Predictivism and avoidance of ad hoc-ness: An empirical study

Predictivism is the thesis that evidence successfully predicted by a scientific theory counts more (or ought to count more) in the confirmation of that theory than already known evidence would. One rationale that has been proposed for predictivism is that predictive success guards against ad hoc hypotheses. Despite the intuitive attraction of predictivism, there is historical evidence that speaks against it. As valuable as the historical evidence may be, however, it is largely indirect evidence for the epistemic attitudes of individual - albeit prominent - scientists. This paper presents the results of an empirical study of scientists' attitudes toward predictivism and ad hoc-ness (n = 492), which will put the debate on a more robust empirical footing. The paper also draws attention to a tension between the ad hoc-ness avoidance rationale of predictivism and the ways philosophers have spelled out the notion of ad hoc-ness.

What are developers talking about information security? A large-scale study using semantic analysis of Q&A posts

Background

Digitalization and rapid technological improvement in the present day bring numerous benefits, but they also raise the complexity and diversity of cyber security risks, putting critical information security issues on the agenda. Growing issues and worries about information security endanger not only the security of individuals and organizations but also global social and economic stability.

Methods

This study investigates the issues and challenges regarding information security by analyzing all the postings on ISSE (Information Security Stack Exchange), a Q&A website focused on information security. In order to identify the primary topics addressed in postings shared on the ISSE platform, we employed a probabilistic topic modeling method called latent Dirichlet allocation (LDA), which is generative in nature and relies on unsupervised machine learning processes.

Results

Through this investigation, a total of 38 topics were identified, demonstrating the present state of information security issues and challenges. Considering these topics, a comprehensive taxonomy of seven categories was devised to address information security issues, taking into account their backgrounds and perspectives. Subsequently, we conducted an examination of the prevalence and complexity of the matters at hand. In addition, we have defined the prevailing technologies utilized in the realm of information security, including tasks, certifications, standards, methods, tools, threats, and defenses. We have provided a number of implications for different stakeholders, including academics, developers, educators, and practitioners, who are working towards advancing the field of information security.

Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses

BACKGROUND

The transitivity assumption is the cornerstone of network meta-analysis (NMA). Violating transitivity compromises the credibility of the indirect estimates and, by extent, the estimated treatment effects of the comparisons in the network. The present study offers comprehensive empirical evidence on the completeness of reporting and evaluating transitivity in systematic reviews with multiple interventions.

METHODS

We screened the datasets of two previous empirical studies, resulting in 361 systematic reviews with NMA published between January 2011 and April 2015. We updated our evidence base with an additional 360 systematic reviews with NMA published between 2016 and 2021, employing a pragmatic approach. We devised assessment criteria for reporting and evaluating transitivity using relevant methodological literature and compared their reporting frequency before and after the PRISMA-NMA statement.

RESULTS

Systematic reviews published after PRISMA-NMA were more likely to provide a protocol (odds ratio (OR): 3.94, 95% CI: 2.79-5.64), pre-plan the transitivity evaluation (OR: 3.01, 95% CI: 1.54-6.23), and report the evaluation and results (OR: 2.10, 95% CI: 1.55-2.86) than those before PRISMA-NMA. However, systematic reviews after PRISMA-NMA were less likely to define transitivity (OR: 0.57, 95% CI: 0.42-0.79) and discuss the implications of transitivity (OR: 0.48, 95% CI: 0.27-0.85) than those published before PRISMA-NMA. Most systematic reviews evaluated transitivity statistically than conceptually (40% versus 12% before PRISMA-NMA, and 54% versus 11% after PRISMA-NMA), with consistency evaluation being the most preferred (34% before versus 47% after PRISMA-NMA). One in five reviews inferred the plausibility of the transitivity (22% before versus 18% after PRISMA-NMA), followed by 11% of reviews that found it difficult to judge transitivity due to insufficient data. In justifying their conclusions, reviews considered mostly the comparability of the trials (24% before versus 30% after PRISMA-NMA), followed by the consistency evaluation (23% before versus 16% after PRISMA-NMA).

CONCLUSIONS

Overall, there has been a slight improvement in reporting and evaluating transitivity since releasing PRISMA-NMA, particularly in items related to the systematic review report. Nevertheless, there has been limited attention to pre-planning the transitivity evaluation and low awareness of the conceptual evaluation methods that align with the nature of the assumption.

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study

BACKGROUND

The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.

METHODS

We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.

RESULTS

Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.

CONCLUSIONS

Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy

This paper provides empirical evidence on the impact of the Covid-19 pandemic on logistics and supply chain processes of five industrial sectors of Italy, namely food & beverage, machine manufacturing, metal mechanical industry, logistics & transport, and textile & fashion. A questionnaire survey, with 82 useful responses, was conducted to investigate various effects of Covid-19 on these businesses, such as the volumes handled and the service performance in the immediate-, short- and medium-term, the countermeasures implemented by companies and the future decision-making strategies. The period of analysis spans from January 2020 to June 2021. Results show that the impact of Covid-19 on volumes and service performance varied across the sectors: the food & beverage and logistics & transport were poorly affected by the pandemic and experienced a general increase in the demand and volumes, while mechanical or textile & fashion industries were mostly affected by a decrease in demand. The positive/negative impacts were particularly evident at the beginning of the pandemics, but, depending on the sector, the effects could cease quite quickly or last in the short-term. The countermeasures adopted against the Covid-19 emergency differ again across sectors; in general, industry fields that were particularly impacted by the pandemic emergency have applied more countermeasures. Typical strategies for risk management (e.g., the diversification in transport modes or the stock increase) turned out to be applied as immediate countermeasures or in plan for the future in few industries only. Differences across sectors were also observed about the sourcing strategies already in use, implemented to counteract the pandemics or expected to be maintained in time. Empirical outcomes offered are expected to help researchers gain a deep understanding of Covid-19 related phenomena, thus inspiring further research activities.

