• Ei tuloksia

The purpose of this research was to explore the opportunities BC offers for BDM by mapping out the challenges in BDM, researching if there are some ways BC could solve them and finding ways to solve the most common problems firms encounter in BC implementation in addition to identifying the most potential industries for the use of BC in BDM. A conceptual process model of BDM was developed based on KM theory and by identifying BD challenges within that model, the potential of BC for BDM was explored by a dual data collection method that combined online data collection and analysis of 40 articles and interviews with ten people with experience and knowledge of BC and BD.

The results show that BC can be used in BD collection, but storing BD on BC is not recommended right now unless the use case requires improving security or trust, because traditional databases are more cost-effective. After a few years though, BC can likely be used for BD storage more widely because the costs will decrease. BC can also be used to improve BDA by ensuring the quality of data, to simplify BD aggregation and to make aggregation and integration more transparent. It can also improve the transparency, accountability and trust related to data, so firms can have greater confidence making decisions based on it. BC also makes it possible to create digital identities to different things, making data more commensurate from identity perspective, which is important in terms of being able to take advantage of BD. Furthermore, the results show BC can improve data sharing between different organizations but using it for intra-organizational data sharing is not recommended.

BD security can also be improved with BC, but using BC for privacy protection is not very advisable because of the GDPR; even though the problem of PII and immutability could be solved by using hashed data or by storing only metadata of PII on the BC, it seems other solutions are better suited for such purposes.

This research makes a contribution to the current literature by combining BC and BDM from a strategic and organizational perspective through the lens of solving BDM challenges. It also provides useful findings and implications to organizations by showing how BC could be used in BDM, what matters need to be considered in the implementation of BC and what kinds of firms should implement BC in BDM.

REFERENCES

Abawajy, J. H., Kelarev, A. & Chowdhury, M. (2014) Large iterative multitier ensemble classifiers for security of big data. IEEE Transactions on Emerging Topics in Computing, 2(3), 352–363.

Abawajy, J. H. (2015) Comprehensive analysis of big data variety landscape.

International Journal of Parallel, Emergent and Distributed Systems, 30(1), 5–14.

Agarwal, R. & Dhar, V. (2014) Editorial – big data, data science, and analytics: the opportunity and challenge for is research. Information Systems Research, 25(3), 443–448.

Alavi, M. & Leidner, D. (2001) Knowledge management and knowledge

management systems: Conceptual foundations and research issues. Management Information Systems Quarterly, 25(1), 107–136.

Alavi, M., Kayworth, T.R. & Leidner, D.E. (2006) An empirical examination of the influence of organizational culture on knowledge management practices. Journal of Management Information Systems, 22(3), 191-224.

Al-Badi, A., Tarhini, A. & Khan, A. I. (2018) Exploring Big Data Governance Frameworks. Procedia Computer Science, 141, 271-277.

Al Nuaimi, E., Al Neyadi, H., Mohamed, N. & Al-Jaroodi, J. (2015) Applications of big data to smart cities. Journal of Internet Services and Applications, 6(1), 1-15.

American Marketing Association (2017) Definitions of Marketing: Definition of Marketing Research. [Online Source] [Published 2017] [Accessed 9.10.2019].

Available: https://www.ama.org/the-definition-of-marketing-what-is-marketing/

Barbierato, E., Gribaudo, M., & Iacono, M. (2014) Performance evaluation of NoSQL big data applications using multi-formalism models. Future Generation Computer Systems, 37, 345–353.

Bellazzi, R. (2014) Big Data and Bio-medical Informatics: A Challenging Opportunity. IMIA Yearbook of Med Inf. 8–13.

Bertot, J.C., Gorham, U., Jaeger, P.T., Sarin, L.C. & Choi, H. (2014) Big data, open government and e-Government: Issues, policies and recommendations.

Information Polity, 19(1), 5-16.

Bhimani, A. & Willcocks, L. (2014) Digitisation, Big Data and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.

