Open Access
Issue
SHS Web Conf.
Volume 56, 2018
International Conference on Leadership and Management (ICLM 2018)
Article Number 05003
Number of page(s) 20
Section Preemptive Global Business Management
DOI https://doi.org/10.1051/shsconf/20185605003
Published online 14 November 2018
  1. Akter, S.,Wamba, S. F.,Gunasekaran, A.,Dubey, R., &Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113-131. doi: https://doi.org/10.1016/j.ijpe.2016.08.018 [CrossRef] [Google Scholar]
  2. Amin, S., &Aslam, S. (2017). Intellectual Capital, Innovation and Firm Performance of Pharmaceuticals: A Study of the London Stock Exchange. Journal of Information & Knowledge Management, 16 (02), 1750017. doi:10.1142/S0219649217500174 [CrossRef] [Google Scholar]
  3. Amini, S.,Gerostathopoulos, I., &Prehofer, C. (2017, 26-28 June 2017). Big data analytics architecture for real-time traffic control. Paper presented at the 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). [Google Scholar]
  4. Arunachalam, D.,Kumar, N., &Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436. doi: https://doi.org/10.1016/j.tre.2017.04.001 [CrossRef] [Google Scholar]
  5. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99-120. doi:10.1177/014920639101700108 [CrossRef] [Google Scholar]
  6. Buhl, H. U.,Röglinger, M.,Moser, F., &Heidemann, J. (2013). Big Data. Business & Information Systems Engineering, 5(2), 65-69. doi:10.1007/s12599-013-0249-5 [CrossRef] [Google Scholar]
  7. Carlsson, B. (2004). The Digital Economy: what is new and what is not?, 15(3), 245-264. [Google Scholar]
  8. Carmeli, A.,Brammer, S.,Gomes, E., &Tarba, S. Y. (2017). An organizational ethic of care and employee involvement in sustainability-related behaviors: A social identity perspective. Journal of Organizational Behavior, 38(9), 1380-1395. doi:10.1002/job.2185 [CrossRef] [Google Scholar]
  9. Chen, H.,Chiang, R. H. L., &Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188 [CrossRef] [Google Scholar]
  10. Chen, M.,Mao, S., &Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0 [CrossRef] [Google Scholar]
  11. Cheng, B.,Ioannou, I., &Serafeim, G. (2013). Corporate social responsibility and access to finance. Strategic Management Journal, 35(1), 1-23. doi:10.1002/smj.2131 [CrossRef] [Google Scholar]
  12. Cockayne, D. G. (2016). Sharing and neoliberal discourse: The economic function of sharing in the digital on-demand economy. Geoforum, 77, 73-82. doi: https://doi.org/10.1016/j.geoforum.2016.10.005 [CrossRef] [Google Scholar]
  13. Côrte-Real, N.,Oliveira, T., &Ruivo, P. (2017). Assessing business value of Big Data Analytics in European firms. Journal of Business Research, 70, 379-390. doi: https://doi.org/10.1016/j.jbusres.2016.08.011 [CrossRef] [Google Scholar]
  14. Cummins, F. A. (2017). Chapter 6 Enterprise Data Management. In F. A. Cummins (Ed.), Building the Agile Enterprise (Second Edition) (pp. 183-208). Boston: Morgan Kaufmann. [CrossRef] [Google Scholar]
  15. Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities: Harvard Business Review Press. [Google Scholar]
  16. Díaz-García, C.,González-Moreno, Á., &Sáez-Martínez, F. J. (2015). Eco-innovation: insights from a literature review. Innovation, 17(1), 6-23. doi:10.1080/14479338.2015.1011060 [CrossRef] [Google Scholar]
  17. Dumbill, E. (2012). Planning for Big Data: O’Reilly Media. [Google Scholar]
  18. Dumbill, E. (2013). Making sense of big data. In: Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA. [Google Scholar]
  19. Falagas, M. E.,Pitsouni, E. I.,Malietzis, G. A., &Pappas, G. (2007). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338-342. doi:10.1096/fj.07-9492LSF [CrossRef] [PubMed] [Google Scholar]
  20. Fan, J.,Han, F., &Liu, H. (2014). Challenges of Big Data analysis. National Science Review, 1(2), 293-314. doi:10.1093/nsr/nwt032 [CrossRef] [Google Scholar]
