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The authors’ conclusions are based on mathematical derivations that are supported by computer simulations and three worked examples in applications of economics and finance. Finally, the authors provide a link to a computer program so that researchers can perform the analyses easily.

Based on a parameter estimation goal, the present work is concerned with determining the minimum sample size researchers should collect so their sample medians can be trusted as good estimates of corresponding population medians. The authors derive two solutions, using a normal approximation and an exact method.

The exact method provides more accurate answers than the normal approximation method. The authors show that the minimum sample size necessary for estimating the median using the exact method is substantially smaller than that using the normal approximation method. Therefore, researchers can use the exact method to enjoy a sample size savings.

In this paper, the a priori procedure is extended for estimating the population median under the skew normal settings. The mathematical derivation and with computer simulations of the exact method by using sample median to estimate the population median is new and a link to a free and user-friendly computer program is provided so researchers can make their own calculations.

The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams.

The authors present relevant mathematical equations, invented examples and real data examples.

G-P diagrams provide a more nuanced understanding of the data than typical summary statistics, effect sizes or significance tests.

Gain-probability diagrams provided a much better basis for making decisions than typical summary statistics, effect sizes or significance tests.

G-P diagrams provide a completely new way to traverse the distance from data to decision-making implications.

The authors derive the necessary mathematics, provide computer simulations, provide links to free and user-friendly computer programs, and analyze real data sets.

Cohen's d, which indexes the difference in means in standard deviation units, is the most popular effect size measure in the social sciences and economics. Not surprisingly, researchers have developed statistical procedures for estimating sample sizes needed to have a desirable probability of rejecting the null hypothesis given assumed values for Cohen's d, or for estimating sample sizes needed to have a desirable probability of obtaining a confidence interval of a specified width. However, for researchers interested in using the sample Cohen's d to estimate the population value, these are insufficient. Therefore, it would be useful to have a procedure for obtaining sample sizes needed to be confident that the sample. Cohen's d to be obtained is close to the population parameter the researcher wishes to estimate, an expansion of the a priori procedure (APP). The authors derive the necessary mathematics, provide computer simulations and links to free and user-friendly computer programs, and analyze real data sets for illustration of our main results.

In this paper, the authors answered the following two questions: The precision question: How close do I want my sample Cohen's d to be to the population value? The confidence question: What probability do I want to have of being within the specified distance?

To the best of the authors’ knowledge, this is the first paper for estimating Cohen's effect size, using the APP method. It is convenient for researchers and practitioners to use the online computing packages.

This research is motivated by the increasing need for international interactions during the gradual recovery of the tourism industry. By recognizing the paucity of research on cultural closeness and accommodation categories, this research aims to illuminate the influencing mechanisms of psychological closeness and travelers’ willingness to book an accommodation-sharing property while booking an accommodation.

The authors employ a mixed-methods approach, including an experiment and semistructured interviews.

Results show that hosts’ higher cultural identity congruence leads to travelers’ higher willingness to book an accommodation-sharing property. Psychological closeness mediates the positive effect of cultural identity congruence on travelers’ willingness to book. The authors further explore the moderating role of room types (entire room vs. private room) and find that the mediation effect is stronger for booking an entire room.

The current research underlines the importance of cultural identity congruence and accommodation type on travelers’ willingness to book an accommodation-sharing property and psychological closeness.

This study aimed to find out whether working capital management policies affect the profitability of manufacturing companies listed on the Qatar Stock Exchange.

To assess the working capital management and profitability relationship, the authors applied a multiple regression analysis methodology in all manufacturing companies listed on the Qatar Stock Exchange (ten firms) between 2015 and 2019. Average collection period, inventory turnover, average payment period and cash conversion cycle were adopted as proxies for working capital management, and profitability was measured by operating profit margin (OPM), return on assets (ROA), return on capital employed (ROCE) and return on equity (ROE).

The study found that companies with shorter receivables collection periods and cash conversion cycles are more profitable. Longer inventory turnover periods and accounts payable payment periods are related to higher profitability of the firms.

Previous studies have assessed the relationship between working capital management and profitability. However, this study is the first one to use these four variables combined (OPM, ROA, ROCE and ROE) to measure profitability; this is what was limited in previous studies. In comparison, the previous studies were not comprehensive in studying the impact of working capital management on profitability from all aspects of profitability's variables [operational (OPM), economic (ROA), capitalist (ROCE) and financial (ROE)]. However, this study focused on all these aspects to make the results of the study more accurate. Also, it is worth mentioning that this study is the first research performed on Qatar Stock Exchange, although Qatar has achieved remarkable progress in the industrial sector in recent years, making it one of the first industrialized countries in the Middle East.

