Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models

References

• Abbott HP (2008) The Cambridge Introduction to Narrative, 2nd ed. (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Alexander I, Maiden N (2004a) Scenarios, stories, and use cases: The modern basis for system development. IEEE Comput. Control Engrg. 15(5):24–29.Crossref, Google Scholar
• Alexander I, Maiden N (2004b) Scenarios, Stories, Use Cases: Through the Systems Development Life-Cycle (Wiley, New York).Google Scholar
• Allen GN, March ST (2012) A research note on representing part-whole relations in conceptual modeling. MIS Quart. 36(3):945–964.Crossref, Google Scholar
• Allen SW, Brooks LR (1991) Specializing the operation of an explicit rule. J. Experiment. Psych. General 120(1):3–19.Crossref, Google Scholar
• Anderson LW, Krathwohl DR (2000) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, abridged 2nd ed. (Allyn & Bacon, Boston).Google Scholar
• Angwin DN, Cummings S, Daellenbach U (2019) How the multimedia communication of strategy can enable more effective recall and learning. Acad. Management Learning Ed. 18(4):527–546.Crossref, Google Scholar
• Arlow J, Emmerich W, Quinn J (1998) Literate modelling—capturing business knowledge with the UML. Bézivin J, Muller P-A, eds. The Unified Modeling Language. UML ’98: Beyond the Notation (Springer, Berlin), 189–199.Google Scholar
• Aspray W, Campbell-Kelly M (1996) Computer: A History of the Information Machine, 1st ed. (Basic Books, New York).Google Scholar
• Atir S, Rosenzweig E, Dunning D (2015) When knowledge knows no bounds: Self-perceived expertise predicts claims of impossible knowledge. Psych. Sci. 26(8):1295–1303.Crossref, Google Scholar
• Atkinson RK, Derry SJ, Renkl A, Wortham D (2000) Learning from examples: Instructional principles from the worked examples research. Rev. Educ. Res. 70(2):181–214.Crossref, Google Scholar
• Bachman CW, Daya M (1977) The role concept in data models. Housel BC, Merten AG, eds. Proc. Third Internat. Conf. Very Large Data Bases, vol. 3 (VLDB Endowment), 464–476.Google Scholar
• Batra D, Hoffer JA, Bostrom RP (1990) Comparing representations with relational and EER models. Comm. ACM 33(2):126–139.Crossref, Google Scholar
• Benyon D, Macaulay C (2004) A scenario-based design method for human-centred interaction design. Alexander I, Maiden N, eds. Scenarios, Stories, Use Cases: Through the Systems Development Life-Cycle (Wiley, New York), 211–235.Google Scholar
• Bera P, Burton-Jones A, Wand Y (2014) How semantics and pragmatics interact in understanding conceptual models. Inform Systems Res. 25(2):401–419.Link, Google Scholar
• Bera P, Soffer P, Parsons J (2019) Using eye tracking to expose cognitive processes in understanding conceptual models. MIS Quart. 43(4):1105–1126.Crossref, Google Scholar
• Bergen BK (2012) Louder Than Words: The New Science of How the Mind Makes Meaning (Basic Books, New York).Google Scholar
• Bloom BS (1956) Taxonomy of Educational Objectives. Handbook 1: The Cognitive Domain (McKay, New York).Google Scholar
• Bodart F, Patel A, Sim M, Weber R (2001) Should optional properties be used in conceptual modeling? A theory and three empirical tests. Inform. Systems Res. 12(4):384–405.Link, Google Scholar
• Browne GJ, Parsons J (2012) More enduring questions in cognitive IS research. J. Assoc. Inform. Systems 13(12):1000–1011.Google Scholar
• Bubenko JA (1979) On the role of ‘understanding models’ in conceptual schema design. Furtado AL, Morgan HL, eds. Proc. Fifth Internat. Conf. Very Large Data Bases (VLDB Endowment), 129–139.Google Scholar
• Bühler C (2001) Empowered participation of users with disabilities in R&D projects. Internat. J. Human Comput. Stud. 55(4):645–659.Crossref, Google Scholar
• Burton-Jones A, Meso PN (2008) The effects of decomposition quality and multiple forms of information on users’ understanding of a domain. J. Assoc. Inform. Systems 9(12):2.Google Scholar
