Measuring and linking social network knowledge exchange and organisational performance

[EN] This paper deals with how to measure social network knowledge exchange and its link with organisational performance. First, it revises both social network analysis and performance measurement techniques, highlighting the main performance elements that may be used. Then, it categorises the main social knowledge network working levels and components from a performance management perspective. It presents the main dimensions of social knowledge networks (actors, knowledge exchange and performance elements) from the performance management point of view. Last but not least, it describes and categorises the main techniques that could be used in order to determine the current and future position inside the social knowledge network (Data Envelopment Analysis). It also emphases the characteristics and pitfalls of the subjective (Analytic Network Processes) and objective techniques (multivariate models), that tend to be used when analyzing the link between social network knowledge exchange and organisational performance.The research reported in this paper is supported by the European Commission for the project “Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)” (FP7-PEOPLE-2013-IAPP 324408)”Rodríguez Rodríguez, R.; Mula, J.; Gómez-Gasquet, P.; Leon, R. (2015). Measuring and linking social network knowledge exchange and organisational performance. InImpact. 8(2):572-583. http://hdl.handle.net/10251/109192S5725838

Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization

[EN] The paper aims to identify the individuals who influence the knowledge sharing processes from an internal social network and to forecast the future knowledge flows that may cross it. Exploratory research is employed, and a four-phase methodology is developed which combines a social network analysis with structural modeling. This is applied to the internal enterprise social network used by a British insurance company. The main results emphasize the most influential groups, their relationships, future knowledge flows, and the connection between the network's heterogeneity and structure, and employees' future knowledge sharing intention. These findings have both theoretical and practical implications. The theory is extended by proving that a social network analysis can be used as a tool for evaluating and predicting future knowledge flows. At the same time, a solution is offered to decision-makers so they will be able to: (i) identify the potential knowledge loss; (ii) determine leaders; (iii) establish who is going to act as a knowledge diffuser, by sharing what they know with their coworkers, and who is going to act as a knowledge repository, by focusing on acquiring increasingly more knowledge; (iv) identify the elements that influence employees' future knowledge sharing intention. (C) 2016 Elsevier Inc. All rights reserved."The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; Rodríguez Rodríguez, R.; Gómez-Gasquet, P.; Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change. 114:103-118. https://doi.org/10.1016/j.techfore.2016.07.032S10311811

An Overview of the Polymorphisms of Circadian Genes Associated With Endocrine Cancer

A major consequence of the world industrialized lifestyle is the increasing period of unnatural light in environments during the day and artificial lighting at night. This major change disrupts endogenous homeostasis with external circadian cues, which has been associated to higher risk of diseases affecting human health, mainly cancer among others. Circadian disruption promotes tumor development and accelerate its fast progression. The dysregulation mechanisms of circadian genes is greatly affected by the genetic variability of these genes. To date, several core circadian genes, also called circadian clock genes, have been identified, comprising the following: ARNTL, CLOCK, CRY1, CRY2, CSNK1E, NPAS2, NR1D1, NR1D2, PER1, PER2, PER3, RORA, and TIMELESS. The polymorphic variants of these circadian genes might contribute to an individual's risk to cancer. In this short review, we focused on clock circadian clock-related genes, major contributors of the susceptibility to endocrine-dependent cancers through affecting circadian clock, most likely affecting hormonal regulation. We examined polymorphisms affecting breast, prostate and ovarian carcinogenesis, in addition to pancreatic and thyroid cancer. Further study of the genetic composition in circadian clock-controlled tumors will be of great importance by establishing the foundation to discover novel genetic biomarkers for cancer prevention, prognosis and target therapies

Platinacycles Containing a Primary Amine Platinum(II) Compounds for Treating Cisplatin-Resistant Cancers by Oxidant Therapy

