Showing papers on "Empirical research published in 2019"

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?

Abstract: The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by teams in practice and the solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address practitioners' needs.

Energy and economic growth

Abstract: Energy use and economic output are positively correlated, though energy intensity has declined over time and is usually lower in richer countries than in poorer countries Numerous factors affect the energy intensity of economies, and energy efficiency is obviously one of the most important However, the rebound effect might limit the possibilities for energy efficiency improvements to reduce energy intensity Natural science suggests that energy is crucial to economic production, and ecological economists and some economic historians argue that increasing energy supply has been a principal driver of growth On the other hand, most mainstream economic growth theories ignore the role of energy These views may diverge because energy scarcity historically imposed constraints on growth, but the increased availability of modern energy sources has reduced energy’s importance as a driver of growth Empirical research on whether energy causes growth or vice versa is inconclusive, but a meta-analysis finds that the role of energy prices is central to understanding the relationship

Physical Literacy, Physical Activity and Health: Toward an Evidence-Informed Conceptual Model.

Abstract: Physical literacy (PL) provides a powerful lens for examining movement in relation to physical activity (PA) and motor skill outcomes, environmental context, and broader social and affective learning processes. To date, limited consideration has been given to the role PL plays in promoting positive health behaviours. There is no clear conceptual framework based on existing empirical evidence that links PL to health, nor has an evidence-informed case been made for such a position. The purpose of this paper is to (1) present a conceptual model positioning PL as a health determinant, and (2) present evidence in support of PL as a health determinant, drawing on research largely from outside physical education. Viewing PL from the perspective of a multidimensional, experiential convergence process enables it to be differentiated from other models. However, parallels between our model and existing models that focus on movement competence are also drawn. Arguing from a pragmatic perspective on PL, we present evidence for positioning PL as a determinant of health from two literature sources: research on motor coordination disorders in children, and associations between motor competence, PA and health in typically developing children. Statistical modelling approaches consistent with the concept of PL are discussed. Results from these approaches-confirmatory factor analysis and cluster analysis-support the idea that measures related to motor competence, motivation and positive affect work in an integrative manner to produce differences in PA and subsequent health outcomes in children. There is increasing interest in PL, particularly in the field of public health. Presenting a model that explicitly links PL to health can lead to the generation of new research questions and the possibility of broadening impact beyond the context of physical education alone. To date, there has been little conceptual attention to what positioning PL as a determinant of health means. By providing an evidence-based model of PL as a determinant of health, we hope to further the discussion and stimulate increased empirical research in the field.

A systematic review of learning computational thinking through Scratch in K-9

Abstract: As computational thinking (CT) is being embraced by educational systems worldwide, researchers and teachers have posed important questions such as “what to teach” and “what can be learned.” These questions are universally crucial to the learning of all subjects. Nevertheless, there is no up-to-date, systematic overview of CT education for K-9 students that attempt to provide answers to these crucial questions. Thus, this systematic review presents a synthesis of 55 empirical studies, providing evidence of the development of computational thinking through programming in Scratch, one of the most popular visual block programming languages in schools. The purpose of this review is to systematically examine the CT skills that can be obtained through Scratch in K-9 based on empirical evidence. This systematic review has adopted Brennan and Resnick's (2012) framework as the basis for defining and identifying the expected CT skills in K-9. The major findings entail what computational thinking skills students in K-9 can learn through Scratch in relation to the framework mentioned above, taking the progression of learning into account. Additional CT skills that are not captured by the framework were identified including input/output, reading, interpreting and communicating code, using multimodal media, predictive thinking, and human-computer interaction. These additional CT skills are not currently presented in Brennan and Resnick's (2012) framework and can be considered as possible supplements to their framework. Furthermore, the paper discusses the difficulties regarding assessment and the progression of the identified skills, as well as problems with study designs. Finally, the paper sets out suggestions for future studies based on the current research gaps.

