Internet of things-based automation design and organizational innovation of manufacturing enterprises

Babu, T.G. Venkatesh; Roopa, H; Shukla, Arvind Kumar; David, Dominique; S, Jayadatta; Rajesh, A. S.

doi:10.1016/j.matpr.2021.10.459

Cited by 5 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This implies that when management prioritizes and encourages the adoption of these innovations, it has a favorable effect on overall business performance. The outcome aligns with the findings of previous investigations [ 44 , 86 ]. On the other hand, there is a strong negative correlation between G_PROC and the CFP.…”

Section: Resultssupporting

confidence: 91%

See 1 more Smart Citation

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

Wang,

Ahmad

2024

Heliyon

View full text Add to dashboard Cite

Section: Resultssupporting

confidence: 91%

“…This outcome is in direct opposition to the conclusions drawn from previous research conducted by Refs. [ 61 , 86 , 87 ].…”

Section: Resultsmentioning

confidence: 99%

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

Wang,

Ahmad

2024

Heliyon

View full text Add to dashboard Cite

“…Our data supports Hypothesis H2, showing that adopting IoT technology boosts competitiveness. IoT adoption helps companies improve their value proposition, differentiate their offers, and adapt to changing market demands (Babu et al, 2022). Companies can develop tailored and targeted marketing strategies, strengthen consumer connections, and enhance brand loyalty by using IoTgenerated data.…”

Section: Discussionmentioning

confidence: 99%

“…H1: Adoption of IOT has a significant and positive impact on Efficiency and productivity. Babu et al (2022) investigated the competitive advantage gained through IoT deployment. Researchers discovered that merchants which used IoT devices to track customer behaviour and preferences acquired a competitive advantage by providing tailored shopping experiences, which resulted in improved customer loyalty and satisfaction.…”

Section: Literature Review Adoption Of Iot and Efficiency And Product...mentioning

confidence: 99%

The Impact of Industrial Internet and the Digital Economy on the Management and Development of Manufacturing Information Systems Triggering Digitization as IoT and Artificial Intelligence

Li,

Ren

2023

J INFORM SYSTEMS ENG

View full text Add to dashboard Cite

Technology adoption is crucial to organizational performance in the fast-changing digital world. Due to their potential to boost efficiency, productivity, and competitiveness, disruptive technologies including data analytics, the Internet of Things (IoT), and information system integration have received attention. This study examined how IoT adoption, data analytics, and information system integration affect organizational efficiency, productivity, and competitive advantage. The study also examined how cybersecurity, cloud infrastructure, and organizational culture mediate the technology-adoption-performance relationship. Quantitative research was conducted using Amos. A diverse sampling of enterprises provided data. Structural equation modelling (SEM) examined direct and indirect impacts by analyzing variable connections. IoT usage, data analytics, and information system integration improved organizational efficiency, productivity, and competitive edge. The study also showed that cybersecurity and cloud infrastructure mediate technology adoption and organizational results. Organizational culture moderated the effect of technology adoption on performance. This study added to the technology adoption literature by examining how IoT adoption, data analytics capabilities, and information system integration affect organizational efficiency, productivity, and competitive advantage. Cybersecurity, cloud infrastructure as mediators and organizational culture as a moderator helped us understand technology adoption and performance results.

show abstract

“…The number of neighbors to be considered to poll is explained by the value of K that is a positive integer. In this analysis, the value of K is 11, that is selected by exploiting the trial and error m [41][4] and it is exhibited in Fig. 4.…”

Section: C) K-nnmentioning

confidence: 99%

An Application of the Filter Feature Selection Method in a Machine Learning Model for the Prognostic of Parkinson's Disease

David¹

2022

View full text Add to dashboard Cite

Parkinson's disease is the most prevalent kind of neurodegenerative sickness that cannot be cured. Neurodegeneration is a word that includes memory loss and other cognitive functions. The conventional methods of medical testing take a lot of time and are not very good at spotting early warning signs. As Parkinson's disease progresses, different treatment approaches are required for patients at different stages of the illness. In this regard, the early identification of Parkinson's disease and the subsequent categorization of its stages may be of great assistance in the process of treating the symptoms of the illness. The objective of the study, on the other hand, is to model a classification approach that could possibly predict the untimely phases of Parkinson's disease by utilizing accurate early-stage gene expression data from the blood that was generated from a clinical Parkinson's dataset. This data is obtained from participants who had the disease. A set of criteria is selected with the use of Information Gain (IG) in order to give sufficient information for differentiating among Normal Control (NC) participants and untimely phases Parkinson's disease (AD) participants. The data is segmented into different sizes, and then 3 unique Machine Learning (ML) methods are used in order to construct the classification approaches: Naive Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). The capability of the algorithms to accurately forecast the condition of cognitive impairment is analyzed, compared, and evaluated by utilizing the Weka software tool, as well as a range of metrics to assess the approaches' performance. According to the most recent data, a classification model based on SVM can properly discriminate cognitively impaired Parkinson's patients from normal healthy persons with a success rate of 96.6 percent. As revealed and verified, a gene expression pattern in the blood correctly separates Parkinson's patients from cognitively healthy controls, suggesting that alterations unique to AD may be identified distant from the disease's core location.

show abstract

Internet of things-based automation design and organizational innovation of manufacturing enterprises

Cited by 5 publications

References 24 publications

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

The Impact of Industrial Internet and the Digital Economy on the Management and Development of Manufacturing Information Systems Triggering Digitization as IoT and Artificial Intelligence

An Application of the Filter Feature Selection Method in a Machine Learning Model for the Prognostic of Parkinson's Disease

Contact Info

Product

Resources

About