Cisplatin is an efficient anticancer drug, but its effects are often lost after several chemotherapy cycles, showing important secondary effects. For these reasons, new anticancer agents, with different coordination properties and mechanisms of action, are needed. Here we describe the reaction of 2-phenylaniline with cis-[PtCl2(dmso)(2)] and sodium acetate to afford a cycloplatinated compound 2 and the synthesis and some biological studies of 3-6 (two neutral and two ionic compounds): [PtCl(C-N)(L)], C-N cycloplatinated 2-phenylaniline with L = PPh3(3) or P(4-FC6H4)(3) (4) and [Pt(C-N)(L-L)]Cl with L-L = Ph2PCH2CH2Ph2(5) or (C6F5)(2)PCH2-CH2(C6F5)(2) (6). Ionic platinacycles 5 and 6 show a greater antiproliferative activity than that of cisplatin in human lung, breast, and colon cancer cell lines (A-549, MDA-MB-231 and MCF-7, and HCT-116), a remarkable result given the fact that they do not show covalent interaction with DNA. 5 and 6 have also been found able to oxidize NADH by a catalytic process prod- oducing H2O2 as ROS. The activity of these complexes to generate ROS seems to be the key factor to explain their potent anticancer activity; it should be noted that platinum(II) complexes showing biocatalytic activity for hydride transfer from NADH have not been described so far. Ionic complex 6 shows low affinity to some target proteins; the presence of perfluoroaromatic rings seems to hinder its interaction with some biomolecules

Current Advances Research in Nutraceutical Compounds of Legumes, Pseudocereals and Cereals

The increase of the Western-type diet and life-style, with high content of highly processed fats, salt and sugar, as well as sedentary life, is directly linked to an increasing incidence of chronic diseases such as diabetes and obesity, cancer, cardiovascular diseases or stroke, and inflammatory-related diseases, which are a great challenge in global health and are usually associated with negative effects of globalization: rapid urbanization, diet and increased sedentary life worldwide. This has brought new interest and increased research into plant-based diets. In this context, the implementation in the diet of legumes, cereals and pseudo-cereals, due to their nutraceutical properties, which is interesting as well as advisable. These foods, in addition of having a high nutritional value themselves, have synergistic properties as part of a balanced diet. For example, most legumes are rich in lysine which is scarce in cereals, and these are rich in sulphur amino acids, such as methionine, while these amino acids are scarce in legumes and are of great importance for the central nervous system development. These foods or part of a food, due to their qualities, and that they provide health benefits can be classified as nutraceuticals. In addition, due to their health benefits beyond nutritional properties, can be classified as functional foods, promoting prevention and treatment for the above mentioned diseases, among others. This double function is due mainly to the proteins and the presence of various secondary metabolites and bioactive compounds in these foods of plant (grain and seed) origin. Last discovered knowledge and research features will be described in the present book chapter

Riociguat treatment in patients with chronic thromboembolic pulmonary hypertension: Final safety data from the EXPERT registry

Objective: The soluble guanylate cyclase stimulator riociguat is approved for the treatment of adult patients with pulmonary arterial hypertension (PAH) and inoperable or persistent/recurrent chronic thromboembolic pulmonary hypertension (CTEPH) following Phase

Business process improvement and the knowledge flows that cross a private online social network: An insurance supply chain case

