Examining post COVID-19 tourist concerns using sentiment analysis and topic modeling

The COVID-19 pandemic has had a destructive effect on the tourism sector, especially on tourists’ fears and risk perceptions, and is likely to have a lasting impact on their intention to travel. Governments and businesses worldwide looking to revive and revamp their tourism sector, therefore, must first develop a critical understanding of tourist concerns starting from the dreaming/planning phase to booking, travel, stay, and experiencing. This formed the motivation of this study, which empirically examines the tourist sentiments and concerns across the tourism supply chain. Natural Language Processing (NLP) using sentiment analysis and Latent Dirichlet Allocation (LDA) approach was applied to analyze the semi-structured survey data collected from 72 respondents. Practitioners and policymakers could use the study findings to enable various support mechanisms for restoring tourist confidence and help them adjust to the ’new normal.

Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic

Governments worldwide have implemented stringent restrictions to curtail the spread of the COVID-19 pandemic. Although beneficial to physical health, these preventive measures could have a profound detrimental effect on the mental health of the population. This study focuses on the impact of lockdowns and mobility restrictions on mental health during the COVID-19 pandemic. We first develop a novel mental health index based on the analysis of data from over three million global tweets using the Microsoft Azure machine learning approach. The computed mental health index scores are then regressed with the lockdown strictness index and Google mobility index using fixed-effects ordinary least squares (OLS) regression. The results reveal that the reduction in workplace mobility, reduction in retail and recreational mobility, and increase in residential mobility (confinement to the residence) have harmed mental health. However, restrictions on mobility to parks, grocery stores, and pharmacy outlets were found to have no significant impact. The proposed mental health index provides a path for theoretical and empirical mental health studies using social media. [Abstract copyright: © 2022 Elsevier Inc. All rights reserved.

Promoting Academic Integrity: A Tale of Two Case Studies

Academic dishonesty is a major concern for academicians and institutions. The topic got wider attention in academic literature for decades and a number of studies focus on the new challenges in this domain including the role of technology. Creating an environment that foster academic integrity is important in promoting a healthy learning environment. In addition to the many short-term solutions to eliminate academic dishonesty, it is important to devise solutions that promote long term changes that instill academic integrity practices in students. This paper addresses the issues of academic dishonesty in summative coursework submissions and presents two case studies. The first case study explores the use of technology in curbing social loafing in group assessments. Social loafing is the tendency of individuals to spend less effort when working collectively than when working individually (Karau and Williams, 1993; Smith, 2017). Focus of the second case study is to improve the integrity practices in individual coursework submissions. The second case study aims to identify the ways to integrate formative assessments in the module and develop strategies to improve the effectiveness of these assessments

Organization vision - Experimentation on its effective communication

Communication of organization strategic statements i.e. the vision and mission statements to its employees is critical to achieve the organizational alignment and to create a sense of belongingness. Mode of communication adopted by the organization significantly affects the extent of internalization of the organization strategic statements by the employees. In this study, effective mode of communication for organization strategic statements and its purpose to employees were experimentally evaluated. Organization strategic statements of a chosen service organization was communicated to the employees of the organization through two different modes namely verbal and non-verbal. A counter-balanced repeated measure experimental design was adopted and impacts were measured at two separated time points with different treatments. Verbal mode of communication was found to be more effective than the non-verbal mode for communicating the purpose of the organization through the strategic statement. Non-verbal mode of communication was found to be stronger than verbal mode in achieving the remembrance of correct words/phrases and identification of incorrect words/phrases in organization strategic statements. Practical and theoretical implications along with limitations of the study are also discussed

E-governance readiness: Challenges for India

Governments and public sector organizations around the globe are relying on information and communication technologies (ICTs) to reform the functioning of the system and provide better service delivery mechanisms for their citizens. E-governance is the effective use of ICTs, particularly the Web-based Internet applications, for better governance and service delivery. Indian government, like its global counterparts, is using ICT and E-governance as part of its broader governance modernization programmes. This article presents an overview of the E-governance scenario in India. It evaluates the readiness, capability and willingness of the government to provide E-services in terms of the factors like telecommunication infrastructure, human capital and web presence. Based on the assessment, the factors which hinder the E-initiatives development and the barriers for the effective implementation are identified. These barriers are then classified into the three dimensions which determine the E-governance readiness.  A strategy for improving the E-governance readiness in India is also proposed

Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Purpose The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news. Design/methodology/approach A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared. Findings The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly. Practical implications Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors. Originality/value While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model

Sun shines in Dubai with Shams: A journey of renewable energy space in the United Arab Emirates

