172 research outputs found
What do evidence-based secondary journals tell us about the publication of clinically important articles in primary healthcare journals?
BACKGROUND: We conducted this analysis to determine i) which journals publish high-quality, clinically relevant studies in internal medicine, general/family practice, general practice nursing, and mental health; and ii) the proportion of clinically relevant articles in each journal. METHODS: We performed an analytic survey of a hand search of 170 general medicine, general healthcare, and specialty journals for 2000. Research staff assessed individual articles by using explicit criteria for scientific merit for healthcare application. Practitioners assessed the clinical importance of these articles. Outcome measures were the number of high-quality, clinically relevant studies published in the 170 journal titles and how many of these were published in each of four discipline-specific, secondary "evidence-based" journals (ACP Journal Club for internal medicine and its subspecialties; Evidence-Based Medicine for general/family practice; Evidence-Based Nursing for general practice nursing; and Evidence-Based Mental Health for all aspects of mental health). Original studies and review articles were classified for purpose: therapy and prevention, screening and diagnosis, prognosis, etiology and harm, economics and cost, clinical prediction guides, and qualitative studies. RESULTS: We evaluated 60,352 articles from 170 journal titles. The pass criteria of high-quality methods and clinically relevant material were met by 3059 original articles and 1073 review articles. For ACP Journal Club (internal medicine), four titles supplied 56.5% of the articles and 27 titles supplied the other 43.5%. For Evidence-Based Medicine (general/family practice), five titles supplied 50.7% of the articles and 40 titles supplied the remaining 49.3%. For Evidence-Based Nursing (general practice nursing), seven titles supplied 51.0% of the articles and 34 additional titles supplied 49.0%. For Evidence-Based Mental Health (mental health), nine titles supplied 53.2% of the articles and 34 additional titles supplied 46.8%. For the disciplines of internal medicine, general/family practice, and mental health (but not general practice nursing), the number of clinically important articles was correlated withScience Citation Index (SCI) Impact Factors. CONCLUSIONS: Although many clinical journals publish high-quality, clinically relevant and important original studies and systematic reviews, the articles for each discipline studied were concentrated in a small subset of journals. This subset varied according to healthcare discipline; however, many of the important articles for all disciplines in this study were published in broad-based healthcare journals rather than subspecialty or discipline-specific journals
Policies, Political-Economy, and Swidden in Southeast Asia
For centuries swidden was an important farming practice found across the girth of Southeast Asia. Today, however, these systems are changing and sometimes disappearing at a pace never before experienced. In order to explain the demise or transitioning of swidden we need to understand the rapid and massive changes that have and are occurring in the political and economic environment in which these farmers operate. Swidden farming has always been characterized by change, but since the onset of modern independent nation states, governments and markets in Southeast Asia have transformed the terms of swiddeners’ everyday lives to a degree that is significantly different from that ever experienced before. In this paper we identified six factors that have contributed to the demise or transformation of swidden systems, and support these arguments with examples from China (Xishuangbanna), Laos, Thailand, Malaysia, and Indonesia. These trends include classifying swiddeners as ethnic minorities within nation-states, dividing the landscape into forest and permanent agriculture, expansion of forest departments and the rise of conservation, resettlement, privatization and commoditization of land and land-based production, and expansion of market infrastructure and the promotion of industrial agriculture. In addition we note a growing trend toward a transition from rural to urban livelihoods and expanding urban-labor markets
Steppe-tundra composition and deglacial floristic turnover in interior Alaska revealed by sedimentary ancient DNA (sedaDNA)