[EN] This paper analyses how the knowledge shared between employees and suppliers within a private enterprise social network affects process improvement. Data was collected from internal documents, and the internal and external enterprise social networks used by an international insurance company; the average cycle time for handling 8494 claims and 3240 messages posted on the internal and external social networks was analysed. Social network analysis techniques were combined with principal component analysis and structural equation modeling, and the results demonstrate that the knowledge shared within the internal and external social network can explain 35.10% of process improvement variability, while the knowledge shared within the internal social network explains 89.90% of external social network variability. The analysis also demonstrates that: (i) the knowledge shared among employees positively affects process improvement; (ii) the knowledge shared among suppliers negatively affects process improvement; and (iii) the knowledge shared among employees positively affects the knowledge shared among supply chain members. These findings have theoretical and practical implications. They extend the literature in the knowledge management and information management field by offering empirical evidence of how the knowledge shared through an enterprise social network affects business process improvement, using the objective data provided by Yammer. They also provide a strategic tool for managers that will allow them to better understand how they can use the enterprise social network for business processes improvement.The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; Rodríguez Rodríguez, R.; Gómez-Gasquet, P.; Mula, J. (2020). Business process improvement and the knowledge flows that cross a private online social network: An insurance supply chain case. Information Processing & Management. 57(4):1-16. https://doi.org/10.1016/j.ipm.2020.102237S116574Aboelmaged, M. G. (2018). Knowledge sharing through enterprise social network (ESN) systems: motivational drivers and their impact on employees’ productivity. Journal of Knowledge Management, 22(2), 362-383. doi:10.1108/jkm-05-2017-0188Al Saifi, S. A., Dillon, S., & McQueen, R. (2016). The relationship between face to face social networks and knowledge sharing: an exploratory study of manufacturing firms. Journal of Knowledge Management, 20(2), 308-326. doi:10.1108/jkm-07-2015-0251Alavi, M., & Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25(1), 107. doi:10.2307/3250961Al-Thuhli, A., Al-Badawi, M., Baghdadi, Y., & Al-Hamdani, A. (2017). A Framework for Interfacing Unstructured Data Into Business Process From Enterprise Social Networks. International Journal of Enterprise Information Systems, 13(4), 15-30. doi:10.4018/ijeis.2017100102Amaratunga, D., Baldry, D., Sarshar, M., & Newton, R. (2002). Quantitative and qualitative research in the built environment: application of «mixed» research approach. Work Study, 51(1), 17-31. doi:10.1108/00438020210415488Aslam, U., Muqadas, F., Imran, M. K., & Ubaid-Ur-Rahman, U.-U.-R. (2018). Exploring the sources and role of knowledge sharing to overcome the challenges of organizational change implementation. International Journal of Organizational Analysis, 26(3), 567-581. doi:10.1108/ijoa-07-2017-1189Bakotic, D., & Krnic, A. (2017). Exploring the relationship between business process improvement and employees’ behavior. Journal of Organizational Change Management, 30(7), 1044-1062. doi:10.1108/jocm-06-2016-0116Balbi, S., Misuraca, M., & Scepi, G. (2018). Combining different evaluation systems on social media for measuring user satisfaction. Information Processing & Management, 54(4), 674-685. doi:10.1016/j.ipm.2018.04.009Barney, J. B. (1996). The Resource-Based Theory of the Firm. Organization Science, 7(5), 469-469. doi:10.1287/orsc.7.5.469Bassano, C., Piciocchi, P., Spohrer, J. («Jim») C., & Pietronudo, M. C. (2018). Managing value co-creation in consumer service systems within smart retail settings. Journal of Retailing and Consumer Services, 45, 190-197. doi:10.1016/j.jretconser.2018.09.008Behrend, F. D., & Erwee, R. (2009). Mapping