Search results

In the wake of lockdown regulations and limited mobility during the COVID-19 pandemic, dining habits shifted towards usage of food delivery apps to avoid physical interaction. Nonetheless, it is unknown whether the COVID-19 pandemic had an influence on the adoption of food delivery apps. Therefore, this study examined factors influencing the adoption of food delivery apps during the COVID-19 pandemic, as well as the moderating effects of education and age.

Data were collected from 282 food delivery application users in South Africa using a web-based survey. Partial least square structural equation modelling analysis was used to test the hypotheses, while partial least squares multigroup analysis was used to examine the moderating effect of education level and age.

The results indicated that perceived ease of use has a significant impact on perceived usefulness and attitudes, perceived usefulness has an impact on attitudes and continuous intention, attitude influences continuous intention and social pressure and convenience influence attitudes. The perceived COVID-19 threat had no impact on attitudes, and education and age had no significant impact on any relationships. The findings are imperative for restaurants and mobile application designers, as they enable more effective strategic management planning.

This study is the first paper to empirically employ technology acceptance model to analyse the adoption of food delivery applications during the COVID-19 pandemic. Its uniqueness is in examining situational influence associated with the pandemic such as social pressure, perceived COVID-19 threat and convenience.

This article explores how companies in multiple controversial industries report their controversial issues. For the first time, the authors use a new conceptualization of controversial industries, focused on harm and solutions, to investigate the reports of 28 companies in seven controversial industries: Agricultural Chemicals, Alcohol, Armaments, Coal, Gambling, Oil and Tobacco.

The authors thematically analyzed company reports to determine if companies in controversial industries discuss their controversial issues in their reporting, if and how they communicate the harm caused by their products or services, and what solutions they provide.

From this study data the authors introduce a new legitimacy reporting method in the controversial industries literature: the solutions companies offer for the harm caused by their products and services. The authors find three solution reporting methods: no solution, misleading solution and less-harmful solution. The authors also develop a new typology of reporting strategies used by companies in controversial industries based on how they report their key controversial issue and the harm caused by their products or services, and the solutions they offer. The authors identify seven reporting strategies: Ignore, Deny, Decoy, Dazzle, Distort, Deflect and Adapt.

Further research can test the typology and identify strategies used by companies in different institutional or regulatory settings, across different controversial industries or in larger populations.

Investors, consumers, managers, activists and other stakeholders of controversial companies can use this typology to identify the strategies that companies use to report controversial issues. They can assess if reports admit to the controversial issue and the harm caused by a company's products and services and if they provide solutions to that harm.

This paper develops a new typology of reporting strategies by companies in controversial industries and adds to the theory and discourse on social and environmental reporting (SER) as well as the literature on controversial industries.

This research aims to study budgetary slack from a behavioural perspective, especially examining the effect of gender and code of ethics on budgetary slack ethical judgment.

This study adopts the experimental method of 2 × 3 between-subjects mixed factorial design with 102 participants to test the hypotheses. The participants are undergraduate and postgraduate accounting students at a major university in Indonesia.

The results show that gender affects budgetary slack ethical judgment, in which women judge budgetary slack as more unethical than men. Additionally, the results indicate that individuals consider budgetary slack more unethical when a code of ethics is present than when it is absent.

This study contributes to the management accounting literature and behavioural research by understanding budgetary slack from an ethical perspective. Additionally, this study contributes to ethics literature by identifying the effect of gender and code of ethics on budgetary slack righteous judgment.

Mapping and Geographic Information Systems (GIS) are widely used in disaster research and practice. While, in some cases, these practices incorporate methods inspired by critical cartography and critical GIS, they rarely engage with the theoretical discussions that animate those fields.

In this commentary, the author considers three such discussions, and draws out their relevance for disaster studies: the turn towards processual cartographies, political economy analysis of datafication and calls for theorising computing of and from the South.

The review highlights how these discussions can contribute to the work of scholars engaged in mapping for disaster risk management and research. First, it can counter the taken-for-granted nature of disaster-related maps, and encourage debate about how such maps are produced, used and circulated. Second, it can foster a reflexive attitude towards the urge to quantify and map disasters. Third, it can help to rethink the role of digital technologies with respect to ongoing conversations on the need to decolonise disaster studies.

The paper aims to familiarise disaster studies scholars with literature that has received relatively little attention in this field and, by doing so, contribute to a repoliticisation of disaster-related maps.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

Globalization and the COVID (post) pandemic continue to pose significant challenges to managing employee performance across geographic locations because there is a need to simultaneously implement procedures that are standardized and yet applicable to different contexts. This study aims to describe five universal principles in performance management that can be adapted to specific contexts to address the performance management standardization vs adaptation dilemma.

Critical literature review of evidence-based recommendations for practice.

This study describe five universal principles in performance management, how they can be adapted to specific contexts around the world and actions that organizations can take to implement them: (1) cultural congruence, (2) strategic congruence, (3) performance evaluation thoroughness, (4) inclusiveness and (5) effective feedback.

This study provide valuable and actionable knowledge for organizations facing performance management challenges around the world.