Simulation-based research in management accounting and control: an illustrative overview
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abernethy, M. A., & Brownell, P. (1997). Management control systems in research and development organizations: the role of accounting, behavior and personnel controls. Accounting, Organizations and Society, 22(3–4), 233–248.
Anderson, P. (1999). Complexity theory and organization science. Organization Science, 10(3), 216–232.
Axelrod, R. (1997a). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena (pp. 21–40). Berlin: Springer.
Axelrod, R. (1997b). Advancing the art of simulation in the social sciences. Santa Fe Institute. Working Paper No. 97–05-048.
Axtell, R. L. (2007). What economic agents do: how cognition and interaction lead to emergence and complexity. The Review of Austrian Economics, 20(2–3), 105–122.
Balakrishnan, R., Hansen, S., & Labro, E. (2011). Evaluating heuristics used when designing product costing systems. Management Science, 75(3), 520–541.
Balakrishnan, R., & Sivaramakrishnan, K. (2001). Sequential solutions to capacity-planning and pricing decisions. Contemporary Accounting Research, 18(1), 1–26.
Balci, O. (1998). Verification, validation, and accreditation. In: Proceedings of the 30th Conference on Winter Simulation (pp 41–44). IEEE Computer Society Press.
Barth, R., Meyer, M., Spitzner, J. (2012). Typical pitfalls of simulation modeling: lessons learned from armed forces and business. Journal of Artificial Societies and Social Simulation, 15(2), 1–14.
Baumann, O., & Martignoni, D. (2011). Evaluating the new: the contingent value of a pro-innovation bias. Schmalenbach Business Review, 63, 393–415.
Behrens, D. A., Berlinger, S., Wall, F. (2014). Phrasing and timing information dissemination in organizations: results of an agent-based simulation. In S. Leitner, F. Wall (Eds.), Artificial Economics and Self-Organization (Vol. 669, pp. 179–190). Lecture Notes in Economics and Mathematical Systems: Springer International Publishing.
Berends, P., & Romme, G. (1999). Simulation as a research tool in management studies. European Management Journal, 17(6), 576–583.
Berg, B. L. (2004). Qualitative research methods for the social sciences (Vol. 5). Boston: Pearson.
Berry, A. J., Coad, A. F., Harris, E. P., Otley, D. T., & Stringer, C. (2009). Emerging themes in management control: a review of recent literature. The British Accounting Review, 41(1), 2–20.
Bonabeau, E. (2002). Agent-Based modeling: methods and techniques for simulating human systems. In Proceedings of the National Academy of Science of the USA (Vol. 99 (3 (Supplement)), pp. 7280–7287).
Boyd, L. H., & Cox, J. F. (2002). Optimal decision making using cost accounting information. International Journal of Production Research, 40(8), 1879–1898.
Burton, R. M. (2003). Computational laboratories for organization science: questions, validity and docking. Computational and Mathematical Organization Theory, 9(2), 91–108.
Carley, K. M. (1995). Computational and mathematical organization theory: perspective and directions. Computational and Mathematical Organization Theory, 1(1), 39–56.
Carley, K. M. (2002). Computational organizational science and organizational engineering. Simulation Modelling Practice and Theory, 10(5–7), 253–269.
Carley, K. M., & Gasser, L. (1999). Computational organization theory. In G. Weiss (Ed.), Multiagent systems: a modern approach to distributed artificial intelligence (pp. 299–330). Cambridge: MIT Press.
Carroll, T., & Burton, R. M. (2000). Organizations and complexity: searching for the edge of chaos. Computational and Mathematical Organization Theory, 6(4), 319–337.
Chang, M.-H., Harrington, J. E. (2006). Agent-based models of organizations. In L. Tesfatsion, K. L. Judd (Eds.), Handbook of computational economics: agent-based computational economics (Vol. 2, pp. 1273–1337). Amsterdam: Elsevier.
Chenhall, R. H. (2003). Management control systems design within its organizational context: findings from contingency-based research and directions for the future. Accounting, Organizations and Society, 28(2–3), 127–168.