An Empirical Verification of the Community of Inquiry Framework

Abstract: The purpose of this paper is to report on the results of a study that examines whether the CoI dimensions of social, teaching and cognitive presence distinctively exist in e-learning environments. The rest of the paper is organized as follows. First, I will briefly review recent studies on the dimensions of this framework: social, cognitive, and teaching presence. Second, I discuss the development of the sample of MBA students in online courses over a two-year period at a Midwestern U.S. university and the items used to measure the CoI dimensions. Next, I will describe the results of an exploratory factor analysis, including an interpretation of the emerging factors. Finally, I will discuss how these findings relate to conclusions presented in Garrison’s review of recent research related to the CoI and present some possible directions for future research.

Working Memory and Attention - A Conceptual Analysis and Review.

Abstract: There is broad agreement that working memory is closely related to attention. This article delineates several theoretical options for conceptualizing this link, and evaluates their viability in light of their theoretical implications and the empirical support they received. A first divide exists between the concept of attention as a limited resource, and the concept of attention as selective information processing. Theories conceptualizing attention as a resource assume that this resource is responsible for the limited capacity of working memory. Three versions of this idea have been proposed: Attention as a resource for storage and processing, a shared resource for perceptual attention and memory maintenance, and a resource for the control of attention. The first of these three is empirically well supported, but the other two are not. By contrast, when attention is understood as a selection mechanism, it is usually not invoked to explain the capacity limit of working memory - rather, researchers ask how different forms of attention interact with working memory, in two areas. The first pertains to attentional selection of the contents of working memory, controlled by mechanisms of filtering out irrelevant stimuli, and removing no-longer relevant representations from working memory. Within working memory contents, a single item is often selected into the focus of attention for processing. The second area pertains to the role of working memory in cognitive control. Working memory contributes to controlling perceptual attention - by holding templates for targets of perceptual selection - and controlling action - by holding task sets to implement our current goals.

Communicating uncertainty about facts, numbers and science.

Abstract: Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty-facts, numbers and science-and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field.

Supply Chain 4.0: concepts, maturity and research agenda

Abstract: © 2019, Emerald Publishing Limited. Purpose: Industry 4.0 is one of the most emergent research topics attracting significant interest by researchers as well as practitioners. Many articles have been published with regards Industry 4.0; however, there is no research that clearly conceptualizes Industry 4.0 in the context of supply chain. This paper aims to propose the term “Supply Chain 4.0” together with a novel conceptual framework that captures the essence of Industry 4.0 within the supply chain context. As Industry 4.0 is inherently a revolution, and as revolutions are evolutionary, this research also aims to capture the evolution of Supply Chain 4.0 from maturity levels perspective to facilitate the formulation and development of Supply Chain 4.0 strategy. Design/methodology/approach: Following a deductive research approach and a qualitative strategy, a systematic literature review (SLR) was adopted as the research method seeking to understand the relationships among supply chain, Industry 4.0 and maturity levels research. The three phases of the SLR process utilized are: planning, conducting and reporting. A concept-oriented technique was applied to the outputs of the SLR to obtain the key constructs that would facilitate the development of the conceptual Supply Chain 4.0 framework. Findings: The SLR showed that there is limited research linking Industry 4.0 to supply chain. Nevertheless, it was possible to extract a set of thematic categories from the analysis of the articles which are referred to as constructs as they form the core of the conceptual Supply Chain 4.0 framework. These constructs are managerial and capability supporters, technology levers, processes performance requirements and strategic outcomes. Each of these constructs consists of a number of elements which are referred to as “dimensions” in this research and a total of 21 dimensions were identified during the SLR. The SLR also demonstrated that maturity propositions for Industry 4.0 are still embrionary and entirely missing in the context of supply chain. Hence, this research develops and proposes a maturity levels framework that is underpinned by the core constructs of Supply Chain 4.0 and the corresponding dimensions. As these proposed frameworks are conceptual, this research also identifies and proposes several research directions to help fortify the Supply Chain 4.0 concept. Research limitations/implications: This research argues that the frameworks are robust because the constructs and dimensions are grounded in the literature, thus demonstrating both theoretical and practical relevance and value. As Supply Chain 4.0 research is still in infancy, there is a range of open research questions suggested based on the frameworks that could serve as guides for researchers to further develop the Supply Chain 4.0 concept. Also, practitioners can use this framework to develop better understanding of Supply Chain 4.0 and be able to evaluate the maturity of their organizations. As the proposed frameworks are conceptual, they require further empirical research to validate them and obtain new insights. Originality/value: The SLR demonstrated a clear gap in literature with regards to Industry 4.0 in the context of supply chain, and also in the context of Industry 4.0 maturity levels for supply chain. This research is unique as it formulates and introduces novel frameworks that close these gaps in literature. The value of this research lies in the fact that it makes significant contribution in terms of understanding of Supply Chain 4.0 with a clear set of constructs and dimensions that form Supply Chain 4.0, which provides the foundation for further work in this area.

Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars

Abstract: Autonomous Vehicles (AVs) have the potential to make motorized transport safer and more sustainable, by integrating clean technologies and supporting flexible shared-mobility services. Leveraging this new form of transport to transform mobility in cities will depend fundamentally on public acceptance of AVs, and the ways in which individuals choose to use them, to meet their daily travel needs. Empirical studies exploring public attitudes towards automated driving technologies and interest in AVs have emerged in the last few years. However, within this strand of research there is a paucity of theory-driven and behaviourally consistent methodologies to unpack the determinants of user adoption decisions with respect to AVs. In this paper, we seek to fill this gap, by advancing and testing four conceptual frameworks which could be deployed to capture the range of possible behavioural influences on individuals’ AV adoption decisions. The frameworks integrate socio-demographic variables and relevant latent behavioural factors, including perceived benefits and perceived ease of use of AVs, public fears and anxieties regarding AVs, subjective norm, perceived behavioural control, and attitudinal factors covering the environment, technology, collaborative consumption, public transit and car ownership. We demonstrate the utility and validity of the frameworks, by translating the latent variables into indicator items in a structured questionnaire, and administering it online to a random sample of adult individuals (n = 507). Using the survey data in confirmatory factor analyses, we specify and demonstrate scale reliability of indicator items, and convergent and discriminant validity of relationships among latent variables. Ultimately, we advance four measurement models. These theory-grounded measurement models are intended for application in research aimed at understanding and predicting (a) AV interest and adoption intentions, and (b) user adoption decisions regarding three different AV modes: ownership, sharing and public transport.

Does Strategic Planning Improve Organizational Performance? A Meta-Analysis

Abstract: Strategic planning is a widely adopted management approach in contemporary organizations. Underlying its popularity is the assumption that it is a successful practice in public and private organizations that has positive consequences for organizational performance. Nonetheless, strategic planning has been criticized for being overly rational and for inhibiting strategic thinking. This article undertakes a meta-analysis of 87 correlations from 31 empirical studies and asks, Does strategic planning improve organizational performance? A random-effects meta-analysis reveals that strategic planning has a positive, moderate, and significant impact on organizational performance. Meta-regression analysis suggests that the positive impact of strategic planning on organizational performance is strongest when performance is measured as effectiveness and when strategic planning is measured as formal strategic planning. This impact holds across sectors (private and public) and countries (U.S. and non-U.S. contexts). Implications for public administration theory, research, and practice are discussed in the conclusion.

The Psychology of Morality: A Review and Analysis of Empirical Studies Published From 1940 Through 2017:

Abstract: We review empirical research on (social) psychology of morality to identify which issues and relations are well documented by existing data and which areas of inquiry are in need of further empirical evidence. An electronic literature search yielded a total of 1,278 relevant research articles published from 1940 through 2017. These were subjected to expert content analysis and standardized bibliometric analysis to classify research questions and relate these to (trends in) empirical approaches that characterize research on morality. We categorize the research questions addressed in this literature into five different themes and consider how empirical approaches within each of these themes have addressed psychological antecedents and implications of moral behavior. We conclude that some key features of theoretical questions relating to human morality are not systematically captured in empirical research and are in need of further investigation.

A Critical Review of Research on Student Self-Assessment

Abstract: This article is a review of research on student self-assessment conducted largely between 2013 and 2018. The purpose of the review is to provide an updated overview of theory and research. The treatment of theory involves articulating a refined definition and operationalization of self-assessment. The review of 76 empirical studies offers a critical perspective on what has been investigated, including the relationship between self-assessment and achievement, consistency of self-assessment and others’ assessments, student perceptions of self-assessment, and the association between self-assessment and self-regulated learning. An argument is made for less research on consistency and summative self-assessment, and more on the cognitive and affective mechanisms of formative self-assessment.

