How home country weaknesses can constrain EMNE growth: the example of India

This paper discusses the opportunities and limitations that the location-specific (L) assets of the home country represent for MNEs, particularly at the early stages of internationalization. The systemic weaknesses of the home country can constrain the long-term competitiveness of its firms, and ultimately, the competitiveness of its MNEs. It is the contention of this paper that many of the emerging countries have a constrained set of L assets from which their firms are able to develop ownership-specific assets. Are their economies developing improved L assets that will promote a new generation of EMNEs? We examine data for the case of India, an economy regarded as having considerable potential to expand to knowledge-intensive sectors. At the macro level, India’s performance is not different from countries of similar economic structure, and its current pockets of excellence are a reflection of its L assets. Our analysis suggests that the failure to foster and upgrade the L assets of emerging economies is likely to stunt the growth of their domestic firms, and ultimately any new MNE activity in the long-term

The Medical Science Research and Development Supported by the Korea Science and Engineering Foundation

This study examined ways of promoting research in the medical sciences by evaluating trends in research funding, and the present status of research funding by the Korea Science and Engineering Foundation (KOSEF). This study analyzed statistics from KOSEF from 1978 to 2003 to examine support for research. In medical science field, group-based programs receive more funding than do individual-based programs. The proportion of research funds allocated to the medical sciences has increased markedly each year. Researchers in the medical sciences have submitted more articles to Science Citation Index (SCI) journals than to non-SCI journals, relative to other fields. Researchers supported by the Mission-Oriented Basic Grants program have published the majority of these papers, followed by those supported by the Programs for Leading Scientists, Regional Scientists, Leading Women Scientists, Young Scientists, and Promising Women Scientists, in that order. Funding by KOSEF reflects many decades of government support for research and development, the development and maintenance of necessary infrastructure, and the education and training of medical scientists

Threats and Supports to Female Students’ Math Beliefs and Achievement

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149563/1/jora12384_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149563/2/jora12384.pd

Relationships, variety & synergy:the vital ingredients for scholarship in engineering education? A case study

This paper begins with the argument that within modern-day society, engineering has shifted from being the scientific and technical mainstay of industrial, and more recently digital change to become the most vital driver of future advancement. In order to meet the inevitable challenges resulting from this role, the nature of engineering education is constantly evolving and as such engineering education has to change. The paper argues that what is needed is a fresh approach to engineering education – one that is sufficiently flexible so as to capture the fast-changing needs of engineering education as a discipline, whilst being pedagogically suitable for use with a range of engineering epistemologies. It provides an overview of a case study in which a new approach to engineering education has been developed and evaluated. The approach, which is based on the concept of scholarship, is described in detail. This is followed by a discussion of how the approach has been put into practice and evaluated. The paper concludes by arguing that within today's market-driven university world, the need for effective learning and teaching practice, based in good scholarship, is fundamental to student success

EXPOSURE TO NANOPARTICLES AND HORMESIS

Nanoparticles are particles with lengths that range from 1 to 100 nm. They are increasingly being manufactured and used for commercial purpose because of their novel and unique physicochemical properties. Although nanotechnology-based products are gener- ally thought to be at a pre-competitive stage, an increasing number of products and mate- rials are becoming commercially available. Human exposure to nanoparticles is therefore inevitable as they become more widely used and, as a result, nanotoxicology research is now gaining attention. However, there are many uncertainties as to whether the unique properties of nanoparticles also pose occupational health risks. These uncertainties arise because of gaps in knowledge about the factors that are essential for predicting health risks such as routes of exposure, distribution, accumulation, excretion and dose-response relationship of the nanoparticles. In particular, uncertainty remains with regard to the nature of the dose-response curve at low level exposures below the toxic threshold. In fact, in the literature, some studies that investigated the biological effects of nanoparticles, observed a hormetic dose-response. However, currently available data regarding this topic are extremely limited and fragmentary. It therefore seems clear that future studies need to focus on this issue by studying the potential adverse health effects caused by low-level exposures to nanoparticles

