scholarly journals Sustainability of the Smart phone applications usage in rural India –An empirical study

2021 ◽  
Vol 58 (2) ◽  
pp. 254-265
Author(s):  
Souvik Roy, Dennis Joseph
Keyword(s):  
Behavioral Intention ◽  
Smart Phone ◽  
Rural India ◽  
Indian Market ◽  
Application Development ◽  
Statistical Tool ◽  
Depth Interview ◽  
Telangana State ◽  
Google Play ◽  
Smart Phone Application

The Indian smart phone applications market is really flourishing with India ranked as number one in terms of the downloads made via Android and Google play store. However the smart phone application penetration is still low in rural India as compared to its urban part. This study is first of its kind which tries to explore two things, one the reasons, why rural Indian market is not growing in terms of smart phone application downloads/usage and second  some antecedents that can affect the behavioral intention of rural customers for increased smart phone application usage. For this study authors resorted to ethnographic in-depth interview during pre-test stage followed by collecting of responses through administrated questionnaires in the second part. Around 346 responses were collected from ten villages in Rangareddy district in Telangana state, in India. SEM was used as the statistical tool to run the hypothesis. In terms of smart phone application development, this study is unique one which tries to throw light immensely on how the marketers/developers can increase the sustainability of smart application usage among rural consumers which happens to be an untouched profitable segment till this point of time.

scholarly journals The Influence of Personality Traits on Mobile Phone Application Preferences

2012 ◽  
Vol 4 (5) ◽  
pp. 252-260 ◽  
Author(s):  
Wilburn Lane
Keyword(s):  
Mobile Phone ◽  
Personality Traits ◽  
Smart Phone ◽  
Big Five Personality ◽  
Application Development ◽  
Mobile Phone Application ◽  
Personality Research ◽  
Smartphone Technology ◽  
And Behavior ◽  
Smart Phone Application

Understanding mobile phone users' preferences and behavior is essential for the commercial success of new application development. This study aims to enhance this understanding by identifying the personality traits associated with smart phone application use. Multiple regressions were used to analyze results from a sample of 233 participants. Consistent with recent personality research, we found that the "Big Five" personality dimensions are related to the application of smartphone technology. Extroverted individuals reported greater importance on gaming applications, but they viewed productivity applications as less important. Also, neurotics placed greater importance on travel applications, while less conscientious people indicated that communication, productivity, and utilities applications were less important to them.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Implementation of Smart Home System Based on Internet of Things

2013 ◽  
Vol 475-476 ◽  
pp. 1150-1153 ◽  
Author(s):  
Yan Zeng Gao ◽  
Ling Yan Wei
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Smart Home ◽  
Smart Phone ◽  
Cloud Service ◽  
Control Center ◽  
Cloud Server ◽  
Novel Method ◽  
Smart Phone Application

Smart home can apply new internet of things concepts along cloud service technologies. This paper introduces a novel method for smart home system building. The system is driven by use case and it is composed of home control center, zigbee end devices, smart phone applications and cloud server. The home control center is based on arm-linux embedded system, it is the relay of cloud server and home devices. Wireless network of smart home devices was designed according to zigbee. A smart phone application was developed as the role of the user interface.

Using UTAUT 2 model to predict mobile app based shopping: evidences from India

2017 ◽  
Vol 9 (3) ◽  
pp. 248-264 ◽  
Author(s):  
Preeti Tak ◽  
Savita Panwar
Keyword(s):  
Behavioral Intention ◽  
Mobile Apps ◽  
Smart Phone ◽  
Mobile App ◽  
Content Type ◽  
Use Of Technology ◽  
Usage Behavior ◽  
Managerial Implications ◽  
Facilitating Conditions ◽  
The Impact

Purpose The purpose of this paper is to understand antecedents of app-based shopping in an Indian context. The paper has used unified theory of acceptance and use of technology (UTAUT) 2 model for examining the impact of various constructs on behavioral intention and usage behavior of smart phone users toward the mobile shopping apps. Design/methodology/approach The constructs were tested and validated by means of a structured questionnaire which was administered on a sample of 350 mobile app shoppers in Delhi. AMOS 20 was used to analyze the collected data. Findings The study revealed that hedonic and habit are the strongest predictors of users’ behavioral intention to use mobile apps for shopping. Respondents are also influenced by the deals that are being offered by the marketers. The research also suggests that facilitating conditions help in usage of mobile apps for shopping. Research limitations/implications Managerial implications simplifying the interface which would encourage the less technologically advanced individuals to use mobile apps. Hedonic element of shopping through mobile apps should also be enhanced. Originality/value This study contributes to the research on intentions and usage behavior of consumer technologies by adopting UTAUT 2 model to explain the intentions and usage behavior toward mobile apps for shopping. The paper also measured the role of deals in influencing the consumers.

scholarly journals The validation study on a three-dimensional burn estimation smart-phone application: accurate, free and fast?

2018 ◽  
Vol 6 ◽  
Author(s):  
A. K. W. Cheah ◽  
T. Kangkorn ◽  
E. H. Tan ◽  
M. L. Loo ◽  
S. J. Chong
Keyword(s):  
Surface Area ◽  
Body Surface Area ◽  
Body Surface ◽  
Validation Study ◽  
Total Body Surface Area ◽  
Three Dimensional ◽  
Smart Phone ◽  
Conventional Methods ◽  
Total Body ◽  
Smart Phone Application

Abstract Background Accurate total body surface area burned (TBSAB) estimation is a crucial aspect of early burn management. It helps guide resuscitation and is essential in the calculation of fluid requirements. Conventional methods of estimation can often lead to large discrepancies in burn percentage estimation. We aim to compare a new method of TBSAB estimation using a three-dimensional smart-phone application named 3D Burn Resuscitation (3D Burn) against conventional methods of estimation—Rule of Palm, Rule of Nines and the Lund and Browder chart. Methods Three volunteer subjects were moulaged with simulated burn injuries of 25%, 30% and 35% total body surface area (TBSA), respectively. Various healthcare workers were invited to use both the 3D Burn application as well as the conventional methods stated above to estimate the volunteer subjects’ burn percentages. Results Collective relative estimations across the groups showed that when used, the Rule of Palm, Rule of Nines and the Lund and Browder chart all over-estimated burns area by an average of 10.6%, 19.7%, and 8.3% TBSA, respectively, while the 3D Burn application under-estimated burns by an average of 1.9%. There was a statistically significant difference between the 3D Burn application estimations versus all three other modalities (p < 0.05). Time of using the application was found to be significantly longer than traditional methods of estimation. Conclusions The 3D Burn application, although slower, allowed more accurate TBSAB measurements when compared to conventional methods. The validation study has shown that the 3D Burn application is useful in improving the accuracy of TBSAB measurement. Further studies are warranted, and there are plans to repeat the above study in a different centre overseas as part of a multi-centre study, with a view of progressing to a prospective study that compares the accuracy of the 3D Burn application against conventional methods on actual burn patients.

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