Luminous Intensity for Traffic Signals: A Scientific Basis for Performance Specifications

Humnan factors experiments on visual responses to simulated traffic signals using incandescent lamps and light-emitting diodes are described

The effectiveness of crowdsourcing in knowledge-based industries: the moderating role of transformational leadership and organisational learning

[EN] Crowdsourcing provides an opportunity for SMEs to exploit collective knowledge that is located outside the organisation. Crowdsourcing allows organisations to keep pace with a fast-changing environment by solving business problems, supporting R&D activities, and fostering innovation cheaply, flexibly, and dynamically. Nevertheless, managing crowdsourcing is difficult, and positive outcomes are not guaranteed. Drawing on the Resource-based View, we study transformational leadership and organisational learning capability as complementary assets to help SMEs deploy crowdsourcing. An empirical study of Spanish telecommunications and biotechnology companies confirmed the moderating effect of organisational learning on the relationship between crowdsourcing and organisational performance.Devece Carañana, CA.; Palacios Marqués, D.; Ribeiro-Navarrete, B. (2019). The effectiveness of crowdsourcing in knowledge-based industries: the moderating role of transformational leadership and organisational learning. Economic Research-Ekonomska Istra ivanja. 32(1):335-351. https://doi.org/10.1080/1331677X.2018.1547204S335351321Amitay, M., Popper, M., & Lipshitz, R. (2005). Leadership styles and organizational learning in community clinics. The Learning Organization, 12(1), 57-70. doi:10.1108/09696470510574269Atapattu, M., & Ranawake, G. (2017). Transformational and Transactional Leadership Behaviours and their Effect on Knowledge Workers’ Propensity for Knowledge Management Processes. Journal of Information & Knowledge Management, 16(03), 1750026. doi:10.1142/s0219649217500265Aragón-Correa, J. A., García-Morales, V. J., & Cordón-Pozo, E. (2007). Leadership and organizational learning’s role on innovation and performance: Lessons from Spain. Industrial Marketing Management, 36(3), 349-359. doi:10.1016/j.indmarman.2005.09.006Bal, A. S., Weidner, K., Hanna, R., & Mills, A. J. (2017). Crowdsourcing and brand control. Business Horizons, 60(2), 219-228. doi:10.1016/j.bushor.2016.11.006Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120. doi:10.1177/014920639101700108Colli, A. (2011). Contextualizing Performances of Family Firms. Family Business Review, 25(3), 243-257. doi:10.1177/0894486511426872Bhatti, W. A., Larimo, J., & Carrasco, I. (2016). Strategy’s effect on knowledge sharing in host country networks. Journal of Business Research, 69(11), 4769-4774. doi:10.1016/j.jbusres.2016.04.028Bruque-Cámara, S., Vargas-Sánchez, A., & Hernández-Ortiz, M. J. (2004). Organizational determinants of IT adoption in the pharmaceutical distribution sector. European Journal of Information Systems, 13(2), 133-146. doi:10.1057/palgrave.ejis.3000490Cáceres, R., Guzmán, J., & Rekowski, M. (2011). Firms as source of variety in innovation: influence of size and sector. International Entrepreneurship and Management Journal, 7(3), 357-372. doi:10.1007/s11365-011-0198-8Chi, H.-K., Lan, C.-H., & Dorjgotov, B. (2012). The Moderating Effect of Transformational Leadership on Knowledge Management and Organizational Effectiveness. Social Behavior and Personality: an international journal, 40(6), 1015-1023. doi:10.2224/sbp.2012.40.6.1015Chiva, R., & Alegre, J. (2005). Organizational Learning and Organizational Knowledge. Management Learning, 36(1), 49-68. doi:10.1177/1350507605049906Chiva, R., Alegre, J., & Lapiedra, R. (2007). Measuring organisational learning capability among the workforce. International Journal of Manpower, 28(3/4), 224-242. doi:10.1108/01437720710755227Coelho, D. A., Nunes, F., & Vieira, F. L. (2016). The impact of crowdsourcing in product development: an exploratory study of Quirky based on the perspective of participants. International Journal of Design Creativity and Innovation, 6(1-2), 114-128. doi:10.1080/21650349.2016.1216331Conant, J. S., Mokwa, M. P., & Varadarajan, P. R. (1990). Strategic