Analysis and Synthesis of Metadata Goals for Scientific Data

The proliferation of discipline-specific metadata schemes contributes to artificial barriers that can impede interdisciplinary and transdisciplinary research. The authors considered this problem by examining the domains, objectives, and architectures of nine metadata schemes used to document scientific data in the physical, life, and social sciences. They used a mixed-methods content analysis and Greenberg’s (2005) metadata objectives, principles, domains, and architectural layout (MODAL) framework, and derived 22 metadata-related goals from textual content describing each metadata scheme. Relationships are identified between the domains (e.g., scientific discipline and type of data) and the categories of scheme objectives. For each strong correlation (\u3e0.6), a Fisher’s exact test for nonparametric data was used to determine significance (p \u3c .05). Significant relationships were found between the domains and objectives of the schemes. Schemes describing observational data are more likely to have “scheme harmonization” (compatibility and interoperability with related schemes) as an objective; schemes with the objective “abstraction” (a conceptual model exists separate from the technical implementation) also have the objective “sufficiency” (the scheme defines a minimal amount of information to meet the needs of the community); and schemes with the objective “data publication” do not have the objective “element refinement.” The analysis indicates that many metadata-driven goals expressed by communities are independent of scientific discipline or the type of data, although they are constrained by historical community practices and workflows as well as the technological environment at the time of scheme creation. The analysis reveals 11 fundamental metadata goals for metadata documenting scientific data in support of sharing research data across disciplines and domains. The authors report these results and highlight the need for more metadata-related research, particularly in the context of recent funding agency policy changes

Do new Ethical Issues Arise at Each Stage of Nanotechnological Development?

The literature concerning ethical issues associated with nanotechnologies has become prolific. However, it has been claimed that ethical problems are only at stake with rather sophisticated nanotechnologies such as active nanostructures, integrated nanosystems and heterogeneous molecular nanosystems, whereas more basic nanotechnologies such as passive nanostructures mainly pose technical difficulties. In this paper I argue that fundamental ethical issues are already at stake with this more basic kind of nanotechnologies and that ethics impacts every kind of nanotechnologies, already from the simplest kind of engineered nanoproducts. These ethical issues are mainly associated with the social desirability of nanotechnologies, with the difficulties to define nanotechnologies properly, with the important uncertainties surrounding nanotechnologies, with the threat of ‘nano-divide’, and with nanotechnology as ‘dual-use technology’

Scientific and Legal Perspectives on Science Generated for Regulatory Activities

This article originated from a conference that asked “Should scientific work conducted for purposes of advocacy before regulatory agencies or courts be judged by the same standards as science conducted for other purposes?” In the article, which focuses on the regulatory advocacy context, we argue that it can be and should be. First, we describe a set of standards and practices currently being used to judge the quality of scientific research and testing and explain how these standards and practices assist in judging the quality of research and testing regardless of why the work was conducted. These standards and practices include the federal Information Quality Act, federal Good Laboratory Practice standards, peer review, disclosure of funding sources, and transparency in research policies. The more that scientific information meets these standards and practices, the more likely it is to be of high quality, reliable, reproducible, and credible. We then explore legal issues that may be implicated in any effort to create special rules for science conducted specifically for a regulatory proceeding. Federal administrative law does not provide a basis for treating information in a given proceeding differently depending on its source or the reason for which it was generated. To the contrary, this law positively assures that interested persons have the right to offer their technical expertise toward the solution of regulatory problems. Any proposal to subject scientific information generated for the purpose of a regulatory proceeding to more demanding standards than other scientific information considered in that proceeding would clash with this law and would face significant administrative complexities. In a closely related example, the U.S. Environmental Protection Agency considered but abandoned a program to implement standards aimed at “external” information

Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information

Women are underrepresented in science, technology, engineering, and mathematics fields, particularly at higher levels of organizations. This article investigates the impact of this underrepresentation on the processes of interpersonal collaboration in nanotechnology. Analyses are conducted to assess: (1) the comparative tie strength of women's and men's collaborations, (2) whether women and men gain equal access to scientific information through collaborators, (3) which tie characteristics are associated with access to information for women and men, and (4) whether women and men acquire equivalent amounts of information by strengthening ties. Our results show that the overall tie strength is less for women's collaborations and that women acquire less strategic information through collaborators. Women and men rely on different tie characteristics in accessing information, but are equally effective in acquiring additional information resources by strengthening ties. This article demonstrates that the underrepresentation of women in science, technology, engineering, and mathematics has an impact on the interpersonal processes of scientific collaboration, to the disadvantage of women scientists.Villanueva-Felez, Á.; Woolley, RD.; Cañibano Sánchez, C. (2015). Nanotechnology researchers' collaboration relationships: A gender analysis of access to scientific information. Social Studies of Science. 45(1):100-129. doi:10.1177/0306312714552347S100129451ACKER, J. (1990). HIERARCHIES, JOBS, BODIES: Gender & Society, 4(2), 139-158. doi:10.1177/089124390004002002Aitken, C., Power, R., & Dwyer, R. (2008). A very low response rate in an on-line survey of medical practitioners. Australian and New Zealand Journal of Public Health, 32(3), 288-289. doi:10.1111/j.1753-6405.2008.00232.xAngrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics. doi:10.1515/9781400829828Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139-1160. doi:10.1177/0018726708094863Beaver, D. D. (2001). Scientometrics, 52(3), 365-377. doi:10.1023/a:1014254214337Boardman, P. C., & Corley, E. A. (2008). University research centers and the composition of research collaborations. Research Policy, 37(5), 900-913. doi:10.1016/j.respol.2008.01.012Bourdieu, P. (1975). The specificity of the scientific field and the social conditions of the progress of reason. Social Science Information, 14(6), 19-47. doi:10.1177/053901847501400602Bourdieu, P. (1977). Outline of a Theory of Practice. doi:10.1017/cbo9780511812507Bourdieu, P. (1989). Social Space and Symbolic Power. Sociological Theory, 7(1), 14. doi:10.2307/202060Bouty, I. (2000). INTERPERSONAL AND INTERACTION INFLUENCES ON INFORMAL RESOURCE EXCHANGES BETWEEN R&D RESEARCHERS ACROSS ORGANIZATIONAL BOUNDARIES. Academy of Management Journal, 43(1), 50-65. doi:10.2307/1556385Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: implications for scientific and technical human capital. Research Policy, 33(4), 599-616. doi:10.1016/j.respol.2004.01.008Bozeman, B., & Gaughan, M. (2011). How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers. Research Policy, 40(10), 1393-1402. doi:10.1016/j.respol.2011.07.002Bozeman, B., & Rogers, J. D. (2002). A churn model of scientific knowledge value: Internet researchers as a knowledge value collective. Research Policy, 31(5), 769-794. doi:10.1016/s0048-7333(01)00146-9Bozeman, B., Dietz, J. S., & Gaughan, M. (2001). Scientific and technical human capital: an alternative model for research evaluation. International Journal of Technology Management, 22(7/8), 716. doi:10.1504/ijtm.2001.002988Brass, D. J. (1985). MEN’S AND WOMEN’S NETWORKS: A STUDY OF INTERACTION PATTERNS AND INFLUENCE IN AN ORGANIZATION. Academy of Management Journal, 28(2), 327-343. doi:10.2307/256204Chompalov, I., Genuth, J., & Shrum, W. (2002). The organization of scientific collaborations. Research Policy, 31(5), 749-767. doi:10.1016/s0048-7333(01)00145-7Cook, C., Heath, F., & Thompson, R. L. (2000). A Meta-Analysis of Response Rates in Web- or Internet-Based Surveys. Educational and Psychological Measurement, 60(6), 821-836. doi:10.1177/00131640021970934Durbin, S. (2010). Creating Knowledge through Networks: a Gender Perspective. Gender, Work & Organization, 18(1), 90-112. doi:10.1111/j.1468-0432.2010.00536.xEcklund, E. H., Lincoln, A. E., & Tansey, C. (2012). Gender Segregation in Elite Academic Science. Gender & Society, 26(5), 693-717. doi:10.1177/0891243212451904Ensign, P. C. (2009). Knowledge Sharing among Scientists. doi:10.1057/9780230617131Etzkowitz, H., Kemelgor, C., & Uzzi, B. (2000). Athena Unbound. doi:10.1017/cbo9780511541414Flyvbjerg, B. (2001). Making Social Science Matter. doi:10.1017/cbo9780511810503FOX, M. F. (2001). WOMEN, SCIENCE, AND ACADEMIA. Gender & Society, 15(5), 654-666. doi:10.1177/089124301015005002Fox, M. F. (2010). Women and Men Faculty in Academic Science and Engineering: Social-Organizational Indicators and Implications. American Behavioral Scientist, 53(7), 997-1012. doi:10.1177/0002764209356234Fox, M. F., & Stephan, P. E. (2001). Careers of Young Scientists: Social Studies of Science, 31(1), 109-122. doi:10.1177/030631201031001006Fox, M. F., Sonnert, G., & Nikiforova, I. (2009). Successful Programs for Undergraduate