Enhancing the effectiveness of case study pedagogy by clubbing complementary teaching strategies for better students learning

Case studies have been used for a long time in social science disciplines like business and marketing as an effective teaching tool. It promotes interaction/discussion, team work, and critical thinking in students. It is helpful in understanding application of the theoretical concepts by using problem solving approach. This study investigates the use of questioning on engaging students mind and critical thinking by using case method in a 300- level marketing course in place of lecture format. The results from this study will be helpful in understanding usefulness of questioning (asking specific questions) in students learning while using case method

Investigating conference attendee’s mobile application adoption behavior: An ecological perspective

The fast growing conference industry has a positive impact on the economy. The industry has been criticized for its resource use intensity, and for having a negative impact on the environment. This study focused on understanding the predictors of green information system (conference apps) adoption behavior, which may help reduce negative environmental impact in future. While there was enough evidence of conference apps implementation by the conference industry, there was limited evidence of its adoption by conference attendees. Thus, the purpose of this study was to investigate conference attendees’ conference apps adoption behavior. As conference apps are helpful in reducing paper waste and carbon emissions, they are considered green information systems (IS). The existing IS literature provides technology adoption models such as Unified Theory of Acceptance and Use of Technology (UTAUT-2) to understand behavior. Thus, UTAUT-2 along with Theory of Reasoned Action and Value Beliefs Norm theory were used in this study to develop a model—Green Information Systems Adoption Model—to understand behavioral intention to adopt conference apps by conference attendees. Structural equation modeling technique with the maximum likelihood estimation method was utilized to identify relationships between variables and to test hypotheses from the model. A survey using online Qualtrics and Amazon Mechanical Turk panel collected 403 usable responses on 29 items. Results were tested for reliability and validity using exploratory factor analysis, confirmatory factor analysis, and structural equation analysis. Results from the model fit indices support the GISAM model, as it fits the data well. The equivalent model and bootstrapping analyses show robustness of the model. Findings suggest that for technologies that provide higher benefits to the environment and conference associations than to the attendees, individuals’ attitudes were based on beliefs that are drawn from biospheric and altruistic values more than self-interest values. Attitude toward conference apps was found to be the strongest predictor of behavioral intention, followed by habit, hedonic motivation, effort expectancy, and ecological beliefs. This study contributes both theoretically and practically by bridging the existing gap in the literature and providing solutions to the conference industry for higher profitability

Studies on the Use of Carbon Waste Generated from Fertiliser Plant in Waste Water Treatment

Carbon waste generated from fertiliser plant has been used for adsorption studies for the removal of chromium, zinc and nickel and COD from industrial effluent from phenol formaldehyde plant, polyester plant, sugar plant. Removal to the extent of 91.40, 86.8, 93.8 percent was achieved at the initial concentration of 10 mg/1 for Cr(VI), Ni(TI) and Zn(II) respectively using carbon waste as adsorbent Removal of these metals were found to be in order of Zn(II) \u3e Cr(VI) \u3e Ni(II). In a multi cationic solution containing these three metals, Cr(VI) adsorbed preferentially over Ni(II) and Zn(II). COD removal of 67.51%, and 86.4 % was obtained in case of polyester and sugar plant effluent respectively. In case of phenol formaldehyde resin COD removal to the extent of 65.00 % was obtained for the initial COD concentration of 1000mg/l

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

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

Exploring the impact of functional, symbolic, and experiential image on approach behaviors among state-park tourists from India, Korea, and the USA

This study provides insights into the influence of state park image, visitor emotions, and place identity on visitors’ revisit intentions by considering the moderating impact of national culture. A quantitative process with the data collected in India, Korea, and the US was used. Hierarchical regression analysis evidences the moderating role of national culture, which is hardly explored in the state park context. Results confirm that most hypotheses are fully or partially accepted, which suggests that brand image and national culture influence visitor intention. This study helps practitioners better understand the relevance of national culture in developing appropriate visitor attraction/retention strategies