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Towards Blockchain Decentralized Autonomous Organizations (DAO) Design

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Abstract

A decentralized autonomous organization (DAO) can be envisioned as an organization or society where its design, processes, and operational tasks are coded in the form of agreements or smart contracts, adhering to guidelines, values, and collective consensus. This paper underpins how organization design constructs (structure, strategy) and organization innovation constructs (innovation complexity, novel delivery) are related and sequenced to DAO transformation excellence (organizational excellence, organizational user expectation). An online survey of 262 blockchain DAO practitioners and researchers was conducted using SmartPLS to demonstrate empirical research findings (probably the first empirical research paper) on the implementation of blockchain DAO in organization design. The study focuses on understanding the role of blockchain DAO in organization design and innovation and how the users can leverage the technology, its substitutes, implementation readiness, and its impacts on organizations. Findings from the paper will help managers develop platforms and tools for various situations related to DAO-led organization design.

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References

  • Abdallah, W., Goergen, M., & O’Sullivan, N. (2015). Endogeneity: How failure to correct for it can cause wrong inferences and some remedies. British Journal of Management, 26(4), 791–804.

    Article  Google Scholar 

  • Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582.

    Article  Google Scholar 

  • Alimŏglu, A., & Ozturan, C. (2017). Design of a smart contract based autonomous organization for sustainable software. 2017 IEEE 13th International Conference on e-Science, 471–476.

  • Antony, J. P., & Bhattacharyya, S. (2010). Measuring organizational performance and organizational excellence of SMEs – Part 1: A conceptual framework. Measuring Business Excellence, 14(2), 3–11.

    Article  Google Scholar 

  • Armstrong, J. S., & Overton, T. S. (1977). Estimating non-response bias in mail surveys. Journal of Marketing Research, 14(3), 396–402.

    Article  Google Scholar 

  • Balcerzak, A. P., Nica, E., Rogalska, E., Poliak, M., Klieštik, T., & Sabie, O. M. (2022). Blockchain Technology and Smart Contracts in Decentralised Governance Systems. Administrative Sciences, 12(3), 96.

    Article  Google Scholar 

  • Beck, R., Müller-Bloch, C., & King, J. L. (2018). Governance in the blockchain economy: A framework and research agenda. Journal of the Association for Information Systems, 19(10), 1.

    Google Scholar 

  • Bellucci, M., Bini, L. & Giunta, F. (2020). Implementing environmental sustainability engagement into business: Sustainability management, innovation, and sustainable business models. In Galanakis, M. (Ed.), Innovation Strategies in Environmental Science, Elsevier, 107–143. https://doi.org/10.1016/B978-0-12-817382-4.00004-6

  • Berg, C., Davidson, S., & Potts, J. (2019). Understanding the Blockchain Economy. An Introduction to Institutional. Cryptoeconomics Edgard Elgar.

    Book  Google Scholar 

  • Binz, C., & Truffer, B. (2017). Global Innovation Systems—a conceptual framework for innovation dynamics in transnational contexts. Research Policy, 46(7), 1284–1298.

    Article  Google Scholar 

  • Brinkhoff, A., Özer, Ö., & Sargut, G. (2015). All you need is trust? An examination of inter-organisational supply chain projects. Production and Operations Management, 24(2), 181–200.

    Article  Google Scholar 

  • Chen, Y., Lu, Y., Bulysheva, L., & Kataev, M. Y. (2022). Applications of blockchain in industry 4.0: A review. Information Systems Frontiers, 1–15. https://doi.org/10.1007/s10796-022-10248-7

  • Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The constructs and measurements. Journal of Operations Management, 22(2), 119–150.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  • Ciriello, R. F. (2021). Tokenized index funds: A blockchain-based concept and a multidisciplinary research framework. International Journal of Information Management, 61, 102400.

    Article  Google Scholar 

  • Cuccuru, P. (2017). Beyond Bitcoin: An Early Overview on Smart Contracts. International Journal of Law and Information, 25(3), 179–195.

    Article  Google Scholar 

  • Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487.

    Article  Google Scholar 

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Davidson, S., De Filippi, P., & Potts, J. (2018). Blockchains and the economic institutions of capitalism. Journal of Institutional Economics, 14(4), 639–658.

