Purpose: The purpose of this paper is to empirically capture the impact of a chief executive officer’s (CEO) personal and organizational characteristics on firm performance in the context of a developing country and to explore whether capital structure mediates the relationship between CEO characteristics and firm performance.Design/methodology/approach: In order to test the hypothesized model, CEO duality, tenure and personal characteristics (age, gender and education) were taken as explanatory variables to study their impact on firm performance. Data were collected from 179 Pakistani companies from 2009–2015. The collected data were processed via panel data regression analysis under fixed effect assumptions.Findings: Results show that CEO duality has a negative impact on firm performance and that a CEO with a dual role is more inclined toward debt financing. Moreover, a CEO with a longer tenure tends to be opportunistic and prioritize his/her personal interest while making strategic financial decisions, thus creating agency costs for the firm. Furthermore, CEO characteristics like age, gender and education have significant effects on firm financial decisions and firm performance. Finally, the debt and equity ratio partially mediates the link between CEO characteristics and firm performance.Research limitations/implications: The findings of this study have limited generalizability due to the specific nature of the sample characteristics.Originality/value: To the best of the authors knowledge, this study is the first to explore the impact of CEO characteristics on capital structure and firm performance. This work is also the first to explore the mediating role of capital structure in the relationship between CEO characteristics and firm performance by using Pakistani data.

The novel coronavirus disease 2019 (COVID-19) has spread globally and the meteorological factors vary greatly across the world. Understanding the effect of meteorological factors and control strategies on COVID-19 transmission is critical to contain the epidemic. Using individual-level data in mainland China, Hong Kong, and Singapore, and the number of confirmed cases in other regions, we explore the effect of temperature, relative humidity, and control measures on the spread of COVID-19. We find that high temperature mitigates the transmission of the disease. High relative humidity promotes COVID-19 transmission when temperature is low, but tends to reduce transmission when temperature is high. Implementing classical control measures can dramatically slow the spread of the disease. However, due to the occurrence of pre-symptomatic infections, the effect of the measures to shorten treatment time is markedly reduced and the importance of contact quarantine and social distancing increases.

Parallel machine scheduling problems are commonly encountered in a wide variety of manufacturing environments and have been extensively studied. This paper addresses a makespan minimisation scheduling problem on identical parallel machines, in which the specific processing time of each job is uncertain, and its probability distribution is unknown because of limited information. In this case, the deterministic or stochastic scheduling model may be unsuitable. We propose a robust (min–max regret) scheduling model for identifying a robust schedule with minimal maximal deviation from the corresponding optimal schedule across all possible job-processing times (called scenarios). These scenarios are specified as closed intervals. To solve the robust scheduling problem, which is NP-hard, we first prove that a regret-maximising scenario for any schedule belongs to a finite set of extreme point scenarios. We then derive two exact algorithms to optimise this problem using a general iterative relaxation procedure. Moreover, a good initial solution (optimal schedule under a mid-point scenario) for the aforementioned algorithms is discussed. Several heuristics are developed to solve large-scale problems. Finally, computational experiments are conducted to evaluate the performance of the proposed methods.

Purpose: This study aims to clarify the effect of team effort allocation between knowledge exploration and exploitation on the generation of extremely good or poor innovations. The influence of previous collaborative experience among team members on the effect of team effort allocation is also investigated to understand the relationship between team members’ collaboration networks and knowledge learning.Design/methodology/approach: This study uses data of all patents granted by the US Patent and Trademark Office between 1984 and 2010. The inventors involved in a patent are regarded as members of the focal team. Logistic regression is used to analyze the data.Findings: Allocating greater effort to exploration than to exploitation is beneficial to achieving breakthrough innovations despite the risk of generating particularly poor innovations. This benefit increases with collaborative experience among team members. Placing an equal emphasis on knowledge exploration and exploitation is not particularly effective in achieving breakthrough innovations; it is, however, the best strategy for avoiding particularly poor innovations.Originality/value: This research not only provides valuable insights for research on innovation and knowledge management by studying the team effort allocation strategy used to achieve breakthroughs and avoid particularly poor innovations but also represents an advancement in bridging two streams of research – knowledge learning and social networks – by highlighting the influence of the team members’ collaborative networks on the effect of team effort allocation between knowledge exploration and exploitation.