knowledge flows in virtual teams with SNA. Journal of Knowledge Management, 13(4), 99-114. doi:10.1108/13673270910971860Berraies, S. (2019). The effect of enterprise social networks use on exploitative and exploratory innovations. Journal of Intellectual Capital, 20(3), 426-452. doi:10.1108/jic-02-2019-0030boyd, danah m., & Ellison, N. B. (2007). Social Network Sites: Definition, History, and Scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230. doi:10.1111/j.1083-6101.2007.00393.xBrajer-Marczak, R. (2016). Elements of knowledge management in the improvement of business processes. Management, 20(2), 242-260. doi:10.1515/manment-2015-0063Capó‐Vicedo, J., Mula, J., & Capó, J. (2011). A social network‐based organizational model for improving knowledge management in supply chains. Supply Chain Management: An International Journal, 16(5), 379-388. doi:10.1108/13598541111155884Carter, C. R., Ellram, L. M., & Tate, W. (2007). THE USE OF SOCIAL NETWORK ANALYSIS IN LOGISTICS RESEARCH. Journal of Business Logistics, 28(1), 137-168. doi:10.1002/j.2158-1592.2007.tb00235.xCetto, A., Klier, M., Richter, A., & Zolitschka, J. F. (2018). «Thanks for sharing»—Identifying users’ roles based on knowledge contribution in Enterprise Social Networks. Computer Networks, 135, 275-288. doi:10.1016/j.comnet.2018.02.012Chandra, R., Iyer, R. S., & Raman, R. (2015). Enabling organizations to implement smarter, customized social computing platforms by leveraging knowledge flow patterns. Journal of Knowledge Management, 19(1), 95-107. doi:10.1108/jkm-11-2014-0455Charoensukmongkol, P. (2014). Effects of support and job demands on social media use and work outcomes. Computers in Human Behavior, 36, 340-349. doi:10.1016/j.chb.2014.03.061Choo, A. S., Linderman, K. W., & Schroeder, R. G. (2007). Method and Psychological Effects on Learning Behaviors and Knowledge Creation in Quality Improvement Projects. Management Science, 53(3), 437-450. doi:10.1287/mnsc.1060.0635Chreim, S., Williams, B. E. (Bernie), & Hinings, C. R. (Bob). (2007). Interlevel Influences on the Reconstruction of Professional Role Identity. Academy of Management Journal, 50(6), 1515-1539. doi:10.5465/amj.2007.28226248Coleman, J. S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94, S95-S120. doi:10.1086/228943Collinson, S., & Wilson, D. C. (2006). Inertia in Japanese Organizations: Knowledge Management Routines and Failure to Innovate. Organization Studies, 27(9), 1359-1387. doi:10.1177/0170840606067248Dartmouth, U.M.ass (2018). 2018 Fortune 500 Social Media Research. Retrieved fromhttps://www.umassd.edu/cmr/socialmediaresearch/2018fortune500/.El Ouirdi, A., El Ouirdi, M., Segers, J., & Henderickx, E. (2015). Employees’ use of social media technologies: a methodological and thematic review. Behaviour & Information Technology, 34(5), 454-464. doi:10.1080/0144929x.2015.1004647Eling, M., & Jia, R. (2019). Efficiency and profitability in the global insurance industry. Pacific-Basin Finance Journal, 57, 101190. doi:10.1016/j.pacfin.2019.101190Fang, R., Chi, L., Chen, M., & Baron, R. A. (2014). Bringing Political Skill into Social Networks: Findings from a Field Study of Entrepreneurs. Journal of Management Studies, 52(2), 175-212. doi:10.1111/joms.12107Fronzetti Colladon, A., & Vagaggini, F. (2017). Robustness and stability of enterprise intranet social networks: The impact of moderators. Information Processing & Management, 53(6), 1287-1298. doi:10.1016/j.ipm.2017.07.001Fulk, J., & Yuan, Y. C. (2013). Location, Motivation, and Social Capitalization via Enterprise Social Networking. Journal of Computer-Mediated Communication, 19(1), 20-37. doi:10.1111/jcc4.12033Galinsky, A. D., Seiden, V. L., Kim, P. H., & Medvec, V. H. (2002). The Dissatisfaction of Having Your First Offer Accepted: The Role of Counterfactual Thinking in Negotiations. Personality and Social Psychology Bulletin, 28(2), 271-283. doi:10.1177/0146167202282012Granovetter, M. (1985). Economic Action and Social Structure: The Problem of Embeddedness. American Journal of Sociology, 91(3), 481-510. doi:10.1086/228311Grant, S. B., & Preston, T. A. (2019). Using social power