How to cite this paper
Rovolis, G & Habibipour, A. (2024). When participatory design meets data-driven decision making: A literature review and the way forward.Management Science Letters , 14(2), 107-126.
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Andrienko, G., Andrienko, N., Jankowski, P., Keim, D., Kraak, M. ‐J., MacEachren, A., & Wrobel, S. (2007). Geovisual analytics for spatial decision support: Setting the research agenda. International Journal of Geographical Information Science, 21(8), 839–857. https://doi.org/10.1080/13658810701349011
Ansell, C., & Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Re-search and Theory, 18(4), 543–571.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27.
Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of So-cial Research Methodology, 8(1), 19–32.
Bason, C. (2018). Leading public sector innovation: Co-creating for a better society. Policy press.
Bass, B. M., & Riggio, R. E. (2006). Transformational leadership.
Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning (Vol. 1). MIT press Cambridge, MA, USA.
Bennett, N. J., Roth, R., Klain, S. C., Chan, K. M. A., Clark, D. A., Cullman, G., Epstein, G., Nelson, M. P., Stedman, R., Teel, T. L., Thomas, R. E. W., Wyborn, C., Curran, D., Greenberg, A., Sandlos, J., & Veríssimo, D. (2017). Mainstream-ing the social sciences in conservation. Conservation Biology, 31(1), 56–66. https://doi.org/10.1111/cobi.12788
Berkes, F. (2004). Rethinking Community-Based Conservation. Conservation Biology, 18(3), 621–630. https://doi.org/10.1111/j.1523-1739.2004.00077.x
Birhane, A. (2021). Algorithmic injustice: A relational ethics approach. Patterns, 2(2), 100205. https://doi.org/10.1016/j.patter.2021.100205
Björgvinsson, E., Ehn, P., & Hillgren, P.-A. (2010). Participatory design and" democratizing innovation". 41–50.
Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57–69). Chapman and Hall/CRC.
Bramer, W. M., Rethlefsen, M. L., Kleijnen, J., & Franco, O. H. (2017). Optimal database combinations for literature searches in systematic reviews: A prospective exploratory study. Systematic Reviews, 6, 1–12.
Brown, P. A. (2008). A Review of the Literature on Case Study Research. Canadian Journal for New Scholars in Educa-tion, 1(1).
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance? Available at SSRN 1819486.
Bryson, J. M., Crosby, B. C., & Bloomberg, L. (2014). Public value governance: Moving beyond traditional public admin-istration and the new public management. Public Administration Review, 74(4), 445–456.
Cameron, K., & Quinn, R. (2000). Diagnosing and changing organizational culture, based on the competing values frame-work, 1999. Reading, Massachusettes: Addison Wesley.
Cargo, M., & Mercer, S. L. (2008). The Value and Challenges of Participatory Research: Strengthening Its Practice. Annu-al Review of Public Health, 29(1), 325–350. https://doi.org/10.1146/annurev.publhealth.29.091307.083824
Carmeli, A., Sheaffer, Z., & Yitzack Halevi, M. (2009). Does participatory decision‐making in top management teams en-hance decision effectiveness and firm performance? Personnel Review, 38(6), 696–714. https://doi.org/10.1108/00483480910992283
Chaix-Couturier, C., Durand-Zaleski, I., Jolly, D., & Durieux, P. (2000). Effects of financial incentives on medical prac-tice: Results from a systematic review of the literature and methodological issues. International Journal for Quality in Health Care, 12(2), 133–142. https://doi.org/10.1093/intqhc/12.2.133
Chen, Chiang, & Storey. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165. https://doi.org/10.2307/41703503
Curry, L. A., Nembhard, I. M., & Bradley, E. H. (2009). Qualitative and Mixed Methods Provide Unique Contributions to Outcomes Research. Circulation, 119(10), 1442–1452. https://doi.org/10.1161/CIRCULATIONAHA.107.742775
Davenport, T. H., & Patil, D. (2012). Data scientist. Harvard Business Review, 90(5), 70–76.
Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., Hsu, R., Katbamna, S., Olsen, R., & Smith, L. (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Medical Research Methodology, 6, 1–13.
