Showing papers on "Empirical research published in 2017"

A manifesto for reproducible science

Abstract: Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.

Analyzing Qualitative Data

Abstract: Evaluation is increasingly recognized as an essential component of HCI research. However, evaluation itself is a changing research area. In particular, the many variations of qualitative research are emerging as important empirical methods. This half-day tutorial is designed for beginning to intermediate audiences. We will focus on the basic methods for analyzing qualitative data using a mixture of talks and hands-on activities. In particular we will consider closed and open coding as well as clustering and categorizing coded data. After completing this tutorial, attendees will have a richer understanding of the benefits and challenges of qualitative empirical research and, more specifically, how to analyze qualitative data.

Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies

Abstract: Background: Many promising technological innovations in health and social care are characterized by nonadoption or abandonment by individuals or by failed attempts to scale up locally, spread distantly, or sustain the innovation long term at the organization or system level. Objective: Our objective was to produce an evidence-based, theory-informed, and pragmatic framework to help predict and evaluate the success of a technology-supported health or social care program. Methods: The study had 2 parallel components: (1) secondary research (hermeneutic systematic review) to identify key domains, and (2) empirical case studies of technology implementation to explore, test, and refine these domains. We studied 6 technology-supported programs-video outpatient consultations, global positioning system tracking for cognitive impairment, pendant alarm services, remote biomarker monitoring for heart failure, care organizing software, and integrated case management via data sharing-using longitudinal ethnography and action research for up to 3 years across more than 20 organizations. Data were collected at micro level (individual technology users), meso level (organizational processes and systems), and macro level (national policy and wider context). Analysis and synthesis was aided by sociotechnically informed theories of individual, organizational, and system change. The draft framework was shared with colleagues who were introducing or evaluating other technology-supported health or care programs and refined in response to feedback. Results: The literature review identified 28 previous technology implementation frameworks, of which 14 had taken a dynamic systems approach (including 2 integrative reviews of previous work). Our empirical dataset consisted of over 400 hours of ethnographic observation, 165 semistructured interviews, and 200 documents. The final nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework included questions in 7 domains: the condition or illness, the technology, the value proposition, the adopter system (comprising professional staff, patient, and lay caregivers), the organization(s), the wider (institutional and societal) context, and the interaction and mutual adaptation between all these domains over time. Our empirical case studies raised a variety of challenges across all 7 domains, each classified as simple (straightforward, predictable, few components), complicated (multiple interacting components or issues), or complex (dynamic, unpredictable, not easily disaggregated into constituent components). Programs characterized by complicatedness proved difficult but not impossible to implement. Those characterized by complexity in multiple NASSS domains rarely, if ever, became mainstreamed. The framework showed promise when applied (both prospectively and retrospectively) to other programs. Conclusions: Subject to further empirical testing, NASSS could be applied across a range of technological innovations in health and social care. It has several potential uses: (1) to inform the design of a new technology; (2) to identify technological solutions that (perhaps despite policy or industry enthusiasm) have a limited chance of achieving large-scale, sustained adoption; (3) to plan the implementation, scale-up, or rollout of a technology program; and (4) to explain and learn from program failures.

How Many Focus Groups Are Enough? Building an Evidence Base for Nonprobability Sample Sizes

Abstract: Few empirical studies exist to guide researchers in determining the number of focus groups necessary for a research study. The analyses described here provide foundational evidence to help research...

Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management

Abstract: Purpose Despite the variety of supply chain management (SCM) research, little attention has been given to the use of Big Data Analytics for increased information exploitation in a supply chain. The purpose of this paper is to contribute to theory development in SCM by investigating the potential impacts of Big Data Analytics on information usage in a corporate and supply chain context. As it is imperative for companies in the supply chain to have access to up-to-date, accurate, and meaningful information, the exploratory research will provide insights into the opportunities and challenges emerging from the adoption of Big Data Analytics in SCM. Design/methodology/approach Although Big Data Analytics is gaining increasing attention in management, empirical research on the topic is still scarce. Due to the limited availability of comparable material at the intersection of Big Data Analytics and SCM, the authors apply the Delphi research technique. Findings Portraying the emerging transition trend from a digital business environment, the presented Delphi study findings contribute to extant knowledge by identifying 43 opportunities and challenges linked to the emergence of Big Data Analytics from a corporate and supply chain perspective. Research limitations/implications These constructs equip the research community with a first collection of aspects, which could provide the basis to tailor further research at the nexus of Big Data Analytics and SCM. Originality/value The research adds to the existing knowledge base as no empirical research has been presented so far specifically assessing opportunities and challenges on corporate and supply chain level with a special focus on the implications imposed through Big Data Analytics.