and influence to mobilise the supply chain into knowledge sharing: A case in insurance. Information & Management, 56(5), 625-639. doi:10.1016/j.im.2018.10.004Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi:10.1108/ebr-11-2018-0203Hayes, A. F., & Krippendorff, K. (2007). Answering the Call for a Standard Reliability Measure for Coding Data. Communication Methods and Measures, 1(1), 77-89. doi:10.1080/19312450709336664Heimbach, I., Gottschlich, J., & Hinz, O. (2015). The value of user’s Facebook profile data for product recommendation generation. Electronic Markets, 25(2), 125-138. doi:10.1007/s12525-015-0187-9Holsapple, C. W., Hsiao, S.-H., & Pakath, R. (2018). Business social media analytics: Characterization and conceptual framework. Decision Support Systems, 110, 32-45. doi:10.1016/j.dss.2018.03.004Hu, Q., Williams, S., Mason, R., & Found, P. (2019). Knowledge management in consultancy-involved process improvement projects: cases from Chinese SMEs. Production Planning & Control, 30(10-12), 866-880. doi:10.1080/09537287.2019.1582095Hung, S.-W., Chen, P.-C., & Chung, C.-F. (2013). Gaining or losing? The social capital perspective on supply chain members’ knowledge sharing of green practices. Technology Analysis & Strategic Management, 26(2), 189-206. doi:10.1080/09537325.2013.850475Huybrechts, B., & Haugh, H. (2017). The Roles of Networks in Institutionalizing New Hybrid Organizational Forms: Insights from the European Renewable Energy Cooperative Network. Organization Studies, 39(8), 1085-1108. doi:10.1177/0170840617717097Idrees, I. A., Vasconcelos, A. C., & Ellis, D. (2018). Clique and elite: inter-organizational knowledge sharing across five star hotels in the Saudi Arabian religious tourism and hospitality industry. Journal of Knowledge Management, 22(6), 1358-1378. doi:10.1108/jkm-07-2017-0276Jones, C., Hesterly, W. S., & Borgatti, S. P. (1997). A General Theory of Network Governance: Exchange Conditions and Social Mechanisms. Academy of Management Review, 22(4), 911-945. doi:10.5465/amr.1997.9711022109Kane, G. C. (2017). The evolutionary implications of social media for organizational knowledge management. Information and Organization, 27(1), 37-46. doi:10.1016/j.infoandorg.2017.01.001Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:10.1016/j.bushor.2009.09.003Khanbabaei, M., Alborzi, M., Sobhani, F. M., & Radfar, R. (2019). Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement. Knowledge and Process Management, 26(2), 123-139. doi:10.1002/kpm.1595Kogut, B., & Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3(3), 383-397. doi:10.1287/orsc.3.3.383Kogut, B., & Zander, U. (1996). What Firms Do? Coordination, Identity, and Learning. Organization Science, 7(5), 502-518. doi:10.1287/orsc.7.5.502Kolleck, N. (2013). Social network analysis in innovation research: using a mixed methods approach to analyze social innovations. European Journal of Futures Research, 1(1). doi:10.1007/s40309-013-0025-2Krumeich, J., Weis, B., Werth, D., & Loos, P. (2014). Event-Driven Business Process Management: where are we now? Business Process Management Journal, 20(4), 615-633. doi:10.1108/bpmj-07-2013-0092Kuegler, M., Smolnik, S., & Kane, G. (2015). What’s in IT for employees? Understanding the relationship between use and performance in enterprise social software. The Journal of Strategic Information Systems, 24(2), 90-112. doi:10.1016/j.jsis.2015.04.001Lee, I. (2018). Social media analytics for enterprises: Typology, methods, and processes. Business Horizons, 61(2), 199-210. doi:10.1016/j.bushor.2017.11.002Lee, S., Ha, T., Lee, D., & Kim, J. H. (2018). Understanding the majority opinion formation process in online environments: An exploratory approach to Facebook. Information Processing & Management, 54(6), 1115-1128. doi:10.1016/j.ipm.2018.08.002Leon, R. – D., Rodríguez-Rodríguez, R., Gómez-Gasquet, P., & Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change, 114, 103-118. doi:10.1016/j.techfore.2016.07.032Li, Y.-M., Liao, T.-F., & Lai, C.