2021
DOI: 10.1007/s11192-020-03807-9
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Big data augmentated business trend identification: the case of mobile commerce

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Cited by 22 publications
(21 citation statements)
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“…A similar approach, based on the analysis of the significance of topics in the media in the iFORA system, is widely used by researchers to identify and describe market trends in various economic sectors (agriculture and food sector [25]; mobile commerce [27]; extractive industries [28,29].…”
Section: Methodsmentioning
confidence: 99%
“…A similar approach, based on the analysis of the significance of topics in the media in the iFORA system, is widely used by researchers to identify and describe market trends in various economic sectors (agriculture and food sector [25]; mobile commerce [27]; extractive industries [28,29].…”
Section: Methodsmentioning
confidence: 99%
“…On the other hand, although m-commerce is a younger concept that does not require a fixed device [26], the concept represents a form of commerce that is continuously evolving while relying significantly on the use of the internet or cellular data. Various sources mention m-commerce as having an "increasing intensity", and trends in its evolution include the following: mobile services, payment, banking, advertising, applications, the internet, and shopping [27]. Transactions are made in any location that offers an internet connection or through mobile data [9] available on smartphone or tablet, with a widely used tool being mobile applications [28], which have proven their usefulness in increasing the quality of services over time [29].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The latter method of computer‐based text mining has been employed since its use was first reported in 1966, 15 with the rapid developments in computer performance. In particular, this method of large data analysis has been developed and widely used in the commercial field for various purposes, such as understanding customer needs 16 . In recent years, this approach has also been employed in the medical field 17 to identify the occurrence of adverse drug reactions (ADRs) based on electronic medical records and to obtain large data sets.…”
Section: Introductionmentioning
confidence: 99%
“…In particular, this method of large data analysis has been developed and widely used in the commercial field for various purposes, such as understanding customer needs. 16 In recent years, this approach has also been employed in the medical field 17 to identify the occurrence of adverse drug reactions (ADRs) based on electronic medical records and to obtain large data sets. This approach has also been harnessed to explore the use of new drugs and nursing records of inpatients to predict and prevent falls.…”
Section: Introductionmentioning
confidence: 99%