Doorn, N. (2021). Artificial intelligence in the water domain: Opportunities for responsible use. Science of The Total Envi-ronment, 755, 142561. https://doi.org/10.1016/j.scitotenv.2020.142561
Elgendy, N., & Elragal, A. (2016). Big Data Analytics in Support of the Decision Making Process. Procedia Computer Sci-ence, 100, 1071–1084. https://doi.org/10.1016/j.procs.2016.09.251
Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analyt-ics. Journal of Decision Systems, 31(4), 337–373. https://doi.org/10.1080/12460125.2021.1894674
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportuni-ties, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347.
Fung, A. (2015). Putting the Public Back into Governance: The Challenges of Citizen Participation and Its Future. Public Administration Review, 75(4), 513–522. https://doi.org/10.1111/puar.12361
Ganapati, S., & Reddick, C. G. (2018). Prospects and challenges of sharing economy for the public sector. Government In-formation Quarterly, 35(1), 77–87.
Gautam, R. S., & Bhimavarapu, V. M. (2022). Data driven decision making: Application in finance. Iconic Research and Engineering Journals, 5(12), 52–56.
Hambrick, D. C. (2007). Upper echelons theory: An update. Academy of Management Review, 32(2), 334–343.
Hart, A., Gagnon, E., Eryigit-Madzwamuse, S., Cameron, J., Aranda, K., Rathbone, A., & Heaver, B. (2016). Uniting Resil-ience Research and Practice With an Inequalities Approach. SAGE Open, 6(4), 215824401668247. https://doi.org/10.1177/2158244016682477
Ho, C. W. L., Soon, D., Caals, K., & Kapur, J. (2019). Governance of automated image analysis and artificial intelligence analytics in healthcare. Clinical Radiology, 74(5), 329–337. https://doi.org/10.1016/j.crad.2019.02.005
Hodge, J., Foley, S., Brankaert, R., Kenning, G., Lazar, A., Boger, J., & Morrissey, K. (2020). Relational, Flexible, Every-day: Learning from Ethics in Dementia Research. Proceedings of the 2020 CHI Conference on Human Factors in Com-puting Systems, 1–16. https://doi.org/10.1145/3313831.3376627
Irvin, R. A., & Stansbury, J. (2004a). Citizen Participation in Decision Making: Is It Worth the Effort? Public Administra-tion Review, 64(1), 55–65. https://doi.org/10.1111/j.1540-6210.2004.00346.x
Irvin, R. A., & Stansbury, J. (2004b). Citizen participation in decision making: Is it worth the effort? Public Administra-tion Review, 64(1), 55–65.
Kabadurmus, O., Kayikci, Y., Demir, S., & Koc, B. (2023). A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks. Socio-Economic Planning Sciences, 85, 101417. https://doi.org/10.1016/j.seps.2022.101417
Kaner, S. (2014). Facilitator’s guide to participatory decision-making. John Wiley & Sons.
Kaplan, R. S., & Norton, D. P. (2015). Balanced Scorecard Success: The Kaplan-Norton Collection (4 Books). Harvard Business Review Press.
Kelleher, J. D., Mac Namee, B., & D’arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: Al-gorithms, worked examples, and case studies. MIT press.
Kellogg, W. (2004). Logic Model Development Guide. Kellogg Foundation.
Kiron, D., Ferguson, R. B., & Prentice, P. K. (2013). From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review, 54(3), 1.
Klievink, B., Romijn, B.-J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and read-iness. Information Systems Frontiers, 19(2), 267–283.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from in-sights to value. MIT Sloan Management Review.
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Medicine, 6(7), e1000100. https://doi.org/10.1371/journal.pmed.1000100
Madsen, D. Ø., & Slåtten, K. (2013). The role of the management fashion arena in the cross-national diffusion of man-agement concepts: The case of the balanced scorecard in the Scandinavian countries. Administrative Sciences, 3(3), 110–142.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. Occasional Paper. Rand Corporation.
Matlock, D. D., & Spatz, E. S. (2014). Design and Testing of Tools for Shared Decision Making. Circulation: Cardiovas-cular Quality and Outcomes, 7(3), 487–492. https://doi.org/10.1161/CIRCOUTCOMES.113.000289
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
Mitchell, T. M. (2007). Machine learning (Vol. 1). McGraw-hill New York.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*, the. (2009). Preferred reporting items for system-atic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269.
