scholarly journals Human Interaction Management Impact on Hospital Labor Planning

10.28945/3847 ◽  
2017 ◽  
Vol 1 ◽  
pp. 125-139
Author(s):  
Richard J Tarpey
Keyword(s):  
New Technology ◽  
Workforce Planning ◽  
Labor Cost ◽  
Human Interaction ◽  
Human Interactions ◽  
Critical Knowledge ◽  
Human Participants ◽  
Department Leader ◽  
Work Structure ◽  
Interaction Management

Labor cost is the single highest expense for hospitals. Rather than relying on new technology, this case study seeks to utilize Human Interaction Management to redesign work structure and process to improve labor forecasting and scheduling outcomes. This study takes a distinctly unique approach to the hospital workforce planning (forecasting and scheduling) problem. The study is differentiated from precedent work in its focus on the structure of the work and the human interactions involved in labor planning, rather than strictly quantitative mathematical models and algorithms. Hospital labor planning involves many dimensions and levels of complexity. Within this complexity, we believe there are many improvement opportunities. This study focused on examining human processes, interactions and work involved with forecasting workload and subsequent labor scheduling. The objective was to redesign necessary components to optimize human interactions, flow of information, and knowledge sharing in order to address the large amounts of complexity and variability. The study concluded that a centralized role-process structure that facilitates and encourages more human interactions and feedback across the different roles resulted in more accurate labor forecasts, subsequently leading to more accurate labor schedules. We found that large amounts of critical knowledge and information was locked within the human participants who did not interact with other roles. There was a lack of a path for the critical information to flow across the roles where needed to successfully perform tasks. The drivers for the improvements were task focus and more information sharing leading to a richer collection of information and knowledge used as input to the work tasks. Redesigning work activities and roles resulted in better forecasting and scheduling outcomes as well as an additional benefit of freeing up clinical department leader time to focus on more patient and employee centric tasks within their departments.

scholarly journals Digital or human touchpoints? Insights from consumer-facing in-store services

2019 ◽  
Vol 33 (1) ◽  
pp. 296-310 ◽  
Author(s):  
Virginia Vannucci ◽  
Eleonora Pantano
Keyword(s):  
Human Services ◽  
Design Methodology ◽  
New Technology ◽  
Structured Interviews ◽  
Content Type ◽  
Face To Face ◽  
Human Interactions ◽  
Key Drivers ◽  
Service Channels ◽  
Digital Or

Purpose Prior research highlights the extent to which consumers largely appreciate the possibility to choose among different digital touchpoints during the in-store experience, which results in a pervasive introduction of digital touchpoints as the first point of contact between retailers and consumers. However, consumers also give value to the human interactions in the service channels. The previous studies do not conclusively indicate the best balance of digital and human services. The purpose of this paper is to understand consumer-facing in-store services in new technology-enriched retail settings. Design/methodology/approach A qualitative approach involving face-to-face semi structured interviews was applied. To this end, the authors recruited 26 participants in Northern Italy between October and November 2017. Findings Results reveal motivations, preferences and discouraging factors leading consumers’ interactions with digital or human touchpoints. Findings ultimately provide useful guidelines to managers on understanding consumers’ attitudes toward digital vs human touchpoints phenomenon. Originality/value By identifying the key drivers of either digital and human touchpoints selection in offline retail settings, the present study figured out the attributes playing the crucial role in determining consumers’ preference regarding the in-store alternatives. Findings allow a further greater clarification of the practical issues, with emphasis on the new of human–machine integration.

scholarly journals Retailing technology: do consumers care?

2020 ◽  
Vol 24 (3) ◽  
pp. 355-375
Author(s):  
Alfonso Valdez Cervantes ◽  
Ana Franco
Keyword(s):  
Technology Implementation ◽  
Purchase Intention ◽  
Field Experiments ◽  
New Technology ◽  
Internal Validity ◽  
Purchase Intentions ◽  
Human Interaction ◽  
Original Research ◽  
Store Image ◽  
Content Type

Purpose Disruptive retailing technologies improve productivity and cost optimization, but there is a lack of academic literature about their effects on shoppers’ perceptions and behaviors. This paper aims to develop and test a conceptual model regarding the effects of retail technology on store image and purchase intentions and to measure how human interaction services (HIS) moderate this relationship. Two relevant retail technologies are explored. Design/methodology/approach The results of this study indicate that retailing technology has notable influences on consumer perceptions. Thus, shopping technologies improve store image perceptions and increase purchase intention, moderated by HIS. Research limitations/implications Future field experiments in actual stores should attempt to corroborate the results of this study and offer greater internal validity. Practical implications The results should help reduce retailers’ resistance to technology adoption. In-store technology can help retailers leverage their store image and increase purchase intentions. HIS could offer a bridge between consumers and new technology. Originality/value This paper is an original research paper, given that few research papers are experimentally based to measure consumer’s reactions to new technology implementation.