An approach to predict the task efficiency of web pages

Usability is generally considered as a metric to judge the efficacy of any interface. This is also true for the web pages of a website. There are different factors - efficiency, memorability, learnability, errors, and aesthetics play significant roles in order to determine usability. In this work, we proposed a computational model to predict the efficiency with which users can do a particular task on a website. We considered seventeen features of web pages that may affect the efficiency of a task. The statistical significance of these features was tested based on the empirical data collected using twenty websites. For each website, a representative task was identified. Twenty participants completed these tasks using a controlled environment within a group. Task completion times were recorded for feature identification. The one Dimensional ANOVA study reveals sixteen out of the seventeen are statistically significant for efficiency measurement. Using these features, a computational model was developed based on the Support Vector Regression. Experimental results show that our model can predict the efficiency of web pages' tasks with an accuracy of 90.64%.

From forced Working-From-Home to voluntary working-from-anywhere: Two revolutions in telework

The COVID-19 outbreak has admittedly caused interruptions to production, transportation, and mobility, therefore, having a significant impact on the global supply and demand chain's well-functioning. But what happened to companies developing digital services, such as software? How has the enforced Working-From-Home (WFH) mode impacted their ability to deliver software, if at all? This article shares our findings from monitoring the WFH during 2020 in an international software company with engineers located in Sweden, the USA, and the UK. We analyzed different aspects of productivity, such as developer job satisfaction and well-being, activity, communication and collaboration, efficiency and flow based on the archives of commit data, calendar invites, Slack communication, the internal reports of WFH experiences, and 30 interviews carried out in April/May and September 2020. We add more objective evidence to the existing COVID-19 studies the vast majority of which are based on self-reported productivity from the early months of the pandemic. We find that engineers continue committing code and carrying out their daily duties, as their routines adjust to "the new norm". Our key message is that software engineers can work from home and quickly adjust their tactical approaches to the changes of unprecedented scale. Further, WFH has its benefits, including better work-life balance, improved flow, and improved quality of distributed meetings and events. Yet, WFH is not challenge free: not everybody feels equally productive working from home, work hours for many increased, while physical activity, socialization, pairing and opportunities to connect to unfamiliar colleagues decreased. Information sharing and meeting patterns also changed. Finally, experiences gained during the pandemic will have a lasting impact on the future of the workplace. The results of an internal company-wide survey suggest that only 9% of engineers will return to work in the office full time. Our article concludes with the InterSoft's strategy for work from anywhere (WFX), and a list of useful adjustments for a better WFH.

An Empirical Investigation of Critical Factors Affecting Acceptance of E-Learning Platforms: A Learner's Perspective

E-learning is evolving as the paradigm of modern-day education. Globally, e-learning has seen a rise; however, failures happen. There is a dearth of studies that discuss why a lot of learners quit e-learning after a preliminary experience. Preceding research studies carried out under diverse task settings have proposed an assortment of factors impacting learners' satisfaction with e-Learning. This study developed an integrated conceptual model with the instructor, course, and learners' dimensions and then empirically validates it. The technology acceptance model (TAM) has been employed for testing the acceptance of various technologies and software within an e-learning context. This study intends to examine the salient factors of effective e-learning acceptance by learners. A survey investigates the critical factors using a self-administered questionnaire influencing the satisfaction of learners in the e-Learning system/platform. The study uses quantitative methodology and data were collected from 348 learners. On performing the structured equation modeling for testing the hypothesized model, outcomes reveal the significant factors influencing learners' perceived satisfaction studied in three dimensions of the instructor, course, and learner. It will facilitate educational institutes and provide directions on improving learners' satisfaction and additionally improve e-Learning implementation.

The effect of node features on GCN-based brain network classification: an empirical study

Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, a graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of BFNs to train a classifier, GCN requires an additional input-node features. To our best knowledge, however, there is no systematic study to analyze their influence on the performance of GCN-based brain disorder classification. Therefore, in this study, we conduct an empirical study on various node feature measures, including (1) original fMRI signals, (2) one-hot encoding, (3) node statistics, (4) node correlation, and (5) their combination. Experimental results on two benchmark databases show that different node feature inputs to GCN significantly affect the brain disease classification performance, and node correlation usually contributes higher accuracy compared to original signals and manually extracted statistical features.

The effect of information level of digital worker guidance systems on assembly performance, user experience and strain

Worker guidance systems provide product-specific and digital assembly information and can make an important contribution to increasing productivity and quality and relieving employees, especially in flexible variant assembly. However, a critical factor here is the successful design of the systems. While much of the research focuses on aspects of the hardware, this article is dedicated to the question of how the configuration of the level of information, i.e. the amount and depth of information in worker guidance systems, is affected. For this purpose, two studies, a laboratory study at the Technical University of Darmstadt (N = 53) and a field study at a company (N = 30), were conducted and two variants of the information level of a worker guidance system were compared. The study results show that assembly quality in particular is significantly influenced by the level of information; to the advantage of detailed information. No significant differences were found for production times. For the subjective evaluation of the system as well as for the objective and subjectively perceived strain, it can be stated that the high level of information also performs better. The results thus help to design worker guidance systems more precisely so that they fulfill the information needs of the employees as well as possible.

On the privacy of mental health apps: An empirical investigation and its implications for app development

An increasing number of mental health services are now offered through mobile health (mHealth) systems, such as in mobile applications (apps). Although there is an unprecedented growth in the adoption of mental health services, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among 3rd-parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10664-022-10236-0.