Women in Science and Engineering: Adapting versus Adopting the Institutional Environment. Research in Higher Education, 50(4), 333-353. doi:10.1007/s11162-009-9120-4Friedkin, N. (1980). A test of structural features of granovetter’s strength of weak ties theory. Social Networks, 2(4), 411-422. doi:10.1016/0378-8733(80)90006-4Gaughan, M. (2005). Introduction to the Symposium: Women in Science. The Journal of Technology Transfer, 30(4), 339-342. doi:10.1007/s10961-005-2579-zGaughan, M., & Corley, E. A. (2010). Science faculty at US research universities: The impacts of university research center-affiliation and gender on industrial activities. Technovation, 30(3), 215-222. doi:10.1016/j.technovation.2009.12.001Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-1380. doi:10.1086/225469Hansen, M. T. (1999). The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits. Administrative Science Quarterly, 44(1), 82. doi:10.2307/2667032Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422. doi:10.2307/2393451Islam, N., & Miyazaki, K. (2009). Nanotechnology innovation system: Understanding hidden dynamics of nanoscience fusion trajectories. Technological Forecasting and Social Change, 76(1), 128-140. doi:10.1016/j.techfore.2008.03.021Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1-18. doi:10.1016/s0048-7333(96)00917-1Koch, N. S., & Emrey, J. A. . (2001). The Internet and Opinion Measurement: Surveying Marginalized Populations. Social Science Quarterly, 82(1), 131-138. doi:10.1111/0038-4941.00012Kyvik, S., & Teigen, M. (1996). Child Care, Research Collaboration, and Gender Differences in Scientific Productivity. Science, Technology, & Human Values, 21(1), 54-71. doi:10.1177/016224399602100103Lee, S., & Bozeman, B. (2005). The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science, 35(5), 673-702. doi:10.1177/0306312705052359Levin, D. Z., & Cross, R. (2004). The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477-1490. doi:10.1287/mnsc.1030.0136Lin, N. (2001). Social Capital. doi:10.1017/cbo9780511815447McFadyen, M. A., & Cannella, A. A. (2004). SOCIAL CAPITAL AND KNOWLEDGE CREATION: DIMINISHING RETURNS OF THE NUMBER AND STRENGTH OF EXCHANGE RELATIONSHIPS. Academy of Management Journal, 47(5), 735-746. doi:10.2307/20159615McFadyen, M. A., Semadeni, M., & Cannella, A. A. (2009). Value of Strong Ties to Disconnected Others: Examining Knowledge Creation in Biomedicine. Organization Science, 20(3), 552-564. doi:10.1287/orsc.1080.0388Manfreda, K. L., Bosnjak, M., Berzelak, J., Haas, I., & Vehovar, V. (2008). Web Surveys versus other Survey Modes: A Meta-Analysis Comparing Response Rates. International Journal of Market Research, 50(1), 79-104. doi:10.1177/147078530805000107Marsden, P. V., & Campbell, K. E. (1984). Measuring Tie Strength. Social Forces, 63(2), 482. doi:10.2307/2579058Mason, M. A., & Ekman, E. M. (2007). Mothers on the Fast TrackHow a New Generation Can Balance Family and Careers. doi:10.1093/acprof:oso/9780195182675.001.0001Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of Organizational Trust. Academy of Management Review, 20(3), 709-734. doi:10.5465/amr.1995.9508080335Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. Academy of Management Review, 23(2), 242-266. doi:10.5465/amr.1998.533225Oliver, A. L., & Liebeskind, J. P. (1997). Three Levels of Networking for Sourcing Intellectual Capital in Biotechnology. International Studies of Management & Organization, 27(4), 76-103. doi:10.1080/00208825.1997.11656719Podolny, J. M., & Baron, J. N. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace. American Sociological Review, 62(5), 673. doi:10.2307/2657354Rhoton, L. A. (2011). Distancing as a Gendered Barrier. Gender & Society, 25(6), 696-716. doi:10.1177/0891243211422717Rothstein, M. G., & Davey, L. M. (1995). Gender differences in network relationships in academia. Women in Management Review, 10(6), 20-25. doi:10.1108/09649429510095999Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386. doi:10.1002/(sici)1097-0266(200003)21:33.0.co;2-mSCOTT, D. B. (1996). SHATTERING THE INSTRUMENTAL-EXPRESSIVE MYTH. Gender & Society, 10(3), 232-247. doi:10.1177/089124396010003003Shapin, S. (1994). A Social History of Truth. doi:10.7208/chicago/9780226148847.001.0001Shrum, W., Chompalov, I., & Genuth, J. (2001). Trust, Conflict and Performance in Scientific Collaborations. Social Studies of Science, 31(5), 681-730. doi:10.1177/030631201031005002Smith-Doerr, L. (2004). Flexibility and Fairness: Effects of the Network Form of Organization on Gender Equity in Life Science Careers. Sociological Perspectives, 47(1), 25-54. doi:10.1525/sop.2004.47.1.25Stix, G. (2001). Little Big Science. Scientific American, 285(3), 32-37. doi:10.1038/scientificamerican0901-32Uzzi, B. (1996). The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect. American Sociological Review, 61(4), 674. doi:10.2307/2096399Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly, 42(1), 35. doi:10.2307/2393808Vinck, D. (2010). The Sociology of Scientific Work. doi:10.4337/9781849807197Fan, W., & Yan, Z. (2010). Factors affecting response rates of the web survey: A systematic review. Computers in Human Behavior, 26(2), 132-139. doi:10.1016/j.chb.2009.10.015Ziman, J. M. (Ed.). (1994). Prometheus Bound. doi:10.1017/cbo978051158506