-Y. (2012). A social recommender mechanism for improving knowledge sharing in online forums. Information Processing & Management, 48(5), 978-994. doi:10.1016/j.ipm.2011.10.004Lin, Z. (John), Yang, H., & Demirkan, I. (2007). The Performance Consequences of Ambidexterity in Strategic Alliance Formations: Empirical Investigation and Computational Theorizing. Management Science, 53(10), 1645-1658. doi:10.1287/mnsc.1070.0712Linderman, K., Schroeder, R. G., & Sanders, J. (2010). A Knowledge Framework Underlying Process Management*. Decision Sciences, 41(4), 689-719. doi:10.1111/j.1540-5915.2010.00286.xLiu, C., Liu, Y.-H., Gedeon, T., Zhao, Y., Wei, Y., & Yang, F. (2019). The effects of perceived chronic pressure and time constraint on information search behaviors and experience. Information Processing & Management, 56(5), 1667-1679. doi:10.1016/j.ipm.2019.04.004Liu, Z., & Jansen, B. J. (2017). Identifying and predicting the desire to help in social question and answering. Information Processing & Management, 53(2), 490-504. doi:10.1016/j.ipm.2016.05.001Mäntymäki, M., & Riemer, K. (2016). Enterprise social networking: A knowledge management perspective. International Journal of Information Management, 36(6), 1042-1052. doi:10.1016/j.ijinfomgt.2016.06.009Massa, F. G. (2016). Guardians of the Internet: Building and Sustaining the Anonymous Online Community. Organization Studies, 38(7), 959-988. doi:10.1177/0170840616670436Massingham, P., & Al Holaibi, M. (2017). Embedding Knowledge Management into Business Processes. Knowledge and Process Management, 24(1), 53-71. doi:10.1002/kpm.1534Modrák, V. (2013). On the Measurement of Business Process Centralization. Procedia Technology, 9, 547-552. doi:10.1016/j.protcy.2013.12.061Nakano, M., & Oji, N. (2016). Success factors for continuous supply chain process improvement: evidence from Japanese manufacturers. International Journal of Logistics Research and Applications, 20(3), 217-236. doi:10.1080/13675567.2016.1219704Neshati, M., Fallahnejad, Z., & Beigy, H. (2017). On dynamicity of expert finding in community question answering. Information Processing & Management, 53(5), 1026-1042. doi:10.1016/j.ipm.2017.04.002Nisar, T. M., Prabhakar, G., & Strakova, L. (2019). Social media information benefits, knowledge management and smart organizations. Journal of Business Research, 94, 264-272. doi:10.1016/j.jbusres.2018.05.005Nonaka, I., & Konno, N. (1998). The Concept of «Ba»: Building a Foundation for Knowledge Creation. California Management Review, 40(3), 40-54. doi:10.2307/41165942Nourani, M., Devadason, E. S., Kweh, Q. L., & Lu, W.-M. (2016). Business excellence: the managerial and value-creation efficiencies of the insurance companies. Total Quality Management & Business Excellence, 28(7-8), 879-896. doi:10.1080/14783363.2015.1133244Olfat, M., Tabarsa, G. A., Ahmadi, S., & Shokouhyar, S. (2019). Disclosing the bright side of SNs in the workplace. Journal of Enterprise Information Management, 32(3), 390-412. doi:10.1108/jeim-10-2018-0215Oostervink, N., Agterberg, M., & Huysman, M. (2016). Knowledge Sharing on Enterprise Social Media: Practices to Cope With Institutional Complexity. Journal of Computer-Mediated Communication, 21(2), 156-176. doi:10.1111/jcc4.12153Panahi, S., Watson, J., & Partridge, H. (2016). Conceptualising social media support for tacit knowledge sharing: physicians’ perspectives and experiences. Journal of Knowledge Management, 20(2), 344-363. doi:10.1108/jkm-06-2015-0229Parise, S. (2009). Social Media Networks: What Do They Mean for Knowledge Management? Journal of Information Technology Case and Application Research, 11(2), 1-11. doi:10.1080/15228053.2009.10856156Pathak, S. D., Wu, Z., & Johnston, D. (2014). Toward a structural view of co-opetition in supply networks. Journal of Operations Management, 32(5), 254-267. doi:10.1016/j.jom.2014.04.001Patroni, J., von Briel, F., & Recker, J. (2016). How Enterprise Social Media Can Facilitate Innovation. IT Professional, 18(6), 34-41. doi:10.1109/mitp.2016.102Putnam, R. D. (1995). Bowling Alone: America’s Declining Social Capital. Journal of Democracy, 6(1), 65-78. doi:10.1353/jod.1995.0002Qi, C., & Chau, P. Y. K. (2017). Will enterprise social networking systems promote knowledge management and organizational learning? An empirical study. Journal