scholarly journals Exploring the Impact of Linguistic Signals Transmission on Patients’ Health Consultation Choice: Web Mining of Online Reviews

2021 ◽  
Vol 18 (19) ◽  
pp. 9969
Author(s):  
Adnan Muhammad Shah ◽  
Mudassar Ali ◽  
Abdul Qayyum ◽  
Abida Begum ◽  
Heesup Han ◽  
...  
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Signaling Theory ◽  
Online Reviews ◽  
Treatment Choice ◽  
Quality Of Information ◽  
Physician Rating ◽  
Sentic Computing ◽  
Negative Sentiment

Background: Patients face difficulties identifying appropriate physicians owing to the sizeable quantity and uneven quality of information in physician rating websites. Therefore, an increasing dependence of consumers on online platforms as a source of information for decision-making has given rise to the need for further research into the quality of information in the form of online physician reviews (OPRs). Methods: Drawing on the signaling theory, this study develops a theoretical model to examine how linguistic signals (affective signals and informative signals) in physician rating websites affect consumers’ decision making. The hypotheses are tested using 5521 physicians’ six-month data drawn from two leading health rating platforms in the U.S (i.e., Healthgrades.com and Vitals.com) during the COVID-19 pandemic. A sentic computing-based sentiment analysis framework is used to implicitly analyze patients’ opinions regarding their treatment choice. Results: The results indicate that negative sentiment, review readability, review depth, review spelling, and information helpfulness play a significant role in inducing patients’ decision-making. The influence of negative sentiment, review depth on patients’ treatment choice was indirectly mediated by information helpfulness. Conclusions: This paper is a first step toward the understanding of the linguistic characteristics of information relating to the patient experience, particularly the emerging field of online health behavior and signaling theory. It is also the first effort to our knowledge that employs sentic computing-based sentiment analysis in this context and provides implications for practice.

scholarly journals Humans rely more on algorithms than social influence as a task becomes more difficult

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Bogert ◽  
Aaron Schecter ◽  
Richard T. Watson
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Social Influence ◽  
Correct Answer ◽  
Online Reviews ◽  
Personal Networks ◽  
Human Judgment ◽  
Individual Decision ◽  
Individual Decision Making

AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

scholarly journals The Effect of Perceived Factors of Decision Support Systems on Actual Usage: Behavioral Intention of Using Decision Support Systems as a Mediating Variable: “An Empirical Study of Jordanian Higher Education Institutions”

2016 ◽  
Vol 12 (1) ◽  
pp. 201
Author(s):  
Bilal Mohammed Salem Al-Momani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Behavioral Intention ◽  
Support Systems ◽  
Higher Education Institutions ◽  
Quality Of Information

Decision support systems (DSS) are interactive computer-based systems that provide information, modeling, and manipulation of data. DSS are clearly knowledge-based information systems to capture, Processing and analysis of information affecting or aims to influence the decision making process, performed by people in scope professional job appointed by a user. Hence, this study describes briefly the key concepts of decision support systems such as perceived factors with a focus on quality  of information systems and quality of information variables, behavioral intention of using DSS, and actual DSS use by adopting and extending the technology acceptance model (TAM) of Davis (1989); and Davis, Bagozzi and Warshaw (1989).There are two main goals, which stimulate the study. The first goal is to combine Perceived DSS factors and behavioral intention to use DSS from both the social perspective and a technology perspective with regard to actual DSS usage, and an experimental test of relations provide strategic locations to organizations and providing indicators that should help them manage their DSS effectiveness. Managers face the dilemma in choosing and focusing on most important factors which contributing to the positive behavioral intention of use DSS by the decision makers, which, in turn, could contribute positively in the actual DSS usage by them and other users to effectively solve organizational problems. Hence, this study presents a model which should provide the useful tool for top management in the higher education institutions- in particular-to understand the factors that determine using behaviors for designing proactive interventions and to motivate the acceptance of TAM in order to use the DSS in a way that contributes to the higher education decision-making plan and IT policy.To accomplish or attain the above mentioned objectives, the researcher developed a research instrument (questionnaire) and distributed it amongst the higher education institutions in Jordan to collect data in order to empirically study hypothesis testing (related to the objectives of study). 341 questionnaires were returned from the study respondents. Data were analyzed by utilizing both SPSS (conducted descriptive analysis) and AMOS (conducting structural equation modelling).Findings of the study indicate that some hypotheses were supported while the others were not. Contributions of the study were presented. In addition, the researcher presented some recommendations. Finally, this study has identified opportunities for further study which has progressed greatly advanced understanding constantly of DSS usage, that can help formulate powerful strategies Involving differentiation between DSS perceived factors.

