Purpose Most research using extended unified theory of acceptance and use of technology (UTAUT2) and other technology acceptance models (TAM) are quantitative studies. This leaves room for interpretation when they are applied to university lecturers’ acceptance of online teaching because the models were originally created for the consumer perspective. This study aims to bridge this gap by integrating existing (quantitative) research with (qualitative) reasoning. Design/methodology/approach This study reflects online and hybrid teaching acceptance by reviewing exemplary existing research using UTAUT2 as the conceptual framework. Findings UTAUT2 TAMs use a broad range of criteria that do not immediately agree with university lecturers’ acceptance of online teaching. This study finds that existing research results are inconclusive and attempts to link criteria when suitable. Performance expectancy should not only encompass individual attitudes and skills but also the nature of the subject taught. Social influence is driven by recognition and student evaluations. Hedonic motivation best fits the elsewhere well-researched concept of intrinsic motivation. This study suggests that universities choose their online teaching technology wisely, promote its ease of use and offer training as well as continuous support to lecturers, especially when addressing future uncertainties. Originality/value This study explains the implications of using TAMs for research of higher education online teaching. Potential reasons and arguments for the inconclusiveness of the studies reviewed are discussed, and measures for university policy and communication improvement are suggested.

No abstract

Equity crowdfunding has been gaining more and more relevance as an alternative way for entrepreneurs to raise capital. This paper examines the impact of retained equity, business angel backing, grants and intellectual property rights on the success of equity crowdfunding. Using data from Crowdcube, one of the leading equity crowdfunding platforms in the world, we obtain 473 pitches within the period from March 2017 to February 2020. We apply three empirical methods to analyse our data set: logistic regression, multiple linear regression and negative binomial regression. By running univariate test and several regression analyses, we find that retained equity has a significantly negative impact on funding success; also, the support of business angels helps entrepreneurs to achieve a funding success. While winning grants is more likely to attract investors, there is no direct evidence associated with funding success. Furthermore, it turns out that intellectual property rights are not relevant for funding success. In an extension, we re-examine our analysis in the light of the Corona crisis by analysing 95 pitches from March to November 2020. We find no structural changes in relevance of the success factors.

No abstract

No abstract