Muller, M. J. (2003). Participatory Design: The Third Space in HCI-Handbook of HCI. Mahway NJ USA: Erlbaum.
Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2021). Big data management and environmental perfor-mance: Role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management, 34(4), 1061–1096.
Nonaka, I., & Takeuchi, H. (2007). The knowledge-creating company. Harvard Business Review, 85(7/8), 162.
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347
O’Flynn, J. (2007). From new public management to public value: Paradigmatic change and managerial implications. Aus-tralian Journal of Public Administration, 66(3), 353–366.
O’neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Orr, G. (2003). Diffusion of innovations, by Everett Rogers (1995). Retrieved January, 21, 2005.
Osman, A. M. S., Elragal, A. A., & Ståhlbröst, A. (2022). Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study. Applied Sciences, 12(3), 1732. https://doi.org/10.3390/app12031732
Papaioannou, D., Sutton, A., & Booth, A. (2016). Systematic approaches to a successful literature review. Systematic Ap-proaches to a Successful Literature Review, 1–336.
Pardo, T. A., & Scholl, H. J. J. (2002). Walking atop the cliffs: Avoiding failure and reducing risk in large scale e-government projects. 1656–1665.
Pedersen, J. S., & Wilkinson, A. (2018). The digital society and provision of welfare services. International Journal of So-ciology and Social Policy, 38(3/4), 194–209. https://doi.org/10.1108/IJSSP-05-2017-0062
Peter, S. (1990). The fifth discipline. The Art & Practice of Learning Organization. Doupleday Currence, New York.
Piotrowski, S. J., & Van Ryzin, G. G. (2007). Citizen attitudes toward transparency in local government. The American Review of Public Administration, 37(3), 306–323.
Provost, F., & Fawcett, T. (2013a). Data science and its relationship to big data and data-driven decision making. Big Da-ta, 1(1), 51–59.
Provost, F., & Fawcett, T. (2013b). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.
Quick, K. S., & Feldman, M. S. (2011). Distinguishing participation and inclusion. Journal of Planning Education and Re-search, 31(3), 272–290.
Rittel, H. W., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.
Robertson, T., & Wagner, I. (2012). Ethics: Engagement, representation and politics-in-action. In Routledge international handbook of participatory design (pp. 64–85). Routledge.
Rowley, J., & Slack, F. (2004). Conducting a literature review. Management Research News, 27(6), 31–39.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc.
Sanders, E. B.-N., Brandt, E., & Binder, T. (2010). A framework for organizing the tools and techniques of participatory design. 195–198.
Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-Design, 4(1), 5–18.
Sarin, S., & McDermott, C. (2003). The effect of team leader characteristics on learning, knowledge application, and per-formance of cross‐functional new product development teams. Decision Sciences, 34(4), 707–739.
Shanley, P., & López, C. (2009). Out of the Loop: Why Research Rarely Reaches Policy Makers and the Public and What Can be Done: Biological Research Beyond Academy. Biotropica, 41(5), 535–544. https://doi.org/10.1111/j.1744-7429.2009.00561.x
Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018). DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications. Mathematical Problems in Engineering, 2018, 1–33. https://doi.org/10.1155/2018/3696457
Sleep, S., Hulland, J., & Gooner, R. A. (2019). THE DATA HIERARCHY: Factors influencing the adoption and implemen-tation of data-driven decision making. AMS Review, 9(3–4), 230–248. https://doi.org/10.1007/s13162-019-00146-8
Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008
Valentine, S. R., Hollingworth, D., & Schultz, P. (2018). Data-based ethical decision making, lateral relations, and organi-zational commitment: Building positive workplace connections through ethical operations. Employee Relations, 40(6), 946–963. https://doi.org/10.1108/ER-10-2017-0240
Wamba, S. F., 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, 165, 234–246.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarter-ly, xiii–xxiii.
Yu, S., Qing, Q., Zhang, C., Shehzad, A., Oatley, G., & Xia, F. (2021). Data-Driven Decision-Making in COVID-19 Re-sponse: A Survey. IEEE Transactions on Computational Social Systems, 8(4), 1016–1029. https://doi.org/10.1109/TCSS.2021.3075955
Ansell, C., & Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Re-search and Theory, 18(4), 543–571.