When tracing vegetation dynamics over long timescales, obtaining enough floristic information to gain a detailed understanding of past communities and their transitions can be challenging. The first high-resolution sedimentary DNA (sedaDNA) metabarcoding record from lake sediments in Alaska—reported here—covers nearly 15,000 years of change. It shows in unprecedented detail the composition of late-Pleistocene “steppe-tundra” vegetation of ice-free Alaska, part of an intriguing late-Quaternary “no-analogue” biome, and it covers the subsequent changes that led to the development of modern spruce-dominated boreal forest. The site (Chisholm Lake) lies close to key archaeological sites, and the record throws new light on the landscape and resources available to early humans. Initially, vegetation was dominated by forbs found in modern tundra and/or subarctic steppe vegetation (e.g., Potentilla, Draba, Eritrichium, Anemone patens), and graminoids (e.g., Bromus pumpellianus, Festuca, Calamagrostis, Puccinellia), with Salix the only prominent woody taxon. Predominantly xeric, warm-to-cold habitats are indicated, and we explain the mixed ecological preferences of the fossil assemblages as a topo-mosaic strongly affected by insolation load. At ca. 14,500 cal yr BP (calendar years before C.E. 1950), about the same time as well documented human arrivals and coincident with an increase in effective moisture, Betula expanded. Graminoids became less abundant, but many open-ground forb taxa persisted. This woody-herbaceous mosaic is compatible with the observed persistence of Pleistocene megafaunal species (animals weighing ≥44 kg)—important resources for early humans. The greatest taxonomic turnover, marking a transition to regional woodland and a further moisture increase, began ca. 11,000 cal yr BP when Populus expanded, along with new shrub taxa (e.g., Shepherdia, Eleagnus, Rubus, Viburnum). Picea then expanded ca. 9500 cal yr BP, along with shrub and forb taxa typical of evergreen boreal woodland (e.g., Spiraea, Cornus, Linnaea). We found no evidence for Picea in the late Pleistocene, however. Most taxa present today were established by ca. 5000 cal yr BP after almost complete taxonomic turnover since the start of the record (though Larix appeared only at ca. 1500 cal yr BP). Prominent fluctuations in aquatic communities ca. 14,000–9,500 cal yr BP are probably related to lake-level fluctuations prior to the lake reaching its high, near-modern depth ca. 8,000 cal yr BP
Trapping \u3ci\u3ePhyllophaga \u3c/i\u3espp. (Coleoptera: Scarabaeidae: Melolonthinae) in the United States and Canada using sex attractants.
The sex pheromone of the scarab beetle, Phyllophaga anxia, is a blend of the methyl esters of two amino acids, L-valine and L-isoleucine. A field trapping study was conducted, deploying different blends of the two compounds at 59 locations in the United States and Canada. More than 57,000 males of 61 Phyllophaga species (Coleoptera: Scarabaeidae: Melolonthinae) were captured and identified. Three major findings included: (1) widespread use of the two compounds [of the 147 Phyllophaga (sensu stricto) species found in the United States and Canada, males of nearly 40% were captured]; (2) in most species intraspecific male response to the pheromone blends was stable between years and over geography; and (3) an unusual pheromone polymorphism was described from P. anxia. Populations at some locations were captured with L-valine methyl ester alone, whereas populations at other locations were captured with L-isoleucine methyl ester alone. At additional locations, the L-valine methyl ester-responding populations and the L-isoleucine methyl ester-responding populations were both present, producing a bimodal capture curve. In southeastern Massachusetts and in Rhode Island, in the United States, P. anxia males were captured with blends of L-valine methyl ester and L-isoleucine methyl ester
Topical rapamycin inhibits tuberous sclerosis tumor growth in a nude mouse model
<p>Abstract</p> <p>Background</p> <p>Skin manifestations of Tuberous Sclerosis Complex (TSC) cause significant morbidity. The molecular mechanism underlying TSC is understood and there is evidence that systemic treatment with rapamycin or other mTOR inhibitors may be a useful approach to targeted therapy for the kidney and brain manifestations. Here we investigate topical rapamycin in a mouse model for TSC-related tumors.</p> <p>Methods</p> <p>0.4% and 0.8% rapamycin ointments were applied to nude mice bearing subcutaneous, TSC-related tumors. Topical treatments were compared with injected rapamycin and topical vehicle. Rapamycin levels in blood and tumors were measured to assess systemic drug levels in all cohorts.</p> <p>Results</p> <p>Treatment with topical rapamycin improved survival and reduced tumor growth. Topical rapamycin treatment resulted in systemic drug levels within the known therapeutic range and was not as effective as injected rapamycin.</p> <p>Conclusion</p> <p>Topical rapamycin inhibits TSC-related tumor growth. These findings could lead to a novel treatment approach for facial angiofibromas and other TSC skin lesions.</p
