User Resistance Behaviors and Management Strategies in IT-Enabled Change

2015 ◽  
Vol 27 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Tim Klaus ◽  
J. Ellis Blanton ◽  
Stephen C. Wingreen
Keyword(s):  
Information Technology ◽  
Management Strategy ◽  
Large Scale ◽  
Management Strategies ◽  
Data Driven ◽  
Structured Interviews ◽  
User Resistance ◽  
System Use ◽  
Data Driven Approach ◽  
Practical Implications

Information Technology (IT) is often used in organizations as a tool to enable change. However, as organizations switch to different vendors, upgrade their systems, or implement new systems, widespread user resistance is often encountered. Resistant behaviors often occur in these large-scale system implementations because the implementation transforms the jobs of employees and mandates system use. In order to understand resistant behaviors better as well as management strategies to minimize these behaviors, this study uses a focus group and qualitative semi-structured interviews. Based on the data collection, this study first creates a resistant behavior framework and a management strategy framework using a data-driven approach. The findings from the user resistance behaviors are classified into four categories. Also, eight preferred management strategies are identified by users, which are grouped into three categories. Then, the Framework-based Theory of User Resistance is proposed, which examines the causes and moderating forces that affect resistant behaviors. The practical implications of these frameworks also are described.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

scholarly journals RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009315
Author(s):  
Ardalan Naseri ◽  
Junjie Shi ◽  
Xihong Lin ◽  
Shaojie Zhang ◽  
Degui Zhi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Scale Up ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Inference Method ◽  
Genome Wide ◽  
Familial Relationship ◽  
Kinship Coefficients ◽  
Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

Resistant Groups in Enterprise System Implementations: A Q-Methodology Examination

2010 ◽  
Vol 25 (1) ◽  
pp. 91-106 ◽  
Author(s):  
Tim Klaus ◽  
Stephen C Wingreen ◽  
J Ellis Blanton
Keyword(s):  
Management Strategy ◽  
Q Methodology ◽  
Large Range ◽  
Management Strategies ◽  
Enterprise System ◽  
User Resistance ◽  
Initial Investigation ◽  
User Groups ◽  
Mandatory Adoption ◽  
Q Sort

This paper is an initial investigation into types of user resistance and the management strategy expectations of users in a mandatory adoption setting. Despite its relationship to adoption, relatively little is known about user resistance. User resistance is investigated in the Enterprise System (ES) environment because the complexity and richness of ES leads users to manifest a large range of resistant behaviors and beliefs. Using Concourse Theory and Q-methodology, ES users are interviewed followed by the development of a Q-sort questionnaire, which was distributed to ES users. The results reveal eight user groups and address the management strategies preferred by each group. The results have implications for both research in the field of user resistance and adoption, and practitioners involved in system implementation.

scholarly journals Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

2018 ◽  
Vol 115 (37) ◽  
pp. 9300-9305 ◽  
Author(s):  
Shuo Wang ◽  
Erik D. Herzog ◽  
István Z. Kiss ◽  
William J. Schwartz ◽  
Guy Bloch ◽  
...  
Keyword(s):  
Network Topology ◽  
Large Scale ◽  
Data Driven ◽  
Electrical Networks ◽  
Chemical Oscillators ◽  
Complex Interactions ◽  
Dynamic Topology ◽  
Estimation Problems ◽  
Data Driven Approach ◽  
Fast Chemical

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.

scholarly journals Screening performance of abbreviated versions of the UPSIT smell test

2018 ◽  
Author(s):  
Theresita Joseph ◽  
Stephen D. Auger ◽  
Luisa Peress ◽  
Daniel Rack ◽  
Jack Cuzick ◽  
...  
Keyword(s):  
Large Scale ◽  
Effective Means ◽  
Cost Effective ◽  
Data Driven ◽  
Screening Performance ◽  
Smell Test ◽  
Identification Test ◽  
Data Driven Approach ◽  
Smell Identification ◽  
Smell Tests

ABSTRACTBackgroundHyposmia features in several neurodegenerative conditions, including Parkinson’s disease (PD). The University of Pennsylvania Smell Identification Test (UPSIT) is a widely used screening tool for detecting hyposmia, but is time-consuming and expensive when used on a large scale.MethodsWe assessed shorter subsets of UPSIT items for their ability to detect hyposmia in 891 healthy participants from the PREDICT-PD study. Established shorter tests included Versions A and B of both the 4-item Pocket Smell Test (PST) and 12-item Brief Smell Identification Test (BSIT). Using a data-driven approach, we evaluated screening performances of 23,231,378 combinations of 1-7 smell items from the full UPSIT.ResultsPST Versions A and B achieved sensitivity/specificity of 76.8%/64.9% and 86.6%/45.9% respectively, whilst BSIT Versions A and B achieved 83.1%/79.5% and 96.5%/51.8% for detecting hyposmia defined by the longer UPSIT. From the data-driven analysis, two optimised sets of 7 smells surpassed the screening performance of the 12 item BSITs (with validation sensitivity/specificities of 88.2%/85.4% and 100%/53.5%). A set of 4 smells (Menthol, Clove, Gingerbread and Orange) had higher sensitivity for hyposmia than PST-A, -B and even BSIT-A (with validation sensitivity 91.2%). The same 4 smells also featured amongst those most commonly misidentified by 44 individuals with PD compared to 891 PREDICT-PD controls and a screening test using these 4 smells would have identified all hyposmic patients with PD.ConclusionUsing abbreviated smell tests could provide a cost-effective means of screening for hyposmia in large cohorts, allowing more targeted administration of the UPSIT or similar smell tests.