  21. Fernández-Miranda, S. S.,Marcos, M.,Peralta, M. E., &Aguayo, F. (2017). The challenge of integrating Industry 4.0 in the degree of Mechanical Engineering. Procedia Manufacturing, 13, 1229-1236. doi:10.1016/j.promfg.2017.09.039 [CrossRef] [Google Scholar]
  22. Fernando, Y., &Hor, W. L. (2017). Impacts of energy management practices on energy efficiency and carbon emissions reduction: A survey of malaysian manufacturing firms. [Google Scholar]
  23. Resources, Conservation and Recycling, 126, 62-73. doi:https://doi.org/10.1016/j.resconrec.2017.07.023 [CrossRef] [Google Scholar]
  24. Fernando, Y., &Wah, W. X. (2016). Moving forward a Parsimonious Model of Eco-Innovation: Results from a Content Analysis. In Handbook of Research on Climate Change Impact on Health and Environmental Sustainability (pp. 619-631): IGI Global. [CrossRef] [Google Scholar]
  25. Fernando, Y., &Wah, W. X. (2017). The impact of eco-innovation drivers on environmental performance: Empirical results from the green technology sector in Malaysia. Sustainable Production and Consumption, 12, 27-43. doi:https://doi.org/10.1016/j.spc.2017.05.002 [CrossRef] [Google Scholar]
  26. Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data: Myths, Misconceptions and Methods: Palgrave Macmillan UK. [CrossRef] [Google Scholar]
  27. Gandomi, A., &Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144. doi:https://doi.org/10.1016/j.ijinfomgt.2014.10.007 [CrossRef] [Google Scholar]
  28. Garifova, L. F. (2015). Infonomics and the Value of Information in the Digital Economy. 23, 738-743. [Google Scholar]
  29. Ghisetti, C.,Marzucchi, A., &Montresor, S. (2015). The open eco-innovation mode. An empirical investigation of eleven European countries. Research Policy, 44(5), 1080-1093. doi:https://doi.org/10.1016/j.respol.2014.12.001 [CrossRef] [Google Scholar]
  30. Glavas, A., &Piderit, S. K. (2009). How Does Doing Good Matter? Effects of Corporate Citizenship on Employees. The Journal of Corporate Citizenship(36), 51-70. [CrossRef] [Google Scholar]
  31. Gunasekaran, A.,Kumar Tiwari, M.,Dubey, R., &Fosso Wamba, S. (2016). Big data and predictive analytics applications in supply chain management. Computers & Industrial Engineering, 101, 525-527. doi:https://doi.org/10.1016/j.cie.2016.10.020 [CrossRef] [Google Scholar]
  32. Günther, W. A.,Rezazade Mehrizi, M. H.,Huysman, M., &Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209. doi:https://doi.org/10.1016/j.jsis.2017.07.003 [CrossRef] [Google Scholar]
  33. Gupta, M., &George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. doi:https://doi.org/10.1016/j.im.2016.07.004 [CrossRef] [Google Scholar]
  34. Hemerly, J. (2013). Public Policy Considerations for Data-Driven Innovation. Computer, 46(6), 25-31. doi:10.1109/MC.2013.186 [CrossRef] [Google Scholar]
  35. Hojeghan, S. B.,Esfangareh, A. N. J. P.-S., &Sciences, B. (2011). Digital economy and tourism impacts, influences and challenges. 19, 308-316. [Google Scholar]
  36. Hojnik, J.,Ruzzier, M., &Antončič, B. (2017). Drivers of eco-innovation: empirical evidence from Slovenia. International Journal of Entrepreneurship and Innovation Management, 21(4-5), 422-440. doi:10.1504/IJEIM.2017.085688 [CrossRef] [Google Scholar]
  37. Inniss, D., &Rubenstein, R. (2017). Chapter 6 The Data Center: A Central Cog in the Digital Economy. In D. Inniss & R. Rubenstein (Eds.), Silicon Photonics (pp. 119-132). Oxford: Morgan Kaufmann. [CrossRef] [Google Scholar]
  38. Irani, Z. (2010). Investment evaluation within project management: an information systemsperspective. Journal of the Operational Research Society, 61(6), 917-928. doi:10.1057/jors.2010.10 [CrossRef] [Google Scholar]
  39. Janssen, M.,van der Voort, H., &Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. doi:https://doi.org/10.1016/j.jbusres.2016.08.007 [CrossRef] [Google Scholar]
