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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 overuse and scarcity of resources emphasize the importance of the circular economy. The technology facilitated by Industry 4.0 stimulates the implementation of the circular economy that aims to reduce resource use and enhance operational efficiency. This study focuses on enhancing delivery efficiency in an online-to-offline (O2O) context from an Industry 4.0 technology-facilitated personal configuration perspective, that is, comparing in-house and crowdsourced delivery efficiency in China's O2O on-demand food delivery context.

The authors collect 128,152 orders from 38 restaurants of an online restaurant chain in China. The authors adopt multiple regression analysis to examine the delivery efficiency gap between in-house and crowdsourced deliverymen and the determinants of this efficiency gap.

The findings of this study reveal that crowdsourced deliverymen exhibit higher delivery efficiency, in terms of a shorter delivery time, than in-house deliverymen. In addition, the authors find that platforms providing monetary incentives or implementing late delivery penalties enlarge this efficiency gap. Furthermore, the authors show that external factors, such as working on weekends and bad weather conditions, contribute to the narrowing of this performance efficiency.

The study's findings suggest that platforms should use advanced technologies facilitated by Industry 4.0 to optimize their personnel configuration to enhance their delivery efficiency and reduce carbon emissions. The effective approaches include using financial incentives and improving working schedules.

The authors' findings contribute to the online fulfillment literature by focusing on delivery efficiency in the O2O context from the Industry 4.0 technology-facilitated personnel configuration perspective. The authors examine how internal and external factors moderate the performance efficiency between these two types of deliverymen.

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 study aims to investigate the impact of cross-border charitable activities on host- and home-country consumers based on the social identity theory.

Through an extensive literature review and two experimental designs, this study establishes the research framework and hypothesises the relationships between the constructs.

National power moderates the impact of cross-border charitable activities on host- and home-country consumers. In particular, compared to countries with high national power, countries with low national power undertaking cross-border charitable activities will receive more positive reactions from the host-country consumers, and, conversely, more negative reactions from the home-country consumers.

From the consumer perspective, this study finds that brand cross-border charitable activities have different influences on consumers in different countries because of an identity transformation mechanism that exists between the “insiders” and the “outsiders”, which is different from the assumptions of western theories.

The findings provide insights for undertaking brand cross-border charitable activities.

Previous studies, which are based on social identity categorisation, assume that cross-border charitable activities have a more positive impact on home-country consumers than host-country consumers. However, this study adopts the research paradigm of social identity relationisation and draws an opposite conclusion, which not only expands the theory of local intergroup interaction, but also clarifies how brand cross-border charitable activities influence Chinese consumers.

This study aims to explore the relationships between consumer data vulnerability, peer privacy concerns and consumers' continued usage intention of sharing accommodation platforms, as well as the moderating effects of the various benefits perceived by consumers.

Data were collected from 327 consumers of sharing accommodation platforms in China. Partial least squares (PLS)-structural equation modeling (SEM) was conducted to test the research hypotheses.

The results suggest that both consumer data vulnerability and peer privacy concerns have negative effects on consumer's continued usage intention of sharing accommodation platforms, which can be further mitigated by consumer perceived economic, social and emotional benefits. This study also finds that consumer data vulnerability has a positive effect on consumer's peer privacy concerns.

This study gives that managers of sharing accommodation platforms a better understanding of how consumers respond to their data vulnerability on sharing accommodation platforms. In addition, this study also highlights the measures that platforms may employ to mitigate the negative influence of consumer data vulnerability and consumers' peer privacy concerns, as well as the measures to reduce consumers' peer privacy concerns.

While previous studies mainly examined the driving forces of consumers' engagement in sharing accommodation, this study focuses on the impediment. With communication privacy management theory to explore the relationships between consumer data vulnerability, peer privacy concerns and continued usage intention of sharing accommodation platforms, as well as the moderating effects of consumers' perceived benefits, this study facilitates a more comprehensive understanding of consumers' engagement in sharing accommodation.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

The purpose of this study is to investigate the motivational process underlying users’ intention to provide feedback on user-contributed knowledge in professional online communities. User feedback can serve as a means of indicating the credibility of the online content, which can help community members in their knowledge-seeking process. Adopting such a user feedback mechanism is beneficial for users to identify relevant and credible content efficiently and for an online community to sustain itself.

Drawing on self-determination theory, an integrated model is proposed. In this model, behavioural intention is defined as the consequence of motivational orientations whose antecedences include various social factors. The model is empirically tested using survey data collected online and the structural equation modelling techniques.

The results show that users’ intention to provide feedback is primarily influenced by autonomous motivation. Autonomous motivation is in turn affected by social factors, including reciprocity, online reputation, trust in the user involvement mechanisms and affective and normative community commitments.

This study adds value to prior studies by stressing the significance and feasibility of user feedback in helping members of professional online communities with their knowledge-seeking process. It also contributes to the literature on user participation in these communities by showing the efficacy of a motivational process perspective and the role of motivational orientations, in particular, in explaining users’ behavioural intention.

The partial privatization of state-owned enterprises (SOEs) is a dynamic process. The main feature of this process lies in not only gradual and sequential privatizations but also privatized shares transfer. For partially privatized SOEs, the introduction of private sector ownership is not the end of the story because the previously introduced private owners may choose to leave the SOEs by transferring the privatized shares after privatization, a process that is called “privatized shares transfer”. This paper aims to investigate the determinants of privatized shares transfer (PST) from the perspective of large shareholders’ control rights.

Considering the pyramidal structure of Chinese listed companies, this paper extends existing analyses to study the impact of the ultimate controller’s control rights on privatized shares transfer. This paper also investigates the relationship between excessive control rights of the largest controlling shareholder and PST in view of the principle of equity of rights and obligations. In addition to a perspective on the holding of key positions by large shareholders, this paper further explores the impacts of the ownership of the largest controlling shareholder on privatized shares transfer.

The results capture the fact that the higher control rights of large shareholders lead to more privatized shares transfer. After exploring the impacts of excessive control rights, the results provide evidence supporting the idea that firms with excessive numbers of directors, senior managers or supervisors who also have positions in the largest controlling shareholder’s entity are more likely to transfer privatized shares owned by private owners. In addition, the largest shareholders’ ownership also plays a role in privatized shares transfer.

This evidence suggests that the large shareholders’ control rights should be limited to an appropriate range during the process of privatization, thereby giving private shareholders more opportunity to participate in the operation of firms, strengthen the state and enhance the competitiveness of state capital.

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.