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes
Multicenter Phase 2 Trial of Sirolimus for Tuberous Sclerosis: Kidney Angiomyolipomas and Other Tumors Regress and VEGF- D Levels Decrease
Tuberous sclerosis (TSC) related tumors are characterized by constitutively activated mTOR signaling due to mutations in TSC1 or TSC2.We completed a phase 2 multicenter trial to evaluate the efficacy and tolerability of the mTOR inhibitor, sirolimus, for the treatment of kidney angiomyolipomas.36 adults with TSC or TSC/LAM were enrolled and started on daily sirolimus. The overall response rate was 44.4% (95% confidence intervals [CI] 28 to 61); 16/36 had a partial response. The remainder had stable disease (47.2%, 17/36), or were unevaluable (8.3%, 3/36). The mean decrease in kidney tumor size (sum of the longest diameters [sum LD]) was 29.9% (95% CI, 22 to 37; n = 28 at week 52). Drug related grade 1-2 toxicities that occurred with a frequency of >20% included: stomatitis, hypertriglyceridemia, hypercholesterolemia, bone marrow suppression (anemia, mild neutropenia, leucopenia), proteinuria, and joint pain. There were three drug related grade 3 events: lymphopenia, headache, weight gain. Kidney angiomyolipomas regrew when sirolimus was discontinued but responses tended to persist if treatment was continued after week 52. We observed regression of brain tumors (SEGAs) in 7/11 cases (26% mean decrease in diameter), regression of liver angiomyolipomas in 4/5 cases (32.1% mean decrease in longest diameter), subjective improvement in facial angiofibromas in 57%, and stable lung function in women with TSC/LAM (n = 15). A correlative biomarker study showed that serum VEGF-D levels are elevated at baseline, decrease with sirolimus treatment, and correlate with kidney angiomyolipoma size (Spearman correlation coefficient 0.54, p = 0.001, at baseline).Sirolimus treatment for 52 weeks induced regression of kidney angiomyolipomas, SEGAs, and liver angiomyolipomas. Serum VEGF-D may be a useful biomarker for monitoring kidney angiomyolipoma size. Future studies are needed to determine benefits and risks of longer duration treatment in adults and children with TSC.Clinicaltrials.gov NCT00126672
Stream denitrification across biomes and its response to anthropogenic nitrate loading
Author Posting. © The Author(s), 2008. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 452 (2008): 202-205, doi:10.1038/nature06686.Worldwide, anthropogenic addition of bioavailable nitrogen (N) to the
biosphere is increasing and terrestrial ecosystems are becoming increasingly N
saturated, causing more bioavailable N to enter groundwater and surface waters.
Large-scale N budgets show that an average of about 20-25% of the N added to the
biosphere is exported from rivers to the ocean or inland basins, indicating
substantial sinks for N must exist in the landscape. Streams and rivers may be
important sinks for bioavailable N owing to their hydrologic connections with
terrestrial systems, high rates of biological activity, and streambed sediment
environments that favor microbial denitrification. Here, using data from 15N
tracer experiments replicated across 72 streams and 8 regions representing several
biomes, we show that total biotic uptake and denitrification of nitrate increase with
stream nitrate concentration, but that the efficiency of biotic uptake and
denitrification declines as concentration increases, reducing the proportion of instream
nitrate that is removed from transport. Total uptake of nitrate was related
to ecosystem photosynthesis and denitrification was related to ecosystem
respiration. Additionally, we use a stream network model to demonstrate that
excess nitrate in streams elicits a disproportionate increase in the fraction of nitrate
that is exported to receiving waters and reduces the relative role of small versus
large streams as nitrate sinks.Funding for this research was provided by the National Science
Foundation
Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms
[EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms.