  • De-Sitter, L. U., Den Hertog, J. F., & Dankbaarl, B. (1997). From complex organizations with simple jobs to simple organizations with complex jobs. Human Relations, 50(5), 497–534.

    Article  Google Scholar 

  • Drummer, D., & Neumann, D. (2020). Is code law? Current legal and technical adoption issues and remedies for blockchain-enabled smart contracts. Journal of Information Technology, 35(4), 337–360.

    Article  Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341–361.

    Article  Google Scholar 

  • DuPont, Q. (2019). Cryptocurrencies and blockchains. John Wiley & Sons.

  • Ehrenberg, A. J., & King, J. L. (2020). Blockchain in context. Information Systems Frontiers, 22, 29–35.

    Article  Google Scholar 

  • Ezzi, F., Abida, M., & Jarboui, A. (2022). The mediating effect of corporate governance on the relationship between blockchain technology and investment efficiency. Journal of the Knowledge Economy, 14(2), 718–734.

  • Faqir-Rhazoui, Y., Arroyo, J., & Hassan, S. (2021). A comparative analysis of the platforms for decentralised autonomous organisations in the Ethereum blockchain. Journal of Internet Services and Applications, 12(1), 1–20.

    Article  Google Scholar 

  • Fischer, A., & Valiente, M. C. (2021). Blockchain governance. Internet. Policy Review, 10(2), 1–10.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Foss, N. J., & Saebi, T. (2017). Fifteen years of research on business model innovation: How far have we come, and where should we go? Journal of Management, 43(1), 200–227.

    Article  Google Scholar 

  • Frost, J. (2017). Multicollinearity in regression analysis: problems, detection, and solutions. Statistics by Jim. Retrieved from: https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis. Accessed April 18, 2022.

  • Fulton, J. R., & King, R. P. (2021). The organisational structure of cooperatives: Centralisation versus decentralisation of decision making authority. In Agricultural cooperatives in transition, Routledge, 103–124. https://doi.org/10.4324/9780429041693-7

  • Gabriela-Livia, C. (2021). EFQM Excellence Model–European Foundation for Quality Management. In 6th International Conference on Education Reform and Modern Management (ERMM 2021), Atlantis Press, 301–303.

  • Ghorbani, M., & Nouri, M. (2005). Organizational excellence in the governmental sector. Journal of Administrative change, 47.

  • Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge Management: An Organizational Capabilities Perspective. Journal of Management Information Systems, 18(1), 185–214.

    Article  Google Scholar 

  • Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. Springer.

  • Gounaris, S., & Koritos, C. (2008). Investigating the drivers of internet banking adoption decision a comparison of three alternative frameworks. International Journal of Bank Marketing, 26(5), 282–304.

    Article  Google Scholar 

  • Grover, P., Kar, A. K., & Janssen, M. (2019). Diffusion of blockchain technology: Insights from academic literature and social media analytics. Journal of Enterprise Information Management, 32(5), 735–757.

    Article  Google Scholar 

  • Guide, V. D. R., & Ketokivi, M. (2015). Notes from the Editors: Redefining some methodological criteria for the journal. Journal of Operations Management, (37), v-viii. https://doi.org/10.1016/S0272-6963(15)00056-X

  • Hair, J. F., William, C. B., Barry, J. B., & Anderson, R. E. (2010). Multivariate Data Analysis. Englewood Cliffs.

    Google Scholar 

  • Hair, J. F., Jr., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017a). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123.

    Article  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017b). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). SAGE Publications.

    Google Scholar 

  • Hair, 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.

    Article  Google Scholar 

  • Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.

    Article  Google Scholar 

  • Hair Jr, J. F. (2006). Successful strategies for teaching multivariate statistics. Proceedings of the 7th international conference on teaching statistics. 1–5.

  • Hassan, S., & De Filippi, P. (2021). Decentralized autonomous organization. Internet. Policy Review, 10(2), 1–10.

    Google Scholar 

  • Helliar, C. V., Crawford, L., Rocca, L., Teodori, C., & Veneziani, M. (2020). Permissionless and permissioned blockchain diffusion. International Journal of Information Management, 54, 102136.

    Article  Google Scholar 

  • Henseler, J. (2017). Partial least squares path modeling Advanced Methods for Modeling Markets, 361-381. Springer.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited.