  40. Jetzek, T.,Avital, M., &Bjørn-Andersen, N. (2014). Generating sustainable value from open data in a sharing society. Paper presented at the International Working Conference on Transfer and Diffusion of IT. [Google Scholar]
  41. Johnson, J. S.,Friend, S. B., &Lee, H. S. (2017). Big Data Facilitation, Utilization, and Monetization: Exploring the 3Vs in a New Product Development Process. Journal of Product Innovation Management, 34(5), 640-658. doi:10.1111/jpim.12397 [CrossRef] [Google Scholar]
  42. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. doi:10.1007/s10708-013-9516-8 [CrossRef] [Google Scholar]
  43. Krisher, T., &Durbin, D.-A. (2017). Tesla or GM? Investors bet on promise over profits. Chicago Tribune. Retrieved from http://www.chicagotribune.com/classified/automotive/sc-tesla-general-motors-autocover-0608-20170601-story.html [Google Scholar]
  44. Lee, K.-H., &Min, B. (2015). Green R&D for eco-innovation and its impact on carbon emissions and firm performance. Journal of Cleaner Production, 108, 534-542. doi:https://doi.org/10.1016/j.jclepro.2015.05.114 [CrossRef] [Google Scholar]
  45. Leonidou, L. C.,Christodoulides, P.,Kyrgidou, L. P., &Palihawadana, D. (2017). Internal Drivers and Performance Consequences of Small Firm Green Business Strategy: The Moderating Role of External Forces. Journal of Business Ethics, 140(3), 585-606. doi:10.1007/s10551-015-2670-9 [CrossRef] [Google Scholar]
  46. Li, F. (2018). The digital transformation of business models in the creative industries: A holistic framework and emerging trends. Technovation. doi:https://doi.org/10.1016/j.technovation.2017.12.004 [Google Scholar]
  47. Li, Q.,Luo, H.,Xie, P.-X.,Feng, X.-Q., &Du, R.-Y. (2015). Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment. Computers in Industry, 70, 23-45. doi:https://doi.org/10.1016/j.compind.2015.01.011 [CrossRef] [Google Scholar]
  48. Lin, Y., &Wu, L.-Y. (2014). Exploring the role of dynamic capabilities in firm performance under the resource-based view framework. Journal of Business Research, 67(3), 407-413. doi:https://doi.org/10.1016/j.jbusres.2012.12.019 [CrossRef] [Google Scholar]
  49. Long, X.,Chen, Y.,Du, J.,Oh, K.,Han, I., &Yan, J. (2017). The effect of environmental innovation behavior on economic and environmental performance of 182 Chinese firms. Journal of Cleaner Production, 166, 1274-1282. doi:https://doi.org/10.1016/j.jclepro.2017.08.070 [CrossRef] [Google Scholar]
  50. Man, J. C. d., &Strandhagen, J. O. (2017). An Industry 4.0 Research Agenda for Sustainable Business Models. Procedia CIRP, 63, 721-726. doi:https://doi.org/10.1016/j.procir.2017.03.315 [CrossRef] [Google Scholar]
  51. Martin, C. J. (2016). The sharing economy: A pathway to sustainability or a nightmarish form of neoliberal capitalism? Ecological Economics, 121, 149-159. doi:https://doi.org/10.1016/j.ecolecon.2015.11.027 [CrossRef] [Google Scholar]
  52. Marz, N., &Warren, J. (2015). Big Data: Principles and best practices of scalable realtime data systems: Manning Publications Co [Google Scholar]
  53. Mat Dahan, S.,Mohd Yusof, S. r., &Taib, M. Y. (2017). Performance measure of eco-process innovation: insights from a literature review. MATEC Web Conf., 131. [CrossRef] [Google Scholar]
  54. McAfee, A., &Brynjolfsson, E. (2012). Big data: the management revolution. [Google Scholar]
  55. McIntyre, J. R.,Ivanaj, S., &Ivanaj, V. (2013). Strategies for sustainable technologies and innovations. [CrossRef] [Google Scholar]
  56. Morabito, V. (2014). Trends and Challenges in Digital Business Innovation: Springer International Publishing. [CrossRef] [Google Scholar]
  57. Morabito, V. (2015). Big Data and Analytics: Strategic and Organizational Impacts: Springer International Publishing. [Google Scholar]
  58. Munodawafa, R. T., &Johl, S. K. (2018). Eco-Innovation And Firm Performance: Is Leadership The Game Changer? Paper presented at the Asia International Multidisciplinary Conference 2017. https://www.futureacademy.org.uk/files/images/upload/AIMC2017F94.pdf [Google Scholar]