Developer discussion topics on the adoption and barriers of low code software development platforms

Low-code software development (LCSD) is an emerging approach to democratize application development for software practitioners from diverse backgrounds. LCSD platforms promote rapid application development with a drag-and-drop interface and minimal programming by hand. As it is a relatively new paradigm, it is vital to study developers' difficulties when adopting LCSD platforms. Software engineers frequently use the online developer forum Stack Overflow (SO) to seek assistance with technical issues. We observe a growing body of LCSD-related posts in SO. This paper presents an empirical study of around 33K SO posts (questions + accepted answers) containing discussions of 38 popular LCSD platforms. We use Topic Modeling to determine the topics discussed in those posts. Additionally, we examine how these topics are spread across the various phases of the agile software development life cycle (SDLC) and which part of LCSD is the most popular and challenging. Our study offers several interesting findings. First, we find 40 LCSD topics that we group into five categories: Application Customization, Database and File Management, Platform Adoption, Platform Maintenance, and Third-party API Integration. Second, while the Application Customization (30%) and Data Storage (25%) topic categories are the most common, inquiries relating to several other categories (e.g., the Platform Adoption topic category) have gained considerable attention in recent years. Third, all topic categories are evolving rapidly, especially during the Covid-19 pandemic. Fourth, the How-type questions are prevalent in all topics, but the What-type and Why-type (i.e., detail information for clarification) questions are more prevalent in the Platform Adoption and Platform Maintenance category. Fifth, LCSD practitioners find topics related to Platform Query the most popular, while topics related to Message Queue and Library Dependency Management as the most difficult to get accepted answers to. Sixth, the Why-type and What-type questions and Agile Maintenance and Deployment phase are the most challenging among practitioners. The findings of this study have implications for all three LCSD stakeholders: LCSD platform vendors, LCSD developers/practitioners, Researchers, and Educators. Researchers and LCSD platform vendors can collaborate to improve different aspects of LCSD, such as better tutorial-based documentation, testing, and DevOps support.

C2M: a maturity model for the evaluation of communication in distributed software development

Communication is essential in any software development project, particularly those globally distributed where geographical, temporal, and cultural distance may hinder the effectiveness of communication. The challenges imposed by distance often characterize communication as still one of the main drawbacks of globally distributed projects. Therefore, establishing communication processes and practices is relevant to support a team's work. These processes and practices need to be updated and aligned with the team's needs. Thus, assessing and evaluating the maturity of such communication processes and practices is paramount. This article presents a Communication Maturity Model called C2M which aims to help organizations identify the maturity of communication-related aspects by providing an approach for revealing what practices need to be improved. The model is composed of 4 levels of maturity (causal, partially managed, managed and reflective) and 4 areas of maturity (people, project, organizational and engineering) which are organized into 15 maturity factors, each factor comprising a set of practices. The model has 58 practices and each has its specific objectives. The model was empirically developed and evaluated in three well-defined phases. In the conception phase, methodological procedures (Tertiary Study, Systematic Literature Review, and Interviews) were carried out in order to gather relevant information for designing the first version of the C2M model (alpha version). Then, in the refinement phase, two focus group meetings were held in two organizations in order to identify how effectively the model attends its purpose. The results led to a second version of the C2M model (beta version), analyzed by a survey with experts who assessed the representation of the third version of the C2M model-omega version (evaluation phase). All results achieved so far suggest that the model can assist in discovering the maturity level of the communication processes and practices in globally distributed projects. Future works will focus on developing a software tool to help with self-assessment.

An empirical study on consumer automobile purchase intentions influenced by the COVID-19 outbreak

The sudden onset of the coronavirus disease 2019 (COVID-19) may influence individuals' automobile purchase decisions, thus bringing great uncertainty to the automobile industry. To this end, the current study investigates individuals' behaviors regarding the purchase of automobiles, both before and after the outbreak of COVID-19. An ICLV (integrated choice and latent variable) model that integrates the socio-demographics, epidemic-related variables and psychological latent variables is applied. A survey of 960 respondents was conducted in China during the epidemic. The results suggest that there was an increase in the demand for automobiles after the COVID-19 outbreak. Firstly, demand was especially high in the groups of females, citizens, high-income earners, and people who own a driving license or who live in high epidemic risk areas. Secondly, although the severity of the epidemic for residences has a positive effect on automobile demand, travelers' perceived vulnerability is the key factor motivating purchases. Thirdly, the epidemic's negative income effects reduced the purchase propensity. Several dynamic policies are proposed to automobile consumption of the special time of the COVID-19 pandemic.

Flight crew fatigue risk assessment for international flights under the COVID-19 outbreak response exemption policy

Background

In response to the COVID-19 outbreak, the Civil Aviation Administration of China (CAAC) has formulated Implementation Measures for Exemption of Crew Duty Periods and Flight Time Restrictions during the COVID-19 Outbreak. This exemption policy imposes temporary deviations from the approved crew duty periods and flight time restrictions for some transport airlines and regulates the use of multiple crews for continuous round-trip flights. However, no research has been conducted on flight crew fatigue under this exemption policy. That is, the exemption policy lacks theoretical analysis and scientific validation.

Methods

Firstly, flight plans for international flights under both the exemption and the CCAR-121 Policy schemes (with three flight departure scenarios: early morning, midday and evening) are designed, and flight plans are simulated based on the SAFE model. The Karolinska Sleepiness Scale (KSS) and the PVT objective test of alertness, both of which are commonly used in the aviation industry, are then selected for use in an empirical experimental study of flight crew fatigue on two flights subject to the exemption and CCAR-121 policies.