    Book  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

    Article  Google Scholar 

  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.

    Article  Google Scholar 

  • Hess, T., Matt, C., Benlian, A. & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), 123–139.

  • Howell, B. E., Potgieter, P. H., & Sadowski, B. M. (2019). Governance of blockchain and distributed ledger technology projects. Retrieved from: SSRN 3365519, 1–24. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3365519. Accessed 06 Jan 2024.

  • Hsieh, Y. Y., Vergne, J. P., Anderson, P., Lakhani, K., & Reitzig, M. (2018). Bitcoin and the rise of decentralized autonomous organizations. Journal of Organization Design, 7(1), 1–16.

    Article  Google Scholar 

  • Hughes, L., Dwivedi, Y. K., Misra, S. K., Rana, N. P., Raghavan, V., & Akella, V. (2019). Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda. International Journal of Information Management, 49, 114–129.

    Article  Google Scholar 

  • Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195–204.

    Article  Google Scholar 

  • Iansiti, M., & Lakhani, K. R. (2017). The truth about blockchain. Harvard Business RevIew, 95(1), 118–127.

    Google Scholar 

  • Jain, G., Shrivastava, A., Paul, J., & Batra, R. (2022). Blockchain for SME Clusters: An ideation using the framework of Ostrom Commons Governance. Information Systems Frontiers, 1–19. https://doi.org/10.1007/s10796-022-10288-z

  • Jaradat, W., Dearle, A., & Barker, A. (2016). Towards an autonomous decentralized orchestration system. Concurrency and Computation: Practice and Experience, 28(11), 3164–3179.

    Article  Google Scholar 

  • Jentzsch, C. (2016). Decentralised autonomous organisation to automate governance. White Paper. Retrieved from: https://lawofthelevel.lexblogplatformthree.com/wp-content/uploads/sites/187/2017/07/WhitePaper-1.pdf. Accessed April 18, 2022.

  • Karamchandani, A., Srivastava, S. K., & Srivastava, R. K. (2020). Perception-based model for analyzing the impact of enterprise blockchain adoption on SCM in the Indian service industry. International Journal of Information Management, 52, 102019.

    Article  Google Scholar 

  • Kennerly, M., Neely, A., & Adams, C. (2003). Survival of the fittest: Measuring performance in a changing business environment. Measuring Business Excellence, 7(4), 37–43.

    Article  Google Scholar 

  • Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486–507.

    Article  Google Scholar 

  • Kenny, D. A. (2015). Measuring model fit. Retrieved from: http://www.davidakenny.net/cm/fit.htm. Accessed April 18, 2022.

  • Klein, M., & Sauer, A. (2016). Celebrating 30 years of innovation system research: what you need to know about innovation systems. Hohenheim Discussion Papers in Business. Economics, and Social Sciences.

    Google Scholar 

  • Kock, N. (2015). A note on how to conduct a factor-based PLS-SEM analysis. International Journal of e-Collaboration, 11(3), 1–9.

    Article  Google Scholar 

  • Kock, N., & Lynn, G. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546–580.

    Article  Google Scholar 

  • Kouhizadeh, M., Saberi, S., & Sarkis, J. (2020). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831.

    Article  Google Scholar 

  • Kumar, S., Lim, W. M., Sivarajah, U., & Kaur, J. (2022). Artificial intelligence and blockchain integration in business: trends from a bibliometric-content analysis. Information Systems Frontiers, 1–26. https://doi.org/10.1007/s10796-022-10279-0

  • Lacity, M. C. (2018). Addressing key challenges to making enterprise blockchain applications a reality. MIS Quarterly Executive, 17(3), 201–222.

    Google Scholar 

  • Liu, Z., Li, Y., Min, Q., & Chang, M. (2022). User Incentive Mechanism in Blockchain-based Online Community: An Empirical Study of Steemit. Information & Management, 103596. https://doi.org/10.1016/j.im.2022.103596

  • Markard, J., Hekkert, M., & Jacobsson, S. (2015). The technological innovation systems framework: Response to six criticisms. Environmental Innovation and Societal Transitions, 16, 76–86.

