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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.

The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.

An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.

The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.

The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.

While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.

In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

This study aims to assesses the factors that affected the development of cloud-based accounting education (DCBAE) and students' academic performance at Oman Universities.

In this paper a questionnaire was used to collect data for a sample of students. Partial least squares (PLS) were used to assess the hypotheses and model.

Results indicate that human factors and cultural and social factors have a direct positive effect on the DCBAE and students' academic performance.

This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Thus, it can help universities (accounting education in particular) in the Sultanate of Oman know the factors that affect the development of accounting education and adopt policies and strategies that depend on cloud computing in education operations, even after the COVID-19 pandemic.

The main contribution of this study is to evaluate the factors that affected the DCBAE and the academic performance of students in the Sultanate of Oman from March 15, 2020 to June 2022, a period that witnessed the application of cloud-based education, either in whole or in part using a questionnaire about the opinions of students.

The aim of this research is to examine the key determinants influencing the success of new service development projects (NSDPs) across four service typologies context.

The researchers used the scenario-based survey method in an NSDP setting. Structural equation modelling (SEM) was used to test the proposed hypotheses based on survey data from 570 managers under four service typologies.

Service firms' cross-functional integration (CFI) and internal project team efficiency (IPTE) positively influenced NSDPs. The results also indicated that both technology infrastructure (TI) and IPTE mediated the relationship between CFI and NSDPs. In addition, the mediation effect of TI existed between the relationship of IPTE and NSDPs. Furthermore, the proposed model confirms that, for NSDPs, the role of knowledge-sharing behaviour (KSB), authentic leadership (AL) and firm's culture (FC) across the four service typologies moderated the relationship.

With a better understanding of the dynamics of the aforementioned variables, service managers and the project team can more effectively develop and execute strategies for an NSDP. The article enables practitioners to expand their current understanding of NSDPs by providing insights of the unique antecedents that are significant for new service development across four service types.

This research is the first of its kind to examine the mediating role of KSB and TI in determining NSDPs. This study provides one of the first empirical examinations on NSDPs in the context of four service typologies from the perspective of a developing country, where the service industry is competitive. The study demonstrates that the critical success factors of NSDPs do not differ across service types, thereby confirming the “One Basket Fits all” assumption in the current NSDP research study.

This study aims to explore the effect of innovation ecosystem stability (IES) on innovation performance of enterprises through the mediating role of knowledge acquisition (KA), and to study how these effects are moderated by unabsorbed slack.

This study draws on data from 327 Chinese enterprises and adopts the multiple linear regression method and bootstrapping method to explore the mediating effect of KA and its moderated mediating effect.

The results demonstrate that IES is positively associated with innovation performance of enterprises, and KA plays a partially mediating role. Moreover, unabsorbed slack negatively moderates the relationship between IES and KA as well as the mediating effect of KA.

This study investigates the relationship between IES and innovation performance, and the mechanism of influence, which has not been previously studied in the field of innovation ecosystem. This study also examines the interaction between unabsorbed slack and IES and further clarifies the mechanism and boundary conditions of the impact of IES on innovation performance.

The shift from paper portfolios to e-portfolios has arrived in educational institutions worldwide. This study investigates e-portfolio systems as a means of improving performance-centered assessment, enriching students’ learning experiences and documenting the students’ progress and achievements. The current study reveals the experience of implementing a course-level framework for e-portfolios and an approach taken in initiating student electronic portfolios in the Department of Educational Technology (DET) at Ajman University of Science and Technology, UAE. Data was obtained in several ways, including Likert scale responses and interviews with the participants; students’ journals and final reports; notes from the Practicum site supervisor and the DET lab technician; and analysis of the electronic portfolio product. The work and responses of the Practicum students were compared for three consecutive Practicum classes. Analysis of the results showed that developing formative and summative portfolios fluctuated extensively between the three Practicum classes of DET graduates, as did the outcomes. In spite of this fact, the findings suggested that the use of e-portfolios could serve as an influential learning and assessment tool when driven by a clear understanding of the desired outcome and the specific skills to be assessed, and when sufficiently mentored, peer-reviewed, and based on sensible principles.

This paper presents the concerns in manufacturing supply chain. Further this study investigates the role of information technology (IT) and information sharing (IS) in manufacturing supply chain and determines its impact towards supply chain integration (SCI), supply chain performance (SCP), and manufacturing firm performance (FP) in Malaysia. The theoretical framework was proposed for the study on the basis of existing literature. The study administered a survey questionnaire to collect data from manufacturing firms in Malaysia with 112 respondents. A multiple regression analysis is conducted to establish the relationship between IT, IS, SCI, and FP. The study finds that IT and sharing has significant positive effect towards and performance. Firms that use IT and practice IS across partners in the supply chain are more likely to integrate their internal and external value chain for better performance both within and across the manufacturing firms in the supply chain. This study can be of interest to the manufacturing industry as well as other industry practitioners interested in improving the performance of the organization and supply chain in total. For supply chain practitioners, this results indicate that the firms should adopt IT and IS practices to strategically improve SCI. This in turn will also improve the supply chain network and firm’s performance. This study employs a newly developed framework which depicts the causal relationship between IT, IS, SCI, Supply Chain Performance, and FP in Malaysia. Furthermore, it closes a gap in existing literature by examining the effect IT and communication (ICT) practices toward manufacturing firms’ performance and SCP in a single setting. In addition, the current study attempted to construct a model which would estimate and interpret SCP and FP simultaneously, and to evaluate this model in an empirical fashion.