Results

The SAFE model simulation found that the fatigue risk results based on flight crews for flights departing in the early morning (4:00), at noon (12:00) and in the evening (20:00) indicate that the fatigue risk levels of flight crews operating under the exemption policy are overwhelmingly lower than or similar to those operating under the CCAR-121 policy. However, there were a few periods when the fatigue risk of crews flying under the exemption policy was higher than that of crews flying under the CCAR-121 policy, but at these times, the crews flying under both policies were either at a lower level of fatigue risk or were in the rest phase of their shifts. In the experimental study section, 40 pilots from each of the early morning (4:00), noon (12:00) and evening (20:00) departures operating under the exemption policy were selected to collect KSS scale data and PVT test data during their duty periods, and a total of 120 other pilots operating under the CCAR-121 policy were selected for the same experiment. First, the KSS scale data results found that flight pilots, whether flying under the exemption policy or under the CCAR-121 policy, had overall similar KSS scores, maintained KSS scores below the fatigue risk threshold (i.e., KSS score < 6) during the flights and that the empirical KSS data and the model simulation results from the KSS data were overall identical at the test nodes during the flight and had nearly identical trends. Finally, the results of the PVT objective test indicators showed that the overall change in 1/RT of the crews flying under the exemption policy was less than or similar to that of the crews flying under the CCAR-121 policy, while the maximum change in 1/RT of the crews under both policies was between 1 and 1.5. This indicates that the overall level of alertness of the crew flying under the exemption policy is higher than or similar to that of the crew flying under the CCAR-121 policy, while the change in alertness level of the crew before and after the mission is relatively small when flying under either policy.

Conclusion

Based on the model simulation results and the results of the empirical study, it was verified that the overall fatigue risk level of flight crews operating under the exemption policy is lower than or similar to the fatigue risk level of flight crews operating under the CCAR-121 policy. Therefore, the exemption policy in response to the COVID-19 outbreak does not result in an overall increase in the level of flight crew fatigue risk compared to the original CCAR-121 policy.

One-off events? An empirical study of hackathon code creation and reuse

CONTEXT

Hackathons have become popular events for teams to collaborate on projects and develop software prototypes. Most existing research focuses on activities during an event with limited attention to the evolution of the hackathon code.

OBJECTIVE

We aim to understand the evolution of code used in and created during hackathon events, with a particular focus on the code blobs, specifically, how frequently hackathon teams reuse pre-existing code, how much new code they develop, if that code gets reused afterwards, and what factors affect reuse.

METHOD

We collected information about 22,183 hackathon projects from Devpost and obtained related code blobs, authors, project characteristics, original author, code creation time, language, and size information from World of Code. We tracked the reuse of code blobs by identifying all commits containing blobs created during hackathons and identifying all projects that contain those commits. We also conducted a series of surveys in order to gain a deeper understanding of hackathon code evolution that we sent out to hackathon participants whose code was reused, whose code was not reused, and developers who reused some hackathon code.

RESULT

9.14% of the code blobs in hackathon repositories and 8% of the lines of code (LOC) are created during hackathons and around a third of the hackathon code gets reused in other projects by both blob count and LOC. The number of associated technologies and the number of participants in hackathons increase reuse probability.

CONCLUSION

The results of our study demonstrates hackathons are not always "one-off" events as the common knowledge dictates and it can serve as a starting point for further studies in this area.

The varying rationality of weakness of the will: an empirical investigation and its challenges for a unified theory of rationality

Weakness of the will remains a perplexing issue. Though philosophers have made substantial progress in homing in on what counts as a weak will, there is little agreement on whether weakness of the will is irrational, and if so, why. In this paper, we take an empirical approach towards the rationality of weakness of the will. After introducing the philosophical debate, we present the results of an empirical study that reveals that people take a "dual sensitivity", as we shall put it, towards assessing the rationality of weak-willed behavior. Put succinctly, intending X against your value judgements is assessed irrational; yet, in the same situation, intending X is assessed significantly less irrational if you judge X as something you ought to do. After discussing this result, we turn to the question of whether there is a plausible theory of rationality than can account for the dual sensitivity of the rationality assessments. We show that a success-based account can make sense of the dual sensitivity our empirical results reveal.

Changes in perceived productivity of software engineers during COVID-19 pandemic: The voice of evidence

BACKGROUND

The COVID-19 pandemic triggered a natural experiment of an unprecedented scale as companies closed their offices and sent employees to work from home. Many managers were concerned that their engineers would not be able to work effectively from home, or lack the motivation to do so, and that they would lose control and not even notice when things go wrong. As many companies announced their post-COVID permanent remote-work or hybrid home/office policies, the question of what can be expected from software engineers who work from home becomes more and more relevant.

AIMS

To understand the nature of home telework we analyze the evidence of perceived changes in productivity comparing office work before the pandemic with the work from home during the pandemic from thirteen empirical surveys of practitioners.

METHOD

We analyzed data from six corporate surveys conducted in four Scandinavian companies combined with the results of seven published surveys studying the perceived changes in productivity in industrial settings. In addition, we sought explanations for the variation in perceived productivity among the engineers from the studied companies through the qualitative analysis of open-ended questions and interviews.

RESULTS

Combined results of 7686 data points suggest that though on average perceived productivity has not changed significantly, there are developers who report being more productive, and developers being less productive when working from home. Positively affected individuals in some surveys form large groups of respondents (up to 50%) and mention benefiting from a better organization of work, increased flexibility and focus. Yet, there are equally large groups of negatively affected respondents (up to 51%) who complain about the challenges related to remote teamwork and collaboration, as well as emotional issues, distractions and poor home office environment and equipment. Finally, positive trends are found in longitudinal surveys, i.e., developers' productivity in the later months of the pandemic show better results than those in the earlier months.