Appelbaum, D., Kogan, A., & Vasarhelyi, M. A. (2017). Big Data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory, 36(4), 1–27.
Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of So-cial Research Methodology, 8(1), 19–32.
Bason, C. (2018). Leading public sector innovation: Co-creating for a better society. Policy press.
Bass, B. M., & Riggio, R. E. (2006). Transformational leadership.
Bengio, Y., Goodfellow, I., & Courville, A. (2017). Deep learning (Vol. 1). MIT press Cambridge, MA, USA.
Bennett, N. J., Roth, R., Klain, S. C., Chan, K. M. A., Clark, D. A., Cullman, G., Epstein, G., Nelson, M. P., Stedman, R., Teel, T. L., Thomas, R. E. W., Wyborn, C., Curran, D., Greenberg, A., Sandlos, J., & Veríssimo, D. (2017). Mainstream-ing the social sciences in conservation. Conservation Biology, 31(1), 56–66. https://doi.org/10.1111/cobi.12788
Berkes, F. (2004). Rethinking Community-Based Conservation. Conservation Biology, 18(3), 621–630. https://doi.org/10.1111/j.1523-1739.2004.00077.x
Birhane, A. (2021). Algorithmic injustice: A relational ethics approach. Patterns, 2(2), 100205. https://doi.org/10.1016/j.patter.2021.100205
Björgvinsson, E., Ehn, P., & Hillgren, P.-A. (2010). Participatory design and" democratizing innovation". 41–50.
Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57–69). Chapman and Hall/CRC.
Bramer, W. M., Rethlefsen, M. L., Kleijnen, J., & Franco, O. H. (2017). Optimal database combinations for literature searches in systematic reviews: A prospective exploratory study. Systematic Reviews, 6, 1–12.
Brown, P. A. (2008). A Review of the Literature on Case Study Research. Canadian Journal for New Scholars in Educa-tion, 1(1).
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance? Available at SSRN 1819486.
Bryson, J. M., Crosby, B. C., & Bloomberg, L. (2014). Public value governance: Moving beyond traditional public admin-istration and the new public management. Public Administration Review, 74(4), 445–456.
Cameron, K., & Quinn, R. (2000). Diagnosing and changing organizational culture, based on the competing values frame-work, 1999. Reading, Massachusettes: Addison Wesley.
Cargo, M., & Mercer, S. L. (2008). The Value and Challenges of Participatory Research: Strengthening Its Practice. Annu-al Review of Public Health, 29(1), 325–350. https://doi.org/10.1146/annurev.publhealth.29.091307.083824
Carmeli, A., Sheaffer, Z., & Yitzack Halevi, M. (2009). Does participatory decision‐making in top management teams en-hance decision effectiveness and firm performance? Personnel Review, 38(6), 696–714. https://doi.org/10.1108/00483480910992283
Chaix-Couturier, C., Durand-Zaleski, I., Jolly, D., & Durieux, P. (2000). Effects of financial incentives on medical prac-tice: Results from a systematic review of the literature and methodological issues. International Journal for Quality in Health Care, 12(2), 133–142. https://doi.org/10.1093/intqhc/12.2.133
Chen, Chiang, & Storey. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165. https://doi.org/10.2307/41703503
Curry, L. A., Nembhard, I. M., & Bradley, E. H. (2009). Qualitative and Mixed Methods Provide Unique Contributions to Outcomes Research. Circulation, 119(10), 1442–1452. https://doi.org/10.1161/CIRCULATIONAHA.107.742775
Davenport, T. H., & Patil, D. (2012). Data scientist. Harvard Business Review, 90(5), 70–76.
Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., Hsu, R., Katbamna, S., Olsen, R., & Smith, L. (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. BMC Medical Research Methodology, 6, 1–13.
Doorn, N. (2021). Artificial intelligence in the water domain: Opportunities for responsible use. Science of The Total Envi-ronment, 755, 142561. https://doi.org/10.1016/j.scitotenv.2020.142561
Elgendy, N., & Elragal, A. (2016). Big Data Analytics in Support of the Decision Making Process. Procedia Computer Sci-ence, 100, 1071–1084. https://doi.org/10.1016/j.procs.2016.09.251
Elgendy, N., Elragal, A., & Päivärinta, T. (2022). DECAS: A modern data-driven decision theory for big data and analyt-ics. Journal of Decision Systems, 31(4), 337–373. https://doi.org/10.1080/12460125.2021.1894674
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportuni-ties, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347.