types, distinctive marketing competencies and organizational performance: A multiple measures-based study. Strategic Management Journal, 11(5), 365-383. doi:10.1002/smj.4250110504Devece, C., Palacios, D., & Martinez-Simarro, D. (2016). Effect of information management capability on organizational performance. Service Business, 11(3), 563-580. doi:10.1007/s11628-016-0320-7Dimitrova, S., & Scarso, E. (2017). The impact of crowdsourcing on the evolution of knowledge management: Insights from a case study. Knowledge and Process Management, 24(4), 287-295. doi:10.1002/kpm.1552Elkins, T., & Keller, R. T. (2003). Leadership in research and development organizations: A literature review and conceptual framework. The Leadership Quarterly, 14(4-5), 587-606. doi:10.1016/s1048-9843(03)00053-5Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38(2), 189-200. doi:10.1177/0165551512437638Flostrand, A. (2017). Finding the future: Crowdsourcing versus the Delphi technique. Business Horizons, 60(2), 229-236. doi:10.1016/j.bushor.2016.11.007García-Morales, V. J., Lloréns-Montes, F. J., & Verdú-Jover, A. J. (2008). The Effects of Transformational Leadership on Organizational Performance through Knowledge and Innovation*. British Journal of Management, 19(4), 299-319. doi:10.1111/j.1467-8551.2007.00547.xGrant, R. M. (1991). The Resource-Based Theory of Competitive Advantage: Implications for Strategy Formulation. California Management Review, 33(3), 114-135. doi:10.2307/41166664King, W. R. (Ed.). (2009). Knowledge Management and Organizational Learning. Annals of Information Systems. doi:10.1007/978-1-4419-0011-1Lang, M., Bharadwaj, N., & Di Benedetto, C. A. (2016). How crowdsourcing improves prediction of market-oriented outcomes. Journal of Business Research, 69(10), 4168-4176. doi:10.1016/j.jbusres.2016.03.020Lee, J., & Seo, D. (2016). Crowdsourcing not all sourced by the crowd: An observation on the behavior of Wikipedia participants. Technovation, 55-56, 14-21. doi:10.1016/j.technovation.2016.05.002Leimeister, J. M., Huber, M., Bretschneider, U., & Krcmar, H. (2009). Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition. Journal of Management Information Systems, 26(1), 197-224. doi:10.2753/mis0742-1222260108Liu, S., Xia, F., Zhang, J., & Wang, L. (2016). How crowdsourcing risks affect performance: an exploratory model. Management Decision, 54(9), 2235-2255. doi:10.1108/md-12-2015-0604Marjanovic, S., Fry, C., & Chataway, J. (2012). Crowdsourcing based business models: In search of evidence for innovation 2.0. Science and Public Policy, 39(3), 318-332. doi:10.1093/scipol/scs009McEvily, S. K., & Chakravarthy, B. (2002). The persistence of knowledge-based advantage: an empirical test for product performance and technological knowledge. Strategic Management Journal, 23(4), 285-305. doi:10.1002/smj.223Melville, Kraemer, & Gurbaxani. (2004). Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value. MIS Quarterly, 28(2), 283. doi:10.2307/25148636Naqshbandi, M. M., & Tabche, I. (2018). The interplay of leadership, absorptive capacity, and organizational learning culture in open innovation: Testing a moderated mediation model. Technological Forecasting and Social Change, 133, 156-167. doi:10.1016/j.techfore.2018.03.017Assis Neto, F. R., & Santos, C. A. S. (2018). Understanding crowdsourcing projects: A systematic review of tendencies, workflow, and quality management. Information Processing & Management, 54(4), 490-506. doi:10.1016/j.ipm.2018.03.006Nonaka, I., & Konno, N. (1998). The Concept of «Ba»: Building a Foundation for Knowledge Creation. California Management Review, 40(3), 40-54. doi:10.2307/41165942Noruzy, A., Dalfard, V. M., Azhdari, B., Nazari-Shirkouhi, S., & Rezazadeh, A. (2012). Relations between transformational leadership, organizational learning, knowledge management, organizational innovation, and organizational performance: an empirical investigation of manufacturing firms. The International Journal of Advanced Manufacturing Technology, 64(5-8), 1073-1085. doi:10.1007/s00170-012-4038-yPalacios‐Marqués, D., Peris‐Ortiz, M., & Merigó, J. M. (2013). The effect of knowledge transfer on firm performance. Management Decision, 51(5), 