CONCLUSIONS

We conclude that behind the average "no change" lays a large variation of experiences, which means that the work from home might not be for everyone. Yet, a longitudinal analysis of the surveys is encouraging, as it shows that the more pessimistic results might be influenced by the initial experiences of an unprecedented crisis. At the end, we put forward the lessons learned during the pandemic that can inspire the new post-pandemic work policies.

Modelling a community resilience index for urban flood-prone areas of Kerala, India (CRIF)

Communities are ever-evolving, cities are constantly expanding, and the threat of natural hazards has escalated like never before. Cities can develop and prosper only if their society is resilient to external shocks. Measuring community resilience over time is crucial with the influence of technology and change in community lifestyles. With the frequent onset of floods in Kerala in recent years, the community must be well-prepared for future calamities. Thus, this paper develops a community resilience index for Kerala's urban flood-prone areas (CRIF) through a rigorous bottom-up approach. The criteria for the index were developed using multi-criteria decision analysis that covered a fuzzy Delphi study, an empirical study using multi-variate probit regression, and an AHP analysis. The fuzzy Delphi study selected seven criteria: 'social', 'economical', 'governance/political', 'health', 'communication/coordination, 'education', and 'infrastructure' from 65 experts. The empirical study helped apprehend the public's viewpoints under each criterion. Finally, the AHP analysis helped assign appropriate weights to the criteria which 28 experts designated. The index is also designed according to the Sendai Framework for Disaster Risk Reduction (2015-2030). Further, the CRIF Index is put into action through a case study of the Kochi Municipal Corporation area, and the results are also validated using the Spearman's rank correlation coefficient method. Results from validation returned a value of 0.7209 for the perceived CRIF method and 0.5798 for the external validation method, which corresponds to a 'high' and 'moderate' correlation, respectively.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11069-022-05299-7.

Assessing the role of industry 4.0 for enhancing swift trust and coordination in humanitarian supply chain

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance.

Assessing the role of industry 4.0 for enhancing swift trust and coordination in humanitarian supply chain

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance.

Assessing the role of industry 4.0 for enhancing swift trust and coordination in humanitarian supply chain

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance.

How Internet of Things responds to the COVID-19 pandemic

The cornovirus disease (COVID-19) pandemic has had a severe impact on our daily lives. As a result, there has been an increasing demand for technological solutions to overcome such challenges. The Internet of Things (IoT) has recently emerged to improve many aspects of human's day-to-day activities and routines. IoT makes it easier to follow the safety guidelines and precautions provided by the World Health Organization (WHO). Prior reports have shown that the world nowadays may need more IoT facilities than ever before. However, little is known about the reaction of the IoT community towards defeating the COVID-19 pandemic, technologies being used, solutions being provided, and how our societies perceive the IoT means available to them. In this paper, we conduct an empirical study to investigate the IoT response to the COVID-19 pandemic. In particular, we study the characteristics of the IoT solutions hosted on a large online IoT community (i.e., Hackster.io) throughout the year of 2020. The study: (a) explores the proportion, types, and nations of IoT solutions/engineers that contributed to defeating COVID-19, (b) characterizes the complexity of COVID-19 IoT solutions, and (c) identifies how IoT solutions are perceived by the surrounding community. Our results indicate that IoT engineers have been actively working towards providing solutions to help their societies, especially in the most affected nations. Our findings (i) provide insights into the aspects IoT practitioners need to pay more attention to when developing IoT solutions for COVID-19 and to (ii) outlines the common IoT solutions and technologies available to humans to deal with the current challenges.

An empirical study on hospital-based prevention of recurrent urinary stone disease in Germany

Purpose

Urinary stone disease is a common disease with a prevalence of 4.7% in Germany. The incidence increased over the last decades, and recurrence rates are up to 50% in the first 5 years after diagnosis. Adequate preventive measures can avoid up to 46% of stone recurrences. These numbers outline the importance of prevention. Especially among high-risk stone formers, specific diagnostics and measures are required. Published data indicate the divergence between the importance of prevention and its implementation in everyday clinical practice. This is the first survey among German urological departments highlighting medical care concerning the prevention of recurrent urinary stone disease, identifying challenges and providing recommendations for improvements.

Methods

Two hundred and seventy urological hospital departments in Germany were anonymously surveyed about measurements to prevent recurrent stone disease. The questionnaire comprised 23 items dealing with diagnostics, counselling, knowledge among doctors concerning preventive measures and difficulties in preventing recurrent urinary stone disease.

Results

Sixty-three urological departments (23.8%) answered the survey. The majority perform stone analysis at first and repeat events. Most patients with urinary stone disease receive general advice on preventive measures during their hospitalization. General recommendations focus on fluid intake and lifestyle changes. However, specific diets are infrequently recommended by inpatient urologists. Diagnostics to identify high-risk stone formers are mostly insufficient, and guideline-compliant urine tests are uncommon.

Conclusion

The quality of secondary prevention needs to improve considerably. The focus should be put on identifying high-risk stone formers and offering those patients specific counselling. Furthermore, general advice on dietary recommendations should be extended.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-021-03813-3.