of Organizational Computing and Electronic Commerce, 28(1), 31-57. doi:10.1080/10919392.2018.1407081Rafique, M., Hameed, S., & Agha, M. H. (2018). Impact of knowledge sharing, learning adaptability and organizational commitment on absorptive capacity in pharmaceutical firms based in Pakistan. Journal of Knowledge Management, 22(1), 44-56. doi:10.1108/jkm-04-2017-0132Reagans, R., Singh, P. V., & Krishnan, R. (2015). Forgotten Third Parties: Analyzing the Contingent Association Between Unshared Third Parties, Knowledge Overlap, and Knowledge Transfer Relationships with Outsiders. Organization Science, 26(5), 1400-1414. doi:10.1287/orsc.2015.0976Riemer, K., Stieglitz, S., & Meske, C. (2015). From Top to Bottom. Business & Information Systems Engineering, 57(3), 197-212. doi:10.1007/s12599-015-0375-3Roberts, P. W., & Sterling, A. D. (2012). Network Progeny? Prefounding Social Ties and the Success of New Entrants. Management Science, 58(7), 1292-1304. doi:10.1287/mnsc.1110.1484Rodriguez, R. R., Saiz, J. J. A., & Bas, A. O. (2009). Quantitative relationships between key performance indicators for supporting decision-making processes. Computers in Industry, 60(2), 104-113. doi:10.1016/j.compind.2008.09.002Roelens, I., Baecke, P., & Benoit, D. F. (2016). Identifying influencers in a social network: The value of real referral data. Decision Support Systems, 91, 25-36. doi:10.1016/j.dss.2016.07.005Ashok Sarkar, S., Ranjan Mukhopadhyay, A., & Ghosh, S. K. (2013). Improvement of claim processing cycle time through Lean Six Sigma methodology. International Journal of Lean Six Sigma, 4(2), 171-183. doi:10.1108/20401461311319347Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998-4010. doi:10.1016/j.jbusres.2016.06.007Melo, A. C. S., Netto, M. A. C., Ferreira Filho, V. J. M., & Fernandes, E. (2010). Knowledge management for improving business processes: an analysis of the transport management process for indivisible exceptional cargo. Pesquisa Operacional, 30(2), 305-330. doi:10.1590/s0101-74382010000200004TOPAL, B., & SAHIN, H. (2019). The Influence of Information Sharing in the Supply Chain Process on Business Performance: An Empirical Study. Studies in Informatics and Control, 27(2), 201-212. doi:10.24846/v27i2y201808Tsai, W., Su, K., & Chen, M.-J. (2011). Seeing Through the Eyes of a Rival: Competitor Acumen Based on Rival-Centric Perceptions. Academy of Management Journal, 54(4), 761-778. doi:10.5465/amj.2011.64870138Turban, E., Bolloju, N., & Liang, T.-P. (2011). Enterprise Social Networking: Opportunities, Adoption, and Risk Mitigation. Journal of Organizational Computing and Electronic Commerce, 21(3), 202-220. doi:10.1080/10919392.2011.590109Wetzels, Odekerken-Schröder, & van Oppen. (2009). Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33(1), 177. doi:10.2307/20650284White, G. R. T., Cicmil, S., Upadhyay, A., Subramanian, N., Kumar, V., & Dwivedi, A. (2019). The soft side of knowledge transfer partnerships between universities and small to medium enterprises: an exploratory study to understand process improvement. Production Planning & Control, 30(10-12), 907-918. doi:10.1080/09537287.2019.1582098Xue, B., Liu, B., & Sun, T. (2018). What Matters in Achieving Infrastructure Sustainability through Project Management Practices: A Preliminary Study of Critical Factors. Sustainability, 10(12), 4421. doi:10.3390/su10124421Xue, Y., Bradley, J., & Liang, H. (2011). Team climate, empowering leadership, and knowledge sharing. Journal of Knowledge Management, 15(2), 299-312. doi:10.1108/13673271111119709Yates, J., Orlikowski, W. J., & Okamura, K. (1999). Explicit and Implicit Structuring of Genres in Electronic Communication: Reinforcement and Change of Social Interaction. Organization Science, 10(1), 83-103. doi:10.1287/orsc.10.1.83Yousfi, A., Batoulis, K., & Weske, M. (2019). Achieving Business Process Improvement via Ubiquitous Decision-Aware Business Processes. ACM Transactions on Internet Technology, 19(1), 1-19. doi:10.1145/3298986Yuen, K. F., & Thai, V. (2017). Barriers to supply chain integration in the maritime logistics industry. Maritime Economics & Logistics, 19(3), 551-572. doi:10.1057/