    Article  Google Scholar 

  • Martens, C., & Woo, C. C. (1997). OASIS: An integrative toolkit for developing autonomous applications in decentralized environments. Journal of Organizational Computing and Electronic Commerce, 7(2–3), 227–251.

    Article  Google Scholar 

  • Miscione, G., Ziolkowski, R., & Schwabe, G. (2020). Decision problems in blockchain systems: Old wine in new bottles of walking in someone. Journal of Management Information Systems, 37(2), 316–348.

  • Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting and information technology innovation. Information Systems Research, 2(3), 192–222.

    Article  Google Scholar 

  • Müller, T., Schuberth, F., & Henseler, J. (2018). PLS path modeling–a confirmatory approach to study tourism technology and tourist behavior. Journal of Hospitality and Tourism Technology, 9(3), 249–266.

    Article  Google Scholar 

  • Murray, A., Kuban, S., Josefy, M., & Anderson, J. (2021). Contracting in the smart era: The implications of blockchain and decentralised autonomous organisations for contracting and corporate governance. Academy of Management Perspectives, 35(4), 622–641.

    Article  Google Scholar 

  • Nielsen, C. & Lund, M. (2014). A brief history of the business model concept. In The Basics of Business Models, Ventus, 21–27. https://vbn.aau.dk/en/publications/a-brief-history-of-the-business-model-concept. Accessed  06 Jan 2024.

  • Nuryyev, G., Wang, Y. P., Achyldurdyyeva, J., Jaw, B. S., Yeh, Y. S., Lin, H. T., & Wu, L. F. (2020). Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs: An empirical study. Sustainability, 12(3), 1256.

    Article  Google Scholar 

  • Ostern, N. K., Holotiuk, F. & Moormann, J. (2021). Organizations' approaches to blockchain: A critical realist perspective. Information & Management, 59(7), 103552.

  • Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, 1. Game Changers, and Challengers, John Wiley & Sons.

    Google Scholar 

  • Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press.

    Book  Google Scholar 

  • Ostrom, E. (2003). Toward a behavioural theory linking trust, reciprocity, and reputation. In E. Ostrom & J. Walker (Eds.), Trust & Reciprocity (pp. 19–29). Interdisciplinary Lesson from Experimental Research Russell Sage Foundation.

    Google Scholar 

  • Ozcan, S., & Islam, N. (2014). Collaborative networks and technology clusters—the case of nanowire. Technological Forecasting and Social Change, 82, 115–131.

    Article  Google Scholar 

  • Panetta K. (2019). Understand the 4 phases of blockchain evolution and explore potential business opportunities, Gartner. Retrieved from: https://www.gartner.com/smarterwithgartner/the-4-phases-of-the-gartner-blockchain-spectrum. Accessed April 18, 2022

  • Panetta K. (2021). 3 Themes Surface in the 2021 Hype Cycle for Emerging Technologies", Gartner. Retrieved from: https://www.gartner.com/smarterwithgartner/3-themes-surface-in-the-2021-hype-cycle-for-emerging-technologies. Accessed April 18, 2022

  • Pazaitis, A., De Filippi, P., & Kostakis, V. (2017). Blockchain and value systems in the sharing economy: The illustrative case of Backfeed. Technological Forecasting and Social Change, 125, 105–115.

    Article  Google Scholar 

  • Pelé, P., Schulze, J., Piramuthu, S., & Zhou, W. (2023). IoT and blockchain based framework for logistics in food supply chains. Information Systems Frontiers, 25(5), 1743–1756.

    Article  Google Scholar 

  • Pereira, J., Tavalaei, M. M., & Ozalp, H. (2019). Blockchain-based platforms: Decentralized infrastructures and its boundary conditions. Technological Forecasting and Social Change, 146, 94–102.

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731.

    Article  Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

    Article  Google Scholar 

  • Qin, R., Ding, W., Li, J., Guan, S., Wang, G., Ren, Y., & Qu, Z. (2022). Web3-Based Decentralised Autonomous Organisations and Operations: Architectures, Models, and Mechanisms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2073–2082.

  • Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70–82.

    Article  Google Scholar 

  • Raddatz, N., Coyne, J., Menard, P., & Crossler, R. E. (2021). Becoming a blockchain user: Understanding consumers' benefits realisation to use blockchain-based applications. European Journal of Information Systems, 32(2), 287–314.

  • Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525.