Identifying, categorizing and mitigating threats to validity in software engineering secondary studies

Abstract: Context Secondary studies are vulnerable to threats to validity. Although, mitigating these threats is crucial for the credibility of these studies, we currently lack a systematic approach to identify, categorize and mitigate threats to validity for secondary studies. Objective In this paper, we review the corpus of secondary studies, with the aim to identify: (a) the trend of reporting threats to validity, (b) the most common threats to validity and corresponding mitigation actions, and (c) possible categories in which threats to validity can be classified. Method To achieve this goal we employ the tertiary study research method that is used for synthesizing knowledge from existing secondary studies. In particular, we collected data from more than 100 studies, published until December 2016 in top quality software engineering venues (both journals and conference). Results Our results suggest that in recent years, secondary studies are more likely to report their threats to validity. However, the presentation of such threats is rather ad hoc, e.g., the same threat may be presented with a different name, or under a different category. To alleviate this problem, we propose a classification schema for reporting threats to validity and possible mitigation actions. Both the classification of threats and the associated mitigation actions have been validated by an empirical study, i.e., Delphi rounds with experts. Conclusion Based on the proposed schema, we provide a checklist, which authors of secondary studies can use for identifying and categorizing threats to validity and corresponding mitigation actions, while readers of secondary studies can use the checklist for assessing the validity of the reported results.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures.

Abstract: Objective:This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and sugges...

Detecting Common Method Bias: Performance of the Harman's Single-Factor Test

Abstract: Lack of careful consideration of common method effects in empirical research can lead to several negative consequences for the interpretation of research outcomes, such as biased estimates of the validity and reliability of the measures employed as well as bias in the estimates of the relationships between constructs of interest, which in turn can affect hypothesis testing. Taken together, these make it very difficult to make any interpretations of the results when those are affected by substantive common method effects. In the literature, there are several preventive, detective, and corrective techniques that can be employed to assuage concerns about the possibility of common method effects underlying observed results. Among these, the most popular has been Harman's Single-Factor Test. Though researchers have argued against its effectiveness in the past, the technique has continued to be very popular in the discipline. Moreover, there is a dearth of empirical evidence on the actual effectiveness of the technique, which we sought to remedy with this research. Our results, based on extensive Monte Carlo simulations, indicate that the approach shows limited effectiveness in detecting the presence of common method effects and may thus be providing a false sense of security to researchers. We therefore argue against the use of the technique moving forward and provide evidence to support our position.

Towards a better understanding of the L2 Learning Experience, the Cinderella of the L2 Motivational Self System

Abstract: The theoretical emphasis within the L2 Motivational Self System has typically been on the two future self-guides representing possible (ideal and ought-to) selves, leaving the third main dimension of the construct, the L2 Learning Experience, somewhat undertheorized. Yet, this third component is not secondary in importance, as evidenced by empirical studies that consistently indicate that the L2 Learning Experience is not only a strong predictor of various criterion measures but is often the most powerful predictor of motivated behavior. This paper begins with an analysis of possible reasons for this neglect and then draws on the notion of student engagement in educational psychology to offer a theoretical framework for the concept. It is proposed that the L2 Learning Experience may be defined as the perceived quality of the learners’ engagement with various aspects of the language learning process.

Exploring blockchain implementation in the supply chain

Abstract: There is great interest in blockchain in the supply chain yet there is little empirical research to support the consideration of the technology. Ferdows (2018) calls for research aimed at learning from pioneers in the field and Gartner points out that the interest in blockchain holds similarities to the interest surrounding RFID 15 years ago. As a result, there may be opportunities to leverage insights from RFID research to inform the consideration of blockchain. The purpose of this paper is to explore how the Reyes et al. (2016) framework for the implementation of RFID may inform the consideration of blockchain in the supply chain.,A two-stage approach is used to explore RFID implementation considerations from the Reyes et al. (2016) RFID implementation framework, using an initial exploration of managers interested in blockchain using a focus group and a survey and to more in depth explore three case companies pioneering blockchain.,Several RFID implementation considerations can inform the consideration of blockchain but there are also differences in considering blockchain. A framework is developed that details considerations found to be relevant by implementation stage.,This paper adds to the limited amount of empirical research on blockchain in the supply chain and advances research beyond the consideration of use cases into the exploration of actual implementation of blockchain in the supply chain. The decision framework developed both leverages and nuances findings from RFID research and can inform managerial decision making. It also adds to research a multi-stage approach to implementation and uncovers rich opportunity to further learn from pioneers.

