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The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

It has been discovered that some results published in studies may not be correct because different researchers using the same dataset and analytical methods were unable to create the same results. This dilemma is called the reproducibility crisis. Currently, there has not been a comprehensive examination of the possible existence of this crisis in the field of autism spectrum research. This chapter does not answer the question, ‘Is there a reproducibility crisis occurring in the field of autism spectrum research?’ Rather, it contains an outline of this crisis, explains some of the most influential factors that have contributed to its development and how scholars who study the autism spectrum can change their research practices so that this crisis does not develop.

The original contribution that this chapter makes to autism spectrum research is to explain how some solutions to the reproducibility crisis can be implemented into the field of autism spectrum research.

Inspection is an activity that controls the outgoing product quality and involves search, detection and measurement or diagnosis. Traditionally, inspection tasks have been allocated to humans. Attempts to automate industrial inspection in order to eliminate errors and alleviate monotony have faced difficulties due to technological limitations and/or prohibitive implementation costs. An occasional compromise is partial automation (hybrid inspection). Reviews published research in manual, hybrid and automated inspection to understand the current research status.

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.

Bailey et al. (2001) queried accounting researchers concerning admitted fraudulent research practices, their beliefs about the prevalence of such practices among their peers, and their perceptions of the causal factors. They used a randomized response technique that assures anonymity, and it remains the only published study to ask these questions explicitly. Over the past two decades, publication pressures have increased, and accounting academia has experienced a shocking instance of fraud. The current study replicates Bailey et al. (2001) and extends the study by asking new questions about the adequacy of participants’ graduate training, the perceived attitudes and practices of mentors and coauthors, and whether their awareness and concern have evolved. Participants’ comments provide insights about the accounting research environment. Importantly, they indicate a lack of consensus about the legitimacy of research practices.

The paper presents results from the implementation and evaluation of a EU/WHO multi‐centre programme on the promotion of children's psychosocial development through primary health care services. The aims of the study were to develop methods for use by primary health care workers in their contact with families on issues pertaining to healthy psychosocial development in the first two years of life, to develop a training programme for primary health care workers to implement in their contact with the families, to evaluate the effectiveness of the programme and to promote the implementation of the developed intervention techniques through the network of primary health care services.

Organizations are considering the influence of workplace attention breadth (mindfulness and absorption) on professional development. Although corporate accountants typically focus on technical skills, soft skills such as mindfulness may also improve performance. In this study, we examine the influence of attention breadth on task performance by demonstrating how mindfulness and absorption vary with respect to improvement to entry, mid, and upper-level accounting tasks. We survey over 700 corporate accounting professionals and find that upper-level manager task performance is related to mindfulness, and mid-level manager task performance is associated with mindfulness and absorption. We also find that mid-level professionals who are unable to transition between mindfulness and absorption states serve a relatively longer tenure before advancing to an upper-level position. This study has important implications for management to assist in improving office productivity and morale.

This paper aims to present a three‐dimensional computational fluid dynamics (CFD) model that simulates the fluid flow, species transport and electric current flow in PEM fuel cells.

The model makes use of a general‐purpose CFD software as a basic tool incorporating fuel cell specific submodels for multi‐component species transport, electrochemical kinetics, water management and electric phase potential analysis in order to simulate various processes that occur in a PEM fuel cell.

Three dimensional results for the flow field, species transport, including waster formations, and electric current distributions are presented for two test flow configurations in the PEM fuel cell. For the two cases presented, reasonable predictions have been obtained, and this provides an insight into the effect of the flow designs to the operation of the fuel cell.

It is appreciated that the CFD modeling of fuel cells is, in general, still facing significant challenges due to the limited understanding of the complex physical and chemical processes existing within the fuel cell. The model is now under further development to improve its capabilities and undergoing further validations.

The model simulations can provide detailed information on some of the key fluid dynamics, physical and chemical/electro‐chemical processes that exist in fuel cells which are crucial for fuel cell design and optimization.

The model can be used to understand the operation of the fuel cell and provide and alternative to experimental investigations in order to improve the performance of the fuel cell.