scholarly journals A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

2021 ◽  
Vol 12 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

scholarly journals CN-Probase: A Data-Driven Approach for Large-Scale Chinese Taxonomy Construction

2019 ◽  
Author(s):  
Jindong Chen ◽  
Ao Wang ◽  
Jiangjie Chen ◽  
Yanghua Xiao ◽  
Zhendong Chu ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Driven Approach

Automatic Detection and Analysis of the “Game Hack” Scam

2020 ◽  
Author(s):  
Emad Badawi ◽  
Guy-Vincent Jourdan ◽  
Gregor Bochmann ◽  
Iosif-Viorel Onut
Keyword(s):  
Social Media ◽  
Market Research ◽  
Large Scale ◽  
Personal Information ◽  
Data Driven ◽  
Search Queries ◽  
Vast Array ◽  
Low Profile ◽  
Data Driven Approach ◽  
Insight Into

The “Game Hack” Scam (GHS) is a mostly unreported cyberattack in which attackers attempt to convince victims that they will be provided with free, unlimited “resources” or other advantages for their favorite game. The endgame of the scammers ranges from monetizing for themselves the victims time and resources by having them click through endless “surveys”, filing out “market research” forms, etc., to collecting personal information, getting the victims to subscribe to questionable services, up to installing questionable executable files on their machines. Other scams such as the “Technical Support Scam”, the “Survey Scam”, and the “Romance Scam” have been analyzed before but to the best of our knowledge, GHS has not been well studied so far and is indeed mostly unknown. In this paper, our aim is to investigate and gain more knowledge on this type of scam by following a data-driven approach; we formulate GHS-related search queries, and used multiple search engines to collect data about the websites to which GHS victims are directed when they search online for various game hacks and tricks. We analyze the collected data to provide new insight into GHS and research the extent of this scam. We show that despite its low profile, the click traffic generated by the scam is in the hundreds of millions. We also show that GHS attackers use social media, streaming sites, blogs, and even unrelated sites such as change.org or jeuxvideo.com to carry out their attacks and reach a large number of victims. Our data collection spans a year; in that time, we uncovered 65,905 different GHS URLs, mapped onto over 5,900 unique domains.We were able to link attacks to attackers and found that they routinely target a vast array of games. Furthermore, we find that GHS instances are on the rise, and so is the number of victims. Our low-end estimation is that these attacks have been clicked at least 150 million times in the last five years. Finally, in keeping with similar large-scale scam studies, we find that the current public blacklists are inadequate and suggest that our method is more effective at detecting these attacks.

scholarly journals Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach

2016 ◽  
Vol 15 ◽  
pp. CIN.S39549
Author(s):  
Jake Luo ◽  
Ron A. Cisler
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Adverse Event ◽  
Adverse Effects ◽  
Adverse Events ◽  
Large Scale ◽  
Data Driven ◽  
Cancer Drugs ◽  
Data Driven Approach ◽  
Grubbs Test

We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. To address this challenge, we present an exploration study that integrates multiple adverse event reports from clinical trials in order to systematically compare adverse events across different cancer drugs. To demonstrate our methods, we first collected data on 186,339 clinical trials from ClinicalTrials.gov and selected 30 common cancer drugs. We identified 1602 cancer trials that studied the selected cancer drugs. Our methods effectively extracted 12,922 distinct adverse events from the clinical trial reports. Using the extracted data, we ranked all 12,922 adverse events based on their prevalence in the clinical trials, such as nausea 82%, fatigue 77%, and vomiting 75.97%. To detect the significant drug outliers that could have a statistically high possibility of causing an event, we used the boxplot method to visualize adverse event outliers across different drugs and applied Grubbs’ test to evaluate the significance. Analyses showed that by systematically integrating cross-trial data from multiple clinical trial reports, adverse event outliers associated with cancer drugs can be detected. The method was demonstrated by detecting the following four statistically significant adverse event cases: the association of the drug axitinib with hypertension (Grubbs’ test, P < 0.001), the association of the drug imatinib with muscle spasm ( P < 0.001), the association of the drug vorinostat with deep vein thrombosis ( P < 0.001), and the association of the drug afatinib with paronychia ( P < 0.01).

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