Fung, A. (2015). Putting the Public Back into Governance: The Challenges of Citizen Participation and Its Future. Public Administration Review, 75(4), 513–522. https://doi.org/10.1111/puar.12361
Ganapati, S., & Reddick, C. G. (2018). Prospects and challenges of sharing economy for the public sector. Government In-formation Quarterly, 35(1), 77–87.
Gautam, R. S., & Bhimavarapu, V. M. (2022). Data driven decision making: Application in finance. Iconic Research and Engineering Journals, 5(12), 52–56.
Hambrick, D. C. (2007). Upper echelons theory: An update. Academy of Management Review, 32(2), 334–343.
Hart, A., Gagnon, E., Eryigit-Madzwamuse, S., Cameron, J., Aranda, K., Rathbone, A., & Heaver, B. (2016). Uniting Resil-ience Research and Practice With an Inequalities Approach. SAGE Open, 6(4), 215824401668247. https://doi.org/10.1177/2158244016682477
Ho, C. W. L., Soon, D., Caals, K., & Kapur, J. (2019). Governance of automated image analysis and artificial intelligence analytics in healthcare. Clinical Radiology, 74(5), 329–337. https://doi.org/10.1016/j.crad.2019.02.005
Hodge, J., Foley, S., Brankaert, R., Kenning, G., Lazar, A., Boger, J., & Morrissey, K. (2020). Relational, Flexible, Every-day: Learning from Ethics in Dementia Research. Proceedings of the 2020 CHI Conference on Human Factors in Com-puting Systems, 1–16. https://doi.org/10.1145/3313831.3376627
Irvin, R. A., & Stansbury, J. (2004a). Citizen Participation in Decision Making: Is It Worth the Effort? Public Administra-tion Review, 64(1), 55–65. https://doi.org/10.1111/j.1540-6210.2004.00346.x
Irvin, R. A., & Stansbury, J. (2004b). Citizen participation in decision making: Is it worth the effort? Public Administra-tion Review, 64(1), 55–65.
Kabadurmus, O., Kayikci, Y., Demir, S., & Koc, B. (2023). A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks. Socio-Economic Planning Sciences, 85, 101417. https://doi.org/10.1016/j.seps.2022.101417
Kaner, S. (2014). Facilitator’s guide to participatory decision-making. John Wiley & Sons.
Kaplan, R. S., & Norton, D. P. (2015). Balanced Scorecard Success: The Kaplan-Norton Collection (4 Books). Harvard Business Review Press.
Kelleher, J. D., Mac Namee, B., & D’arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: Al-gorithms, worked examples, and case studies. MIT press.
Kellogg, W. (2004). Logic Model Development Guide. Kellogg Foundation.
Kiron, D., Ferguson, R. B., & Prentice, P. K. (2013). From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review, 54(3), 1.
Klievink, B., Romijn, B.-J., Cunningham, S., & de Bruijn, H. (2017). Big data in the public sector: Uncertainties and read-iness. Information Systems Frontiers, 19(2), 267–283.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from in-sights to value. MIT Sloan Management Review.
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Medicine, 6(7), e1000100. https://doi.org/10.1371/journal.pmed.1000100
Madsen, D. Ø., & Slåtten, K. (2013). The role of the management fashion arena in the cross-national diffusion of man-agement concepts: The case of the balanced scorecard in the Scandinavian countries. Administrative Sciences, 3(3), 110–142.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. Occasional Paper. Rand Corporation.
Matlock, D. D., & Spatz, E. S. (2014). Design and Testing of Tools for Shared Decision Making. Circulation: Cardiovas-cular Quality and Outcomes, 7(3), 487–492. https://doi.org/10.1161/CIRCOUTCOMES.113.000289
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
Mitchell, T. M. (2007). Machine learning (Vol. 1). McGraw-hill New York.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*, the. (2009). Preferred reporting items for system-atic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269.
Muller, M. J. (2003). Participatory Design: The Third Space in HCI-Handbook of HCI. Mahway NJ USA: Erlbaum.