973-985. doi:10.1108/md-08-2012-0562Palacios, M., Martinez-Corral, A., Nisar, A., & Grijalvo, M. (2016). Crowdsourcing and organizational forms: Emerging trends and research implications. Journal of Business Research, 69(5), 1834-1839. doi:10.1016/j.jbusres.2015.10.065Peris-Ortiz, M., Devece-Carañana, C. A., & Navarro-Garcia, A. (2018). Organizational learning capability and open innovation. Management Decision, 56(6), 1217-1231. doi:10.1108/md-02-2017-0173Piezunka, H., & Dahlander, L. (2015). Distant Search, Narrow Attention: How Crowding Alters Organizations’ Filtering of Suggestions in Crowdsourcing. Academy of Management Journal, 58(3), 856-880. doi:10.5465/amj.2012.0458Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. doi:10.1037/0021-9010.88.5.879Prpić, J., Shukla, P. P., Kietzmann, J. H., & McCarthy, I. P. (2015). How to work a crowd: Developing crowd capital through crowdsourcing. Business Horizons, 58(1), 77-85. doi:10.1016/j.bushor.2014.09.005Qin, S., Van Der Velde, D., Chatzakis, E., McStea, T., & Smith, N. (2016). Exploring barriers and opportunities in adopting crowdsourcing based new product development in manufacturing SMEs. Chinese Journal of Mechanical Engineering, 29(6), 1052-1066. doi:10.3901/cjme.2016.0808.089Rai, Patnayakuni, & Seth. (2006). Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities. MIS Quarterly, 30(2), 225. doi:10.2307/25148729RAVICHANDRAN, T., LERTWONGSATIEN, C., & LERTWONGSATIEN, C. (2005). Effect of Information Systems Resources and Capabilities on Firm Performance: A Resource-Based Perspective. Journal of Management Information Systems, 21(4), 237-276. doi:10.1080/07421222.2005.11045820Ray, G., Barney, J. B., & Muhanna, W. A. (2003). Capabilities, business processes, and competitive advantage: choosing the dependent variable in empirical tests of the resource-based view. Strategic Management Journal, 25(1), 23-37. doi:10.1002/smj.366Schlagwein, D., & Bjorn-Andersen, N. (2014). Organizational Learning with Crowdsourcing: The Revelatory Case of LEGO. Journal of the Association for Information Systems, 15(11), 754-778. doi:10.17705/1jais.00380Schmallegger, D., & Carson, D. (2008). Blogs in tourism: Changing approaches to information exchange. Journal of Vacation Marketing, 14(2), 99-110. doi:10.1177/1356766707087519Stanko, M. A., Molina-Castillo, F.-J., & Harmancioglu, N. (2014). It Won’t Fit! For Innovative Products, Sometimes That’s for the Best. Journal of Product Innovation Management, 32(1), 122-137. doi:10.1111/jpim.12238STOCK, R. M., & SCHNARR, N. L. (2016). EXPLORING THE PRODUCT INNOVATION OUTCOMES OF CORPORATE CULTURE AND EXECUTIVE LEADERSHIP. International Journal of Innovation Management, 20(01), 1650009. doi:10.1142/s1363919616500092Templeton, G. F., Lewis, B. R., & Snyder, C. A. (2002). Development of a Measure for the Organizational Learning Construct. Journal of Management Information Systems, 19(2), 175-218. doi:10.1080/07421222.2002.11045727Tohidi, H., & Jabbari, M. M. (2012). Measuring organizational learning capability. Procedia - Social and Behavioral Sciences, 31, 428-432. doi:10.1016/j.sbspro.2011.12.079Vukovic, M. (2009). Crowdsourcing for Enterprises. 2009 Congress on Services - I. doi:10.1109/services-i.2009.56Wade, & Hulland. (2004). Review: The Resource-Based View and Information Systems Research: Review, Extension, and Suggestions for Future Research. MIS Quarterly, 28(1), 107. doi:10.2307/25148626Wu, I.-L., & Chen, J.-L. (2014). Knowledge management driven firm performance: the roles of business process capabilities and organizational learning. Journal of Knowledge Management, 18(6), 1141-1164. doi:10.1108/jkm-05-2014-0192Xie, X., Wang, L., & Zeng, S. (2018). Inter-organizational knowledge acquisition and firms’ radical innovation: A moderated mediation analysis. Journal of Business Research, 90, 295-306. doi:10.1016/j.jbusres.2018.04.038Ye, H. (Jonathan), & Kankanhalli, A. (2015). Investigating the antecedents of organizational task crowdsourcing. Information & Management, 52(1), 98-110. doi:10.1016/j.im.2014.10.007Zollo, M., & Winter, S. G. (2002). Deliberate Learning and the Evolution of Dynamic Capabilities. Organization Science, 13(3), 339-351. doi:10.1287/orsc.13.3.339.278