    Article  Google Scholar 

  • Rikken, O., Janssen, M., & Kwee, Z. (2019). Governance challenges of blockchain and decentralised autonomous organisations. Information Polity, 24(4), 397–417.

    Article  Google Scholar 

  • Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. Or Spectrum, 36(1), 251–276.

    Article  Google Scholar 

  • Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. SmartPLS GmbH, Boenningstedt. SmartPLS GmbH. Retrieved from http://www.smartpls.com. Accessed  06 Jan 2024.

  • Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). Free Press.

    Google Scholar 

  • Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.

    Google Scholar 

  • Rosling, H., Reonnlund, A. R., & Rosling, O. (2018). Factfulness: Ten Reasons We’re Wrong about the World-Aand Why Things Are Better than You Think. Flatiron Books.

    Google Scholar 

  • Roth, T., Stohr, A., Amend, J., Fridgen, G., & Rieger, A. (2022). Blockchain as a driving force for federalism: A theory of cross-organizational task-technology fit. International Journal of Information Management, 68, 102476.

  • Rozas, D., Tenorio-Forn´es, A., Díaz-Molina, S. & Hassan, S. (2021). When ostrom meets blockchain: exploring the potentials of blockchain for commons governance. SAGE Open, 11 (1). https://doi.org/10.1177/21582440211002526

  • Santana, C., & Albareda, L. (2022). Blockchain and the emergence of Decentralized Autonomous Organizations (DAOs): An integrative model and research agenda. Technological Forecasting and Social Change, 182, 121806.

    Article  Google Scholar 

  • Singh, A. & Hess, T. (2017). How chief digital officers promote the digital transformation of their companies. MIS Quarterly Executive, 16(1), 202–220.

  • Singh, M., & Kim, S. (2019). Blockchain technology for decentralized autonomous organizations. Advances in Computers, Elsevier, 115, 115–140.

    Article  Google Scholar 

  • Sulkowski, A. J. (2019). The Tao of DAO: Hardcoding business ethics on blockchain. Business & Finance Law Review, 3, 146.

    Google Scholar 

  • Swan, M. (2015). Blockchain: Blueprint for a new economy. O'Reilly Media, Inc..

  • Tan, E., Mahula, S., & Crompvoets, J. (2022). Blockchain governance in the public sector: A conceptual framework for public management. Government Information Quarterly, 39(1), 101625.

    Article  Google Scholar 

  • Tapscott, D., & Tapscott, A. (2017a). How blockchain will change organisations. MIT Sloan Management Review, 58(2), 10.

    Google Scholar 

  • Tapscott, D., & Tapscott, A. (2017). The BlockChain revolution and higher education. Educause Review, 52(2), 11–24.

    Google Scholar 

  • Unalan, S., & Ozcan, S. (2020). Democratising systems of innovations based on Blockchain platform technologies. Journal of Enterprise Information Management, 33(6), 1511–1536.

    Article  Google Scholar 

  • Upadhyay, N. (2020). Demystifying blockchain: A critical analysis of challenges, applications and opportunities. International Journal of Information Management, 54, 102120.

    Article  Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Article  Google Scholar 

  • Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134.

    Article  Google Scholar 

  • Wang, S., Ding, W., Li, J., Yuan, Y., Ouyang, L., & Wang, F. Y. (2019). Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems, 6(5), 870–878.

    Article  Google Scholar 

  • Wang, Y. Y., Tao, F. & Wang, J. (2022). Information disclosure and blockchain technology adoption strategy for competing platforms. Information & Management, 59(7), 103506.

  • Warner, K. S., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349.

    Article  Google Scholar 

  • Warnke, P., Koschatzky, K., D€onitz, E., Zenker, A., Stahlecker, T., Som, O., & Guth, S. (2016). Opening up the Innovation System Framework towards New Actors and Institutions, 49. Fraunhofer ISI Discussion Papers Innovation Systems and Policy Analysis.

    Google Scholar 

  • Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1–32.

    Google Scholar 

  • Woodside, J. M., Augustine, F. K., Jr., & Giberson, W. (2017). Blockchain technology adoption status and strategies. Journal of International Technology and Information Management, 26(2), 65–93.

