Pathways for Design Research on Artificial Intelligence

References

  • Abbasi A, Chen H (2008) CyberGate: A design framework and system for text analysis of computer-mediated communication. MIS Quart. 32(4):811–837.CrossrefGoogle Scholar
  • Abbasi A, Chiang RH, Xu J (2023) Data science for social good. J. Assoc. Inform. Systems 24(6):1439–1458.Google Scholar
  • Abbasi A, Sarker S, Chiang RH (2016) Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inform. Systems 17(2):1jais.00423.Google Scholar
  • Abbasi A, Dobolyi D, Vance A, Zahedi FM (2021) The phishing funnel model: A design artifact to predict user susceptibility to phishing websites. Inform. Systems Res. 32(2):410–436.LinkGoogle Scholar
  • Abbasi A, Li J, Clifford G, Taylor H (2018) Make “fairness by design” part of machine learning. Harvard Bus. Rev. (August 1), https://hbr.org/2018/08/make-fairness-by-design-part-of-machine-learning.Google Scholar
  • Agarwal R, Dhar V (2014) Big data, data science, and analytics: The opportunity and challenge for IS research. Inform. Systems Res. 25(3):443–448.LinkGoogle Scholar
  • Ahsen ME, Ayvaci MUS, Raghunathan S (2019) When algorithmic predictions use human-generated data: A bias-aware classification algorithm for breast cancer diagnosis. Inform. Systems Res. 30(1):97–116.LinkGoogle Scholar
  • Aiken MW, Liu Sheng OR, Vogel DR (1991) Integrating expert systems with group decision support systems. ACM Trans. Inform. Systems 9(1):75–95.CrossrefGoogle Scholar
  • Ananthaswamy A (2023) In AI, is bigger always better. Nature 615:202–205.Google Scholar
  • Arazy O, Woo C (2007) Enhancing information retrieval through statistical natural language processing: A study of collocation indexing. MIS Quart. 31(3):525–546.CrossrefGoogle Scholar
  • arXiv (2023) Usage statistic reports. Accessed May 12, 2023, https://arxiv.org/stats/monthly_submissions.Google Scholar
  • Bailey DE, Barley SR (2020) Beyond design and use: How scholars should study intelligent technologies. Inform. Organ. 30(2):100286.CrossrefGoogle Scholar
  • Baskerville R (2008) What design science is not. Eur. J. Inform. Systems 17(5):441–443.CrossrefGoogle Scholar
  • Baskerville RL, Kaul M, Storey VC (2015) Genres of inquiry in design-science research. MIS Quart. 39(3):541–564.CrossrefGoogle Scholar
  • Bauer K, von Zahn M, Hinz O (2023) Expl (AI) ned: The impact of explainable artificial intelligence on users’ information processing. Inform. Systems Res. 34(4):1582–1602.LinkGoogle Scholar
  • Benbya H, Pachidi S, Jarvenpaa S (2021) Special issue editorial: Artificial intelligence in organizations: Implications for information systems research. J. Assoc. Inform. Systems 22(2):10.Google Scholar
  • Bender EM, Gebru T, McMillan-Major A, Shmitchell S (2021) On the dangers of stochastic parrots: Can language models be too big? Proc. 2021 ACM Conf. Fairness Accountability Transparency, 610–623.Google Scholar
  • Berente N, Gu B, Recker J, Santhanam R (2021) Managing artificial intelligence. MIS Quart. 45(3):1433–1450.CrossrefGoogle Scholar
  • Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans. Assoc. Comput. Linguistics 5:135–146.Google Scholar
  • Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, von Arx S, et al. (2022) On the opportunities and risks of foundation models. Preprint, submitted August 16, https://arxiv.org/abs/2108.07258.Google Scholar
  • Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A (2020) Language models are few-shot learners. Adv. Neural Inform. Processing Systems 33:1877–1901.Google Scholar
  • Burton-Jones A, Boh WF, Oborn E, Padmanabhan B (2021) Advancing research transparency at MISQ: A pluralistic approach. MIS Quart. 45(2):iii–xviii.Google Scholar
  • Burton-Jones A, Storey VC, Sugumaran V, Ahluwalia P (2005) A semiotic metrics suite for assessing the quality of ontologies. Data Knowledge Engrg. 55(1):84–102.CrossrefGoogle Scholar