Issues and Challenges for Teaching Successful Online Courses in Higher Education: A Literature Review

Abstract: Online education changes all components of teaching and learning in higher education. Many empirical studies have been conducted to examine issues in delivering online courses; however, few have sy...

Psychological Safety: A Meta-Analytic Review and Extension

Abstract: Although psychological safety research has flourished in recent years, and despite the empirical support for the important role of psychological safety in the workplace, several critical questions remain. In order to address these questions, we aggregate theoretical and empirical works, and draw on 136 independent samples representing over 22,000 individuals and nearly 5,000 groups, to conduct a comprehensive meta-analysis on the antecedents and outcomes of psychological safety. We not only present the nomological network of psychological safety but also extend this research in 4 important ways. First, we compare effect sizes to determine the relative effectiveness of antecedents to psychological safety. Second, we examine the extent to which psychological safety influences both task performance and organizational citizenship behaviors over and beyond related concepts such as positive leader relations and work engagement. Third, we examine whether research design characteristics and national culture alter validities within the nomological network, thus promoting a more accurate and contextualized understanding of psychological safety. Finally, we test the homology assumption by comparing the effect sizes of the antecedents and outcomes of psychological safety across individual and group levels of analysis. We conclude with a discussion of the areas in need of future examination.

Literature review on disruption recovery in the supply chain

Abstract: Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified.

On the promotion of human flourishing

Abstract: Many empirical studies throughout the social and biomedical sciences focus only on very narrow outcomes such as income, or a single specific disease state, or a measure of positive affect. Human well-being or flourishing, however, consists in a much broader range of states and outcomes, certainly including mental and physical health, but also encompassing happiness and life satisfaction, meaning and purpose, character and virtue, and close social relationships. The empirical literature from longitudinal, experimental, and quasiexperimental studies is reviewed in attempt to identify major determinants of human flourishing, broadly conceived. Measures of human flourishing are proposed. Discussion is given to the implications of a broader conception of human flourishing, and of the research reviewed, for policy, and for future research in the biomedical and social sciences.

The Power of Bias in Economics Research

Abstract: We investigate two critical dimensions of the credibility of empirical economics research: statistical power and bias. We survey 159 empirical economics literatures that draw upon 64,076 estimates of economic parameters reported in more than 6,700 empirical studies. Half of the research areas have nearly 90% of their results under-powered. The median statistical power is 18%, or less. A simple weighted average of those reported results that are adequately powered (power ≥ 80%) reveals that nearly 80% of the reported effects in these empirical economics literatures are exaggerated; typically, by a factor of two and with one-third inflated by a factor of four or more.

E-learning success determinants: Brazilian empirical study

Abstract: E-learning is a web-based learning ecosystem for the dissemination of information, communication, and knowledge for education and training. Understanding the impact of e-learning on society, as well as its benefits, is important to link e-learning systems to their success drivers. The aim of this study is to find the determinants of user perceived satisfaction, use, and individual impact of e-learning. This study proposes a theoretical model integrating theories of information systems' satisfaction and success in the e-learning systems. The model was empirically validated in higher education institutions and university centers in Brazil through a quantitative method of structural equation modeling. Collaboration quality, information quality, and user perceived satisfaction explain e-learning use. The drivers of user perceived satisfaction are information quality, system quality, instructor attitude toward e-learning, diversity in assessment, and learner perceived interaction with others. System quality, use, and user perceived satisfaction explain individual impact.

Gender and attitudes toward technology use: A meta-analysis

Abstract: Gender difference in the attitude toward technology use has long been a concern in education. The last meta-analysis on this issue covered the empirical studies up to about 20 years ago. Since then, technology use has increased exponentially, and many more empirical studies have examined this issue, but showed inconsistent findings. As a result, there is a lack of clear understanding about if such gender difference still persists. The purpose of this research is to re-examine this issue by meta-analyzing the empirical research studies on this issue in the last two decades, and to examine the potential moderators that may have contributed to the heterogeneity of the research findings. A total of 50 articles from 1997 to 2014 were identified and used in this meta-analysis. The findings indicated that males still hold more favorable attitudes toward technology use than females, but such different would be characterized as small effect sizes. The comparison between this study and the last meta-analysis of about two decades ago suggested that there was only minimal reduction in the gender attitudinal gap in general. But when the general attitude was broken down to different dimensions of attitude, the present study showed a reduction of gender difference in the dimension of Affect and Self-efficacy , but not in the dimension of Belief . The limitations of the study were noted, and the implications and future research directions were discussed.

Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review

Abstract: Purpose The purpose of this paper is to analyse the concept of supply chain resilience (SCRES) within a concept mapping framework to seek conceptual clarity, with an emphasis on SCRES definitions, essential elements and managerial practices. Design/methodology/approach A systematic literature review was conducted of 103 peer-reviewed journal articles from the year 2000 to 2015, with the aim of answering a focus review question. Findings Through analysis and synthesis of the literature, the study revealed three major constructs used to define SCRES: phases of resilience, resilience strategies and the capabilities needed to be resilient. Emerging from the capabilities construct are five core SCRES capabilities: the ability to anticipate, to adapt, to respond, to recover and to learn. Also, given the need to consolidate the various constructs of SCRES, the study identified 13 essential elements and 84 managerial practices that support firms to achieve the five capabilities, which are then linked to SCRES strategies and phases to establish the connections that provide an integrated view of the concept. Research limitations/implications The explorative nature of this study and the role of the concept mapping framework, which does not empirically test the relationships in the model, are considered as limitations, to be addressed by the authors in future research. Originality/value The originality of this paper lies in the classification of different features of SCRES through a comprehensive concept mapping framework that establishes relationships and interactions between them. This study, therefore, lays a foundation for testing these connections in future empirical studies. The paper brings together fragmented literature from multiple studies to create a solid body of knowledge that addresses the need for conceptual clarity in SCRES literature.

Knowledge driven preferences in informal inbound open innovation modes. An explorative view on small to medium enterprises

Abstract: Purpose This paper aims to investigate three key factors (i.e. cognitive dimensions, the knowledge-driven approach and absorptive capacity) that are likely to determine the preference for informal inbound open innovation (OI) modes, through the lens of the OI model and knowledge-based view (KBV). The innovation literature has differentiated these collaborations into informal inbound OI entry modes and formal inbound OI modes, offering an advocative and conceptual view. However, empirical studies on these collaborations are still limited. Design/methodology/approach Building on the above-mentioned theoretical framework, the empirical research was performed in two stages. First, data were collected via a closed-ended questionnaire distributed to all the participants from the sample by e-mail. Second, to assess the hypotheses, structural equation modelling (SEM) via IBM® SPSS® Amos 20 was applied. Findings The empirical research was conducted on 175 small to medium enterprises in the United Kingdom, suggesting that the knowledge-driven approach is the strongest determinant, leading to a preference for informal inbound OI modes. The findings were obtained using SEM and are discussed in line with the theoretical framework. Research limitations/implications Owing to the chosen context and sector of the empirical analysis, the research results may lack generalisability. Hence, new studies are proposed. Practical implications The paper includes implications for the development of informal inbound OI led by knowledge-driven approach. Originality/value This paper offers an empirical research to investigate knowledge-driven preferences in informal inbound OI modes.

Thinking together: What makes Communities of Practice work?

Abstract: In this article, we develop the founding elements of the concept of Communities of Practice by elaborating on the learning processes happening at the heart of such communities. In particular, we provide a consistent perspective on the notions of knowledge, knowing and knowledge sharing that is compatible with the essence of this concept – that learning entails an investment of identity and a social formation of a person. We do so by drawing richly from the work of Michael Polanyi and his conception of personal knowledge, and thereby we clarify the scope of Communities of Practice and offer a number of new insights into how to make such social structures perform well in professional settings. The conceptual discussion is substantiated by findings of a qualitative empirical study in the UK National Health Service. As a result, the process of ‘thinking together’ is conceptualized as a key part of meaningful Communities of Practice where people mutually guide each other through their understandings of the same problems in their area of mutual interest, and this way indirectly share tacit knowledge. The collaborative learning process of ‘thinking together’, we argue, is what essentially brings Communities of Practice to life and not the other way round.

Presidential Address: The Scientific Outlook in Financial Economics

Abstract: Given the competition for top journal space, there is an incentive to produce “significant” results. With the combination of unreported tests, lack of adjustment for multiple tests, and direct and indirect p-hacking, many of the results being published will fail to hold up in the future. In addition, there are basic issues with the interpretation of statistical significance. Increasing thresholds may be necessary, but still may not be sufficient: if the effect being studied is rare, even t > 3 will produce a large number of false positives. Here I explore the meaning and limitations of a p-value. I offer a simple alternative (the minimum Bayes factor). I present guidelines for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk-taking (from the perspective of authors and editors) to advance our field. SUMMARY Empirical research in financial economics relies too much on p-values, which are poorly understood in the first place. Journals want to publish papers with positive results and this incentivizes researchers to engage in data mining and “p-hacking.” The outcome will likely be an embarrassing number of false positives—effects that will not be repeated in the future. The minimum Bayes factor (which is a function of the p-value) combined with prior odds provides a simple solution that can be reported alongside the usual p-value. The Bayesianized p-value answers the question: What is the probability that the null is true? The same technique can be used to answer: What threshold of t-statistic do I need so that there is only a 5% chance that the null is true? The threshold depends on the economic plausibility of the hypothesis.