    Article  Google Scholar 

  • Wu, I., & Chuang, C. (2010). Examining the Diffusion of Electronic Supply Chain Management with External Antecedents and Firm Performance: A Multi-stage Analysis. Decision Support Systems, 50(1), 103–115.

    Article  Google Scholar 

  • Yadav, J., Misra, M., & Goundar, S. (2020a). An overview of food supply chain virtualisation and granular traceability using blockchain technology. International Journal of Blockchains and Cryptocurrencies, 1(2), 154.

    Article  Google Scholar 

  • Yadav, J., Misra, M., & Goundar, S. (2020). Autonomous Agriculture Marketing Information System Through Blockchain: A Case Study of e-NAM Adoption in India In Blockchain Technologies Applications and Cryptocurrencies, 115-138. World scientific.

    Google Scholar 

  • Yadav, J., Misra, M., Rana, N. P., Singh, K., & Goundar, S. (2022). Netizens’ behavior towards a blockchain-based esports framework: A TPB and machine learning integrated approach. International Journal of Sports Marketing and Sponsorship, 23(4), 665–683.

    Article  Google Scholar 

  • Zachariadis, M., Hileman, G., & Scott, S. V. (2019). Governance and control in distributed ledgers: Understanding the challenges facing blockchain technology in financial services. Information and Organization, 29(2), 105–117.

    Article  Google Scholar 

  • Zavolokina, L., Ziolkowski, R., Bauer, I., & Schwabe, G. (2020). Management, governance and value creation in a blockchain consortium. MIS Quarterly Executive, 19(1), 3.

    Article  Google Scholar 

  • Zheng, X. R., & Lu, Y. (2021). Blockchain technology–recent research and future trend. Enterprise Information Systems, 16(12), 1939895.

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  1. Indian Institute of Management, Shillong, Meghalaya, India, 793014

    Kumar Saurabh & Neelam Rani

  2. Information Systems and Analytics, Indian Institute of Management, Shillong, Meghalaya, 793014, India

    Parijat Upadhyay

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  3. Neelam Rani
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Correspondence to Parijat Upadhyay.

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Appendices

Appendix 1

Figure 5

Fig. 5
figure 5

Literature review method, synthesis and filtration

Full size image

Appendix 2

Respondents comprised CEOs and MDs, offering leaders, chief architects, chief strategy officers, chief technology officers, cloud architects, vice-president–marketing and brand communication, sales head, IT manager, technology leader, co-founder, IT architect, distinguished engineer and VP, Delivery leader, general manager, transformation leader, offering leader, global architect leader, delivery manager, HR manager, sales head, compliance leader, infrastructure manager, sales manager, presales head, IP attorney, senior technical specialist, technical solutions manager, associate technical engineer, lead architect, architect, migration architect, discovery analyst, specialist, developer, QA analyst, and test manager.

Appendix 3

Constructs

 

Items

Section 1- DAOrg Design

 

DAOrg Structure

 
 

SI1

Organizations which use DAO can effectively implement areas of specialization (type and numbers of job/tasks)

 

SI2

Organizations which use DAO can realize automated workflow processes for various LOBs

 

SI3

Organizations which use DAO can easily achieve shape (span of control)

 

SI4

Organizations which use DAO can efficiently distribute power using decentralized characteristics

 

SI5

Organizations which use DAO can better manage consensus among the node participants (stakeholders and employees)

 

DAOrg Strategy

 
 

ST1

Organizations which use DAO can achieve better strategic outcomes

 

ST2

Organizations which use DAO can effectively meet strategic vision, goals, and objectives

 

ST3

Organizations which use DAO can set more transparent criteria (priorities) to achieve the directions to be chased

 

ST4

Organizations which use DAO can implement better strategic initiatives

 

ST5

Organizations which use DAO can evaluate and monitor strategic initiatives effectively

Section 2- DAO Innovation

 

DAOrg Inno Complexity

 
 

IC1

Before deciding whether to use DAO, we can have resources to understand it better

 

IC2

DAO is available to us with enough learning collaterals

 

IC3

It is permitted to use DAO on a trial basis long enough to see what it can do

 

IC4

We have adequate opportunities (use cases) to try out different things on DAO

 

IC5

We have resources to develop DAO skills for implementations

 

DAOrg Novel Delivery

 
 

ND1

It is easy to see many others using DAO

 