  • Burton-Jones A, Recker J, Indulska M, Green P, Weber R (2017) Assessing representation theory with a framework for pursuing success and failure. MIS Quart. 41(4):1307–1334.CrossrefGoogle Scholar
  • Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186.CrossrefGoogle Scholar
  • Chang MK, Woo CC (1994) A speech-act-based negotiation protocol: Design, implementation, and test use. ACM Trans. Inform. Systems 12(4):360–382.CrossrefGoogle Scholar
  • Chau M, Li TM, Wong PW, Xu JJ, Yip PS, Chen H (2020) Finding people with emotional distress in online social media: A design combining machine learning and rule-based classification. MIS Quart. 44(2):933–955.CrossrefGoogle Scholar
  • Chen H, Dhar V (1991) Cognitive process as a basis for intelligent retrieval systems design. Inform. Processing Management 27(5):405–432.CrossrefGoogle Scholar
  • Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: From big data to big impact. MIS Quart. 36(4):1165–1188.Google Scholar
  • Chua CEH, Indulska M, Lukyanenko R, Maass W, Storey VC (2022) Data management. MIS Quart. Online 1–10.Google Scholar
  • De Jong T, Ferguson-Hessler MG (1996) Types and qualities of knowledge. Educational Psychologist 31(2):105–113.Google Scholar
  • Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. Proc. NAACL-HLT, 4171–4186.Google Scholar
  • Dhar V, Tuzhulin A (1993) Data driven pattern discovery in databases. IEEE Trans. Knowledge Data Engrg. 5(6):926–938.CrossrefGoogle Scholar
  • Donoho D (2017) 50 years of data science. J. Comput. Graphical Statist. 26(4):745–766.CrossrefGoogle Scholar
  • Etudo U, Yoon VY (2024) Ontology-based information extraction for labeling radical online content using distant supervision. Inform. Systems Res. 35(1):203–225.LinkGoogle Scholar
  • Garg N, Schiebinger L, Jurafsky D, Zou J (2018) Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl. Acad. Sci. USA 115(16):E3635–E3644.CrossrefGoogle Scholar
  • Gladwell M (2009) What the Dog Saw: And Other Adventures (Hachette UK, London).Google Scholar
  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. (2020) Generative adversarial networks. Commun. ACM 63(11):139–144.CrossrefGoogle Scholar
  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Bengio Y, et al. (2014) Generative adversarial networks. Adv. Neural Inform. Processing Systems. 27:1–9.Google Scholar
  • Goul M, Henderson JC, Tonge FM (1992) The emergence of artificial intelligence as a reference discipline for decision support systems research. Decision Sci. 23(6):1263–1276.CrossrefGoogle Scholar
  • Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(1):337–355.CrossrefGoogle Scholar
  • Guo Y, Yang Y, Abbasi A (2022) Auto-debias: Debiasing masked language models with automated biased prompts. Proc. 60th Annual Meeting Assoc. Comput. Linguistics, 1012–1023.Google Scholar
  • Gupta A (2018) Traits of successful research contributions for publication in ISR: Some thoughts for authors and reviewers. Inform. Systems Res. 29(4):779–786.LinkGoogle Scholar
  • Gupta A (2022) There are promises to keep and miles to go before I leave …. Inform. Systems Res. 33(4):1119–1125.LinkGoogle Scholar
  • He X, Zhao K, Chu X (2021) AutoML: A survey of the state-of-the-art. Knowledge Based Systems 212:106622.CrossrefGoogle Scholar
  • Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Quart. 28(1):75–105.CrossrefGoogle Scholar
  • Hofman JM, Watts DJ, Athey S, Garip F, Griffiths TL, Kleinberg J, et al. (2021) Integrating explanation and prediction in computational social science. Nature 595(7866):181–188.CrossrefGoogle Scholar
  • Hume D (1777) An enquiry concerning human understanding. Enquiries concerning the human understanding, and concerning the principles of morals. The Project Gutenberg EBook. Accessed April 15, 2023, https://www.gutenberg.org/files/4705/4705-h/4705-h.htm#link2H_PART3.Google Scholar
  • Jia N, Luo X, Fang Z, Liao C (2024) When and how artificial intelligence augments employee creativity. Acad. Management J. 67(1):5–32.CrossrefGoogle Scholar