scholarly journals Quality Assurance in Knowledge Data Warehouse

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Making ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Noisy Data ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Quality Of Information ◽  
External Data ◽  
Existing Data

Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision-making for an organization. Combining multiple operational databases and external data create the data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.

scholarly journals SentiProdBR: Building Domain-Specific Sentiment Lexicons for the Portuguese Language

2021 ◽  
Author(s):  
Tiago de Melo
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Bayes Theorem ◽  
Experimental Results ◽  
Product Categories ◽  
Domain Specific ◽  
Alternative Approaches ◽  
The Web

Online reviews are readily available on the Web and widely used for decision-making. However, only a few studies on Portuguese sentiment analysis are reported due to the lack of resources including domain-specific sentiment lexical collections. In this paper, we present an effective methodology using probabilities of the Bayes’ Theorem for building a set of lexicons, called SentiProdBR, for 10 different product categories for the Portuguese language. Experimental results indicate that our methodology significantly outperforms several alternative approaches of building domain-specific sentiment lexicons.

scholarly journals Tracking patients healthcare experiences during the COVID-19 outbreak: Topic modeling and sentiment analysis of doctor reviews

2021 ◽  
Vol 9 (3A) ◽  
Author(s):  
Adnan M. Shah ◽  
◽  
Xiangbin Yan ◽  
Samia tariq ◽  
Syed Asad A. Shah ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Swot Analysis ◽  
Vaccine Development ◽  
Time Slice ◽  
Healthcare Organizations ◽  
Physician Rating ◽  
Physician Rating Website

Emerging voices of patients in the form of opinions and expectations about the quality of care can improve healthcare service quality. A large volume of patients’ opinions as online doctor reviews (ODRs) are available online to access, analyze, and improve patients’ perceptions. This paper aims to explore COVID-19-related conversations, complaints, and sentiments using ODRs posted by users of the physician rating website. We analyzed 96,234 ODRs of 5,621 physicians from a prominent health rating website in the United Kingdom (Iwantgreatcare.org) in threetime slices (i.e., from February 01 to October 31, 2020). We employed machine learning approach, dynamic topic modeling, to identify prominent bigrams, salient topics and labels, sentiments embedded in reviews and topics, and patient-perceived root cause and strengths, weaknesses, opportunities, and threats (SWOT) analyses to examine SWOT for healthcare organizations. This method finds a total of 30 latent topics with 10 topics across each time slice. The current study identified new discussion topics about COVID-19 occurring from time slice 1 to time slice 3, such as news about the COVID-19 pandemic, violence against the lockdown, quarantine process and quarantine centers at different locations, and vaccine development/treatment to stop virus spread. Sentiment analysis reveals that fear for novel pathogen prevails across all topics. Based on the SWOT analysis, our findings provide a clue for doctors, hospitals, and government officials to enhance patients’ satisfaction and minimize dissatisfaction by satisfying their needs and improve the quality of care during the COVID-19 crisis.

scholarly journals Evaluating COVID-19 Vaccination Website using DISCERN and QUality Evaluation Scoring (QUEST) Tools

2021 ◽  
Author(s):  
Akash Gajanan Prabhune ◽  
Aishwarya Mallavaram ◽  
Sachin Bhat ◽  
Samridhi Pandey ◽  
Ayesha Mehar Shagufta ◽  
...  
Keyword(s):  
Decision Making ◽  
United Arab Emirates ◽  
Ease Of Use ◽  
Average Score ◽  
Quality Of Information ◽  
Member States ◽  
Critical Information ◽  
Minor Elements ◽  
Selection Of

Abstract Background: The objective of this paper was to evaluate the COVID-19 vaccine registration website across UN-recognized member states for their portal quality, reliability, Ease Use and help to the general population in informed decision making.Methods: 12 UN member states (Countries) were selected based on the inclusion and exclusion criteria mentioned in the methodology section of this paper. PPS technique was used for sampling and selection of 12 countries from 193 UN member states. Post selection of UN member states the study used 2 step evaluation techniques, Step 1 The DISCERN checklist consists of 16 questions in three sections and is aimed to assess the reliability of information and quality of information; Step 2 The QUEST tool consists of 6 items with a subitem, Authorship, Attribution, Conflict of Interest, Complementarity, Currency, Tone. The authors developed a standard set of instructions for evaluating Vaccination Portals to bring uniformity in understanding and context setting.Results: DISCERN tool overall reliability score on the Likert scale of 0 to 5 was 4 (SD ± 1.28). On Quality of information regarding treatment choices, the average score was 3.4 (SD ± 1.67). The QUEST tool on ease of use, concision, and comprehensiveness demonstrated an average score of 18.1 (SD ± 8.3) out of 28.The vaccine registration portal of the Czech Republic was found to be most informative and was able to provide a piece of scientifically valid information on safety, efficacy, long-term short effects, choice of vaccine with attributable authors details. India, Bangladesh, Nigeria, South Africa scored relatively low to missing critical information on the website. The United Arab Emirates, Republic of South Korea, Indonesia, Australia, and Argentina had minor elements missing.Conclusions: COVID-19 vaccination portals vary in the quality of information and many were found unable to provide critical information for decision making on getting vaccinated