ND2

We have seen what others do using DAO

 

ND3

It is easy for us to see the outcomes of using DAO

 

ND4

We have seen many competitors and partners in the market working on similar solutions like DAO

 

ND5

We believe using DAO can achieve improved results over the traditional models

Section 3- DAO Technology Transformation

 

DAOrg Excellence

 
 

DE1

Using DAO for organization design reduces cost

 

DE2

Using DAO for organization design enhances organization efficiency

 

DE3

Using DAO for organizational design enhances quality of products/offerings

 

DE4

Using DAO for organization design enhances service effectiveness

 

DE5

Using DAO for organization design improves competitive advantage

 

DAOrg User Expectation (UEx)

 
 

UEx1

Overall, we believe that DAO is easy to use

 

UEx2

Learning to operate DAO is easy for us

 

UEx3

We believe it is easy to get DAO to perform desired activities

 

UEx4

Using DAO gives us ease in managing organizational activities

 

UEx5

Using DAO makes the operation’s work easier

 

DAO Implementation Readiness

 
 

DR1

We plan to use DAO in the future

 

DR2

We intend to use DAO in the future

 

DR3

We predict we would use DAO in the future

 

DR4

We will strongly recommend to use DAO in the future

 

DR5

We will use DAO on a regular basis in the future

  1. Note: Our study variable used a five-point (1–5) Likert scale, which indicated 1 as “strongly disagree”, 2 as “moderately disagree”, 3 as “neutral” 4 as “moderately agree”, and “5 as strongly agree”

Appendix 4

Mediation effects

Literature analysis confirms the influence of blockchain DAO technology transformations on the DAO implementation and readiness construct that best predicts the characteristics and user perceptions of blockchain DAO for organization design (Nuryyev et al., 2020; Woodside et al., 2017). Therefore, it can be concluded that user perceptions and opinions mediate managerial interferences on blockchain DAO technology implementations in organization designs. Consequently, managers work on the user perceptions and opinions of innovation and organizational designs, leveraging learning interventions, training collaterals, and expert support (Beck et al., 2018; Ezzi et al., 2022; Fischer & Valiente, 2021; Liu et al., 2022; Tan et al., 2022; Zachariadis et al., 2019). Therefore, we hypothesize the following.

  • Hypothesis 6 (H6): DAO technology transformation of (a) DAO excellence and (b) DAO user expectation (UEx) mediates the organization structure and DAO implementation readiness in organization design.

  • Hypothesis 7 (H7): DAO technology transformation of (a) DAO excellence and (b) DAO user expectation (UEx) mediates the organization strategy and DAO implementation readiness in organization design.

  • Hypothesis 8 (H8): DAO technology transformation of (a) DAO excellence and (b) DAO user expectation (UEx) mediates the innovation constructs complexity and DAO implementation readiness in organization design.

  • Hypothesis 9 (H9): DAO technology transformation of (a) DAO excellence and (b) DAO user expectation (UEx) mediates the innovation construct novel delivery and DAO implementation readiness in organization design.

  • Hypothesis 10 (H10): (a) DAOrg Structure, (b) DAOrg Strategy, (c) DAOrg Innovation Complexity, and (d) DAOrg Novel Delivery are positively related to DAO implementation readiness in organization design.