Cho, S.-H., & Eppinger, S. D. (2005). A simulation-based process model for managing complex design projects. IEEE Transactions on Engineering Management, 52(3), 316–328.
Christensen, C. M., Kaufman, S. P., & Shih, W. C. (2008). Innovation killers. Harvard Business Review, 86(1), 98–105.
Christensen, J. (2010). Accounting errors and errors of accounting. The Accounting Review, 85(6), 1827–1838.
Christensen, J., & Demski, J. (1997). Product costing in the presence of endogenous subcost functions. Review of Accounting Studies, 2(1), 65–87.
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. Academy of Management Review, 32(2), 480–499.
Dawson, R. E. (1962). Simulation in the social sciences. In H. Guetzkow (Ed.), Simulation in social science. Englewood Cliffs: Prentice Hall.
Deckert, A., & Klein, R. (2010). Agentenbasierte Simulation Zur Analyse Und Lösung Betriebswirtschaftlicher Entscheidungsprobleme. Journal für Betriebswirtschaft, 60(2), 89–125.
Demski, J. S. (1998). Performance measure manipulation. Contemporary Accounting Research, 15(3), 261–285.
Denrell, J., & March, J. G. (2001). Adaptation as information restriction: the hot stove effect. Organization Science, 12(5), 523–538.
Dhavale, D. G. (2007). Product costing for decision making in certain variable-proportion technologies. Journal of Management Accounting Research, 19(1), 51–70.
Domschke, W., & Drexel, A. (2005). Einführung in operations research (Vol. 5). Berlin: Springer.
Dosi, G., Levinthal, D., & Marengo, L. (2003). Bridging contested terrain: linking incentive-based and learning perspectives on organizational evolution. Industrial and Corporate Change, 12(2), 413–436.
Duscher, I., Meyer, M., & Spitzner, J. (2012). Volatilität Kalkulieren Und Steuern Im Sinne Eines Wertorientierten Investitionscontrollings. Controlling & Management, 56(2), 46–51.
Ethiraj, S. K., & Levinthal, D. (2004). Modularity and innovation in complex systems. Management Science, 50(2), 159–173.
Feng, Y., D’Amours, S., & Beauregard, R. (2010). Simulation and performance evaluation of partially and fully integrated sales and operations planning. International Journal of Production Research, 48(19), 5859–5883.
Ferreira, A., & Otley, D. (2009). The design and use of performance management systems: an extended framework for analysis. Management Accounting Research, 20(4), 263–282.
Fioretti, G. (2013). Agent-based simulation models in organization science. Organizational Research Methods, 16(2), 227–242.
Forrester, J. W. (1971). Counterintuitive behavior of social systems. Technology Review, 73(3), 52–68.
Gavetti, G., & Levinthal, D. (2000). Looking forward and looking backward: cognitive and experiential search. Administrative Science Quarterly, 45, 113–137.
Gavetti, G., Levinthal, D. A., & Rivkin, J. W. (2005). Strategy making in novel and complex worlds: the power of analogy. Strategic Management Journal, 26(8), 691–712.
Ghemawat, P., & Levinthal, D. (2008). Choice interactions and business strategy. Management Science, 54(9), 1638–1651.
Gilbert, N. (1998). Editorial. Journal of Artificial Societies and Social Simulation, 1(1), 1.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Buckingham: Open University Press.
Goldberg, D. E. (1989). Genetic algorithms. In Search of optimizaton and machine learning. Reading: Wesley Publishing Company.
Goldenberg, J., & Efroni, S. (2001). Using cellular automata modeling for the emergence of innovations. Technological Forecasting and Social Change, 68(3), 293–308.
Goldratt, E. M., Cox, J., & Whitford, D. (1992). The goal: a process of ongoing improvement (Vol. 2). Great Barrington: North River Press.
Guerrero, O. A., Axtell, R. (2011). Using agentization for exploring firm and labor dynamics: a methodological tool for theory exploration and validation. In S. Osinga, G. J. Hofstede, T. Verwaart (Eds.), Emergent results of artificial economics (Vol. 652, pp. 139–150). Lecture Notes on Economics and Mathematical Systems. Berlin, Heidelberg: Springer.
