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This issue is a selected bibliography covering the subject of leadership.

The authors study whether CEO beauty influences management guidance.

The authors calculate an attractiveness score based on facial symmetry and perform regression analyses to examine the relation between CEO beauty and management guidance.

The authors find that attractive CEOs are more likely to issue voluntary management earnings guidance. After controlling for this appearance-based self-selection, the authors document that management forecasts provided by attractive CEOs are more optimistic yet less precise. Consistent with this result, the authors find that analysts' consensus forecast error following management forecasts made by attractive CEOs is larger than such error following management forecasts made by unattractive CEOs. The authors further find that the perceived credibility of management forecasts by attractive CEOs is not different from that by unattractive CEOs.

These findings suggest that attractive CEOs are more active but less skillful in issuing management forecasts. This adds to the emerging accounting literature on the relation between facial appearance and information delivery.

To present the hypergraph as a systems model that is more versatile than are the single‐purpose models known from literature.

Parting from a previously known approach, pertinent hypergraph structures are specified, three new propositions are proved and the role of system elements and relations is analyzed.

Unlike a simple graph and a simple logical analysis, hypergraphs make it possible to apply a single theoretical approach to modelling a variety of systems. A worked‐out example illustrates the procedure.

Hypergraphs as a research tool appear sparsely in scientific literature. This paper fills the gap by presenting the hypergraph‐based systems model as being more versatile than are the models known from literature. It also highlights the effect of system elements and relations on the make‐up of the system in point.

Social media have been playing a critical role in seeking and sharing health related information and consequently shaping individuals’ health behaviors. This study investigates how information seeking about Covid-19 vaccine on social media is related to vaccine receiving intentions. The study furthers explores the association of trust in social media and uncertainty about Covid-19 with information seeking and the moderating role of prior social media experience on this association.

We developed a questionnaire and collected data from 525 educated social media users through “Google Forms.” Further, we applied ordinary least squares (OLS) regress to test the study hypothesis.

We noted that trust in social media and uncertainty about Covid-19 vaccine positively influenced information seeking which further positively affected vaccine receiving intentions. However, the moderating effect of prior social media experience was not only noted as weak but also found negatively affecting the associations of trust in social media and uncertainty about Covid-19 vaccine with information seeking.

The findings provide insights into understanding of public perceptions regarding Covid-19 vaccine in the cultural contexts of a developing country. Further, it informs about the public patterns of seeking information related to health issues on social media, an understanding which may likely benefit policymakers, health care providers and researchers to understand the antecedents and behavioral outcomes of seeking information through social media during health crisis. The study also elucidates the leveraging power of social media to motivate the public to accept the Covid-19 vaccines.

The study uniquely combines the antecedents and behavioral outcomes of information seeking through social media in the particular context of Covid-19. It further extends the literature by introducing the conditional role of prior social media experience.

Motivated by the need for research on the relationship between health app usage and health-related outcomes in the form of health status and life satisfaction, this study builds on self-regulation theory to construct a research model for elucidating how health app quality affects health information literacy, health app usage and physical activity.

To empirically validate the proposed research model, a large-scale questionnaire survey on health app usage was administered on a sample of 6,948 respondents recruited from a university in China. Structural equation modeling was employed for data analysis.

Empirical findings demonstrate that health app quality positively affects self-regulation with respect to health app usage, health information literacy and physical activity. Taken together, these self-regulated behaviors drive health-related outcomes for health status and life satisfaction.

This study advances extant literature on health app usage through the application of self-regulation theory to investigate the effects of technological interventions in healthcare. Findings offer practical implications for how health apps can be leveraged to realize positive health-related outcomes.

Pulchronomics studies the economics of beauty. The purpose of this paper is to research CEO pulchronomics by examining whether a beauty premium exists in CEO compensation and whether this beauty premium is justified by differences in CEO performance.

The authors calculate a facial attractiveness scores (FAS) based on facial symmetry, facial structure and the golden ratio. The authors then perform OLS regressions to examine the effect of CEO beauty on CEO compensation and firm performances.

The authors find that base salaries for attractive CEOs are higher than those for unattractive CEOs, but incentive pays for attractive CEOs are not different from those for unattractive CEOs. The latter is likely due to the fact that attractive CEOs do not outperform unattractive CEOs in operations, innovation, corporate social responsibility and financial reporting quality.

Since the CEO beauty premium is not supported by the superior performance of attractive CEOs, this paper provides new evidence of appearance discrimination in CEO compensation.

A neural network controller is proposed for the motion control of robot manipulators with force/torque feedback signals. This controller is trained with reinforcement learning algorithms and a model is extracted from the synaptic weights within the neural network. This model is continuously refined by the feedback signals to ensure its validity even in a stochastic and non‐stationary environment. With this model and the real‐time force/torque feedback data, the robot can acquire a fine skill for a particular assembly task for which it is trained.

The purpose of this paper is to explore cycling tourists’ experience in Singapore using an exploratory case study.

Tourist’s spontaneous reviews (n=409) posted on TripAdvisor were collected and analyzed through Leximancer.

The software identified 31 concepts grouped into five dominant themes of tourists’ experiences. In order of relative importance, the themes were tour, bike, guide, experience, and cycling. It revealed that most tourists have a favorable impression of their cycling experience in Singapore.

The findings suggest cycling tours can provide tourists with pleasurable experiences. Tourists embark on cycling tours in Singapore to learn more about the city and enjoy new experiences. However, to meet tourists’ expectations, bicycles must be well-maintained and accessible. In addition, tour guides should be attentive and knowledgeable. These have a direct effect on tourists’ satisfaction level with cycling tours.

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

Effective planning requires the participation of different functions and may be hampered by lack of integration and information quality (IQ). This paper aims to investigate the relationships among integration, uncertainty, IQ and performance, in the context of the production planning and control function. The literature lacks in-depth studies that consider these factors altogether, showing how they interact and how they contribute to improve business performance.

The authors introduce the variable of planning performance, which represents the quality of the production plans/planning process and is related to the frequency and causes of modifications to these plans. The relationships among the mentioned constructs are investigated by means of multiple case studies.

The results illustrate that integration is positively related to planning performance, and this relationship is mediated by IQ and moderated by uncertainty.

The presented analysis may help practitioners to foster interfunctional integration, better cope with uncertainty and improve information management, aiming to achieve better planning performance. The managers can choose integration and IQ improvement mechanisms that better fit to their environment/reality, using the four different cases as a benchmark. Moreover, this research contributes to the literature exploring this contingency perspective by means of in-depth case studies, considering that most of the existing research adopting this perspective is survey-based.