Knowledge-Intensive Entrepreneurship in the Economy of the European Union

2014 ◽  
pp. 43-52
Author(s):  
G. Olevsky
Keyword(s):  
Decision Making ◽  
Knowledge Production ◽  
The European Union ◽  
Quality Of Information ◽  
The Matrix ◽  
Knowledge Intensive ◽  
Knowledge Intensive Entrepreneurship ◽  
The Eu

The article studies role of knowledge in business and analyzes tendencies of the formation of knowledge-intensive entrepreneurship (business) in the EU. It is shown that for small and medium-sized enterprises prospects of expansion of knowledge production and sales of products and services are associated with the internationalization of business. The author proposes the matrix of decision-making entrepreneurs, depending on the completeness and quality of information at their disposal on the market.

scholarly journals Do multidisciplinary cancer care teams suffer decision-making fatigue: an observational, longitudinal team improvement study

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e027303 ◽  
Author(s):  
Tayana Soukup ◽  
Tasha A K Gandamihardja ◽  
Sue McInerney ◽  
James S A Green ◽  
Nick Sevdalis
Keyword(s):  
Decision Making ◽  
Cancer Care ◽  
Information Quality ◽  
Primary Outcome ◽  
Audit And Feedback ◽  
Case Review ◽  
Quality Of Information ◽  
High Workload ◽  
Assess Quality

ObjectiveThe objective of this study was to examine effectiveness of codesigned quality-improving interventions with a multidisciplinary team (MDT) with high workload and prolonged meetings to ascertain: (1) presence and impact of decision-making (DM) fatigue on team performance in the weekly MDT meeting and (2) impact of a short meeting break as a countermeasure of DM fatigue.Design and interventionsThis is a longitudinal multiphase study with a codesigned intervention bundle assessed within team audit and feedback cycles. The interventions comprised short meeting breaks, as well as change of room layout and appointing a meeting chair.Setting and participantsA breast cancer MDT with 15 members was recruited between 2013 and 2015 from a teaching hospital of the London (UK) metropolitan area.MeasuresA validated observational tool (Metric for the Observation of Decision-making) was used by trained raters to assess quality of DM during 1335 patient reviews. The tool scores quality of information and team contributions to reviews by individual disciplines (Likert-based scores), which represent our two primary outcome measures.ResultsData were analysed using multivariate analysis of variance. DM fatigue was present in the MDT meetings: quality of information (M=16.36 to M=15.10) and contribution scores (M=27.67 to M=21.52) declined from first to second half of meetings at baseline. Of the improvement bundle, we found breaks reduced the effect of fatigue: following introduction of breaks (but not other interventions) information quality remained stable between first and second half of meetings (M=16.00 to M=15.94), and contributions to team DM improved overall (M=17.66 to M=19.85).ConclusionQuality of cancer team DM is affected by fatigue due to sequential case review over often prolonged periods of time. This detrimental effect can be reversed by introducing a break in the middle of the meeting. The study offers a methodology based on ‘team audit and feedback’ principle for codesigning interventions to improve teamwork in cancer care.

eWOW of Guests Regarding Their Hotel Experience

2020 ◽  
pp. 295-308
Author(s):  
Zelia Breda ◽  
Rui Costa ◽  
Gorete Dinis ◽  
Amandine Angie Martins
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
User Generated Content ◽  
Sources Of Information ◽  
Value For Money ◽  
Quality Of Sleep ◽  
Online Comments ◽  
Analytical Response ◽  
Source Of Information

Online comments are increasingly mentioned as an important source of information, simplifying consumers' buying decisions. Online user-generated content has become one of the main sources of information for tourists, who themselves become creators of their own online content. This chapter focuses on sentiment analysis of comments made on TripAdvisor regarding one resort located in the Algarve region, in Portugal. The resort has good reviews, which means that the eWOM is positive. The highest scores relate to the resort's cleanliness, location and quality of sleep, and those that were less relevant were the value for money, the rooms and the service. The most dominant emotion is joy, followed by an analytical response. Negative emotions, such as sadness and anger, were not found very often in the online reviews. These results could be explained by the quality of the service, the kindness of the staff, the facilities for children, the entertainment, and the location, attributes that were often highlighted in the comments.

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