Harrison, J. R., Zhiang, L. I. N., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32(4), 1229–1245.
Hauschild, S., & Knyphausen-Aufseß, D. (2013). The resource-based view of diversification success: conceptual issues, methodological flaws, and future directions. Review of Managerial Science, 7(3), 327–363.
Hesford, J. W., Lee, S.-H. S., Van der Stede, W. A., Young, S. M. (2007). Management accounting: a bibliographic study. In C. S. Chapman, A. G. Hoopwood, M. D. Shields (Eds.), Handbook of management accounting research (Vol. 1, pp. 3–26). Amsterdam: Elsevier.
Hilgers, D. (2008). Performance management. Springer-Verlag.
Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: a review. European Journal of Operational Research, 203(1), 1–13.
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: heuristics and biases. Cambridge: Cambridge University Press.
Kauffman, S. (1995). At home in the universe. The search for laws of self-organization and complexity. New York: Oxford Univ. Press.
Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of Theoretical Biology, 128, 11–45.
Keys, B., & Wolfe, J. (1990). The role of management games and simulations in education and research. Journal of management, 16(2), 307–336.
Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), 183–230.
Knudsen, T., & Levinthal, D. A. (2007). Two faces of search: alternative generation and alternative evaluation. Organization Science, 18(1), 39–54.
Kollock, P. (1993). An eye for an eye leaves everyone blind: cooperation and accounting systems. American Sociological Review, 58(6), 768–786.
Kreutzer, W. (1986). System Simulation programming styles and languages. Addison-Wesley Longman Publishing Co., Inc.
Labro, E., & Vanhoucke, M. (2007). A simulation analysis of interactions among errors in costing systems. The Accounting Review, 82(4), 939–962.
Labro, E., & Vanhoucke, M. (2008). Diversity in resource consumption patterns in costing system robustness to errors. Management Science, 54(10), 1715–1730.
Lea, B.-R., & Fredendall, L. D. (2002). The impact of management accounting, product structure, product mix algorithm, and planning horizon on manufacturing performance. International Journal of Production Economics, 79(3), 279–299.
Lea, B.-R., & Min, H. (2003). Selection of management accounting systems in just-in-time and theory of constraints-based manufacturing. International Journal of Production Research, 41(13), 2879–2910.
Leitner, S. (2012a). Information Quality and Management Accounting (Vol. 664). Lecture Notes in Economics and Mathematical Systems. Berlin, Heidelberg, New York: Springer.
Leitner, S. (2012b). Interactions among biases in costing systems: a simulation approach. In A. Teglio, S. Alfarano, E. Camacho-Guena, & M. Gilnés-Vilar (Eds.), Managing market complexity (Vol. 662, pp. 209–220)., Springer Heidelberg, New York: Berlin.
Leitner, S. (2014). A simulation analysis of interactions among intended biases in costing systems and their effects on the accuracy of decision-influencing information. Central European Journal of Operations Research, 22(1), 113–138.
Leitner, S., Behrens, D. A. (2014a). On the efficiency of hurdle rate-based coordination mechanisms. Mathematical and Computer Modelling of Dynamical Systems, pp. 1–19.
Leitner, S., Behrens, D. A. (2014b). On the robustness of coordination mechanims involving ’incompetent’ agents. In S. Leitner F. Wall (Eds.), Artificial economics and self-organization (Vol. 669, pp. 191–203). vol Lecture Notes in Economics and Mathematical Systems. Berlin, Heidelberg, New York: Springer.
Leitner, S., & Behrens, D. A. (2015). On the fault (in)tolerance of coordination mechanisms for distributed investment decisions. Central European Journal of Operations Research, 23(1), 251–278.
Leitner, S., Brauneis, A., Rausch, A. (2015a). Does collaboration pay? An investigation for the domain of distributed investment decisions. In F. Amblard, F. J. Miguel, A. Blanchet, B. Gaudou (Eds.), Advances in Artificial Economics (Vol. 676, pp. 1–26). Lecture Notes in Economics and Mathematical Systems. Berlin, Heidelberg: Springer.