  • Kaur D, Uslu S, Rittichier KJ, Durresi A (2022) Trustworthy artificial intelligence: A review. ACM Comput. Surveys 55(2):1–38.CrossrefGoogle Scholar
  • Keen PGW (1980) MIS research: Reference disciplines and a cumulative tradition. ICIS 1980 Proc. 9 (AIS Electronic Library, Atlanta).Google Scholar
  • Lalor JP, Abbasi A, Oketch K, Yang Y, Forsgren N (2024) Should fairness be a metric or a model? A model-based framework for assessing bias in machine learning pipelines. ACM Trans. Inform. Systems 42(4):99.CrossrefGoogle Scholar
  • Lalor JP, Yang Y, Smith K, Forsgren N, Abbasi A (2022) Benchmarking intersectional biases in NLP. Proc. 2022 Conf. NAACL-HLT, 3598–3609.Google Scholar
  • Lebovitz S, Levina N, Lifshitz-Assaf H (2021) Is AI ground truth really ‘true’? The dangers of training and evaluating AI tools based on experts’ know-what. MIS Quart. 45(3):1501–1525.CrossrefGoogle Scholar
  • Lee K, Ram S (2024) Explainable deep learning for false information identification: An argumentation theory approach. Inform. Systems Res. 35(2):890–907.Google Scholar
  • Lee D, Cheng Z, Mao C, Manzoor E (2024) Guided diverse concept miner (GDCM): Uncovering relevant constructs for managerial insights from text. Inform. Systems Res., ePub ahead of print May 10, https://doi.org/10.1287/isre.2020.0494.LinkGoogle Scholar
  • Leroy G, Tulu B, Liu X (2023) Introduction to the special issue on design and data science research in healthcare. ACM Trans. Management Inform. Systems 14(2):1–4.CrossrefGoogle Scholar
  • Li J, Larsen K, Abbasi A (2020) TheoryOn: A design framework and system for unlocking behavioral knowledge through ontology learning. MIS Quart. 44(4):1733–1772.CrossrefGoogle Scholar
  • Li TM, Chau M, Yip PS, Wong PW (2014) Temporal and computerized psycholinguistic analysis of the blog of a Chinese adolescent suicide. Crisis 35(3):168–175.CrossrefGoogle Scholar
  • Liu D, Feng XL, Ahmed F, Shahid M, Guo J (2022) Detecting and measuring depression on social media using a machine learning approach: Systematic review. JMIR Mental Health 9(3):e27244.CrossrefGoogle Scholar
  • Liu Y, Pant G, Sheng OR (2020) Predicting labor market competition: Leveraging interfirm network and employee skills. Inform. Systems Res. 31(4):1443–1466.LinkGoogle Scholar
  • Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al. (2019) Roberta: A robustly optimized Bert pretraining approach. Preprint, submitted July 26, https://arxiv.org/abs/1907.11692.Google Scholar
  • Luitse D, Denkena W (2021) The great transformer: Examining the role of large language models in the political economy of AI. Big Data Soc. 8(2):20539517211047734.CrossrefGoogle Scholar
  • Lukyanenko R, Parsons J, Wiersma YF, Maddah M (2019) Expecting the unexpected: Effects of data collection design choices on the quality of crowdsourced user-generated content. MIS Quart. 43(2):623–648.CrossrefGoogle Scholar
  • Lyytinen KJ (1985) Implications of theories of language for information systems. MIS Quart. 9(1):61–74.CrossrefGoogle Scholar
  • Macha M, Foutz NZ, Li B, Ghose A (2024) Personalized privacy preservation in consumer mobile trajectories. Inform. Systems Res. 35(1):249–271.LinkGoogle Scholar
  • Malhotra A, Jindal R (2022) Deep learning techniques for suicide and depression detection from online social media: A scoping review. Appl. Soft Comput. 130:109713.CrossrefGoogle Scholar
  • Man Tang P, Koopman J, McClean ST, Zhang JH, Li CH, De Cremer D, et al. (2022) When conscientious employees meet intelligent machines: An integrative approach inspired by complementarity theory and role theory. Acad. Management J. 65(3):1019–1054.CrossrefGoogle Scholar
  • March ST, Smith GF (1995) Design and natural science research on information technology. Decision Support Systems 15(4):251–266.CrossrefGoogle Scholar
  • Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv. Neural Inform. Processing Systems 26:1–9.Google Scholar
  • Miranda S, Berente N, Seidel S, Safadi H, Burton-Jones A (2022) Editor’s comments: Computationally intensive theory construction: A primer for authors and reviewers. MIS Quart. 46(2):iii–xviii.CrossrefGoogle Scholar
  • Nunamaker JF, Whinston A (1972) A planning and cost allocation procedure for computer system management. ACM SIGCSIM Installation Management Rev. 2(si1):11–26.Google Scholar
  • Nunamaker JF Jr, Chen M, Purdin TD (1990) Systems development in information systems research. J. Management Inform. Systems 7(3):89–106.CrossrefGoogle Scholar
  • Nunamaker JF Jr, Briggs RO, Derrick DC, Schwabe G (2015) The last research mile: Achieving both rigor and relevance in information systems research. J. Management Inform. Systems 32(3):10–47.CrossrefGoogle Scholar
  • Padmanabhan B, Sahoo N, Burton-Jones A (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.CrossrefGoogle Scholar
  • Pant G, Sheng OR (2015) Web footprints of firms: Using online isomorphism for competitor identification. Inform. Systems Res. 26(1):188–209.LinkGoogle Scholar
  • Peffers K, Tuunanen T, Rothenberger MA, Chatterjee S (2007) A design science research methodology for information systems research. J. Management Inform. Systems 24(3):45–77.CrossrefGoogle Scholar
  • Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. Moschitti A, Pang B, Daelemans W, eds. Proc. 2014 Conf. Empirical Methods Natl. Language Processing (EMNLP) (Association for Computational Linguistics, Kerrville, TX), 1532–1543.Google Scholar
  • Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. Proc. NAACL, vol. 5.Google Scholar
  • Pinker S (2009) Malcolm Gladwell, eclectic detective. New York Times (November 7), https://www.nytimes.com/2009/11/15/books/review/Pinker-t.html.Google Scholar
  • Provost F, Martens D, Murray A (2015) Finding similar mobile consumers with a privacy-friendly geosocial design. Inform. Systems Res. 26(2):243–265.Google Scholar
  • Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training. Technical report, OpenAI, San Francisco.Google Scholar
  • Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. Technical report, OpenAI, San Francisco.Google Scholar
  • Rai A (2017a) Avoiding type III errors: Formulating IS research problems that matter. MIS Quart. 41(2):III–VII.Google Scholar
  • Rai A (2017b) Editor’s comments: Diversity of design science research. MIS Quart. 41(1):iii–xviii.CrossrefGoogle Scholar
  • Rai A (2018) Editor’s comments: Beyond outdated labels: The blending of IS research traditions. MIS Quart. 42(1):iii–vi.Google Scholar
  • Recker JC, Lukyanenko R, Jabbari Sabegh M, Samuel B, Castellanos A (2021) From representation to mediation: A new agenda for conceptual modeling research in a digital world. MIS Quart. 45(1):269–300.CrossrefGoogle Scholar
  • Rhue L (2023) The anchoring effect, algorithmic fairness, and the limits of information transparency for emotion artificial intelligence. Inform. Systems Res., ePub ahead of print December 19, https://doi.org/10.1287/isre.2019.0493.LinkGoogle Scholar
  • Samtani S, Zhu H, Padmanabhan B, Chai Y, Chen H, Nunamaker JF Jr (2023) Deep learning for information systems research. J. Management Inform. Systems 40(1):271–301.CrossrefGoogle Scholar
  • Sarker S, Chatterjee S, Xiao X, Elbanna A (2019) The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quart. 43(3):695–720.CrossrefGoogle Scholar
  • Sarker S, Whitley EA, Goh KY, Hong Y, Mähring M, Sanyal P, et al. (2023) Some thoughts on reviewing for Information Systems Research and other leading information systems journals. Inform. Systems Res. 34(4):1321–1338.LinkGoogle Scholar
  • Sastry G (2018) AI and compute. Open AI Blogs (May 16, 2018), https://openai.com/research/ai-and-compute.Google Scholar
  • Seidel S, Berente N, Lindberg A, Lyytinen K, Nickerson JV (2018) Autonomous tools and design: A triple-loop approach to human-machine learning. Commun. ACM 62(1):50–57.CrossrefGoogle Scholar
  • Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.CrossrefGoogle Scholar
  • Simon HA (1988) The science of design: Creating the artificial. Design Issues 4(1/2):67–82.CrossrefGoogle Scholar
  • Simon HA (1996) The Sciences of the Artificial, 3rd ed. (MIT Press, Cambridge, MA).Google Scholar
  • Storey VC (1993) Understanding semantic relationships. VLDB J. 2:455–488.CrossrefGoogle Scholar
  • Suarez D, Gomez C, Medaglia AL, Akhavan-Tabatabaei R, Grajales S (2024) Integrated decision support for disaster risk management: Aiding preparedness and response decisions in wildfire management. Inform. Systems Res. 35(2):609–628.LinkGoogle Scholar
  • Susarla A, Gopal R, Thatcher JB, Sarker S (2023) The Janus effect of generative AI: Charting the path for responsible conduct of scholarly activities in information systems. Inform. Systems Res. 34(2):399–408.LinkGoogle Scholar
  • Tarafdar M, Shan G, Bennett Thatcher J, Gupta A (2022) Intellectual diversity in IS research: Discipline-based conceptualization and an illustration from information systems research. Inform. Systems Res. 33(4):1490–1510.Google Scholar
  • Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, et al. (2023) Llama 2: Open foundation and fine-tuned chat models. Preprint, submitted July 19, https://arxiv.org/pdf/2307.09288.pdf.Google Scholar
  • Tremblay M, VanderMeer D, Beck R (2018) The effects of the quantification of faculty productivity: Perspectives from the design science research community. Comm. Assoc. Inform. Systems 43:625–661.Google Scholar
  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. (2017) Attention is all you need. Adv. Neural Inform. Processing Systems 30:1–11.Google Scholar
  • Verdonck M, Gailly F, Pergl R, Guizzardi G, Martins B, Pastor O (2019) Comparing traditional conceptual modeling with ontology-driven conceptual modeling: An empirical study. Inform. Systems 81:92–103.Google Scholar
  • Vom Brocke J, Winter R, Hevner A, Maedche A (2020) Special issue editorial—Accumulation and evolution of design knowledge in design science research: A journey through time and space. J. Assoc. Inform. Systems 21(3):1jais.00611.Google Scholar
  • Walls JG, Widmeyer GR, El Sawy OA (1992) Building an information system design theory for vigilant EIS. Inform. Systems Res. 3(1):36–59.LinkGoogle Scholar
  • Wand Y, Weber R (1993) On the ontological expressiveness of information systems analysis and design grammars. Inform. Systems J. 3(4):217–237.CrossrefGoogle Scholar
  • Wand Y, Monarchi DE, Parsons J, Woo CC (1995) Theoretical foundations for conceptual modelling in information systems development. Decision Support Systems 15(4):285–304.CrossrefGoogle Scholar
  • Wang RY, Storey VC, Firth CP (1995) A framework for analysis of data quality research. IEEE Trans. Knowledge Data Engrg. 7(4):623–640.CrossrefGoogle Scholar
  • Yang K, Lau RY, Abbasi A (2023) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.LinkGoogle Scholar
  • Yarkoni T, Westfall J (2017) Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect. Psych. Sci. 12(6):1100–1122.Google Scholar
  • Zhang N, Xu H (2024) Fairness of ratemaking for catastrophe insurance: Lessons from machine learning. Inform. Systems Res. 35(2):469–488.Google Scholar
  • Zhang J, Adomavicius G, Gupta A, Ketter W (2020) Consumption and performance: Understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Inform. Systems Res. 31(1):76–101.LinkGoogle Scholar
  • Zhang H, Zhao X, Fang X, Chen B (2024) Proactive resource request for disaster response: A deep learning-based optimization model. Inform. Systems Res. 35(2):528–550.Google Scholar
  • Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, et al. (2020) A comprehensive survey on transfer learning. Proc. IEEE 109(1):43–76.CrossrefGoogle Scholar
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