor identified from there were grouped together through a morphological analysis (MA), and interlinkages between them were explored through a cross-consistency matrix.FindingsThis research work identified 20 critical dimensions of human factor and have grouped them under five important categories, namely, cohesive force, motivating force, regulating force, supporting force and functional force that drive quality performance in the supply chain domain.Originality/valueIn line with the requirements of the present “Industry 4.0” and the forthcoming “Industry 5.0”, where the need to collaborate human factor with smart system gets priority, the paper made a novel attempt in presenting the critical human factors and categorizing them under important driving forces. The research also contributed in linking DDSCQMPs with organizational performance. The proposed framework can guide the future researchers in expanding the theoretical constructs through initiating further cross-cultural studies across industries.

Big data-driven strategic orientation in international marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahriar Akter ◽  
Md Afnan Hossain ◽  
Qiang (Steven) Lu ◽  
S.M. Riad Shams
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
International Marketing ◽  
Strategic Orientation ◽  
Data Driven ◽  
Future Research ◽  
Content Type ◽  
Strategic Orientations ◽  
Marketing Activities

PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.FindingsThe findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.Research limitations/implicationsThe study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.Originality/valueThe study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

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