Leitner, S., Brauneis, A., Rausch, A. (2015b). Shared investment projects and forecasting errors: setting framework conditions for coordination and sequencing data quality activities. PLoS One, 10(3), e0121362. doi: 10.1371/journal.pone.0121362 .
Leitner, S., Wall, F. (2011). Effectivity of multi criteria decision-making in organisations: results of an agent-based simulation. In S. Osinga, G. J. Hofstede, T. Verwaart (Eds.), Emergent Results of Artificial Economics, vol 652. Lecture Notes in Economics and Mathematical Systems (pp. 79–90). Berlin Heidelberg: Springer. doi: 10.1007/978-3-642-21108-9_7 .
Leitner, S., Wall, F. (2014). Multi objective decision making policies and coordination mechanisms in hierarchical organizations: results of an agent-based simulation. The Scientific World Journal 2014, 12.
Lorscheid, I., Heine, B.-O., & Meyer, M. (2012). Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Computational and Mathematical Organization Theory, 18(1), 22–62.
Ma, T., & Nakamori, Y. (2005). Agent-based modeling on technological innovation as an evolutionary process. European Journal of Operational Research, 166(3), 741–755.
Malmi, T., & Brown, D. A. (2008). Management control systems as a package-opportunities, challenges and research directions. Management Accounting Research, 19(4), 287–300.
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.
Markland, R.E., Sweigart, J.R., Vickery, S. K. (1987). Quantitative methods: applications to managerial decision making. Wiley: New York.
Merchant, K.A., Otley, D.T. (2006). A Review of the literature on control and accountability. In: S. Christopher, A. G. H. Chapman, D.S. Michael (Eds.), Handbooks of Management Accounting Research (Vol. 2, pp 785–802). Elsevier.
Meyer, M., Lorscheid, I., & Troitzsch, K. G. (2009). The development of social simulation as reflected in the first ten years of jasss: a citation and co-citation analysis. Journal of Artificial Societies and Social Simulation, 12(4), 12.
Müller, B., Balbi, S., Buchmann, C. M., de Sousa, L., Dressler, G., Groeneveld, J., et al. (2014). Standardised and transparent model descriptions for agent-based models: current status and prospects. Environmental Modelling & Software, 55, 156–163.
Nelson, B. L. (2004). 50th anniversary article: stochastic simulation research in management science. Management Science, 50(7), 855–868.
Otley, D. (1999). Performance management: a framework for management control systems research. Management Accounting Research, 10(4), 363–382.
Otley, D. (2003). Management control and performance management: whence and whither? The British Accounting Review, 35(4), 309–326.
Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences. A practical guide. Oxford: Blackwell Publishing.
Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision Support Systems, 26(4), 275–286.
Reeves, C. R., & Rowe, J. E. (2003). Genetic algorithms—principles and perspectives: a guide to ga theory. Berlin, Heidelberg: Springer.
Reiss, J. (2011). A plea for (good) simulations: nudging economics toward an experimental science. Simulation & Gaming, 42(2), 243–264.
Richiardi, M. G., Leombruni, R., Saam, N., & Sonnessa, M. (2006). A common protocol for agent-based social simulation. Journal of Artificial Societies and Social Simulation, 9(1).
Rivkin, J. W., & Siggelkow, N. (2003). Balancing search and stability: interdependencies among elements of organizational design. Management Science, 49(3), 290–311.
Rivkin, J. W., & Siggelkow, N. (2007). Patterned interactions in complex systems: implications for exploration. Management Science, 53(7), 1068–1085.
Robinson, S. (2008a). Conceptual modelling for simulation part I: definition and requirements. Journal of the Operational Research Society, 59(3), 278–290.
Robinson, S. (2008b). Conceptual modelling for simulation part II: a framework for conceptual modelling. Journal of the Operational Research Society, 59(3), 291–304.
Rybacki, S., Haack, F., Wolf, K., Uhrmacher, A.M. (2014). Developing simulation models-from conceptual to executable model and back-an artifact-based workflow approach. In Proceedings of the 7th International ICST Conference on Simulation Tools and Techniques (pp 21–30). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).