  59. Newbert, S. L. (2008). Value, rareness, competitive advantage, and performance: a conceptual-level empirical investigation of the resource-based view of the firm. Strategic Management Journal, 29(7), 745-768. doi:10.1002/smj.686 [CrossRef] [Google Scholar]
  60. Newsham, G. R.,Mancini, S., &Birt, B. J. (2009). Do LEED-certified buildings save energy? Yes, but…. Energy and Buildings, 41(8), 897-905. doi:https://doi.org/10.1016/j.enbuild.2009.03.014 [CrossRef] [Google Scholar]
  61. Ociepa-Kubicka, A., &Pachura, P. (2017). Eco-innovations in the functioning of companies. Environmental Research, 156, 284-290. doi:https://doi.org/10.1016/j.envres.2017.02.027 [CrossRef] [Google Scholar]
  62. OECD. (2017). Green Finance and Investment Mobilising Bond Markets for a Low-Carbon Transition: OECD Publishing. [Google Scholar]
  63. Pereira, A. C., &Romero, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13, 1206-1214. doi:10.1016/j.promfg.2017.09.032 [CrossRef] [Google Scholar]
  64. Pries, K. H., &Dunnigan, R. (2015). Big Data Analytics: A practical guide for managers: Auerbach Publications. [CrossRef] [Google Scholar]
  65. Provost, F., &Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51-59. doi:10.1089/big.2013.1508 [CrossRef] [Google Scholar]
  66. Rennings, K. (2000). Redefining innovation — eco-innovation research and the contribution from ecological economics. Ecological Economics, 32(2), 319-332. doi:https://doi.org/10.1016/S0921-8009(99)00112-3 [CrossRef] [Google Scholar]
  67. Reypens, C.,Lievens, A., &Blazevic, V. (2016). Leveraging value in multi-stakeholder innovation networks: A process framework for value co-creation and capture. Industrial Marketing Management, 56, 40-50. doi:https://doi.org/10.1016/j.indmarman.2016.03.005 [CrossRef] [Google Scholar]
  68. Sagiroglu, S., &Sinanc, D. (2013, 20-24 May 2013). Big data: A review. Paper presented at the 2013 International Conference on Collaboration Technologies and Systems (CTS). [Google Scholar]
  69. Schneider, S., &Spieth, P. J. I. J. o. I. M. (2013). Business model innovation: Towards an integrated future research agenda. 17(01), 1340001. [Google Scholar]
  70. Schuster, A. J. (2017). Understanding Information: From the Big Bang to Big Data: Springer International Publishing. [Google Scholar]
  71. Secundo, G.,Del Vecchio, P.,Dumay, J., &Passiante, G. (2017). Intellectual capital in the age of Big Data: establishing a research agenda. Journal of Intellectual Capital, 18(2), 242-261. doi:10.1108/JIC-10-2016-0097 [CrossRef] [Google Scholar]
  72. Seliger, G. (2001). Product Innovation – Industrial Approach. CIRP Annals, 50(2), 425-443. doi:https://doi.org/10.1016/S0007-8506(07)62989-8 [CrossRef] [Google Scholar]
  73. Shaharudin, M. S.,Fernando, Y., &Wah, W. X. (2016). Does a firm’s innovation category matter in practising eco-innovation? Evidence from the lens of Malaysia companies practicing green technology. Journal of Manufacturing Technology Management, 27(2), 208-233. doi:10.1108/JMTM-02-2015-0008 [CrossRef] [Google Scholar]
  74. Sheng, J.,Amankwah-Amoah, J., &Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97-112. doi:https://doi.org/10.1016/j.ijpe.2017.06.006 [CrossRef] [Google Scholar]
  75. Shrivastava, P. (2013). Sustainable Innovation Responses to Global Climate Change. In: Edward Elgar Publishing. [Google Scholar]
  76. Shrivastava, P.,Ivanaj, S., &Ivanaj, V. (2016). Strategic technological innovation for sustainable development. International Journal of Technology Management, 70(1), 76-107. doi:10.1504/IJTM.2016.074672 [CrossRef] [Google Scholar]
  77. Sivarajah, U.,Kamal, M. M.,Irani, Z., &Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. doi:https://doi.org/10.1016/j.jbusres.2016.08.001 [CrossRef] [Google Scholar]