Bizer, C., Boncz, P., Brodie, M.L. & Erling, O. (2012) The meaningful use of big data: Four perspectives - Four challenges. SIGMOD Record, 40(4), 56–60.

Blumer H. (1954) What is wrong with social theory. Am Sociol Rev 19(1), 3 –10.

Boyd, D. & Crawford, K. (2012) Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information Communication and Society, 15(5), 662-679.

Brands, K. (2014) Big Data and Business Intelligence for Management Accountants. Strategic Finance, 95(12), 64-65.

Bryman A. (1995) Quantity and quality in social research. London: Unwin Hyman.

Cai, L & Zhu, Y (2015) The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1-10.

Casey, M.J. & Wong, P. (2017) Global Supply Chains Are About to Get Better, Thanks to Blockchain. Harvard Business Review. [Online Article] [Published 13.3.2017] [Accessed 19.4.2019]. Available: https://hbr.org/2017/03/global-supply-chains-are-about-to-get-better-thanks-to-blockchain

Catalini, C. (2017) How Blockchain Applications Will Move Beyond Finance.

Harvard Business Review. [Online Article] [Published 2.3.2017] [Accessed 20.4.2019]. Available: https://hbr.org/2017/03/how-blockchain-applications-will-move-beyond-finance

Chen, C.L.P. & Zhang, C.Y. (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Information Sciences 275, 314-347.

Chen, G., Chen, K., Jiang, D., Ooi, B. C., Shi, L., Vo, H. T. & Wu, S. (2012) E3: an elastic execution engine for scalable data processing. Journal of Information Processing, 20(1), 65–76.

Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S. & Zhou, X. (2013) Big data challenge: a data management perspective. Frontiers of Computer Science, 7(2), 157–164.

Chen, M., & Liu, S. M. Y. (2014) Big data: A survey. Mobile Networks and Applications 19(2), 171-209.

Chen, Y., Argentinis, E. & Weber, G. (2016) IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research.

Clinical Therapeutics 38(4), 688-701.

Davenport, T., Barth, P. & Bean, R. (2012) How Big Data Is Different. MIT Sloan Management Review, 54(1), 43-46.

Davenport, T, DeLong, D. & Beers, M. (1998) Successful knowledge management projects. Sloan Management Review, 39, 43–57.

DGI (2020) Goals and Principles for Data Governance. Data Governance Institute.

[Online Source] [Cited 27.4.2020] Available:

http://www.datagovernance.com/adg_data_governance_goals/

Dedonate, M. J. & Sánchez de Pablo, J. D. (2015) The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of

Business Research 68, 360–370.

Dhillon, V., Metcalf, D. & Hooper, M. (2017) Blockchain Enabled Applications:

Understand the Blockchain Ecosystem and How to Make it Work for You. Apress, New York, USA.

DIAS (2020) Asuntokauppa on nyt digitaalista. DIAS. [Online Source] [Cited 6.6.2020] Available: https://dias.fi/

Dierickx, I. & Cool, K. (1989) Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504-1511.

Disparte, D. (2017) Blockchain Could Make the Insurance Industry Much More Transparent. Harvard Business Review. [Online Article] [Published 12.7.2017]

[Accessed 19.4.2019]. Available: https://hbr.org/2017/07/blockchain-could-make-the-insurance-industry-much-more-transparent

Drescher, D. (2017) Blockchain Basics: A Non-Technical Introduction in 25 Steps.

Springer, Frankfurt am Main, Germany.

Dubois, A. & Gadde, L.-E. (2002) Systematic combining: An abductive approach to case research. Journal of Business Research, 55, 553–560.

Dubois, A. & Gadde, L.-E. (2014) “Systematic combining”— A decade later. Journal of Business Research, 67, 1277–1284.

Dunning, J.H. & Lundan, S. M. (2008) Multinational Enterprises and the Global Economy, Second Edition. Edward Elgar Publishing Limited, Great Britain.

