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In this paper the authors aim to argue that the existence of a strong corporate governance mechanism (a formal credibility-enhancing mechanism) and the presence of a more trustworthy-looking CEO (an informal credibility-enhancing mechanism) are substitutes.

By using machine-learning-based facial-feature-point detection technique, the authors construct a proprietary facial-trustworthiness database for a large-scale of CEOs in the US listed companies. First, the authors manually search for qualifying CEO image from websites and annual reports. Second, by following the neuroscience and psychology literature, the authors use the machine-learning-based face detector to identify the facial features in the CEO photos to calculate a rich and reliable set of facial-trustworthiness measures. The authors then construct a composite facial-trustworthiness index for each CEO. After obtaining accounting data, the authors’ final sample comprises 16,201 firm-year observations for 3,186 CEOs in the sample period of 2000-2018.

The results of the authors’ regression analyses show a negative association between board monitoring intensity and CEOs' facial trustworthiness, indicating that board directors may factor CEOs' facial trustworthiness into their monitoring decisions. Moreover, the authors find that these results are mainly driven by CEOs whose tenure is below the third quartile (i.e. eight years). The authors further find stronger results for externally hired CEOs than internally promoted CEOs. Finally, the authors’ results remain robust when using change models or subsample of CEO photos in recent years.

First, to the best of the authors’ knowledge, this is the first study that adopts a large sample to provide systematic evidence on the directors' use of facial trustworthiness. This study extends the literature by documenting the impacts of CEOs' individual characteristics on the board monitoring intensity. Second, the results of this study emphasized the important role of perceptions based on executives' facial appearance in firm valuation, executive compensation and audit fee, and by presenting empirical evidence that CEOs' facial trustworthiness affects board monitoring intensity. Third, this study responds to the call for research on personalized trust by Hsieh et al. (2020).

This study examines the effect of employees' perceptions of political connections on performance measurement systems (PMS) design choice and firm performance. In addition, this study explores the moderating effect of social networking, a very common and widely used factor by domestic and foreign multinational firms operating in China, and its joint effect with political connections or PMS design choice on firm performance. We collected survey responses from a sample of 110 managers from manufacturing firms in China. Our results reveal that highly politically connected managers use nonfinancial measures, leading to improved firm performance. Our results suggest that social networking interacts significantly with political connections, and nonfinancial and financial measures on firm performance. The theoretical and practical implications of our findings are discussed.

The purpose of this paper is to study the relationship between institutional holdings and analyst coverage in the context of the heterogeneous nature of institutional investors.

Similar to prior studies (e.g. Ke and Ramalingegowda; Ramalingegowda and Yu), this paper obtains institutional investors' trading classifications (transient, dedicated, and quasi‐indexing) from Brian Bushee directly. To examine the hypotheses, the paper uses a two‐step instrumental variable approach demonstrated in O'Brien and Bhushan to mitigate the simultaneity relationship between the change in analyst coverage and the change in the number of heterogeneous institutional investors.

The findings suggest that such relations are different among transient, dedicated, and quasi‐indexing institutional investors. Specifically, there are three major results. First, a change to the number of analyst coverage has the lowest impact on the change in the number of dedicated institutional investors. Second, a change in the number of transient institutional investors has a higher impact on change in analyst coverage than those for change in the number of dedicated and quasi‐indexing institutional investors. Third, changes to analysts' buy or sell recommendations have the least impact on the change in the number of dedicated institutions, relative to transient and quasi‐indexing institutions.

The findings suggest that institutional investors are not homogeneous. Research studies on institutional investors need to disentangle the differences among different types of institutions.

The paper provides a comprehensive study on different institutional investors and analyst coverage. The findings show the complex nature of the interaction between institutional investors and analyst coverage.

The purpose of this paper is to conduct an assessment of the research productivity of the accounting and finance community in UK higher education institutions (HEIs) during 1991-2010 using 44 high-quality accounting and finance journals.

The authors follow Chan et al. (2011) to use their 22 finance journals. For accounting journals, the paper includes a set of 24 accounting journals that were used in a global accounting ranking study by Chan et al. (2007). The paper uses the number of coauthors (n) and coaffiliations (M) to derive the weighted articles as the measurement metric.

In general, the research output in terms of weighted articles steadily increases during the 20-year period. The University of Manchester, London School of Economics, and London Business School are the top-three HEIs using 44 accounting and finance journals for the full sample. The authors also find that it is a challenge to publish multiple articles. If an author is able to manage five total appearances, he/she is in the top 16 percent among the 1,447 UK authors. Furthermore, the paper finds that many highly productive authors are able to move to different jobs during the 20-year period.

The assessment of research productivity is, unavoidably, based on a set of selected accounting and finance journals. Hence, no matter what journal screening criteria the paper uses, there is always a subjective element in the process. If other journals or more/less journals were to be included in a similar study, different results may emerge. As a way to extend the value of the research, it would be interesting to obtain broader institutional knowledge, such as the tenure requirements of HEIs in UK, and information on the institutions where faculty members obtained their doctoral degrees, so that the authors can better evaluate the research productivity among accounting and finance community in the UK.

The paper conducts an assessment of the research productivity of accounting and finance community in UK HEIs during 1991-2010 using 44 high-quality accounting and finance journals. The study fills the gap of the extant literature to compliment the assessment of the UK accounting and finance departments in RAEs.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.