  78. Tan, K. H.,Zhan, Y.,Ji, G.,Ye, F., &Chang, C. (2015). Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. International Journal of Production Economics, 165, 223-233. doi:https://doi.org/10.1016/j.ijpe.2014.12.034 [CrossRef] [Google Scholar]
  79. Tantalo, C., &Priem, R. L. (2014). Value creation through stakeholder synergy. Strategic Management Journal, 37(2), 314-329. doi:10.1002/smj.2337 [CrossRef] [Google Scholar]
  80. Teece, D. J. (2014). A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. Journal of International Business Studies, 45(1), 8-37. doi:10.1057/jibs.2013.54 [CrossRef] [Google Scholar]
  81. Terzi, D. S.,Terzi, R., &Sagiroglu, S. (2017, 5-8 Oct. 2017). Big data analytics for network anomaly detection from netflow data. Paper presented at the 2017 International Conference on Computer Science and Engineering (UBMK). [Google Scholar]
  82. Tippins, M. J., &Sohi, R. S. (2003). IT competency and firm performance: is organizational learning a missing link? Strategic Management Journal, 24(8), 745-761. doi:10.1002/smj.337 [CrossRef] [Google Scholar]
  83. Tjahjono, B.,Esplugues, C.,Ares, E., &Pelaez, G. (2017). What does Industry 4.0 mean to Supply Chain? Procedia Manufacturing, 13, 1175-1182. doi:https://doi.org/10.1016/j.promfg.2017.09.191 [CrossRef] [Google Scholar]
  84. Tulder, R. v. (2014). Managing the transition to a sustainable enterprise : lessons from frontrunner companies. [Google Scholar]
  85. Vargo, S. L.,Wieland, H., &Akaka, M. A. (2015). Innovation through institutionalization: A service ecosystems perspective. Industrial Marketing Management, 44, 63-72. doi:https://doi.org/10.1016/j.indmarman.2014.10.008 [CrossRef] [Google Scholar]
  86. Wamba, S. F.,Gunasekaran, A.,Akter, S.,Ren, S. J.-f.,Dubey, R., &Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. doi:https://doi.org/10.1016/j.jbusres.2016.08.009 [CrossRef] [Google Scholar]
  87. Wang, G.,Gunasekaran, A.,Ngai, E. W. T., &Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.doi:https://doi.org/10.1016/j.ijpe.2016.03.014 [CrossRef] [Google Scholar]
  88. Watson, R.,Wilson, H. N.,Smart, P., &Macdonald, E. K. (2017). Harnessing Difference: A Capability-Based Framework for Stakeholder Engagement in Environmental Innovation. Journal of Product Innovation Management, 35(2), 254-279. doi:10.1111/jpim.12394 [CrossRef] [Google Scholar]
  89. Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171-180. doi:10.1002/smj.4250050207 [CrossRef] [Google Scholar]
  90. Wheelen, T. L.,Hunger, J. D.,Hoffman, A. N., &Bamford, C. E. (2014). Strategic management and business policy : globalization, innovation, and sustainability. [Google Scholar]
  91. Xavier, A. F.,Naveiro, R. M.,Aoussat, A., &Reyes, T. (2017). Systematic literature review of eco-innovation models: Opportunities and recommendations for future research. Journal of Cleaner Production, 149, 1278-1302. doi:https://doi.org/10.1016/j.jclepro.2017.02.145 [CrossRef] [Google Scholar]
  92. Zhang, F.,Wang, Y.,Li, D., &Cui, V. (2017). Configurations of Innovations across Domains: An Organizational Ambidexterity View. Journal of Product Innovation Management, 34(6), 821-841. doi:10.1111/jpim.12362 [CrossRef] [Google Scholar]
  93. Zhong, R. Y.,Huang, G. Q.,Lan, S.,Dai, Q. Y.,Chen, X., &Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260-272. doi:https://doi.org/10.1016/j.ijpe.2015.02.014 [CrossRef] [Google Scholar]
  94. Zhong, R. Y.,Xu, X.,Klotz, E., &Newman, S. T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616-630. doi:https://doi.org/10.1016/J.ENG.2017.05.015 [CrossRef] [Google Scholar]
  95. Zikopoulos, P.,Deroos, D.,Parasuraman, K.,Deutsch, T.,Giles, J., &Corrigan, D. (2013). Harness the power of big data: The IBM big data platform: McGraw-Hill New York, NY. [Google Scholar]
  96. Zikopoulos, P., &Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data: McGraw-Hill Osborne Media. [Google Scholar]

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