Eosinophils in Colorectal Cancer: Emerging Insights into Anti-Tumoral Mechanisms and Clinical Implications

Eosinophils are myeloid effector cells whose main homing is the gastrointestinal tract. There, they take part in type I and type II immune responses. They also contribute to other non-immunological homeostatic functions like mucus production, tissue regeneration, and angiogenesis. In colorectal cancer (CRC), eosinophils locate in the center of the tumor and in the front of invasion and play an anti-tumoral role. They directly kill tumor cells by releasing cytotoxic compounds and eosinophil extracellular traps or indirectly by activating other immune cells via cytokines. As CRC progresses, the number of infiltrating eosinophils decreases. Although this phenomenon is not fully understood, it is known that some changes in the microenvironmental milieu and microbiome can affect eosinophil infiltration. Importantly, a high number of intratumoral eosinophils is a favorable prognostic factor independent from the tumor stage. Moreover, after immunotherapy, responding patients usually display eosinophilia, so eosinophils could be a good biomarker candidate to monitor treatment outcomes. Finally, even though eosinophils seem to play an interesting anti-tumoral role in CRC, much more research is needed to fully understand their interactions in the CRC microenvironment. This review explores the multifaceted roles of eosinophils in colorectal cancer, highlighting their anti-tumoral effects, prognostic significance, and potential as a biomarker for treatment outcomes

Melatonin as an antioxidant: biochemical mechanisms and pathophysiological implications in humans.

This brief resume enumerates the multiple actions of melatonin as an antioxidant. This indoleamine is produced in the vertebrate pineal gland, the retina and possibly some other organs. Additionally, however, it is found in invertebrates, bacteria, unicellular organisms as well as in plants, all of which do not have a pineal gland. Melatonin's functions as an antioxidant include: a), direct free radical scavenging, b), stimulation of antioxidative enzymes, c), increasing the efficiency of mitochondrial oxidative phosphorylation and reducing electron leakage (thereby lowering free radical generation), and 3), augmenting the efficiency of other antioxidants. There may be other functions of melatonin, yet undiscovered, which enhance its ability to protect against molecular damage by oxygen and nitrogen-based toxic reactants. Numerous in vitro and in vivo studies have documented the ability of both physiological and pharmacological concentrations to melatonin to protect against free radical destruction. Furthermore, clinical tests utilizing melatonin have proven highly successful; because of the positive outcomes of these studies, melatonin's use in disease states and processes where free radical damage is involved should be increased