• Burton-Jones A, Weber R (2014) Building conceptual modeling on the foundation of ontology. Topi H, Tucker A, eds. Computing Handbook: Information Systems and Information Technology (CRC Press, Boca Raton, FL), 316–339.Google Scholar
• Burton-Jones A, Clarke R, Lazarenko K, Weber R (2012) Is use of optional attributes and associations in conceptual modeling always problematic? Theory and empirical tests. ICIS 2012 Proc., vol. 4. https://aisel.aisnet.org/icis2012/proceedings/HumanComputerInteractions/4.Google Scholar
• Castellanos A, Tremblay MC, Lukyanenko R, Samuel B (2020) Basic classes in conceptual modeling: Theory and practical guidelines. J. Assoc. Inform. Systems 21(4):1001–1044.Google Scholar
• Chen PP-S (1976) The entity-relationship model—Toward a unified view of data. ACM Trans. Database Systems 1(1):9–36.Crossref, Google Scholar
• Cockburn A (2000) Writing Effective Use Cases, 1st ed. (Addison-Wesley, Boston).Google Scholar
• Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (Erlbaum Associates, Hillsdale, NJ).Google Scholar
• Compeau D, Marcolin B, Kelley H, Higgins C (2012) Generalizability of information systems research using student subjects—A reflection on our practices and recommendations for future research. Inform. Systems Res. 23(4):1093–1109.Link, Google Scholar
• Cooper G, Sweller J (1987) Effects of schema acquisition and rule automation on mathematical problem-solving transfer. J. Ed. Psych. 79(4):347–362.Crossref, Google Scholar
• Currim F, Ram S (2012) Modeling spatial and temporal set-based constraints during conceptual database design. Inform. Systems Res. 23(1):109–128.Link, Google Scholar
• Damasio AR (2010) Self Comes to Mind: Constructing the Conscious Brain (Pantheon, New York).Google Scholar
• Dansereau DF, Simpson DD (2009) A picture is worth a thousand words: The case for graphic representations. Professional Psych. Res. Practice 40(1):104–110.Crossref, Google Scholar
• Davenport TH, Harris JG, Morison R (2010) Analytics at Work: Smarter Decisions, Better Results (Harvard Business Press, Boston).Google Scholar
• Davies I, Green P, Rosemann M, Indulska M, Gallo S (2006) How do practitioners use conceptual modeling in practice? Data Knowledge Engrg. 58(3):358–380.Crossref, Google Scholar
• Day SB, Goldstone RL (2012) The import of knowledge export: Connecting findings and theories of transfer of learning. Ed. Psych. 47(3):153–176.Crossref, Google Scholar
• De Simoni G (2021) Metadata is the fish finder in data lakes. Report, Gartner Research, https://www.gartner.com/en/documents/3996684.Google Scholar
• De Simoni G, Judah S, Zaidi E (2015) Market guide for metadata management solutions. Gartner Research, https://www.gartner.com/en/documents/3092921.Google Scholar
• Dunn CL, Gerard GJ, Grabski SV (2011) Diagrammatic attention management and the effect of conceptual model structure on cardinality validation. J. Assoc. Inform. Systems 12(8):585–605.Google Scholar
• Edjlali R, Sicular S (2017) Four data management best practices for AI. Report, Gartner Research, https://www.gartner.com/en/documents/3787064.Google Scholar
• Elmasri R, Navathe S (2010) Fundamentals of Database Systems, 6th ed. (Addison-Wesley, Boston).Google Scholar
• Eppler MJ, Mengis J (2004) The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inform. Soc. 20(5):325–344.Crossref, Google Scholar
• Ericsson KA, Charness N, Feltovich PJ, Hoffman DL (2006) The Cambridge Handbook of Expertise and Expert Performance (Cambridge University Press, New York).Crossref, Google Scholar
• Eriksson O, Agerfalk PJ (2021) Speaking things into existence: Ontological foundations of identity representation and management. Inform. Systems J. 32(1):33–60.Crossref, Google Scholar