Psychologically Plausible Cognitive Models for Simulating Interactive Human Behaviors

2005 ◽  
Vol 49 (12) ◽  
pp. 1205-1209 ◽  
Author(s):  
Michael L. Bernard ◽  
Patrick Xavier ◽  
Paul Wolfenbarger ◽  
Derek Hart ◽  
Russel Waymire ◽  
...  
Keyword(s):  
Cognitive Modeling ◽  
Human Interaction ◽  
Human Model ◽  
Motor Functioning ◽  
Human Behaviors ◽  
Human Interactions ◽  
Cognitive Framework ◽  
Interaction Modeling ◽  
Cognition Model ◽  
High Level

The intent of Sandia National Laboratories' Human Interactions (HI) project is to demonstrate initial virtual human interaction modeling capability. To accomplish this, we have begun the process of simulating human behavior in a manner that produces life-like characteristics and movement, as well as creating the framework for models that are based on the most current experimental research in cognition, perception, physiology, and cognitive modeling. Currently the simulated human models can sense each other, react to each other, and move about in a simulated 3D environment. A preliminary action generation or motor-level cognition model, which transforms abstract actions generated by high-level cognition to actions that can be carried out by a simulated physical human model, has also been developed. Our work has yielded models of perceptual, spatial, and motor functioning and memory that will be embedded in upgrades to the cognitive framework.

scholarly journals GOSSIP ON WEIGHTED NETWORKS

2012 ◽  
Vol 15 (supp01) ◽  
pp. 1250061 ◽  
Author(s):  
MURSEL TASGIN ◽  
HALUK O. BINGOL
Keyword(s):  
Complex Networks ◽  
Weight Distribution ◽  
Human Interaction ◽  
Interaction Networks ◽  
Weighted Network ◽  
Weighted Networks ◽  
Human Interactions ◽  
Spreading Model ◽  
Good Parameter ◽  
Weighted Complex Networks

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.

scholarly journals Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Behzad Vahedi ◽  
Morteza Karimzadeh ◽  
Hamidreza Zoraghein
Keyword(s):  
Social Connectedness ◽  
Predictive Power ◽  
Human Mobility ◽  
Human Interaction ◽  
County Level ◽  
Infection Rates ◽  
Baseline Model ◽  
Human Interactions ◽  
Prediction Horizon ◽  
Socioeconomic Composition

AbstractMeasurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

scholarly journals Human Interaction with Creators and Fellow Creatures (Study of Communication Relations of XYZ in Alamin Theory)

2019 ◽  
Vol 2 (4) ◽  
pp. 85-98
Author(s):  
Muhammad Aminullah
Keyword(s):  
Qualitative Research ◽  
Content Analysis ◽  
Research Method ◽  
Interaction Process ◽  
Human Interaction ◽  
Analysis Approach ◽  
Human Relations ◽  
Human Interactions ◽  
The Relationship ◽  
The Universe

These studies are important to understand the process of interaction in human relations with the creator and also relationships with fellow creatures. The research method used is a qualitative research based on content analysis approach, with the aim to be able to explore the theory of alamin used in this study. The results found that interaction was formed by the existence of one of the most basic objectives in communication, namely necessity. This concept can be understood that everything needed by humans, then must have a process of relationships in the form of interaction to be able to achieve whatever objects are needed. But the interaction process is different. Therefore human interactions have two goals. First, human interaction with the creator called the relationship X with Y. This interaction is carried out by X to Y in the form of a relationship as creator to X, provider of living facilities to X, trustee Y, and X servant Y. Second is human interaction with the universe this is called the relationship X with Z. This interaction is carried out by X to Z in the form of a Z relationship as a reference for X, a provider of mass space for X, a power provider for X, and a proof of space for the implementation of the X assignment.

scholarly journals ACCELERATION TECHNOLOGY ON BOOKS AS CULTURAL MEDIA ON HUMAN COMMUNICATION IN THE ERA OF INTERACTIVE COMMUNICATION

2016 ◽  
pp. 157
Author(s):  
Asthararianty Asthararianty
Keyword(s):  
Interpersonal Communication ◽  
Human Interaction ◽  
Internet Technology ◽  
Human Communication ◽  
Cultural Shift ◽  
Interactive Communication ◽  
Human Interactions ◽  
Reading Culture ◽  
Conventional Manner ◽  
Cultural Media

Dromology is a speed that characterize progress. One of the affected is the culture of reading books. In the past people reading a book in the conventional manner, but in recent years, Internet technology has brought man reading a book in a different way, namely through the e-book. These changes ultimately led to a cultural shift in communication, especially in reading the book. The method used in this research is the study of literature. Results from the study showed that the reading culture (human interactions in a conventional book) has been turned into a reading culture that is synonymous with technology and also acceleration. Characteristics, sensations and experiences have changed. Technology (e-book) has become the new devices in cultured (communication / human interaction). Keywords: book, dromology, interpersonal communication, new culture

scholarly journals Neural Networks for Predicting Human Interactions in Repeated Games

2019 ◽  
Author(s):  
Yoav Kolumbus ◽  
Gali Noti
Keyword(s):  
Neural Networks ◽  
Repeated Games ◽  
Economic Value ◽  
Sufficient Information ◽  
Human Interactions ◽  
Economic Information ◽  
History Of ◽  
Predicting Behavior ◽  
Human Participants ◽  
Play Action

We consider the problem of predicting human players' actions in repeated strategic interactions. Our goal is to predict the dynamic step-by-step behavior of individual players in previously unseen games. We study the ability of neural networks to perform such predictions and the information that they require. We show on a dataset of normal-form games from experiments with human participants that standard neural networks are able to learn functions that provide more accurate predictions of the players' actions than established models from behavioral economics. The networks outperform the other models in terms of prediction accuracy and cross-entropy, and yield higher economic value. We show that if the available input is only of a short sequence of play, economic information about the game is important for predicting behavior of human agents. However, interestingly, we find that when the networks are trained with long enough sequences of history of play, action-based networks do well and additional economic details about the game do not improve their performance, indicating that the sequence of actions encode sufficient information for the success in the prediction task.

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