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Even though global markets are transferring from offline to online, offline sales are rapidly increasing in two top global furniture firms in Korea and Japan each—IKEA and Nitori. This study aims to answer the following research question: 'What are the differences and similarities between IKEA and Nitori in the open innovation dynamics of furniture design and function?' Diverse naturalistic qualitative research methods such as participatory observation, or focus group interviews were used. IKEA is mainly pursuing engineering open innovation strategies, including advanced furniture design with engineering knowledge, fusion with new IT and other technologies for new and innovative furniture function and design, and pursuit of creative design idea frontiers. Nitori is mainly pursuing a strategy towards customer open innovation, which is focused on domestic and regional requirements.

AbstractPrior studies suggest that external corporate social responsibility (CSR) practices could lead to positive responses from employees, but little attention has been paid to its potential negative impacts on employees' responses. To have a comprehensive understanding of the relationship in question, we propose that there is a S‐curve relationship between external CSR and labor productivity. Found on samples of Chinese listed firms during the period from 2011 to 2017, we have drawn some conclusions as follows. First, there is a S‐curve relationship between external CSR practices and labor productivity, with labor productivity declining at low and high levels of external CSR but increasing at moderate level of external CSR. Second, internal CSR will moderate the relationship between external CSR and labor productivity that firms affiliated with high internal CSR are more likely to perform better in terms of labor productivity than firms with low internal CSR. However, the proposed negative moderating role of government subsidy on the relationship in question is not supported. It is the match between government subsidy and external CSR that may predict labor productivity.

This article looks into dynamics of open social enterprises. We used several research methods—literature review, content analysis and case studies through intensive interviews via semi-structured questionnaire. First, we developed a research framework, expressed as the social open innovation dynamics model through literature reviews on social economy, sharing economy, collaborative innovation, open innovation and social enterprise. Second, we applied the model to ten Korean social enterprises and determined the success factors of social open innovation, as well as the concrete dynamics behind it. Main finding of this research are as follows. The success of social enterprises depends on the extent to which they strive to move towards open innovation. Adopting open innovation strategies seem to be a fruitful pathway for social enterprises to progress and grow in their operations.

The study aims to explore how to measure firms' open innovation from financial statements. So, our research question is as follows: How can we determine firms' open innovation signals directly or indirectly from financial statements? This study used data from the US financial statements and patent registration database from 2016 to 2018 to answer this research question. Three manifest signals of open innovation in financial data were found. First, subsidiary or related firm investment in financial data may have a negative relationship with open innovation because open innovation (i.e., the co-application of patents) could decrease subsidiary or related firm investment. But there are differences between the top and bottom twenty firms. Second, internal R&D investment (I R&D) in financial data may have a positive relationship with open innovation because I R&D could trigger inward open innovation. If I R&D combines with an open innovation strategy, it increases the size of subsidiary or related firm investment as a kind of inward open innovation. Third, free cash flow (FCF) in financial data may have a positive relationship with open innovation because high FCF could support outward open innovation.

The digital transformation can serve as a window of opportunity for those late-comers who are equipped with the capability to create complementary assets for grabbing new opportunities of technological leapfrogging as a way of catch-up while penalising the forerunner. In this context, we want to answer the following research question. Is there any difference in open innovation effect on the firm according to the changing of belonging sectorial innovation system from catch-up to post-catch-up? We statistically analysed the moderating effects of open innovation between catching-up, and the performance with the patents which were registered in United States Patent and Trademark Office, which were applied from China in telecommunication, from South Korea in Semiconductors, and from Japan in biotechnology on 1995–1999, and 2005–2009. We found three results from this study: first, from this research, as the signal of post catching-up, open innovation effects on the performance of firm, and the moderating effects of open innovation between catching-up, and the performance of the firm were found; second, the appearance of new dominant design after post catching-up was explained through the powerful open innovation and third, open innovation could be a useful new strategy for firms in the post catching-up to use.

Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data. ; anadian Institutes of Health Research (CIHR) KRS-134297 Fonds de recherche du Quebec -Sante (FRQS) Canadian Institutes of Health Research (CIHR) Canadian Institutes of Health Research (CIHR) FRQS Masters Training Awards Vanier Canada Graduate Scholarship FRQS Postdoctoral Training Fellowship Research Institute of the McGill University Health Centre G.R. Caverhill Fellowship from the Faculty of Medicine, McGill University Cumming School of Medicine, University of Calgary Alberta Health Services through the Calgary Health Trust Hotchkiss Brain Institute Senior Health Scholar award from Alberta Innovates Health Solutions Health Research Council of New Zealand Lundbeck International Tehran University of Medical Sciences M-288 Department of Education, National Institute on Disability and Rehabilitation Research, Spinal Cord Injury Model Systems: University of Washington H133N060033 Baylor College of Medicine H133N060003 University of Michigan System H133N060032 National Health and Medical Research Council of Australia 1002160 Safe Work Australia Australian Research Council FT130101444 European Foundation for Study of Diabetes Chinese Diabetes Society Lilly Foundation Asia Diabetes Foundation Liao Wun Yuk Diabetes Memorial Fund United States National Institute of Mental Health (NIMH) grant 5F30MH096664 United States Department of Health & Human Services National Institutes of Health (NIH) - USA United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH Fogarty International Center (FIC) United States Department of Health & Human Services National Institutes of Health (NIH) - USA National Cancer Center United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Heart Lung & Blood Institute (NHLBI) NIH Office of Research for Women's Health through the Fogarty Global Health Fellows Program Consortium 1R25TW00934001 American Recovery and Reinvestment Act United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) R24MH071604 / R34 MH072925/ K02 MH65919 / P30 DK50456 / R24 MH56858 / RO1 MH073687 /RO1-MH069666 / R34MH084673 /R24 MH071604 United States Department of Health & Human Services Centers for Disease Control & Prevention - USA R49 CE002093 St Anne's Community Services, Leeds, UK US National Center for Medical Rehabilitation Research RO1 HD39415 Federal Ministry of Education & Research (BMBF) 01GY1150 United States Department of Health & Human Services National Institutes of Health (NIH) - USA T37 MD001449 / T32 GM07356 Ohio Board of Regents Research and Development Administration Office, University of Macau MYRG2015-00109-FSS Federal Ministry of Education & Research (BMBF) 01 GD 9802/4 ; 01 GD 0101 Federation of German Pension Insurance Institute Federal Ministry of Education & Research (BMBF) Perpetual Trustees Flora and Frank Leith Charitable Trust Jack Brockhoff Foundation Grosvenor Settlement Sunshine Foundation Danks Trust Canadian Institutes of Health Research (CIHR) FRN 83518 Scleroderma Society of Canada Scleroderma Society of Ontario Scleroderma Society of Saskatchewan Sclerodermie Quebec Cure Scleroderma Foundation Inova Diagnostics Inc Euroimmun FRQS Canadian Arthritis Network Lady Davis Institute of Medical Research of the Jewish General Hospital, Montreal, QC FRQS Senior Investigator Award National Strategic Reference Framework European Union (EU) Greek Ministry of Education, Lifelong Learning and Religious Affairs (ARISTEIA-ABREVIATE) 1259 Ministry of Health, Labour and Welfare, Japan UK National Institute for Health Research under its Programme Grants for Applied Research Programme RP-PG-0606-1142 Canada Research Chair in Neurological Health Services Research AIHS Population Health Investigator Award National Health and Medical Research Council of Australia 1088313 Netherlands Organization for Health Research and Development 945-03-047 National Health Research Institutes - Taiwan NHRI-EX97-9706PI Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand 49086 Reitoria de Pesquisa da Universidade de Sao Paulo 09.1.01689.17.7 Banco Santander 10.1.01232.17.9 Pfizer medical faculty of the University of Heidelberg, Germany 121/2000 Research University Grant Scheme from Universiti Putra Malaysia, Malaysia Postgraduate Research Student Support Accounts of the University of Auckland, New Zealand National Program for Centers of Excellence (PRONEX/FAPERGS/CNPq, Brazil) Pfizer US Pharmaceutical Inc. 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