Literature suggests investigating the mediation effects empirically in any research model (Brinkhoff et al., 2015). We have used Preacher and Hayes’s (2004, 2008) work to test the same. It is emphasized to calculate the variance accounted for (VAF) to support the mediation effect if there exists a direct relationship. We have used the results of PLS-SEM-based specific indirect effects to investigate it in the absence of a direct relationship. Table 7 shows specific indirect effects with relevant p-values. The results show a significant mediation effect exists for DAOrg UEx (DAO technology transformation) between DAOrg Strategy and DAO Implementation Readiness; DAOrg Excellence (DAO technology transformation) between DAOrg Innovation Complexity and DAO Implementation Readiness of DAO blockchain technology use in organization design; DAOrg Excellence (DAO technology transformation) between DAOrg Novel Delivery and DAO Implementation Readiness; and DAOrg Excellence between DAOrg Structure and DAO Implementation Readiness of DAO blockchain technology implementation in organization design. Hence, we can say the alternative hypotheses H6a, H7b, H8a, and H9a are supported, confirming the mediation effects on influencing DAO blockchain implementation readiness in organization design, as suggested by the respondents. At the same time, H6b, H7a, H8b, and H9b failed to reject the hypotheses. As suggested by the respondents, we can state that the hypotheses do not have any significant mediation effect on influencing blockchain DAO implementation readiness in organization design. Moreover, the Sobel test was used to examine the intermediary variable robustness. The Sobel test p-value is 0, and the test statistic is greater than 1.96 for the following action paths: DAOrg Structure—> DAOrg UEx—> DAO Implementation Readiness; DAOrg Strategy—> DAOrg Excellence—> DAO Implementation Readiness; DAOrg Strategy—> DAOrg UEx—> DAO Implementation Readiness; DAOrg Innovation Complexity—> DAOrg UEx—> DAO Implementation Readiness; DAOrg Innovation Complexity—> DAOrg Excellence—> DAO Implementation Readiness; DAOrg Novel Delivery—> DAOrg Excellence—> DAO Implementation Readiness; DAOrg Structure—> DAOrg Excellence—> DAO Implementation Readiness. This specifies that the mediating variable passed the test, which is not valid for all the mediations in bootstrapping method, as per Table 7.

Appendix 5

figure a

SEM model for organization design, innovation, and transformation excellence factors impacting the implementation of DAOs (after removing the loading of “DR1” and after changing other loadings to “DR1 to DR4”)

Appendix 6

We used f 2 (effect size) to evaluate the effect (Hair et al., 2019). The f 2 value for DAOrg Excellence to DAO Implementation Readiness was 0.961( indicating strong effect i.e. f 2 >  = 0.35), DAOrg Innovation Complexity to DAOrg Excellence was 0.273 ( indicating moderate effect, i.e. 0.15 <  = f 2 < 0.35), DAOrg UEx indicating weak effect to DAO Implementation Readiness with the value of 0.047 (indicating weak effect, i.e. 0.02 <  = f 2 < 0.15), DAOrg Strategy to DAOrg UEx was 0.096 (indicating weak effect, i.e. 0.02 <  = f 2 < 0.15), DAOrg Structure to DAOrg UEx was 0.029 (indicating weak effect, i.e. 0.02 <  = f 2 < 0.15), DAOrg Structure to DAOrg Excellence was 0.051 (indicating weak effect i.e. 0.02 <  = f 2 < 0.15) (Hair et al., 2017a, b) . Also, we studied the direct relationship statistics between the independent variables (DAOrg Structure, DAOrg Strategy, DAOrg Innovation Complexity, and DAOrg Novel Delivery) and the dependent variable (DAO Implementation Readiness). The alternative hypotheses H10b (β = 0.357, p = 0.000) and H10d (β =  − 0.156, p = 0.002) were supported, and both positively affect DAO Implementation Readiness. However, H10a and H10c do not significantly affect blockchain DAO implementation in organization design, as suggested by the respondents, which fails to reject the hypotheses.

We have used blindfolding to investigate further the predictive relevance with the omission distance (d) of 5. The goal of the blindfolding activity in a single round is not to utilize an entire set of observations but to drop every fifth observation (data point) within the target construct’s indicators. The following are the predictive relevance for DAO Implementation Readiness, DAOrg UEx, and DAOrg Excellence with the value of blindfolding-based Q2 (= 1 − SSE/SSO) improving to 0.392 (indicating a strong effect, i.e., Q2 >  = 0.35), 0.192 (indicating moderate effect, i.e., 0.15 <  = Q2 < 0.35), and 0.38 (indicating strong effect, i.e., Q2 >  = 0.35) (Hair et al., 2019). Finally, we worked on standardized root mean square residual (SRMR) values to establish the global model fitness and explanatory power. The value of our model was 0.078, which is less than the critical value (0.085) (Henseler et al., 2016). Hence, based on the SRMR value, we can conclude that the estimated correlation matrix fits well for the global model fit index (Hair et al., 2020).

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Saurabh, K., Upadhyay, P. & Rani, N. Towards Blockchain Decentralized Autonomous Organizations (DAO) Design. Inf Syst Front (2024). https://doi.org/10.1007/s10796-023-10455-w

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