Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Kar, Arpan Kumar; Dwivedi, Yogesh K.

doi:10.1016/j.ijinfomgt.2020.102205

Cited by 194 publications

(102 citation statements)

References 73 publications

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“…In recent years, big data research and their challenges have been the subject of literature ( Abbasi et al., 2016 ; Fang et al., 2015 ; Kar and Dwivedi, 2020 ). This study uses methodological approaches that are based on big data.…”

Section: Methodsmentioning

confidence: 99%

The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic

Haman

2020

Heliyon

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The article examines how many leaders used Twitter during the COVID-19 pandemic, in what way, and the impact they had on the public. In the context of Twitter, the impact on the public refers to the growth in followers as it signifies the increased interest of the public about information. 50,872 tweets were collected from 143 state leaders and an original dataset was created containing information on the growth of followers. Ordinary least squares regression models were used for the analysis. It was found that 64.8% of UN member states had a leader that tweeted about COVID-19. Furthermore, a significant increase in the number of followers during the pandemic compared to months prior was noted. Since March, the pandemic has been a dominant topic on Twitter. During the COVID-19 pandemic, the highest percentage increase in gaining Twitter followers was experienced by politicians who frequently tweeted and those who had a lower ratio of the number of followers to internet users. The research implies that citizens are interested in being informed about emergencies through social networks, and government officials should use them.

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Section: Methodsmentioning

confidence: 99%

The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic

Haman

2020

Heliyon

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“…Big-data is present almost in every domain. However, the authors of this paper bring back the importance of use of the information system, rather than the current use of technologies like text mining, neural networks for manipulation of data ( Kar and Dwivedi, 2020 ). In this paper, a neural network based method of learning tree is used which enhances picture information storage efficiency ( Han et al., 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Chauhan

Palivela²,

Tiwari

2021

International Journal of Information Management Data Insights

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It’s been more than a year that the entire world is fighting against Covid-19 pandemic. Starting from the Wuhan city in China, COVID-19 has conquered the entire world with its rapid progression. But seeking the importance towards the human situation, it has become essential to build such an automated model to diagnose COVID-19 within less computational time easily. As the disease has spread, there is not enough data to implement an accurate COVID-19 predicting model. But technology is a boon, which makes it possible. Effective techniques based on medical imaging using artificial intelligence have approached to assist humans in needful time. It has become very essential to detect COVID-19 in humans at an early stage to prevent it from becoming more infectious. The neural networks have shown promising results in medical imaging. In this research, a deep learning-based approach is used for image classification to detect COVID-19 using chest X-ray images (CXR). A CNN classifier have been used to classify the normal-healthy images from the COVID-19 images, using transfer learning. The concept of early stopping is used to enhance the accuracy of the proposed DenseNet model. The results of the system have been evaluated using accuracy, precision, recall and F1-score metrics. An automated comparative analysis among multiple optimizers, LR Scheduler and Loss Function is performed to get the highest accuracy suitable for the proposed system. The Adamax optimizer with Cross Entropy loss function and StepLR scheduler have outperformed with 98.45% accuracy for normal-healthy CXR images and 98.32% accuracy for COVID-19 images.

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“…Whatever may be the limitations of the study, it is to state that future participation (F) and co-production (P) appears to be the effective predictors of co-creation (C) that impacts on business benefit (BB) of the firm and this F-P-C-B framework is effective since it could achieve high explanative power (76%). Big data generated via various social media platforms (Kar and Dwivedi 2020;Rathore et al 2016) can also be utilised for conducting such analysis that will help to get a more objective view on this topic.…”

Section: Limitations and Future Research Directionsmentioning

confidence: 99%

Assessing Consumers’ Co‐production and Future Participation On Value Co‐creation and Business Benefit: an F-P-C-B Model Perspective

2021

Self Cite

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Co-production and active participation of the consumers are considered to have enhanced the value co-creation activities that would ensure business benefits of a firm. The marketing literature available does not explicitly explain the philosophy that would motivate the consumers to help to increase values for co-creation activities. In this context, attempts have been made to identify the factors that would impact on co-production and consumers’ participation to co-create values. By studying literature and theories such as theory of co-creation, theory of value creation, information processing theory, marketing theory and expectancy value theory, a conceptual model called F-P-C-B (Future Participation (F) - Co-production (P) - Co-creation (C) - Business Benefit (B)) has been developed along with nine hypotheses. The data was from 362 respondents in India and the model was tested using PLS based analysis. The study shows that it is important for the firms to shift from product-oriented activities to customer-related strategies. It is also found that for obtaining more profitability and better business results, customers should be involved in business activities by way of involving in co-design, idea generation, and other relevant activities of the firms. Moreover, the study highlights that knowledge sharing between the customers and the firm authorities ensures better business values.

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Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Cited by 194 publications

References 73 publications

The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic

The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic

Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging

Assessing Consumers’ Co‐production and Future Participation On Value Co‐creation and Business Benefit: an F-P-C-B Model Perspective

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