Eenmaa-Dimitrieva, H. & Schmidt-Kessen, M.J. (2019) Creating markets in no-trust environments: The Law and Economics of Smart Contracts. Computer Law and Security Review, 35(1), 69-88.

Einav, L. & Levin, J.D. (2013) The data revolution and economic analysis. National Bureau of Economic Research, 364(6210), 715-722.

Epps, T., Carey, B. & Upperton, T. (2019) Revolutionizing global supply chains one block at a time: Growing international trade with blockchain: Are international rules up to the task? Global Trade and Customs Journal, 14(4), 136-145.

Eriksson, P. (2017) The Role of Co-Creation in Enhancing Explorative and Exploitative Learning in Project-Based Settings. Project Management Journal, 48(4), 22-38.

Estrada, I., Faems, D. & de Faria, P. (2016) Coopetition and product innovation performance: The role of internal knowledge sharing mechanisms and formal knowledge protection mechanisms. Industrial Marketing Management, 53, 56-65.

Eurostat (2017) Big data conversion techniques including their main features and characteristics: 2017 Edition. Eurostat. [Online Document] [Published 1.8.2017]

[Cited 20.4.2020] Available:

https://ec.europa.eu/eurostat/documents/3888793/8123371/KS-TC-17-003-EN-N.pdf/ad617aaa-6d34-4f05-a341-fa8db6043045

Fan, J., Han, F. & Liu, H. (2014) Challenges of big data analysis. National Science Review, 1(2), 293-314.

Felin, T. & Lakhani, K. (2018) What Problems Will You Solve with Blockchain?

Massachusetts Institute of Technology. MIT Sloan Management Review Fall 2018, 32-38.

Ferguson, M. (2018) Preparing for a Blockchain Future. Massachusetts Institute of Technology. MIT Sloan Management Review Fall 2018.

Ferraris, A., Santoro, G. & Bresciani, S. (2017) Open innovation in multinational companies’ subsidiaries: the role of internal and external knowledge. European Journal of International Management, 11(4), 452-468.

Ferraris, A., Mazzoleni, A. & Devalle, A. (2019) Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision 12 September 2019, 57(8), 1923-1936.

Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G. & Gnanzou, D. (2015) How

‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study. International Journal of Production Economics.

Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S. Ji-fan., Dubey, R. &

Childe, S. J. (2017) Big data analytics and firm performance: effects of dynamic capabilities. Journal of Business Research, 70, 356-365.

Frické, M. (2009) The knowledge pyramid: Critique of the DIKW hierarchy. Journal of Information Science, 35 (2), 131–142

Gandomi, A. & Haider, M. (2015) Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Gasik, S. (2011) A model of project knowledge management. Project Management Journal, 42(3), 23-44.

Glaser, B. & Strauss, A. (1967) The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine Publishing.

Gold, A.H., Malhotra, A. & Segars, A.H. (2001) Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185-214.

Grady, M.P. (1998) Qualitative and Action Research: A Practitioner Handbook. Phi Delta Kappa Educational Foundation, Bloomington.

Grant, R. M. (1996) Toward a knowledge-based theory of the firm. Strategic Management Journal, 17 (Special Issue), 109–122.

Gray, P. H. & Meister, D. B. (2004) Knowledge sourcing effectiveness.

Management Science 50, 821–834.

Gupta, A.K., Smith, K.G. and Shalley, C.E. (2006) The interplay between exploration and exploitation, Academy of Management Journal, 49, 693-706.

Gupta, V. (2017) The Promise of Blockchain Is a World Without Middlemen.

Harvard Business Review. [Online Article] [Published 6.3.2017] [Accessed 20.4.2019]. Available: https://hbr.org/2017/03/the-promise-of-blockchain-is-a-world-without-middlemen

Hagel, J. (2015) Bringing Analytics to Life. Journal of Accountancy, 219(2), 24-25.