A methodological systematic review of meta-ethnography conduct to articulate the complex analytical phases

Abstract: Decision making in health and social care requires robust syntheses of both quantitative and qualitative evidence. Meta-ethnography is a seven-phase methodology for synthesising qualitative studies. Developed in 1988 by sociologists in education Noblit and Hare, meta-ethnography has evolved since its inception; it is now widely used in healthcare research and is gaining popularity in education research. The aim of this article is to provide up-to-date, in-depth guidance on conducting the complex analytic synthesis phases 4 to 6 of meta-ethnography through analysis of the latest methodological evidence. We report findings from a methodological systematic review conducted from 2015 to 2016. Fourteen databases and five other online resources were searched. Expansive searches were also conducted resulting in inclusion of 57 publications on meta-ethnography conduct and reporting from a range of academic disciplines published from 1988 to 2016. Current guidance on applying meta-ethnography originates from a small group of researchers using the methodology in a health context. We identified that researchers have operationalised the analysis and synthesis methods of meta-ethnography – determining how studies are related (phase 4), translating studies into one another (phase 5), synthesising translations (phase 6) and line of argument synthesis - to suit their own syntheses resulting in variation in methods and their application. Empirical research is required to compare the impact of different methods of translation and synthesis. Some methods are potentially better at preserving links with the context and meaning of primary studies, a key principle of meta-ethnography. A meta-ethnography can and should include reciprocal and refutational translation and line of argument synthesis, rather than only one of these, to maximise the impact of its outputs. The current work is the first to articulate and differentiate the methodological variations and their application for different purposes and represents a significant advance in the understanding of the methodological application of meta-ethnography.

Building tourism organizational resilience to crises and disasters: A dynamic capabilities view

Abstract: The importance of resilience for tourism organizations facing crises and disasters is indisputable. Yet little is known about how these organizations become resilient. This paper proposes that dynamic capabilities provide a mechanism that enables tourism organizations to respond to disruptive environmental changes through a process of routine transformation, resource allocation, and utilization. The resulting theoretical framework takes a processual view to show how an organization's existing operational routines transform into new ones that are resilient to disruptive events, enabled by dynamic capabilities and slack resources. The paper outlines six research propositions and suggests methods for future empirical research.

Scientific Paper Recommendation: A Survey

Abstract: Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

Women’s entrepreneurship policy research: a 30-year review of the evidence

Abstract: This paper focuses on women’s entrepreneurship policy as a core component of the entrepreneurial ecosystem. We use a systematic literature review (SLR) approach to critically explore the policy implications of women’s entrepreneurship research according to gender perspective: feminist empiricism, feminist standpoint theory, and post-structuralist feminist theory. Our research question asks whether there is a link between the nature of policy implications and the different theoretical perspectives adopted, and whether scholars’ policy implications have changed as the field of women’s entrepreneurship research has developed. We concentrate on empirical studies published in the “Big Five” primary entrepreneurship research journals (SBE, ETP, JBV, JSBM, and ERD) over a period of more than 30 years (1983–2015). We find that policy implications from women’s entrepreneurship research are mostly vague, conservative, and center on identifying skills gaps in women entrepreneurs that need to be “fixed,” thus isolating and individualizing any perceived problem. Despite an increase in the number of articles offering policy implications, we find little variance in the types of policy implications being offered by scholars, regardless of the particular theoretical perspective adopted, and no notable change over our 30-year review period. Recommendations to improve the entrepreneurial ecosystem for women from a policy perspective are offered, and avenues for future research are identified.

Explaining models: an empirical study of how explanations impact fairness judgment

Abstract: Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact people's fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance people's confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.