Social commerce in emerging markets and its impact on online community engagement

YesThis study aims to build on the understanding of social commerce in the emerging markets and how it influences online community engagement. The conceptual model was proposed using theories including the social support theory, the trust theory, the social presence theory, the flow theory and the service-dominant logic theory. Using Facebook online community, the data were collected from 400 respondents from Jordan and analysed using AMOS based structural equation modelling. Results revealed that social commerce constructs positively influence social support, community members’ trust and social presence. Furthermore, it was found that social support and social presence positively affect community members’ trust. We also found that community members’ trust positively influence flow whereas both community members’ trust and flow positively influence community engagement

Sentiment analysis of products’ reviews containing English and Hindi texts

YesThe online shopping is increasing rapidly because of its convenience to buy from home and comparing products from their reviews written by other purchasers. When people buy a product, they express their emotions about that product in the form of review. In Indian context, it is found that the reviews contain Hindi text along with English. It is also found that most of the Hindi text contains opinionated words like bahut achha, bakbas, pesa wasool etc. We have tried to find out different Hindi texts appearing in product reviews written on Indian E-commerce portals. We have also developed a system which takes all those reviews containing Hindi as well as English texts and find out the sentiment expressed in that review for each attribute of the product as well as a final review of the product

Authenticity of Geo-Location and Place Name in Tweets

The place name and geo-coordinates of tweets are supposed to represent the possible location of the user at the time of posting that tweet. However, our analysis over a large collection of tweets indicates that these fields may not give the correct location of the user at the time of posting that tweet. Our investigation reveals that the tweets posted through third party applications such as Instagram or Swarmapp contain the geo-coordinate of the user specified location, not his current location. Any place name can be entered by a user to be displayed on a tweet. It may not be same as his/her exact location. Our analysis revealed that around 12% of tweets contains place names which are different from their real location. The findings of this research can be used as caution while designing location-based services using social media

A deep multi-modal neural network for informative Twitter content classification during emergencies

YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents

Citizen's adoption of an e-government system: Validating extended social cognitive theory (SCT)

YesBy employing an extended social cognitive theory, this study examines factors (such as outcome expectation, affect, anxiety, self-efficacy and social influence) influencing intention to adopt an electronic government system called online public grievance redressal system (OPGRS) in context of India. The extended social cognitive theory (SCT) was validated using 419 responses collected from eight selected cities in India. The empirical outcomes of the proposed model indicated the significant relationships of seven hypothesised relationships between six constructs. This is the first study, which has used the SCT model to understand the adoption of an e-government system. The policy implication provided in this research can help the government to improve upon the effectiveness and quality of the system and the level of social impact on the users by employing the project champions. It also helps in enhancing their positive feelings toward adopting this system and fully utilise the potential of the OPGRS as a useful tool toward a transparent and corruption free society

E-GOVERNMENT ADOPTION RESEARCH: A META-ANALYSIS OF FINDINGS

The purpose of this paper is to undertake a meta-analysis of findings reported in existing research on adoption and diffusion of e-government. Usable data relating to e-government adoption research were collected from 112 papers out of 779 research articles identified from the ISI Web of Knowledge database and journals dedicated to electronic government research. The findings indicate that there are some variables such as: perceived ease of use, perceived usefulness, intention to use, attitude, satisfaction, actual use, subjective norm, and perceived behavioral control, which are common and drive the research from citizens as well as from employees’ perspective. The meta-analysis of the existing e-government adoption studies found that the majority of the construct relationships demonstrated the significant range of average summative correlation, and effect size, but the influence of ‘facilitating condition’, and ‘perceived risk’ on ‘intention to use’ and of ‘service quality’ on ‘satisfaction’ was found as non-significant. The broader analysis of the e-government adoption and diffusion research also reflects that although a large number of theories and theoretical constructs were borrowed from the reference disciplines, their utilization by e-government researchers appears to be largely random in approach. The paper also acknowledges the theoretical contributions, limitations and suggests further research directions

Can clicking promote learning? measuring student learning performance using clickers in the undergraduate information systems class

YesPurpose The purpose of this paper is to explore the impact of factors such as attention, preparation, participation, feedback and engagement on the student learning performance. Design/methodology/approach Students of an undergraduate business course of a British university took part in the survey. The survey questionnaire was distributed to students during the revision week of the course and a total of 61 valid responses were gathered from them. The linear regression analysis using statistical package for the social sciences was performed to analyse the data. Findings The results indicated the significant relationships for all six hypotheses. The model explains variance of 43.2 per cent in learning performance, which indicates that independent constructs contribute significantly on the research model's performance. Research limitations/implications First, the sample only provides the students' views about the use of clickers in the classroom setting. Second, the sample size for the gathered data is small. Third, the variance explained by the research model is reasonably moderate and hence can be improved further. Originality/value This is the first study to explore the impact of factors such as attention, preparation, participation, feedback and engagement on the student learning performance in the UK educational setting

Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model

YesMobile banking is one of the most promising technologies that has emerged in recent years and could prove to have considerable value to both banks and customers. Thus, this study recognises the need to test the main factors that could predict the use of mobile banking as well as how using such a system could contribute to both customer satisfaction and customer loyalty. The conceptual model of this study combines two models (i.e. UTAUT2 and the D&M IS Success Model). A questionnaire survey was conducted to collect the required data from convenience sampling of Saudi bank customers. The main factors – performance expectancy, price value, facilitating conditions, hedonic motivation, habit, system quality and service quality – were found to have a significant impact on actual use behaviour. This study was cross-sectional, therefore future studies should implement longitudinal studies in order to re-collect the findings. Further, this study adopted convenience sampling of Saudi M-Banking users. This may adversely impact the issue of generalisability to the whole population. The gap in the M-Banking literature in Saudi Arabia would be bridged by proposing a comprehensive conceptual model that scrupulously clarifies the use of M-Banking from the perspective of Saudi users. Furthermore, this study would consider the adoption of numeric data in order to inferentially analyse them using SEM. This in turn would assist in generalising the findings to the whole Saudi population

Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach

YesSmart cities are shifting the presence of people from physical world to cyber world (cyberspace). Along with the facilities for societies, the troubles of physical world, such as bullying, aggression and hate speech, are also taking their presence emphatically in cyberspace. This paper aims to dig the posts of social media to identify the bullying comments containing text as well as image. In this paper, we have proposed a unified representation of text and image together to eliminate the need for separate learning modules for image and text. A single-layer Convolutional Neural Network model is used with a unified representation. The major findings of this research are that the text represented as image is a better model to encode the information. We also found that single-layer Convolutional Neural Network is giving better results with two-dimensional representation. In the current scenario, we have used three layers of text and three layers of a colour image to represent the input that gives a recall of 74% of the bullying class with one layer of Convolutional Neural Network.Ministry of Electronics and Information Technology (MeitY), Government of Indi