An empirical study of supply chain sustainability with financial performances of Indian firms

In this research, we examine empirically the impact of sustainable supply chain practices on financial performances, considering the case of Indian firms. Here, we use a sample of 25 Indian firms listed for their sustainability performances in the Thomson Reuters Environmental, Social and Governance (ESG) scores. The sustainability performance data have been accessed from the Bloomberg terminal, where the overall sustainability performance on ESG is measured as a discounted score on ESG considering various controversies on ESG reported for the firm. And for the study, we associate financial data using the profit indicators of firms. We perceive that the sustainable supply chain practices considering environmental, social and governance performances may not positively impact the financial performance measured by the Return on Asset (ROA) and Return on Equity (ROE), during the considered period of five years for the study. We construct the empirical model and use Partial Least Square (PLS) regression modeling to analyze the results. The study can be further extended for many Indian firms and for firms across different developing economies, as well. The major implications of this research are to observe for firms and their supply chains whether the implementation of Environmental, Social and Governance (ESG) practices can help them in achieving financial benefits, along with other competitive advantages. The research is built on the concept and theory of ecological modernization, which suggests for the economic benefits of environmentalism.

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency-inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks.

Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series

BACKGROUND

The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets.

METHODS

A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods.

RESULTS

From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series.

CONCLUSIONS

The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

An assessment of student satisfaction with e-learning: An empirical study with computer and software engineering undergraduate students in Turkey under pandemic conditions

As COVID-19 reached Turkey in March 2020, all universities switched to e-learning in a very short period. Computer and software engineering (CE/SE) undergraduate students studying at university campuses have switched to e-learning. This paper seeks to understand the e-learning experience of CE/SE undergraduate students. A questionnaire was created and applied to CE/SE undergraduate students in Turkish universities. The data were analyzed using quantitative and qualitative techniques. The questionnaire received 290 usable responses. The highlights from the findings include: the participants (1) used video recordings intensively for e-learning and found them useful; (2) found face-to-face lectures more beneficial compared to digital live lectures; (3) used external online resources to improve their learning performance in courses; (4) thought that the materials and methods utilized for assessment should be adapted to e-learning for a better and fair evaluation; (5) perceived significantly less instructor support and classmate interaction and collaboration in e-learning compared to on-campus education settings; (6) rated their perceived satisfaction from e-learning as 2.85, slightly under the mid-level of the 5-point Likert scale; (7) perceived instructor support, student interaction and collaboration, and student autonomy as noteworthy factors in high-quality e-learning.

Treatment efficacy analysis of traditional Chinese medicine for novel coronavirus pneumonia (COVID-19): an empirical study from Wuhan, Hubei Province, China

Background

A novel coronavirus was identified in December, 2019 in Wuhan, China, and traditional Chinese medicine (TCM) played an active role in combating the novel coronavirus pneumonia (NCP) caused by this fast-spreading virus COVID-19. Thus, we aimed to explore TCM characteristics of clinical efficacy to NCP, as well as to optimize Qingfei Paidu decoction (QFPDD) and the recommended formulas to NCP by National Health Commission (NHC).

Methods

Chinese medical sciences theory and clinical application of TCM were analyzed. A total of 54 NCP patients were observed in a hospital from Wuhan, whose clinical characteristics and utilization of Chinese Medicines (CMs) were described. Paired t test was used to measure the change of patients' hemogram during hospitalization period, indicating the effect of CMs. Multiple linear regression analysis was applied to explore the factors affecting the length of hospital stay. Network pharmacology analysis was applied to figure out the performance of NHC-recommended formulas of five disease stages at levels of compounds, targets and pathways.

Result

The average length of hospital stay was 8.96 days. Patients over 45 stayed 9.79 days in hospital in average, longer than 7.64 days of patients under 45. Comparing the hemograms between admission and discharge of hospital, the number of leukocytes, neutrophil, lymphocyte and platelet increased, while the numbers of erythrocytes, hemoglobin concentration and hematocrit decreased. According to the standard coefficients of regression, the factor affecting the length of stay for the most was CMs in category of invigorating spleen and removing dampness (ISRD), followed by administrating CMs, male, and cough. Thirty-two CMs were screened after deleting duplication from QFPDD and NHC-recommended formulas. Compound quercetin, luteolin, kaempferol, acacetin etc., were all involved in the treatment of various disease stages on the compound level both in generality and individuality.

Conclusion

TCM has a systemic theoretical understanding on the pathological evolution and a positive clinical efficacy on NCP. The CMs of ISRD improved patients' recovery, suggesting the importance of regulating intestinal function and keeping microenvironmental balance in TCM treatment of NCP. The active compounds from QFPDD and NHC-recommended formulas contribute to recovery of varied disease progresses during TCM treating NCP.

Reliability of crowdsourcing as a method for collecting emotions labels on pictures

Objective

In this paper we study if and under what conditions crowdsourcing can be used as a reliable method for collecting high-quality emotion labels on pictures. To this end, we run a set of crowdsourcing experiments on the widely used IAPS dataset, using the Self-Assessment Manikin (SAM) emotion collection instrument, in order to rate pictures on valence, arousal and dominance, and explore the consistency of crowdsourced results across multiple runs (reliability) and the level of agreement with the gold labels (quality). In doing so, we explored the impact of targeting populations of different level of reputation (and cost) and collecting varying numbers of ratings per picture.

Results

The results tell us that crowdsourcing can be a reliable method, reaching excellent levels of reliability and agreement with only 3 ratings per picture for valence and 8 per arousal, with only marginal difference between target populations. Results for dominance were very poor, echoing previous studies on the data collection instrument used. We also observed that specific types of content generate diverging opinions in participants (leading to higher variability or multimodal distributions), which remain consistent across pictures of the same theme. These can inform the data collection and exploitation of crowdsourced emotion datasets.