• Eriksson O, Johannesson P, Bergholtz M (2019) The case for classes and instances—A response to representing instances: The case for reengineering conceptual modelling grammars. Eur. J. Inform. Systems 28(6):681–693.Crossref, Google Scholar
• Eysenck MW (1991) Working memory. Eysenck MW, ed. The Blackwell Dictionary of Cognitive Psychology (Blackwell, Malden, MA), 372–375.Google Scholar
• Fettke P (2009) How conceptual modeling is used. Comm. Assoc. Inform. Syst. 25:43.Google Scholar
• Gemino A (2004) Empirical comparisons of animation and narration in requirements validation. Requirements Engrg. 9(3):153–168.Google Scholar
• Gemino A, Parker D (2009) Use case diagrams in support of use case modeling: Deriving understanding from the picture. J. Database Management 20(1):1–24.Crossref, Google Scholar
• Gemino A, Wand Y (2005) Complexity and clarity in conceptual modeling: Comparison of mandatory and optional properties. Data Knowledge Engrg. 55(3):301–326.Crossref, Google Scholar
• Gibson CF, Nolan RL (1973) Organizing and managing computer personnel: Conceptual approaches for the MIS manager. Proc. 11th Annual SIGCPS Comput. Personnel Res. Conf. (ACM, New York), 19–45.Google Scholar
• Graves A, Hendler J (2013) Visualization tools for open government data. Proc. 14th Annual Internat. Conf. Digital Government Res. (ACM, New York), 136–145.Google Scholar
• Gupta A, Poels G, Bera P (2018) A proposal of using conceptual models for user story development and maintenance. 17th AIS SIGSAND Sympos., Syracuse, NY, May 24–25.Google Scholar
• Gupta A, Poels G, Bera P (2019a) An algorithm to create multiple conceptual models from user stories. 18th AIS SIGSAND Sympos., New York, June 1–2.Google Scholar
• Gupta A, Poels G, Bera P (2019b) Creation of Multiple Conceptual Models from User Stories—a Natural Language Processing Approach (Springer, Cham, Switzerland), 47–57.Crossref, Google Scholar
• Gustavsson R, Ståhl B (2010) The empowered user—The critical interface to critical infrastructures. 2010 Fifth Internat. Conf. Critical Infrastructure (CRIS) (IEEE, Piscataway, NJ), 1–3.Google Scholar
• He X, Zhao K, Chu X (2021) AutoML: A survey of the state-of-the-art. Knowledge-Based Systems 212:106622.Crossref, Google Scholar
• Hirschheim R, Klein HK (2012) A glorious and not-so-short history of the information systems field. J. Assoc. Inform. Systems 13(4):188–235.Google Scholar
• Hirschheim R, Klein HK, Lyytinen K (1995) Information Systems Development and Data Modeling: Conceptual and Philosophical Foundations (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Howson C (2013) Successful Business Intelligence: Unlock the Value of BI and Big Data (McGraw-Hill, New York).Google Scholar
• Hvalshagen M (2011) Harnessing the power of narratives to understand user requirements. Unpublished doctoral dissertation, Kelley School of Business, Indiana University.Google Scholar
• Jabbari Sabegh MA, Recker J, Green P, Werder K (2019) How do individuals understand multiple conceptual modeling scripts? J. Assoc. Inform. Systems 20(8):2.Google Scholar
• Jiang Z, Benbasat I (2007) The effects of presentation formats and task complexity on online consumers’ product understanding. MIS Quart. 31(3):475–500.Crossref, Google Scholar
• Junglas I, Goel L, Ives B, Harris J (2014) Consumer IT at work: Development and test of an IT empowerment model. Proc. 35th Internat. Conf. Inform. Systems, Auckland, New Zealand, December 14–17.Google Scholar
• Kahneman D (2011) Thinking, Fast and Slow (Farrar, Straus and Giroux, New York).Google Scholar
• Kalyuga S (2009) Knowledge elaboration: A cognitive load perspective. Learning Instruction 19(5):402–410.Crossref, Google Scholar
• Kalyuga S (2010) Schema acquisition and sources of cognitive load. Plass JL, Moreno R, Brünken R, eds. Cognitive Load Theory (Cambridge University Press, Cambridge, UK), 48–64.Crossref, Google Scholar
