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Formative assessment is crucial for teachers to gauge students’ understanding of subject content, and applying formative assessment in an online context is more challenging than in a traditional Physical Sciences classroom. This study examines challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment as per the research question. The empirical investigation adopted a generic qualitative design in which three purposively selected Grade 10 Physical Sciences teachers from three different schools and quintiles within the Tshwane North District in South Africa. Data were collected through individual and focus group interviews. Interviews were video recorded, transcribed and thematically analysed using grounded theory to construct a thematic framework for supporting the study. Technological, pedagogical, and content knowledge (TPACK) was utilised as a theoretical framework underpinning the study to identify challenges experienced by Grade 10 Physical Sciences teachers when enacting online formative assessment. Challenges identified in this study include the enforced utilisation of Annual Teaching Plans, lack of technological knowledge, lack of internet connectivity, and poor digital infrastructure at schools, and in towns and cities in South Africa. The study aims to show that the Department of Basic Education faces the key imperative to provide continuous teacher professional development and concomitant online learning materials that can facilitate meaningful enactment of online formative assessment in various educational settings while policy is needed to ensure that every student has access to data and internet resources.

This paper provides new evidence on gender bias in teaching evaluations. We exploit a quasi-experimental dataset of 19,952 student evaluations of university faculty in a context where students are randomly allocated to female or male instructors. Despite the fact that neither students' grades nor self-study hours are affected by the instructor's gender, we find that women receive systematically lower teaching evaluations than their male colleagues. This bias is driven by male students' evaluations, is larger for mathematical courses, and particularly pronounced for junior women. The gender bias in teaching evaluations we document may have direct as well as indirect effects on the career progression ofwomen by affecting juniorwomen's confidence and through the reallocation of instructor resources away from research and toward teaching.

Research Summary Strategy research addresses endogeneity by incorporating econometric techniques, including Heckman's two‐step method. The economics literature theorizes regarding optimal usage of Heckman's method, emphasizing the valid exclusion condition necessary in the first stage. However, our meta‐analysis reveals that only 54 of 165 relevant papers published in the top strategy and organizational theory journals during 1995–2016 claim a valid exclusion restriction. Without this condition being met, our simulation shows that results using the Heckman method are often less reliable than OLS results. Even where Heckman is not possible, we recommend that other rigorous identification approaches be used. We illustrate our recommendation to use a triangulation of identification approaches by revisiting the classic global strategy question of the performance implications of cross‐border market entry through greenfield or acquisition. Managerial Summary Managers make strategic decisions by choosing the best option given the particular circumstances of their firm. However, researchers had previously not taken into consideration these circumstances when evaluating the outcome of that choice. The Heckman method importantly addresses this situation, but requires that the researcher have some variable that effects the best option for the firm, but not the outcome. We show that researchers frequently do not utilize such a variable, and demonstrate that the Heckman method can exacerbate estimation issues in this case. We then provide an approach that researchers can use to address the challenge of determining the outcome of a strategic decision, and illustrate it with an empirical examination of the performance implications of cross‐border market entry through greenfield or acquisition.

We model the dynamics of discrimination and show how its evolution can identify the underlying source. We test these theoretical predictions in a field experiment on a large online platform where users post content that is evaluated by other users on the platform. We assign posts to accounts that exogenously vary by gender and evaluation histories. With no prior evaluations, women face significant discrimination. However, following a sequence of positive evaluations, the direction of discrimination reverses: women’s posts are favored over men’s. Interpreting these results through the lens of our model, this dynamic reversal implies discrimination driven by biased beliefs.

We propose a framework for assessing the accuracy of nonmarket values. This involves adapting two widely-used concepts. Reliability addresses variance and validity addresses potential biases. These concepts are formally defined and adapted to assess the accuracy of individual nonmarket valuation studies and the potential accuracy of valuation methods. We illustrate the framework by considering, in a preliminary way, the reliability and validity of the contingent-valuation and travel-cost methods.

As a remedy against fake news on social media, we examine the effectiveness of three different mechanisms for source ratings that can be applied to articles when they are initially published: expert rating (where expert reviewers fact-check articles, which are aggregated to provide a source rating), user article rating (where users rate articles, which are aggregated to provide a source rating), and user source rating (where users rate the sources themselves). We conducted two experiments and found that source ratings influenced social media users' beliefs in the articles and that the rating mechanisms behind the ratings mattered. Low ratings, which would mark the usual culprits in spreading fake news, had stronger effects than did high ratings. When the ratings were low, users paid more attention to the rating mechanism, and, overall, expert ratings and user article ratings had stronger effects than did user source ratings. We also noticed a second-order effect, where ratings on some sources led users to be more skeptical of sources without ratings, even with instructions to the contrary. A user's belief in an article, in turn, influenced the extent to which users would engage with the article (e.g., read, like, comment and share). Lastly, we found confirmation bias to be prominent; users were more likely to believe - and spread - articles that aligned with their beliefs. Overall, our results show that source rating is a viable measure against fake news and propose how the rating mechanism should be designed.

Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based structural equation modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based structural equation modeling.

In this research, the authors investigate the prevalence, robustness, and possible reasons underlying the polarity of online review distributions, with the majority of the reviews at the positive end of the rating scale, a few reviews in the midrange, and some reviews at the negative end of the scale. Compiling a large data set of online reviews—over 280 million reviews from 25 major online platforms—the authors find that most reviews on most platforms exhibit a high degree of polarity, but the platforms vary in the degree of polarity on the basis of how selective customers are in reviewing products on the platform. Using cross-platform and multimethod analyses, including secondary data, experiments, and survey data, the authors empirically confirm polarity self-selection, described as the higher tendency of consumers with extreme evaluations to provide a review as an important driver of the polarity of review distributions. In addition, they describe and demonstrate that polarity self-selection and the polarity of the review distribution reduce the informativeness of online reviews.

Researchers often struggle when applying ‘golden rules of thumb’ to evaluate structural equation models. This paper questions the notion of universal thresholds and calls for adjusted orientation points that account for sample size, factor loadings, the number of latent variables and indicators, as well as data (non-)normality. This research explores the need for flexible cutoffs and their accuracy in single- and two-index strategies. Study 1 reveals that many indices are biased; thus, rigid cutoffs can become imprecise. Flexible cutoff values are shown to compensate for the unique distorting patterns and prove to be particularly beneficial for moderate misspecification. Study 2 sheds further light on this ‘gray’ area of misspecification and disentangles the different sources of misspecification. Study 3 finally investigates the performance of flexible cutoffs for non-normal data. Having substantiated higher performance for flexible reference values, this paper provides to managers an easy-to-use tool that facilitates the determination of adequate cutoffs.

Firms'incentives to manufacture biased user reviews impede review usefulness. We examine the differences in reviews for a given hotel between two sites: Expedia. com (only a customer can post a review) and TripAdvisor. com (anyone can post). We argue that the net gains from promotional reviewing are highest for independent hotels with single-unit owners and lowest for branded chain hotels with multiunit owners. We demonstrate that the hotel neighbors of hotels with a high incentive to fake have more negative reviews on TripAdvisor relative to Expedia; hotels with a high incentive to fake have more positive reviews on TripAdvisor relative to Expedia.