Havens, T.C., Bezdek, J.C., Leckie, C., Hall, L.O. & Palaniswami, M. (2012) Fuzzy c-Means Algorithms for Very Large Data. Fuzzy Systems, IEEE Transactions on, 20(6), 1130-1146.

Herschel, R. T., Nemati, H. & Steiger, D. (2001) Tacit to explicit knowledge conversion: knowledge exchange protocols. Journal of Knowledge Management, 5(1), 107-116.

Higdon, R., Haynes, W., Stanberry, L., Stewart, E., Yandl, G., Howard, C., Broomall, W., Kolker, N. & Kolker, E. (2013) Unraveling the Complexities of Life Sciences Data. Big data, 1(1), 42-50.

ICC/ESOMAR (2016) International Code on Market, Opinion and Social Research and Data Analytics. [Online Source] [Published 2016] [Accessed 9.10.2019].

Available: https://www.esomar.org/uploads/public/knowledge-and-standards/codes-and-guidelines/ICCESOMAR_Code_English_.pdf

Iansiti, M. & Lakhani, K.R. (2017) The Truth About Blockchain. Harvard Business Review 95, no. 1 (January-February 2017), 118-127.

Irani, Z., Sharif, A., Kamal, M.M. & Love, P.E. (2014). Visualising a knowledge mapping of information systems investment evaluation. Expert Systems with Applications, 41(1), 105-125.

Ito, J., Narula, N. & Ali, R. (2017) The Blockchain Will Do to the Financial System What the Internet Did to Media. Harvard Business Review. [Online Article]

[Published 8.3.2017] [Accessed 20.4.2019]. Available: https://hbr.org/2017/03/the-blockchain-will-do-to-banks-and-law-firms-what-the-internet-did-to-media

Janssen, M., Estevez, E. & Janowski, T. (2014) Interoperability in big, open, and linked data - Organizational maturity, capabilities, and data portfolios. IEEE Computer Society.

Janssen, M. & Kuk, G. (2016) Big and Open Linked Data (BOLD) in research, policy, and practice. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 3-13.

Janssen, M., van der Voort, H. & Wahyudi, A. (2017) Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345.

Jennex, M. E. & Bartczak, S. E. (2013) A Revised Knowledge Pyramid.

International Journal of Knowledge Management, 9(3), 19-30

Jin, X., Wah, B. W., Cheng, X. & Wang, Y. (2015) Significance and challenges of big data research. Big Data Research, 2(2), 59–64.

Johansson, P. E., Eerola, M., Innanen, A. & Viitanen, J. (2019) Lohkoketju:

Tiekartta päättäjille. Alma Talent, Helsinki.

Johnson, B.D. (2012) The Secret Life of Data. The Futurist, 46(4), 20-23.

Junni, P., Sarala, R., Taras, V., & Tarba, S. (2013) Organizational ambidexterity and performance: A meta-analysis. Academy of Management Perspectives, 27(4), 299–

312.

Karacapilidis, N., Tzagarakis, M. & Christodoulou, S. (2013) On a meaningful exploitation of machine and human reasoning to tackle data-intensive decisionmaking. Intelligent Decision Technologies, 7(3), 225–236.

Kim, H. Y. & Cho, J.-S. (2018) Data governance framework for big data implementation with NPS Case Analysis in Korea. Journal of Business and Retail Management Research, 12(3), 36-46.

Kim, T. H., Lee, J., Chun, J. U. & Benbasat, I. (2014) Understanding the effect of knowledge management strategies on knowledge management performance: A contingency perspective. Information & Management, 51(4), 398-416.

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.

Knapp, M.M. (2013) Big Data. Journal of Electronic Resources in Medical Libraries Dec 2013 10(4), 215-222.

Koltay, T. (2016) Data governance, data literacy and the management of data quality. IFLA Journal, 42(4), 303-312.

Koltz, F. (2018) Navigating the Next Wave of Blockchain Innovation: Smart Contracts. Case Study. Harvard Business Review.