• Khatri V, Ramesh V, Vessey I, Clay P (2006) Comprehension of conceptual schemas: Exploring the role of application and is domain knowledge. Inform. Systems Res. 17(1):81–99.Link, Google Scholar
• Khatri V, Samuel BM (2019) Analytics for managerial work. Comm. ACM 62(4):100–100.Crossref, Google Scholar
• Kim Y-G, March ST (1995) Comparing data modeling formalism. Comm. ACM 38(6):103–115.Crossref, Google Scholar
• Kim J, Hahn J, Hahn H (2000) How do we understand a system with (so) many diagrams? Cognitive integration processes in diagrammatic reasoning. Inform. Systems Res. 11(3):284–303.Link, Google Scholar
• Kuechler WL, Vaishnavi V (2006) So, talk to me: The effect of explicit goals on the comprehension of business process narratives. MIS Quart. 30(4):961–979.Crossref, Google Scholar
• Larsen KR, Becker DS (2021) Automated Machine Learning for Business (Oxford University Press, New York).Crossref, Google Scholar
• Lee AS, Baskerville RL (2012) Conceptualizing generalizability: New contributions and a reply. MIS Quart. 36(3):749–761.Crossref, Google Scholar
• Legner C, Eymann T, Hess T, Matt C, Böhmann T, Drews P, Mädche A, Urbach N, Ahlemann F (2017) Digitalization: Opportunity and challenge for the business and information systems engineering community. Bus. Inform. Systems Engrg. 59(4):301–308.Crossref, Google Scholar
• Lukyanenko R, Parsons J, Samuel B (2018) Artifact sampling: Using multiple information technology artifacts to increase research rigor. Proc. 51st Hawaii Internat. Conf. System Sci., Big Island, HI, January 2–6, 235–244.Google Scholar
• Lukyanenko R, Parsons J, Samuel B (2019a) Representing instances: The case for reengineering conceptual modelling grammars. Eur. J. Inform. Systems 28(1):68–90.Crossref, Google Scholar
• Lukyanenko R, Samuel BM, Evermann J, Parsons J (2016) Toward artifact sampling in IS design research. Workshop Inform. Tech. Systems, Dublin, Ireland, December 15–16.Google Scholar
• Lukyanenko R, Castellanos A, Parsons J, Chiarini Tremblay M, Storey VC (2019b) Using conceptual modeling to support machine learning. Cappiello C, Ruiz M, eds. Information Systems Engineering in Responsible Information Systems (Springer, Cham, Switzerland), 170–181.Crossref, Google Scholar
• Malinova M, Mendling J (2021) Cognitive diagram understanding and task performance in systems analysis and design. MIS Quart. 45(4):2101–2158.Crossref, Google Scholar
• Mayer RE, ed. (2005) The Cambridge Handbook of Multimedia Learning, 1st ed. (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Mayer RE (2014) Cognitive theory of multimedia learning. Mayer RE, ed. The Cambridge Handbook of Multimedia Learning, 2nd ed. (Cambridge University Press, Cambridge, UK), 43–71.Crossref, Google Scholar
• Mayr HC, Thalheim B (2021) The triptych of conceptual modeling. Software Systems Modeling 20(1):7–24.Crossref, Google Scholar
• McNamara DS, Scott JL (2001) Working memory capacity and strategy use. Memory Cognition 29(1):10–17.Crossref, Google Scholar
• Meulen Rd, Pettey C (2018) Gartner says self-service analytics and BI users will produce more analysis than data scientists will by 2019. Press release, Gartner Research, January 25, https://www.gartner.com/en/newsroom/press-releases/2018-01-25-gartner-says-self-service-analytics-and-bi-users-will-produce-more-analysis-than-data-scientists-will-by-2019.Google Scholar
• Møller NH, Bossen C, Pine KH, Nielsen TR, Neff G (2020) Who does the work of data? Interaction 27(3):52–55.Crossref, Google Scholar
• Moody D (2009) The “physics” of notations: Toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Software Engrg. 35(6):756–779.Crossref, Google Scholar
• Nathan MJ (2012) Rethinking formalisms in formal education. Ed. Psych. 47(2):125–148.Crossref, Google Scholar
• Newman J, Vidler E (2006) Discriminating customers, responsible patients, empowered users: Consumerism and the modernisation of health care. J. Soc. Policy 35(2):193–209.Crossref, Google Scholar