Nisar, Q. A., Nasir, N., Jamshed, S., Naz, S., Ali, M., & Ali, S. (2021). Big data management and environmental perfor-mance: Role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management, 34(4), 1061–1096.
Nonaka, I., & Takeuchi, H. (2007). The knowledge-creating company. Harvard Business Review, 85(7/8), 162.
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns, 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347
O’Flynn, J. (2007). From new public management to public value: Paradigmatic change and managerial implications. Aus-tralian Journal of Public Administration, 66(3), 353–366.
O’neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
Orr, G. (2003). Diffusion of innovations, by Everett Rogers (1995). Retrieved January, 21, 2005.
Osman, A. M. S., Elragal, A. A., & Ståhlbröst, A. (2022). Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study. Applied Sciences, 12(3), 1732. https://doi.org/10.3390/app12031732
Papaioannou, D., Sutton, A., & Booth, A. (2016). Systematic approaches to a successful literature review. Systematic Ap-proaches to a Successful Literature Review, 1–336.
Pardo, T. A., & Scholl, H. J. J. (2002). Walking atop the cliffs: Avoiding failure and reducing risk in large scale e-government projects. 1656–1665.
Pedersen, J. S., & Wilkinson, A. (2018). The digital society and provision of welfare services. International Journal of So-ciology and Social Policy, 38(3/4), 194–209. https://doi.org/10.1108/IJSSP-05-2017-0062
Peter, S. (1990). The fifth discipline. The Art & Practice of Learning Organization. Doupleday Currence, New York.
Piotrowski, S. J., & Van Ryzin, G. G. (2007). Citizen attitudes toward transparency in local government. The American Review of Public Administration, 37(3), 306–323.
Provost, F., & Fawcett, T. (2013a). Data science and its relationship to big data and data-driven decision making. Big Da-ta, 1(1), 51–59.
Provost, F., & Fawcett, T. (2013b). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media, Inc.
Quick, K. S., & Feldman, M. S. (2011). Distinguishing participation and inclusion. Journal of Planning Education and Re-search, 31(3), 272–290.
Rittel, H. W., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169.
Robertson, T., & Wagner, I. (2012). Ethics: Engagement, representation and politics-in-action. In Routledge international handbook of participatory design (pp. 64–85). Routledge.
Rowley, J., & Slack, F. (2004). Conducting a literature review. Management Research News, 27(6), 31–39.
Russell, S. J. (2010). Artificial intelligence a modern approach. Pearson Education, Inc.
Sanders, E. B.-N., Brandt, E., & Binder, T. (2010). A framework for organizing the tools and techniques of participatory design. 195–198.
Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-Design, 4(1), 5–18.
Sarin, S., & McDermott, C. (2003). The effect of team leader characteristics on learning, knowledge application, and per-formance of cross‐functional new product development teams. Decision Sciences, 34(4), 707–739.
Shanley, P., & López, C. (2009). Out of the Loop: Why Research Rarely Reaches Policy Makers and the Public and What Can be Done: Biological Research Beyond Academy. Biotropica, 41(5), 535–544. https://doi.org/10.1111/j.1744-7429.2009.00561.x
Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018). DEMATEL Technique: A Systematic Review of the State-of-the-Art Literature on Methodologies and Applications. Mathematical Problems in Engineering, 2018, 1–33. https://doi.org/10.1155/2018/3696457
Sleep, S., Hulland, J., & Gooner, R. A. (2019). THE DATA HIERARCHY: Factors influencing the adoption and implemen-tation of data-driven decision making. AMS Review, 9(3–4), 230–248. https://doi.org/10.1007/s13162-019-00146-8
Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008
Valentine, S. R., Hollingworth, D., & Schultz, P. (2018). Data-based ethical decision making, lateral relations, and organi-zational commitment: Building positive workplace connections through ethical operations. Employee Relations, 40(6), 946–963. https://doi.org/10.1108/ER-10-2017-0240
Wamba, S. F., 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, 165, 234–246.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarter-ly, xiii–xxiii.
Yu, S., Qing, Q., Zhang, C., Shehzad, A., Oatley, G., & Xia, F. (2021). Data-Driven Decision-Making in COVID-19 Re-sponse: A Survey. IEEE Transactions on Computational Social Systems, 8(4), 1016–1029. https://doi.org/10.1109/TCSS.2021.3075955