Krishnamurthy, R., & Desouza, K. C. (2014) Big data analytics: the case of the social security administration. Information Polity, 19(3/4), 165–178.

Laney, D. (2001) 3D data management: Controlling data volume, velocity and variety. META Group Research Note 6.

Lee, J.-H., & Pilkington, M. (2017) How the Blockchain Revolution Will Reshape the Consumer Electronics Industry. IEEE Consumer Electronics Magazine, 19-23.

Lewis, R., McPartland, J. & Ranjan, R. (2017) Blockchain and financial market innovation. Federal Reserve Bank of Chicago. Economic Perspectives 7/2017.

L'Heureux, A., Grolinger, K., Elyamany, H. F. & Capretz, M. A. M. (2017) Machine Learning with Big Data: Challenges and Approaches. IEEE Access, 5, 7776-7797.

Liao, Z., Yin, Q., Huang Y. & Sheng, L. (2015) Management and application of mobile big data. International Journal of Embedded Systems. 7(1), 63-70.

Liebeskind, J. P. (1996) Knowledge, strategy, and the theory of the firm. Strategic Management Journal, 17: 93–107.

Liu, S. (2020) Knowledge Management: An Interdisciplinary Approach for

Business Decisions. Kogan Page Limited, London, United Kingdom & New York, United States.

Lu, R., Zhu, H., Liu, X., Liu, J.K. & Shao, J. (2014) Toward efficient and privacy-preserving computing in big data era. IEEE Network, 28(4), 46-50.

Magnier-Watanabe, R. & Senoo, D. (2010) Shaping knowledge management:

organization and national culture. Journal of Knowledge Management, 14(2), 214-227.

Mantelero, A. & Vaciago, G. (2015) Data protection in a big data society. Ideas for a future regulation. Digital Investigation, 15, 104-109.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. & Byers, A.

H. (2011) Big data: the next frontier for innovation, competition and productivity:

McKinsey Global Institute.

Marr, B. (2018) How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Forbes. [Online Article] [Published 21.5.2018]

[Accessed 23.11.2019] Available:

https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#1b810d9c60ba

March, J. (1991) Exploration and exploitation in organizational learning.

Organization Science, 2(1), 71–87.

Marx, V. (2013). Biology: The big challenges of big data. Nature, 498(7453), 255–

260.

Mattila, J., Seppälä, T. & Holmström, J. (2016) Product-centric Information Management: A Case Study of a Shared Platform with Blockchain Technology.

Industry Studies Association Conference, 24.5-26.5.2016, Minneapolis, USA. UC Berkeley.

Mazzei, M.J. & Noble, D. (2017) Big data dreams: A framework for corporate strategy. Business Horizons 60, 405-414.

Michelman, P. & Catalini, C. (2017) Seeing Beyond the Blockchain Hype. MIT Sloan Management Review 58 (4), 17-19.

Miles, M. B. (1979) Qualitative data as an attractive nuisance: The problem of analysis. Administrative Science Quarterly, 24(4), 590–601.

Mooi, E., Sarstedt, M. & Mooi-Reci, I. (2018) Market Research: The Process, Data, and Methods Using Stata. Singapore: Springer Singapore.

Morabito, V. (2017) Business Innovation Through Blockchain: The B³ Perspective.

Springer, Cham, Switzerland.

Myers, M. D. (2013) Qualitative research in business & management, 2nd ed.

Thousand Oaks (CA), Sage.

Nahapiet J, Ghoshal S. (1998) Social capital, intellectual capital, and the organizational advantage. Academy of Management Review 23(2), 242–58.

Nguyen, H. N. & Mohamed, S. (2011) Leadership behaviors, organizational culture and knowledge management practices: An empirical investigation. Journal of Management Development, 30(2), 206–221.

Nofer, M., Gomber, P., Hinz, O. & Schiereck, D. (2017) Blockchain. Business &

Information Systems Engineering 59 (3), 183–187.

