Impact of personalized review summaries on buying decisions: An experimental study

This study evaluates the impact of personalization of review summaries on consumers’ cognitive efforts and buying decision. Following an experimental procedure we tested four hypotheses pertaining to online buyers’ decision process. Our results show that personalized review summary significantly reduces the information processing effort and information requirements of those who received personalized review summaries as compared to those who did not. This study thus contributes to e-commerce literature on online buyer behavior and recommender systems strategy

The Persuasive Nature of Web Personalization on Online Users’ Product Perception: A Mental Accounting Perspective

E-commerce firms strive to enhance engagement by providing augmented experiences to online users. This research focuses on one such shopping experience enhancement technique—Web personalization. In this study, we examine how personalization affects online users’ perceptions and how different personalization levels differentially impact those perceptions. Drawing on mental accounting theory, we argue that personalization, by providing convenience in online buying, increases transaction utility and, thus, influence online users’ product perceptions. We conducted a laboratory experiment in a public university in Southern India where users took buying decisions at four different personalization levels: zero, low, medium, and high. The findings from this study suggest that product prices affect users’ perceived product quality, which, in turn, affects their perceived product values and, subsequently, their final purchase decision. Web personalization plays a moderating role in all cause-effect relations above. This study contributes to the existing literature on the Web personalization strategy and online user behavior. We find empirical evidence to show that personalization plays a moderating role in the relationship between user perception and intention to purchase

Predicting Unplanned Hospital Readmissions using Patient Level Data

The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020. Hence, this issue has received considerable critical attention with the policy makers. Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program. In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data. Our results are promising, and they benchmark previous results in predicting hospital readmissions. The contributions of this study add to existing literature on healthcare analytics

The Effect of Comment Errors in Multilingual Electronic Meetings

Previous studies of multilingual electronic meetings have shown that members often comprehend the automatic translations, but accuracy is affected by errors in the source comments. However, no study has investigated to what extent these text errors reduce understanding. An experiment with six groups of students showed that participants were able to comprehend translations from German to English, even when the source text contained 5% word errors, but translations from source comments with no errors were understood better. Further, the differences in comprehension affected students’ perceptions of the system’s ease of use and usefulness

Estimation of tuberculosis incidence at subnational level using three methods to monitor progress towards ending TB in India, 2015–2020

Objectives We verified subnational (state/union territory (UT)/district) claims of achievements in reducing tuberculosis (TB) incidence in 2020 compared with 2015, in India.Design A community-based survey, analysis of programme data and anti-TB drug sales and utilisation data.Setting National TB Elimination Program and private TB treatment settings in 73 districts that had filed a claim to the Central TB Division of India for progress towards TB-free status.Participants Each district was divided into survey units (SU) and one village/ward was randomly selected from each SU. All household members in the selected village were interviewed. Sputum from participants with a history of anti-TB therapy (ATT), those currently experiencing chest symptoms or on ATT were tested using Xpert/Rif/TrueNat. The survey continued until 30 Mycobacterium tuberculosis cases were identified in a district.Outcome measures We calculated a direct estimate of TB incidence based on incident cases identified in the survey. We calculated an under-reporting factor by matching these cases within the TB notification system. The TB notification adjusted for this factor was the estimate by the indirect method. We also calculated TB incidence from drug sale data in the private sector and drug utilisation data in the public sector. We compared the three estimates of TB incidence in 2020 with TB incidence in 2015.Results The estimated direct incidence ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia Hills, Meghalaya) per 100 000 population. Indirect estimates of incidence ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population. The incidence using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra.Conclusion TB incidence in 1 state, 2 UTs and 35 districts had declined by at least 20% since 2015. Two districts in India were declared TB free in 2020