The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS).
Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. In the same vein, they results confirm the presence of the cyclic movement of innovative outcome with the Exploitation.In addition, this research is part of the Project ECO2015-71380-R funded by the Spanish Ministry of Economy, Industry and Competitiveness and the State Research Agency. Co-financed by the European Regional Development Fund (ERDF).Vargas-Mendoza, NY.; Lloria, MB.; Salazar Afanador, A.; Vergara Domínguez, L. (2018). Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms. International Entrepreneurship and Management Journal. 14(4):1053-1069. https://doi.org/10.1007/s11365-018-0496-5S10531069144Alegre, J., & Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: an empirical test. Technovation, 28, 315–326.Amara, N., Landry, R., Becheikh, N., & Ouimet, M. (2008). Learning and novelty of innovation in established manufacturing SMEs. Technovation, 28, 450–463.Aragón-Mendoza, J., Pardo del Val, M., & Roig, S. (2016). The influence of institutions development in venture creation decision: a cognitive view. Journal of Business Research, 69(11), 4941–4946.Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice: motivators, barriers, and enablers. Advances in Developing Human Resources, 10(4), 541–554.Argyris, C., & Schön, D. (1978). Organizational learning: a theory of action perspective. Reading: Addison Wesley.Bagozzi, R. P., Yi, Y., & Singh, S. (1991). On the use of structural equation models in experimental designs: two extensions international. Journal of Research in Marketing, 8, 125–140.Belda, J., Vergara L., Salazar, A., & Safont G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs, accepted for publication in Signal Processing.Boland, R. J. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in communities of knowing. Organization Science, 6(4), 350–372.Bontis, N., (1998). Intellectual capital: an exploratory study that develops measures models. Management Decision, 36, 63–76.Bontis, N. (1999). Managing an organizational learning system by aligning stocks and flows of knowledge: an empirical examination of intellectual capital, knowledge management, and business performance. 1999. Management of Innovation and New Technology Research Centre, McMaster University.Bontis, N., Keow, W., & Richardson, S. (2000). Intellectual capital and the nature of business in Malaysia. Journal of Intellectual Capital, 1(1), 85–100Bontis, N., Hullan, J., & Crossan, M. (2002). Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies, 39, 438–469.Brachos, D., Kostopulos, K., Sodersquist, K. E., & Prastacos, G. (2007). Knowledge effectiveness, social context and innovation. Journal of Knowledge Management, 11(5), 31–44.Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31, 515–524.Chang, T. J., Yeh, S. P., & Yeh, I. J. (2007). The effects of joint rewards system in new product development. International Journal of Manpower, 28(3/4), 276–297.Chin, W. (1998). The partial least square approach to structural equation modeling. In G. A. Marcoulides (Ed.) (pp. 294–336). New Jersey: Lawrence Erlbaum Associates.Cho, N., Li, G., & Su, C. (2007). An empirical study on the effect of individual factors on knowledge sharing by knowledge type. Journal of Global Business and Technology, 3(2), 1–15.Cohen, W. M., & Levin, R. C. (1989). Empirical studies of innovation and market structure. In R. Schmalansee & R. D. Willing (Eds.), Handbook of industrial organization II. New York: Elsevier.Cohen, W. M., & Levinthal, D. A. (1990). Absorptive-capacity – a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.Cooper, R. G. (2000). New product performance: what distinguishes the star products. Austrian Journal of Management, 25, 17–45.Crossan, M., & Berdrow, I. (2003). Organizational learning and strategic renewal. Strategic Management Journal, 24, 1087–1105.Crossan, M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: a systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.Crossan, M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. Academy of Management Review, 24, 522–537.Damanpour, F., & Aravind, D. (2012). Managerial innovation: conceptions, processes, and antecedents. Management and Organization Review, 8(2), 423–454.Damanpour, F., & Shanthi, G. (2001). The dynamics of the adoption of products and process innovations in organizations. Journal of Management Studies, 38(1), 21–65.Decarolis, D. M., & Deeds, D. L. (1999). The impact of stock and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20, 953–968.Demartini, C. (2015). Relationships between social and intellectual capital: empirical Evidence from IC statements. Knowledge and Process Management, 22(2), 99–111.Dupuy, F. (2004). Sharing knowledge: they why and how of organizational change. Hampshire: Palgrave Macmillan.Fornell, C., & Bookstein, F. I. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.Ganter, A., & Hecker, A. (2013). Deciphering antecedents of organizational innovation. Journal of Business Research, 66(5), 575–584.Ganter, A., & Hecker, A. (2014). Configurational paths to organizational innovation: qualitative comparative analyses of antecedents and contingencies. Journal of Business Research, 67, 1285–1292.Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. International Journal of Management Science, 25, 15–28.Hedberg, B. (1981). How organizations learn and unlearn. In P. Nystrom & W. Starbuck (Eds.), Handbook of organizational design. New York: Oxford University.Hedlund, G., & Nonaka, I. (1993). Models of knowledge management in the west and Japan. In: P. Lorange, B. Chacravrarthy, J. Ross, and J. Van de ven (Eds.) Cambridge: Basil Blackwell.Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use the partial least squares path modeling. In: R. Sinkovics and N. Pervez (Eds.) 277–319.Hsu, I. (2006). Enhancing employee tendencies to share knowledge-case studies on nine companies in Taiwan. International Journal of Information Management, 26(4), 326–338.Hsu, I. (2008). Knowledge sharing practices as a facilitating factor for improving organizational performance though human capital: a preliminary test. Expert Systems with Application, 35, 316–1326.Huang, Q., Davison, R., & Gu, J. (2008). Impact of personal and cultural factors on knowledge sharing in China. Asia Pacific Journal Management, 25(3), 451–471.Ibarra, H. (1993). Network centrality, power, and innovation involvement – determinants of technical and administrative roles. Academy of Management Journal, 36(3), 471–501.Iebra, I. L., Zegarra, P. S., & Zegarra, A. S. (2011). Learning for sharing: an empirical analysis of organizational learning and knowledge sharin. International Entrepreneurship Management Journal, 7, 509–518.Ipe, M. (2003). Knowledge sharing in organizations: a conceptual framework. Human Resource Development Review, 2(4), 337–359.Jenkin, T. (2013). Extending the 4I organizational learning model: information sources, foraging processes and tools. Administrative Sciences, 3, 96–109.Jiménez-Jiménez, D., & Sanz-Valle, R. (2011). Innovation, organizational learning, and performance. Journal of Business Research, 64, 408–417.Kane, G. C., & Alavi, M. (2007). Information technology and organizational learning: an investigation of exploration and exploitation processes. Organization Science, 18(5), 796–812.Kleinbaum, D. G., Kupper, N. N., Muller, K. E. (1988). Applied regression analysis and other Multivariable’s methods, PWS KENT.Klomp, L., & Van Leeuwen, G. (2001). Linking innovation and firm performance: a new approach. International Journal of the Economics of Business, 8(3), 343–364.Lansisalmi, H., Kivimaki, M., Aalto, P., & Ruoranen, R. (2006). Innovation in healthcare: a systematic review of recent research. Nursing Science Quarterly, 19(1), 66–72.Laperrière, A., & Spence, M. (2015). Enacting international opportunities: the role of organizational learning in knowledge-intensive business services. Journal of International Entrepreneurship, 13(3), 212–241.Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.Lin, H. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315–332.Lloria, M. B., & Moreno-Luzón, M. D. (2014). Organizational learning: proposal of an integrative scale and research instrument. Journal of Business Research, 67, 692–697.March, J. G. (1991). Exploration and exploitation in organizational learning. Organizational Science, 2, 71–87.Matikainen, M., Terho, H., Parvinen, P., & Juppo, A. (2016). The role and impact of firm’s strategic orientations on launch performance: significance of relationship orientation. Journal of Business & Industrial Marketing, 31(5), 625–639.Mone, M. A., McKinley, W., & Barker, V. L. (1998). Organizational decline and innovation: a contingency framework. Academy of Management Review, 23, 115–132.Moreno-Luzón, M. D., & Lloria, B. (2008). The role of non-structural and informal mechanisms of integration and integration as forces in knowledge creation. British Journal of Management, 19, 250–276.Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: how patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55, 69–75.Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. How Japanese companies create the dynamics of innovation. New York: Oxford University Press.Nonaka, I., & von Krogh, G. (2009). Perspective tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organization Science, 20(3), 635–652.Parida, V., Lahti, T., & Wincent, J. (2016). Exploration and exploitation and firm performance variability: a study of ambidexterity in entrepreneurial firms. International Entrepreneurship Management Journal, 12, 1147–1164.Pew, H., Plowman, D., & Hancock, P. (2008). The involving research on intellectual capital. Journal of Intellectual Capital, 9, 585–608.Potter, R. E., & Balthazard, P. A. (2004). The role of individual memory and attention processes during electronic brainstorming. MIS Quarterly, 28(4), 621–643.Ramadani, V., Hyrije, A. A., Léo-Paul, D., Gadaf, R., & Sadudin, I. (2017). The impact of knowledge spillovers and innovation on firm-performance: findings from the Balkans countries. International Entrepreneurship Management Journal, 13, 299–325.Ren, S., Shu, R., Bao, Y., & Chen, X. (2016). Linking network ties to entrepreneurial opportunity discovery and exploitation: the role of affective and cognitive trust. International Entrepreneurship and Management Journal, 12(2), 465–485.Ringle, C. M., Wende, S., & Will, A. (2005). Smart PLS 2.0 (M3) beta, Hamburg: http://www.smartpls.de .Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM. MIS Quarterly, 36(1), iii–xiv.Sanchez, R., & Heene, A. (1997). A competence perspective on strategic learning and knowledge management. En Sanchez, R. and Heene, A. (eds.) Strategic learning and knowledge management. John Wiley and Sons.Seidler-de Alwis, R., & Hartmann, E. (2008). The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises. Journal of Knowledge Management, 12(1), 133–147.Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20, 7–28.Tansky, J., Ribeiro, D., & Roig, S. (2010). Linking entrepreneurship and human resources in globalization. Human Resource Management, 49(2), 217–223.Teece, D. (2012). Dynamic capabilities: routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401.Tenenhaus, M., Vinzi, V., Chatelin, Y., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 49, 159–205.vande Vrande, V., de Jong, J., Vanhaverbeke, W., & Rochemont, M. (2009). Open innovation in SMEs: trends, motives and management challenges. Technovation, 29, 423–437.Vargas, N., & Lloria, M. B. (2014). Dynamizing intellectual capital through enablers and learning flows. Industrial Management and Data Systems, 114(1), 2–20.Vargas, N., & Lloria, M. B. (2017). Performance and intellectual capital: how enablers drive value creation in organisations. Knowledge and Process Management, 24(2), 114–124.Vargas, N., Lloria, M. B., & Roig-Dobón, S. (2016). Main drivers of human capital, learning and performance. The Journal of Technology Transfer, 41(5), 961–978.Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., & Iglesias, S. (2017). Signal processing on graphs for improving automatic credit card fraud detection. Proceeding of 2017 I.E. 51st international Carnahan Conference on Security Technology (ICCST 2017), https://doi.org/10.1109/CCST.2017.8167820 , 23–26 Oct, 2017, Madrid, Spain.Wallin, M. W., & Von Krogh, G. (2010). Organizing for open innovation: focus o the integration of knowledge. Organizational Dynamics, 39(2), 145–154.Wang, C. L., & Ahmed, P. K. (2004). Linking innovation and firm performance: a new approach. European International Journal of Technology Management, 27, 674–688.Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). Cambridge: Academic Press.Wold, H. (1985). Factors influencing the outcome of economic sanctions. In Sixto Ríos Honorary. Trabajos de Estadística and de Investigación Operativa, 36(3), 325–337
- …