The literature review aims to facilitate a broader understanding of on-demand service platform operations management and proposes potential research directions for scholars.

This study searches four databases for relevant literature on on-demand service platform operations management and selects 72 papers for this review. According to the research context, the literature can be divided into research on “a single platform” and research on “multiple platforms”. According to the research methods, the literature can be classified into “Mathematical Models”, “Empirical Studies”, “Multiple Methods” and “Literature Review”. Through comparative analysis, we identify research gaps and propose five future research agendas.

This paper proposes five research agendas for future research on on-demand service platform operations management. First, research can be done to combine classic research problems in the field of operations management with platform characteristics. Second, both the dynamic and steady-state issues of on-demand service platforms can be further explored. Third, research employing mathematical models and empirical analysis simultaneously can be more fruitful. Fourth, more research efforts on the various interactions among two or more platforms can be pursued. Last but not least, it is worthwhile to examine new models and paths that have emerged during the latest development of the platform economy.

Through categorizing the literature into two research contexts as well as classifying it according to four research methods, this article clearly shows the research progresses made so far in on-demand service platform operations management and provides future research directions.

This study investigates the extent to which structural equivalence between acquiring and target firms is associated with post-merger and acquisition (M&A) performance—a relationship that is proposed to be moderated by industry-level vertical relatedness between acquiring and target firms.

Applying social network analysis and regression, this study analyzes a buyer–supplier relationship network dataset of 279 M&A deals completed between 2010 and 2017 to test the hypotheses. Structural equivalence is measured as the proportion of common customers and suppliers between an acquiring firm and a target firm.

Supporting a view about the importance of supply chains in explaining M&As outcomes, the results suggest that the structural equivalence in the supplier network is positively associated with post-M&A firm performance. The results also show that the effect of the structural equivalence in the customer network is moderated by vertical relatedness between two merging firms (i.e. structural equivalence contributes to post-M&A performance when vertical industry relatedness is high).

This study contributes to the M&A and supply network literature by investigating the performance implications of structural equivalence in supplier and customer networks, demonstrating the importance of taking a supply chain view when explaining M&As outcomes. Specifically, the authors suggest considering structural equivalence as a new type of relatedness between merging firms (i.e. relatedness in network resources in explaining post-M&A performance). It also indicates how industry-level vertical resource relatedness, which is about relatedness in internal resources between the two firms, could interact with firm-level network resource relatedness, which is about relatedness in external supply chain resources between the two firms, in affecting post-M&A performance.

This paper aims to study the impact of human resource heterogeneity on firms’ cash-holding policies.

The authors construct a proxy for human resource heterogeneity using the dissimilarity in employees’ skill structure between the firm and its peers in the same industry.

The authors report evidence that firms with heterogeneous human resources hold more cash than other firms. This effect is more pronounced in labor-intensive firms and firms more susceptible to hold-up by employees, i.e. firms located in regions with more labor disputes and firms surrounded by more external employment opportunities. In addition, the authors demonstrate that high cash holdings triggered by human resource heterogeneity reduce the scale and efficiency of firms’ capital investment.

This study highlights the role of human resource heterogeneity in determining firms’ cash policies. This paper adds to the understanding of labor adjustment costs within the firm and provides insights into firms’ cash-holding decisions.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

The role of entrepreneurship education in promoting entrepreneurial actions remains unclear. The purpose of this paper is to investigate the logic of different types of entrepreneurship education and the effect of learning characteristics in promoting entrepreneurial actions among student entrepreneurs in the higher education setting.

The study employs a quantitative approach involving the use of survey data collected via an Internet tool. The constructs of variables are measured using previously tested scales. The data were analysed using partial least squares modelling because it can handle formative and reflective constructs in the same model and is capable of testing for moderation.

The findings illustrate that voluntary entrepreneurship education generates learning outcomes in terms of students' entrepreneurial actions, which is important because without action, a venture will never be launched. This is especially so if students show a deep learning orientation, while mastery motivation showed a significant and negative moderating effect. This is not the case for compulsory entrepreneurship education.

Embedded in construal level theory, this paper offers knowledge that can help to advance entrepreneurship education research (1) by uncovering the role of different types of entrepreneurship education interventions, (2) by considering students' entrepreneurial actions as the dependent variable and (3) by unravelling the role of students' learning characteristics in the efficacy of entrepreneurship education interventions. By doing this, the study addresses recent repeated calls for more fine-grained research focused on how university students learn in entrepreneurship in higher education and its effects.