• Niehaves B, Köffer S, Ortbach K (2012) IT consumerization—A theory and practice review. Proc. AMCIS 2012, Seattle, August 9–12.Google Scholar
• O’Reilly CA (1980) Individuals and information overload in organizations: Is more necessarily better? Acad. Management J. 23(4):684–696.Crossref, Google Scholar
• Olivé A (2007) Conceptual Modeling of Information Systems (Springer, Berlin).Google Scholar
• Ottensooser A, Fekete A, Reijers HA, Mendling J, Menictas C (2012) Making sense of business process descriptions: An experimental comparison of graphical and textual notations. J. Systems Software 85(3):596–606.Crossref, Google Scholar
• Paas F, van Gog T (2006) Optimizing worked example instruction: Different ways to increase germane cognitive load. Learning Instruction 16(2):87–91.Crossref, Google Scholar
• Paas F, Van Merriënboer JJ (1994) Instructional control of cognitive load in the training of complex cognitive tasks. Ed. Psych. Rev. 6(4):351–371.Crossref, Google Scholar
• Paas F, Renkl A, Sweller J (2003) Cognitive load theory and instructional design: Recent developments. Ed. Psych. 38(1):1–4.Crossref, Google Scholar
• Pilone D, Pitman N (2005) UML 2.0 in a Nutshell, 2nd ed. (O'Reilly, Sebastopol, CA).Google Scholar
• Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog (February 14), https://openai.com/blog/better-language-models/.Google Scholar
• Ram S, Khatri V (2005) A comprehensive framework for modeling set-based business rules during conceptual database design. Inform. Systems 30(2):89–118.Crossref, Google Scholar
• Recker J, Green P (2019) How do individuals interpret multiple conceptual models? A theory of combined ontological completeness and overlap. J. Assoc. Inform. Systems 20(8):1.Google Scholar
• Recker J, Rosemann M, Green P, Indulska M (2011) Do ontological deficiencies in modeling grammars matter? MIS Quart. 35(1):57–79.Crossref, Google Scholar
• Recker J, Lukyanenko R, Jabbari M, Samuel BM, Castellanos A (2021) From representation to mediation: A new agenda for conceptual modeling research in a digital world. MIS Quart. 45(1):269–300.Crossref, Google Scholar
• Renkl A (2005) The worked-out-example principle in multimedia learning. Mayer RE, ed. The Cambridge Handbook of Multimedia Learning, 1st ed. (Cambridge University Press, Cambridge, UK), 229–245.Crossref, Google Scholar
• Renkl A, Atkinson RK (2010) Learning from worked-out examples and problem solving. Plass JL, Moreno R, Brünken R, eds. Cognitive Load Theory (Cambridge University Press, Cambridge, UK), 91–108.Crossref, Google Scholar
• Renkl A, Hilbertm T, Schworm S (2009) Example-based learning in heuristic domains: A cognitive load theory account. Ed. Psych. Rev. 21(1):67–78.Crossref, Google Scholar
• Roger EK, Kirk A (1968) Experimental Design: Procedures for the Behavioral Sciences (Brooks/Cole, Belmont, CA).Google Scholar
• Romero S, Fernandez-Feijoo B, Ruiz S (2014) Perceptions of quality of assurance statements for sustainability reports. Soc. Responsibility J. 10(3):480–499.Crossref, Google Scholar
• Rosemann M, Vessey I (2008) Toward improving the relevance of information systems research to practice: The role of applicability checks. MIS Quart. 32(1):1–22.Crossref, Google Scholar
• Roussopoulos N, Mylopoulos J (1989) Using semantic networks for data base management. Mylopolous J, Brodie M, eds. Readings in Artificial Intelligence and Databases (Elsevier, Amsterdam), 112–137.Crossref, Google Scholar
• Ryan M-L (2007) Toward a definition of narrative. Herman D, ed. The Cambridge Companion to Narrative (Cambridge University Press, Cambridge, UK), 22–35.Crossref, Google Scholar
• Saghafi A, Wand Y, Parsons J (2021) Skipping class: Improving human-driven data exploration and querying through instances. Eur. J. Inform. Systems, ePub ahead of print January 29, https://doi.org/10.1080/0960085X.2020.1869507.Google Scholar