Saeed Mohammed Al Tayer, MD and CEO of Dubai Electricity and Water Authority (DEWA), has a dream for the future of the United Arab Emirates (UAE) that involves renewable energy significantly contributing to the energy mix of the country. Shams Dubai was launched as a smart initiative to connect solar energy to buildings, a part of the Distributed Renewable Resources Generation programme. The case encourages students to analyze the business process, innovation, and value chain of Shams Dubai. It highlights the viability of the process of expanding renewable energy in the context of the UAE and discusses the current and long-term effectiveness of Shams Dubai. The business question deals with the scalability of Shams Dubai and addresses the concern of the strategic planning head, Mr. Ahmad, in meeting the Demand Side Management targets of 2030. Another business question involves the feasibility of Shams Dubai meeting the objectives of individuals and organizations in the installation of solar rooftops. Shams Dubai was launched in 2014 in response to Executive Council Resolution 46 that called for the connection of solar energy to the distribution grid of Dubai. Dubai Government’s Supreme Council of Energy had set a target of renewable energy supplying 1% of Dubai’s energy mix by 2020 and 5% by 2030 under the Dubai Integrated Energy Strategy 2030 plan. Initial results are encouraging and suggest that this project will be successful. It will be interesting to see if sustainable growth of Shams Dubai and the Demand Side Management strategy is realized. Will the targets of 2030 and 2050 be met? Will the policy mechanisms and stakeholder structures that have been put in place be sufficiently robust to drive this in the future? Is Shams Dubai viable, and will it meet the objectives of a sustainable future for the UAE

Decision-making system for university selection: a priority comparison of pre- and post-COVID-19

University selection is always a complicated task for the aspirants from a decision making perspective. The process of developing a decision support system for this task had its challenges due to the availability of university data on various parameters of decision making. This study works on a university selector system by scrapping LinkedIn Education data of various universities and their alumni data. The final decision-making tool was hosted on the web to collect responses from potential students. The data collection on the portal was conducted twice to understand the differences of priorities in university selection pre- and post-COVID pandemic. It was found that the respondents had significant changes in their selection criteria on four parameters- Cost (went high), Ranking (went low), Presence of E-Learning mode (went high), and Student Life (went low)

Decision-making system for higher education university selection: Comparison of priorities pre- and post-COVID-19

Purpose University selection in higher education is a complex task for aspirants from a decision-making perspective. This study first aims to understand the essential parameters that affect potential students' choice of higher education institutions. It then aims to explore how these parameters or priorities have changed given the impact of the COVID-19 pandemic. Learning about the differences in priorities for university selection pre- and post-COVID-19 pandemic might help higher education institutions focus on relevant parameters in the post-pandemic era. Design/methodology/approach This study uses a mixed-method approach, with primary and secondary data (university parameters from the website and LinkedIn Insights). We developed a university selector system by scraping LinkedIn education data of various universities and their alumni records. The final decision-making tool was hosted on the web to collect potential students' responses (primary data). Response data were analyzed via a multicriteria decision-making (MCDM) model. Portal-based data collection was conducted twice to understand the differences in university selection priorities pre- and post-COVID-19 pandemic. A one-way MANOVA was performed to find the differences in priorities related to the university decision-making process pre- and post-COVID-19. Findings This study considered eight parameters of the university selection process. MANOVA demonstrated a significant change in decision-making priorities of potential students between the pre- and post-COVID-19 phases. Four out of eight parameters showed significant differences in ranking and priority. Respondents made significant changes in their selection criteria on four parameters: cost (went high), ranking (went low), presence of e-learning mode (went high) and student life (went low). Originality/value The current COVID-19 pandemic poses many uncertainties for educational institutions in terms of mode of delivery, student experience, campus life and others. The study sheds light on the differences in priorities resulting from the pandemic. It attempts to show how social priorities change over time and influence the choices students make

Leveraging smart technologies for connected tourist experiences – The case of Dubai

Advancements in technologies have led to the emergence of smart tourism. Given that smart tourism is no longer an option but a must to survive in the competitive global tourism space, governments worldwide are investing heavily in developing infrastructure that supports smart tourism. While individual technological advances can improve tourist experiences, it is the integration, synchronization, and concerted use of different technologies that deliver connected tourist experiences. This study aims to explore the interconnected smart tourism landscape required in providing a seamless tourist experience. A secondary case study research methodology is adopted in this study. The Dubai tourism sector, which has played a pivotal role in transforming Dubai to a modern economy is used as an exemplary case to comprehend how Dubai has leveraged smart tourism capabilities to become one of the leading tourist destinations in the world. The lessons learned from this study is useful for practitioners and policymakers elsewhere responsible for promoting smart tourism