[Multi-center empirical study of virtual gastroscopy simulator in the examination of clinical practice skills of specialist physicians]

Objective: To study the role of virtual gastroscopy simulator in the examination of clinical practice skills of gastrointestinal specialists. Methods: A multi-center empirical study was carried out. In June 2018, ninety participants from three tertiary care teaching hospitals in China, including Zhongshan Hospital of Fudan University, Shanghai Tongji Hospital and Xiangya Hospital of Central South University, took part in the examination. Participants were selected as follows: 30 specialists without any experience of gastroscopy exams, 30 with basic knowledge (gastroscopy exams <500 cases) and 30 with good skill (gastroscopy exams ≥500 cases). These 90 students participated in this empirical study after theoretical study and simulator training. Among them, 50 (55.6%) were undergraduates, 25 (27.8%) masters, and 15 (16.7%) doctors; 42 (46.7%) were residents, 39 (43.3%) attending physicians, and 9 (10.0%) deputy chief physicians. The assessment was divided into two parts. The first part was to use the simulator (GI mentor II) for ballooning within 10 minutes, with a total score of 40 points. The second part was a complete gastroscopy exam on the simulator, including preparation before gastroscopy exam, gastroscopy exam, post - exam management and related knowledge quiz. The difficulty and discrimination of the assessment plan were evaluated by the extreme grouping method parameters (difficulty P definition: <0.3 was difficult, 0.3 to 0.7 was medium, ≥0.7 was easy; discrimination degree D definition: <0 was poor, 0 to 0.2 was medium, ≥0.2 was good), and by comparing the virtual gastroscopy assessment scores of different experience, academic, and professional title groups. The score analysis of the student group was expressed using the mastering rate (the average score divided by the total score). The data of the normal distribution was represented by (mean±SD) (range), and the data of the nonnormal distribution was represented by M (P25, P75). Kruskal-Wallis non-parametric test was used for comparison between groups. Results: The total score of 90 students was (82.9±10.9) (55.8 to 99.0), and the mastering rate was 82.9%. The overall difficulty of the first and the second parts were 0.78 and 0.76, respectively, and the difficulty level was easy. The overall discrimination of the first and the second parts were 0.70 and 0.67, respectively, and the discrimination was good. The stratified analysis was performed according to the professional title. The median score was 83.2 (75.5, 89.0) in the residents, 82.5 (71.7, 93.6) in the attending physicians, and 93.5 (88.5, 99.0) in the deputy chief physicians, and the difference was statistically significant (H=6.213, P=0.045). According to the stratification analysis of academic qualifications, the median score was 81.7 (73.0, 87.5) in participants with undergraduate degree, 91.0 (79.0, 95.0) in those with master degree and 88.0 (81.7, 93.5) in those with doctor degree, whose difference was statistically significant (H=9.233, P=0.010). The stratified analysis of the second part showed that the median scores of the "operational process" part of the low, medium and high basis group were 33.0 (30.5, 36.5), 34.0 (32.0, 36.0) and 37.0 (35.0, 37.5), respectively, whose difference was statistically significant (H=7.603, P=0.022). The median scores of the "operational process" section of the resident, attending physician, and deputy chief physician were 33.0 (30.5, 35.0) points, 36.0 (34.0, 37.5) points and 37.0 (37.0, 37.5) points, respectively, whose difference was statistically significant (H=18.563, P=0.001). Conclusions: The virtual gastroscopy simulator can reflect the true level of gastroscopy exam skills of the students. The examination questions are moderately difficult, and there is a good discrimination of the exam.

An innovative medical consultation model in mainland China

PURPOSE

The purposes of this paper are two-fold: first, to introduce a new concept of primary care consultation system at a mainland Chinese hospital in response to healthcare reform; and second, to explore the factors associated with change resistance and acceptance from both patients' and medical staff's perspectives.

DESIGN/METHODOLOGY/APPROACH

A survey design study, with two questionnaires developed and distributed to patients and medical staff. Convenience and stratified random sampling methods were applied to patient and medical staff samples.

FINDINGS

A 5-dimension, 21-item patient questionnaire and a 4-dimension, 16-item staff questionnaire were identified and confirmed, with 1020 patients (91.07 percent) and 202 staff (90.18 percent) as effective survey participants. The results revealed that patient resistance mainly stems from a lack of personal experiences with visiting general practice (GP) and being educated or having lived overseas; while staff resistance came from occupation, education, GP training certificate, and knowledge and experience with specialists. Living in overseas and knowledge of GP concepts, gender and education are associated with resistance of accepting the new practice model for both patients and staff.

ORIGINALITY/VALUE

There are few Chinese studies on process reengineering in the medical sector; this is the first study to adopt this medical consultation model and change in patients' consultation culture in Mainland China. Applying organizational change and process reengineering theories to medical and healthcare services not only extends and expands hospital management theory but also allows investigation of modern hospital management practice. The experience from this study can serve as a reference to promote this new consultation model in Chinese healthcare reform.

Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.

[Digital health literacy in Germany - requested, but not supported? Results of the empirical study TK-DiSK]

In Germany, the digital transformation is considered the key to "growth and prosperity". Beyond the debate on the required technical conditions, digital health literacy is seen as a prerequisite for a democratic and patient oriented digitization of the health system. The study "TK-DiSK: digital - self-determined - competent" aimed at examining the perspectives and status of stakeholders in the German health system and of further developing the concept of digital health literacy. Document analysis, surveys and interviews revealed a paradox about digital health literacy: though it is generally seen as a "highly relevant' issue, there is hardly any substantial discussion of it. Interviewed experts did not share a common understanding of its content, disagreed on ways of promoting it, and disputed the agencies to be held responsible for ensuring digital health literacy. Focus groups with patients clearly showed that the facilitation of patient autonomy is a central aspect for their assessment of digital health technologies. This article develops a timely definition of digital health literacy and argues for a comprehensive and targeted promotion addressing individuals as well as organizations.