• Samuel BM, Khatri V, Ramesh V (2018) Exploring the effects of extensional versus intentional representations on domain understanding. MIS Quart. 42(4):1187–1209.Google Scholar
• Schank RC, Abelson RP (1995) Knowledge and memory: The real story. Wyer RS, ed. Knowledge and Memory: The Real Story (Lawrence Erlbaum Associates, Hillsdale, NJ), 1–85.Google Scholar
• Shanks G, Tansley E, Nuredini J, Tobin D, Weber R (2008) Representing part–whole relations in conceptual modeling: An empirical evaluation. MIS Quart. 32(3):553–573.Crossref, Google Scholar
• Sharma S, Patil K (2017) Past, Present and Future of Collaborative Design: From User Centric to User Driven Design (Springer, Singapore), 1025–1036.Google Scholar
• Shoval P, Frumermann I (1994) OO and EER conceptual schemas: A comparison of user comprehension. J. Database Management 5(4):28–38.Crossref, Google Scholar
• Sutskever I, Martens J, Hinton GE (2011) Generating text with recurrent neural networks. Proc. 28th Internat. Conf. Machine Learning (ICML-11), Bellevue, Washington, June 28–July 2, 1017–1024.Google Scholar
• Sweller J (1994) Cognitive load theory, learning difficulty, and instructional design. Learning Instruction 4(4):295–312.Crossref, Google Scholar
• Sweller J (2006) The worked example effect and human cognition. Learning Instruction 16(2):165–169.Crossref, Google Scholar
• Sweller J (2010) Cognitive load theory: Recent theoretical advances. Plass JL, Moreno R, Brünken R, eds. Cognitive Load Theory (Cambridge University Press, Cambridge, UK), 29–47.Crossref, Google Scholar
• Sweller J, Cooper GA (1985) The use of worked examples as a substitute for problem solving in learning algebra. Cognition Instruction 2(1):59–89.Crossref, Google Scholar
• Tabachnick B, Fidell L (2000) Computer-Assisted Research Design and Analysis, 1st ed. (Pearson, New York).Google Scholar
• Tarca A, Hancock P, Woodliff D, Brown P, Bradbury M, Van Zijl T (2008) Identifying decision useful information with the matrix format income statement. J. Internat. Financial Management Accounting 19(2):184–217.Crossref, Google Scholar
• Thalheim B (1992) Fundamentals of Cardinality Constraints. Pernul G, Tjoa AM, eds. Entity-Relationship Approach—ER ’92 (Springer, Berlin), 7–23.Crossref, Google Scholar
• The Economist (2022) What if all workers wrote software, not just the geek elite? (January 29), https://www.economist.com/business/2022/01/29/what-if-all-workers-wrote-software-not-just-the-geek-elite.Google Scholar
• Topi H, Ramesh V (2002) Human factors research on data modeling: A review of prior research, an extended framework and future research directions. J. Database Management 13(2):3–19.Crossref, Google Scholar
• van den Broek P (2010) Using texts in science education: Cognitive processes and knowledge representation. Science 328(5977):453–456.Crossref, Google Scholar
• van Merriënboer JJG, Sweller J (2005) Cognitive load theory and complex learning: Recent developments and future directions. Ed. Psych. Rev. 17(2):147–177.Crossref, Google Scholar
• Wand Y, Weber R (2002) Information systems and conceptual modeling—A research agenda. Inform. Systems Res. 13(4):363–376.Link, Google Scholar
• Wand Y, Storey VC, Weber R (1999) An ontology analysis of the relationship construct in conceptual modeling. ACM Trans. Database Systems 24(4):494–528.Crossref, Google Scholar
• Wand Y, Monarchi DE, Parson J, Woo CC (1995) Theoretical foundations for conceptual modeling in information systems development. Decision Support Systems 15(4):285–304.Crossref, Google Scholar
• Xiaoyu M, Jiang Z (2018) The magic of cinemagraphs: Investigation of different image formats in online product presentation. Proc. 39th Internat. Conf. Inform. Systems, San Francisco, December 13–16, 1–9.Google Scholar
• Zuboff S (1988) In the Age of the Smart Machine: The Future of Work and Power (Basic, New York).Google Scholar