Nonaka, I. (1994) A Dynamic Theory of Organizational Knowledge Creation.

Organization Science, 5(1), 14-37.

North, K. & Kumta, G. (2014). Knowledge Management: Value Creation Through Organizational Learning. Springer International Publishing, Cham, Switzerland.

North, K. & Kumta, G. (2018). Knowledge Management: Value Creation Through Organizational Learning. Second Edition. Springer International Publishing, Cham, Switzerland.

Nowiński, W. & Kozma M. (2017) How Can Blockchain Technology Disrupt the Existing Business Models? Entrepreneurial Business and Economics Review 5 (3), 173-188.

Nunan, D. & Di Domenico, M. (2017) Big Data: A Normal Accident Waiting to Happen? Journal of Business Ethics 145, 481–491.

Obitade, P. (2019) Big data analytics: A link between knowledge management capabilities and superior cyber protection. Journal of Big Data, 6(1), 1-28.

O’Dell C, Grayson C. (1998) If only we knew what we know: identification and transfer of internal best practices. California Management Review 40(3), 154–74.

OECD (2015) The OECD Guidelines for Multinational Enterprises. Organisation for Economic Co-operation and Development. [Online Document] [Published 6/2015]

[Accessed 14.10.2019] Available: http://biac.org/wp-content/uploads/2015/06/FIN-15-06-GUIDELINES-BROCHURE.pdf

Office of the Data Protection Ombudsman (2020a) If you would like to have your data erased. [Online Source] [Cited 7.6.2020] Available: https://tietosuoja.fi/en/if-you-would-like-to-have-all-of-your-data-erased

Office of the Data Protection Ombudsman (2020b) If you would like to have your data erased. [Online Source] [Cited 7.6.2020] Available: https://tietosuoja.fi/en/if-you-want-to-have-your-data-rectified

O’Leary, D.E. (2013) Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99.

Olszak, C. M. (2016) Toward better understanding and use of business

intelligence in organizations. Information Systems Management, 33(2), 105–123.

O'Reilly, C.A. (1982) Variations in decision makers' use of information sources:

The impact of quality and accessibility of information. Academy of Management Journal, 25(4), 756-771.

O’Reilly, C. & Tushman, M. (2004) The ambidextrous organization. Harvard Business Review, 82(4), 74–81.

O’Toole, T. (2020) What’s the Best Approach to Data Analytics? Harvard Business Review. [Online Article] [Published 2.3.2020] [Accessed 5.3.2020] Available:

https://hbr.org/2020/03/whats-the-best-approach-to-data-analytics

Paris, J., Donnal, J.S. & Leeb, S.B. (2014) NilmDB: the non-intrusive load monitor database. Smart Grid, IEEE Transactions on, 5(5), 2459-2467.

Pasquale, F. (2015) The black box society. The secret algorithms that control money and information. Cambridge: Harvard University Press.

Pattinson, H. M. & Woodside, A. G. (2007) Innovation and diffusion of software technology: Mapping strategies. Oxford: Elsevier.

Qiu, J., Wu, Q., Ding, G., Xu, Y. & Feng, S. (2016) A survey of machine learning for big data processing. Journal on Advances in Signal Processing, 67, 1-16.

Rajan, R. G. & Zingales, L. (2001) The firm as a dedicated hierarchy: A theory of the origins and growth of firms. Quarterly Journal of Economics, 116: 805–851.

Ragin, C. (2008) Redesigning social inquiry: Fuzzy sets and beyond. Chicago:

Chicago University Press.

Rayport, J.F. & Sviokla, J.J. (1995) Exploiting the virtual value chain. Harvard business review, 73(6), 75-85.

Redman, T. C. (2018) 5 Ways Your Data Strategy Can Fail. Harvard Business Review. [Online Article] [Published 11.10.2018] [Accessed 5.3.2020] Available:

https://hbr.org/2018/10/5-ways-your-data-strategy-can-fail

Riggins, F. J. & Klamm, B. K. (2017) Data governance case at Krause McMahon LLP in an era of self-service BI and Big Data, Journal of Accounting Education, (38), 23-36.