Safarzyńska, K., & van den Bergh, J. (2010). Evolutionary models in economics: a survey of methods and building blocks. Journal of Evolutionary Economics, 20(3), 329–373.
Sah, R. K., & Stiglitz, J. E. (1986). The architecture of economic systems: hierarchies and polyarchies. American Economic Review, 76(4), 716–727.
Sargent, R.G. (2005). Verification and validation of simulation models. In Proceedings of the 37th Conference on Winter Simulation, 2005. Winter Simulation Conference (pp. 130–143).
Shannon, R. E. (1975). Systems simulation. The art and science. Englewood Cliffs: Prentice-Hall.
Shannon, R.E. (1998). Introduction to the art and science of simulation. In Proceedings of the 30th conference on Winter simulation (pp. 7–14). IEEE Computer Society Press.
Siggelkow, N., & Rivkin, J. W. (2005). Speed and search: designing organizations for turbulence and complexity. Organization Science, 16(2), 101–122.
Simons, R. (1995). Levers of control: how managers use innovative control systems to drive strategic renewal. Boston: Harvard Business School Press.
Smith, J. S. (2003). Survey on the use of simulation for manufacturing system design and operation. Journal of manufacturing systems, 22(2), 157–171.
Sorenson, O. (2002). Interorganizational complexity and computation. In J. A. C. Baum (Ed.), Companion to organizations (pp. 664–685). Oxford: Blackwell.
Springer, C. W., & Borthick, A. F. (2004). Business simulation to stage critical thinking in introductory accounting: rationale, design, and implementation. Issues in accounting education, 19(3), 277–303.
Sterman, J. D. (1994). Learning in and about complex systems. System Dynamics Review, 10(2–3), 291–330.
Sterman, J. D. (2001). System dynamics modeling: tools for learning in a complex world. California Management Review, 43(4), 8–25.
Tesfatsion, L. (2006). Agent-based computational economics: a constructive approach to economic theory. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics: agent-based computational economics (Vol. 2, pp. 831–880). Amsterdam: Elsevier.
Touran, A., & Lopez, R. (2006). Modeling cost escalation in large infrastructure projects. Journal of construction engineering and management, 132(8), 853–860.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science, 185(4157), 1124–1131.
Van Landeghem, H., & Vanmaele, H. (2002). Robust planning: a new paradigm for demand chain planning. Journal of Operations Management, 20(6), 769–783.
Waldrop, M. M. (1992). Complexity: the emerging science at the edge of order and chaos. New York: Simon and Schuster.
Wall, F. (2010). The (beneficial) role of informational imperfections in enhancing organizational performance. In M. LiCalzi, L. Milone, & P. Pellizzari (Eds.), Progress in artificial economics: computational and agent-based models (pp. 101–122). Berlin, New York, Heidelberg: Springer.
Wall, F. (2011). Diversity of the knowledge base in organizations: results of an agent-based simulation. In Y. Demezeau, M. Pechoucek, J. M. Cochado, & J. B. Pérez (Eds.), Advances on practical applications of agents and multiagent systems (Vol. 88, pp. 13–20). Advances in Intelligent and Soft Computing. Berlin, Heidelberg: Springer.
Wall, F. (2014). Agent-based modeling in managerial science: an illustrative survey and study. Review of Managerial Science, pp. 1–59.
Wall, F., & Greiling, D. (2011). Accounting information for managerial decision-making in shareholder management versus stakeholder management. Review of Managerial Science, 5(2–3), 91–135.
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems, 12(4), 5–34.
Wolfram, S. (1986a). Approaches to complexity engineering. Physica D: Nonlinear Phenomena, 22(1–3), 385–399.
Wolfram, S. (1986b). Theory and applications of cellular automata. Singabore: World Scientific.
Wynder, M. (2004). Facilitating creativity in management accounting: a computerized business simulation. Accounting Education, 13(2), 231–250.
Zhang, X., Luo, L., Yang, Y., Li, Y., Schlick, C. M., & Grandt, M. (2009). A simulation approach for evaluation and improvement of organisational planning in collaborative product development projects. International Journal of Production Research, 47(13), 3471–3501.