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The setting up of e-university has been slow-going. Much of e-university slow progress has been attributed to poor business models, branding, disruptive technologies, lack of organisational structure that accommodates such challenges, and failure to integrate a blended approach. One of the stumbling blocks, among many, is the handling of evaluation process. E-university models do not provide much automation compared to the original brick-and-mortar classroom model of delivery. The underlining technologies may not have been supportive; however, the conditions are changing, and more evaluation tools are becoming available for academics. The paper aims to discuss these issues.

This paper identifies the extent of current online evaluation processes. In this process, the team reviews the case study of a UK E-University using Adobe Connect learning model that mirrors much of the physical processes as well as online exams and evaluation tools. Using the Riva model, the paper compares the physical with the online evaluation processes for e-universities to identify differences in these processes to evaluate the benefits of e-learning. As a result, the models can help us to identify the processes where improvements can take place for automating the process and evaluate the impact of this change.

The paper concludes that this process can be significantly shortened and provide a fairer outcome but there remain some challenges for e-university processes to overcome.

This paper examines the vital quality assurance processes in academia as more universities move towards process automation, blended or e-university business models. Using the case study of Arden University online distance learning, the paper demonstrates, through modelling and analysis that the process of online automation of the evaluation process is achieved with significant efficiency.

This paper aims to highlight the role and impact of corporate governance in combating fraud by drawing on insights from the literature, identify gaps in the literature and suggest new directions for future research.

The paper is based on a comprehensive general literature review using multiple search engines and databases.

This paper finds that effective corporate governance can help reduce fraud risk, prevent fraud and detect fraud, particularly corporate fraud, insider fraud and asset diversion. Some companies use corporate governance mechanisms to bolster their reputation following fraud detection. Ineffective corporate governance increases fraud risk, provides the opportunity for perpetrating fraud and reduces the likelihood of fraud detection. The paper sheds light on several governance mechanisms that could help in mitigating fraud risk, as reported in the literature. The paper categorises these governance mechanisms into four broad governance aspects, including board leadership and the role of ethics; (b) board characteristics, composition and structure; ownership structure; accountability. The paper proposes a guide summarising these broad fundamental governance aspects, including specific anti-fraud controls and examples of how organisations could enhance ethical cultures and the tone at the top.

To the best of the author’s knowledge, this is the first paper to elucidate the role of corporate governance in countering fraud and develop guidance in this area. The proposed guidance could be helpful to businesses leaders, policymakers, researchers and academics alike.

This paper aims to examine the impact of the daily growth rate of COVID-19 cases in the USA (COVIDg), the Federal Fund Rate (FFR) and the trade-weighted US dollar index (USDX) on S&P500 index daily returns and its 11 constituent sectors’ indices for the time period between January 22, 2020, until June 30, 2020.

The study uses the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model to gauge the impacts over the whole period of study, as well as over two sub-periods; first, January 22, 2020, until March 30, 2020, reflecting uncertainty in the US markets and second, from April 1, 2020, until June 30, 2020, reflecting the lockdown.

Results of the MGARCH model reveal a negative and significant relation between COVIDg and S&P500 index daily returns over the first sub-period and the whole study period in the following sectors, namely, communications, consumer discretionary, consumer staples, health, technology and materials. Yet, COVIDg showed a positive and significant relation with S&P500 index daily returns during the second time period in the following sectors, namely, communication, consumer discretionary, financial, industrial, information technology (IT) and utilities. Besides, USDX showed a negative significant effect on S&P500 index daily returns and on the daily return on each of its 11 constituent sectors over the second sub-period and the whole period. Further, FFR showed a significant effect only in the second sub-period, specifically, a negative effect on the daily return of the financial sector and a positive effect on the daily return of the technology sector index. Nevertheless, FFR had a positive significant effect on the daily return of the utilities sector index for the whole period under study.

The impact of the crisis on the S&P500 index can be assessed only with some limitations owing to available global data and the limited time frame of the lock-down.

The study proposes supporting a smooth, functioning and resilient financial system; increasing fiscal measures by the US Government to increase liquidity on constraints; measures by The Federal Reserve to alleviate US dollar funding shortages; support market integrity; ensure continuous transparency and sharing of information; support the health sector, as well as consumer-based sectors that faced demand shocks and facilitate investments in the technology sector.

The originality of this paper lies in the examination of the impact of the novel COVID-19 pandemic on each of the 11 sectors constituting the S&P500 index separately, reflecting how the main economic sectors formulating the US economy reacted to the shock during the peak time of the pandemic to observe a full picture of the economic consequences amid the pandemic.

This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.

The data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).

The findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.

Extant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.