Rowley, J. (2006) The wisdom hierarchy: representations of the DIKW hierarchy.

Journal of Information Science, 33 (2), 163–180

Ruddin, P. (2006). You can generalize stupid! Social scientists, Bent Flyvbjerg and case study methodology. Qualitative Inquiry, 12(4), 797–812.

Sabherwal, R. & Sabherwal, S. (2005) Knowledge management using information technology: determinants of short-term impact on firm value. Decision Sciences, 36(4), 531-567.

Sandberg, J. & Tsoukas, H. (2011) Grasping the logic of practice: Theorizing through practical rationality. Academy of Management Review, 36(2), 338–360.

Sarajärvi, A. & Tuomi, J. (2018) Laadullinen tutkimus ja sisällönanalyysi. Tammi, Helsinki.

Shah, T., Rabhi, F. & Ray, P. (2015) Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions. Cluster Computing, 76(1), 351-367.

Shrier, D., Sharma, D. & Pentland, A. (2016) Blockchain & Financial Services: The Fifth Horizon of Networked Innovation. Massachusetts Institute of Technology.

Shrier, D., Iarossi, J., Sharma, D. & Pentland, A. (2016) Blockchain &

Transactions, Markets and Marketplaces: Part 2. Massachusetts Institute of Technology.

Shrier, D., Wu, W. & Pentland, A. (2016) Blockchain & Infrastructure (Identity, Data Security): Part 3. Massachusetts Institute of Technology.

Siggelkow, N. (2007). Persuasion with case studies. Academy of Management Journal, 50(1), 20–24.

Simonet, A., Fedak, G. & Ripeanu, M. (2015) Active Data: A programming model to manage data life cycle across heterogeneous systems and infrastructures. Future Generation Computer Systems, 53, 25–42.

Simsek, Z., Vaara, E., Paruchuri, S., Nadkarni, S. & Shaw, J.D. (2019) From the Editors: New Ways of Seeing Big Data. Academy of Management Journal, 62(4), 971-978.

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.

Staelin, R. (1987) Effects of quality and quantity of information on decision effectiveness. Journal of Consumer Research, (1986-1998), 14(2), 200-213.

Suddaby, R. (2006) What grounded theory is not. Academy of Management Journal, 49(4), 633–642.

Tapscott, A. & Tapscott, A. (2016) The Impact of the Blockchain Goes Beyond Financial Services. Harvard Business Review. [Online Article] [Published

10.5.2016] [Accessed 20.4.2019] Available at: https://hbr.org/2016/05/the-impact-of-the-blockchain-goes-beyond-financial-services

Tapscott, A. & Tapscott, D. (2017) How Blockchain Is Changing Finance. Harvard Business Review. [Online Article] [Published 1.3.2017] [Accessed 11.4.2019]

Available at: https://hbr.org/2017/03/how-blockchain-is-changing-finance

Tonidandel, S., King, E.B. & Cortina, J.M. (2018) Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21, 525-547.

Tseng, S-M. (2014) The impact of knowledge management capabilities and supplier relationship management on corporate performance. Int. J. Production Economics 154, 39–47.

Tucker, C. & Catalini C. (2018) What Blockchain Can’t Do. Harvard Business Review. [Online Article] [Published 28.6.2018] [Accessed 20.4.2019] Available at:

https://hbr.org/2018/06/what-blockchain-cant-do

Treiblmaier, H. (2018) The impact of the blockchain on the supply chain: a theory-based research framework and a call for action. Supply Chain Management, 23(6), 545-559.

Tuomi, J. & Sarajärvi, A. (2018) Laadullinen tutkimus ja sisällönanalyysi. Tammi,

Tuomi, J. & Sarajärvi, A. (2018) Laadullinen tutkimus ja sisällönanalyysi. Tammi,