Health Informatics Scientists' Perception About Big Data Technology

The aim of this paper is to present the perceptions of the Health Informatics Scientists about the Big Data Technology in Healthcare. An empirical study was conducted among 46 scientists to assess their knowledge about the Big Data Technology and their perceptions about using this technology in healthcare. Based on the study findings, 86.7% of the scientists had knowledge of Big data Technology. Furthermore, 59.1% of the scientists believed that Big Data Technology refers to structured data. Additionally, 100% of the population believed that Big Data Technology can be implemented in Healthcare. Finally, the majority does not know any cases of use of Big Data Technology in Greece while 57,8% of the them mentioned that they knew use cases of the Big Data Technology abroad.

Project description and crowdfunding success: an exploratory study

Existing research on antecedent of funding success mainly focuses on basic project properties such as funding goal, duration, and project category. In this study, we view the process by which project owners raise funds from backers as a persuasion process through project descriptions. Guided by the unimodel theory of persuasion, this study identifies three exemplary antecedents (length, readability, and tone) from the content of project descriptions and two antecedents (past experience and past expertise) from the trustworthy cue of project descriptions. We then investigate their impacts on funding success. Using data collected from Kickstarter, a popular crowdfunding platform, we find that these antecedents are significantly associated with funding success. Empirical results show that the proposed model that incorporated these antecedents can achieve an accuracy of 73 % (70 % in F-measure). The result represents an improvement of roughly 14 percentage points over the baseline model based on informed guessing and 4 percentage points improvement over the mainstream model based on basic project properties (or 44 % improvement of mainstream's performance over informed guessing). The proposed model also has superior true positive and true negative rates. We also investigate the timeliness of project data and find that old project data is gradually becoming less relevant and losing predictive power to newly created projects. Overall, this study provides evidence that antecedents identified from project descriptions have incremental predictive power and can help project owners evaluate and improve the likelihood of funding success.

An experiment with content distribution methods in touchscreen mobile devices

This paper compares the usability of three different content distribution methods (scrolling, paging and internal links) in touchscreen mobile devices as means to display web documents. Usability is operationalized in terms of effectiveness, efficiency and user satisfaction. These dimensions are then measured in an experiment (N = 23) in which users are required to find words in regular-length web documents. Results suggest that scrolling is statistically better in terms of efficiency and user satisfaction. It is also found to be more effective but results were not significant. Our findings are also compared with existing literature to propose the following guideline: "try to use vertical scrolling in web pages for mobile devices instead of paging or internal links, except when the content is too large, then paging is recommended". With an ever increasing number of touchscreen web-enabled mobile devices, this new guideline can be relevant for content developers targeting the mobile web as well as institutions trying to improve the usability of their content for mobile platforms.

Empirical evaluation of consistency and accuracy of methods to detect differentially expressed genes based on microarray data

BACKGROUND

In this study, we empirically evaluated the consistency and accuracy of five different methods to detect differentially expressed genes (DEGs) based on microarray data.

METHODS

Five different methods were compared, including the t-test, significance analysis of microarrays (SAM), the empirical Bayes t-test (eBayes), t-tests relative to a threshold (TREAT), and assumption adequacy averaging (AAA). The percentage of overlapping genes (POG) and the percentage of overlapping genes related (POGR) scores were used to rank the different methods on their ability to maintain a consistent list of DEGs both within the same data set and across two different data sets concerning the same disease. The power of each method was evaluated based on a simulation approach which mimics the multivariate distribution of the original microarray data.

RESULTS

For smaller sample sizes (6 or less per group), moderated versions of the t-test (SAM, eBayes, and TREAT) were superior in terms of both power and consistency relative to the t-test and AAA, with TREAT having the highest consistency in each scenario. Differences in consistency were most pronounced for comparisons between two different data sets for the same disease. For larger sample sizes AAA had the highest power for detecting small effect sizes, while TREAT had the lowest.

DISCUSSION

For smaller sample sizes moderated versions of the t-test can generally be recommended, while for larger sample sizes selection of a method to detect DEGs may involve a compromise between consistency and power.

The public disclosure of accreditation information in Australia: stakeholder perceptions of opportunities and challenges

Public disclosure is increasingly a requirement of accrediting agencies and governments. There are few published empirical evaluations of disclosure interventions that inform evidence-based implementation or policy. This study investigated the practices associated with the public disclosure of healthcare accreditation information, in addition to multi-stakeholder perceptions of key challenges and opportunities for improvement. We conducted a mixed methods study comprising analysis of disclosure practices by accreditation agencies, and 47 semi-structured individual or group interviews involving 258 people. Participants were diverse stakeholders associated with Australian primary, acute and residential aged care accreditation programmes. Four interrelated issues were identified. First, there was broad agreement that accreditation information should be publicly disclosed, although the three accreditation agencies differed in the information they made public. Second, two implementation issues emerged: the need to educate the community about accreditation information, and the practical question of the detail to be provided. Third, the impact, both positive and negative, of disclosing accreditation information was raised. Fourth, the lack of knowledge about the impact on consumers was discussed. Public disclosure of accreditation information is an idea that has widespread support. However, translating the idea into practice, so as to produce appropriate, meaningful information, is a challenge.

Shall we dance? - The effect of information presentations on negotiation processes and outcomes

The way information is presented influences human decision making and is consequently highly relevant to electronically supported negotiations. The present study analyzes in a controlled laboratory experiment how information presentation in three alternative formats (table, history graph and dance graph) influences the negotiators' behavior and negotiation outcomes. The results show that graphical information presentation supports integrative behavior and the use of non-compensatory strategies. Furthermore, information about the opponents' preferences increases the quality of outcomes but decreases post-negotiation satisfaction of negotiators. The implications for system designers are discussed.