Numerical analysis and experimental study of the error of magnetic field strength measurements with single sheet testers

The error of the measurement of the magnetic field strength with a single sheet tester has been studied. Two different methods, determination by means of field sensing coils (1) and from the magnetizing current (2), have been compared. The errors of methods(1) and (2) were calculated by the finite element method (FEM), different parameters having been varied, and method (2) was additionally studied experimentally. SSTs with wound yokes and stacked yokes were considered. The results will help to decide whether the more complicated and more accurate H coil method or the easier to handle, but less accurate m.c.method is chosen.</p

Application of semidefinite programming to maximize the spectral gap produced by node removal

The smallest positive eigenvalue of the Laplacian of a network is called the spectral gap and characterizes various dynamics on networks. We propose mathematical programming methods to maximize the spectral gap of a given network by removing a fixed number of nodes. We formulate relaxed versions of the original problem using semidefinite programming and apply them to example networks.Comment: 1 figure. Short paper presented in CompleNet, Berlin, March 13-15 (2013

Mixed perturbative expansion: the validity of a model for the cascading

A new type of perturbative expansion is built in order to give a rigorous derivation and to clarify the range of validity of some commonly used model equations. This model describes the evolution of the modulation of two short and localized pulses, fundamental and second harmonic, propagating together in a bulk uniaxial crystal with non-vanishing second order susceptibility $\chi^(2)$ and interacting through the nonlinear effect known as ``cascading'' in nonlinear optics. The perturbative method mixes a multi-scale expansion with a power series expansion of the susceptibility, and must be carefully adapted to the physical situation. It allows the determination of the physical conditions under which the model is valid: the order of magnitude of the walk-off, phase-mismatch,and anisotropy must have determined values.Comment: arxiv version is already officia

Crystal structure of mixed fluorites Ca(1-x)Sr(x)F(2) and Sr(1-x)Ba(x)F(2) and luminescence of Eu(2+) in the crystals

Within the framework of the virtual crystal method implemented in the shell model and pair potential approximation the crystal structure of mixed fluorites Ca(1-x)Sr(x)F(2) and Sr(1-x)Ba(x)F(2) has been calculated. The impurity center Eu(2+) and the distance Eu(2+)-F in this crystals have been also calculated. The low level position of excited 4f65d configuration of the Eu(2+) ion has been expressed using phenomenological dependence on distance E(2+)-F. The dependences of Stokes shift and Huang-Rhys factor on concentration x have been received for yellow luminescence in Sr(1-x)Ba(x)F(2):Eu(2+). The value x, for which the eg -level of Eu(2+) ion will be in conduction band in Sr(1-x)Ba(x)F(2):Eu(2+) has been calculated.Comment: 8 pages, 3 figures. The manuscript is sent to journal 'Physics of the solid state'. The results will be submitted on inernational conference SCINTMAT'2002 in oral session (june,20-22,2002,Ekaterinburg,Russia). Corresponding author e-mail: [email protected]

Functional Big-step Semantics

When doing an interactive proof about a piece of software, it is important that the underlying programming language’s semantics does not make the proof unnecessarily difficult or unwieldy. Both smallstep and big-step semantics are commonly used, and the latter is typically given by an inductively defined relation. In this paper, we consider an alternative: using a recursive function akin to an interpreter for the language. The advantages include a better induction theorem, less duplication, accessibility to ordinary functional programmers, and the ease of doing symbolic simulation in proofs via rewriting. We believe that this style of semantics is well suited for compiler verification, including proofs of divergence preservation. We do not claim the invention of this style of semantics: our contribution here is to clarify its value, and to explain how it supports several language features that might appear to require a relational or small-step approach. We illustrate the technique on a simple imperative language with C-like for-loops and a break statement, and compare it to a variety of other approaches. We also provide ML and lambda-calculus based examples to illustrate its generality

Two-fluid dynamics in driven YBa₂Cu₃O_6.48

Coherent optical excitation of certain phonon modes in YBa2Cu3O6+x has been shown to induce superconducting-like interlayer coherence at temperatures higher than Tc. Recent work has associated these phenomena to a parametric excitation and amplification of Josephson plasma polaritons, which are overdamped above Tc but are made coherent by the phonon drive. However, the dissipative response of uncondensed quasiparticles, which do not couple in the same way to the phonon drive, has not been addressed. Here, we investigate both the enhancement of the superfluid density, ωσ2(ω), and the dissipative response of quasiparticles, σ1(ω), by systematically tuning the duration and energy of the mid-infrared pulse while keeping the peak field fixed. We find that the photo-induced superfluid density saturates to the zero-temperature equilibrium value for pulses made longer than the phonon dephasing time, whilst the dissipative component continues to grow with increasing pulse duration. We show that superfluid and dissipation remain uncoupled as long as the drive is on, and identify an optimal regime of pump pulse durations for which the superconducting response is maximum and dissipation is minimized