Business model simulation is a computer-based model for understanding business processes and firm dynamics. Simulating a business model in a laboratory helps one to understand the operational and strategic aspects of firms quickly, reliably and cost-effectively, enabling them to develop new financial and operational strategies. Not many studies have focused on antecedents of the business model simulation and their consequences. Therefore, the aim of this study is to determine the antecedents and consequences of business model simulation and to further analyse the moderating effects of leadership support. A theoretical model is proposed that takes inputs from the literature and resource-based view and dynamic capability theories. The partial least squares–structural equation modelling technique tested the model by analysing the quantified responses of 327 respondents. This study finds that business model simulation is significantly impacted by firms’ competitive intensity, innovative activities and technology strategy. The study also demonstrates that leadership support moderates the relationship between business model simulation and actual business practices. The study contributes that business model simulation, which is affected by salient predictors, can impact firm performance when mediated through actual business process and practices and with the moderating effects of leadership support.
ÅgeL.-J. (2018). Business maneuvering: A dynamic view of B2B selling processes. In AnderssonP., AxelssonB. & RosenqvistC. (Eds.), Organizing marketing and sales (pp. 113–123). Emerald Publishing Limited.
2.
AkterS., Fosso WambaS., & DewanS. (2017). Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality. Production Planning & Control, 28(11/12), 1011–1021.
3.
AkterS., BandaraR., HaniU., WambaS. F., ForoponC., & PapadopoulosT. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48, 85–95. https://doi.org/10.1016/j.ijinfomgt.2019.01.020
4.
AndersenN. Å., & PorsJ. G. (2019). When no is not an option: The immunization against silence in a motivational interview about marijuana use. Social Theory & Health, 17(4), 443–462.
5.
AndersenN. Å., & StennerP. (2020). Social immune mechanisms: Luhmann and perennialization technologies. Theory, Culture & Society, 37(2), 79–103.
6.
ApostolopoulosN., & LiargovasP. (2016). Regional parameters and solar energy enterprises: Purposive sampling and group AHP approach. International Journal of Energy Sector Management, 10(1), 19–37.
7.
BabuV., KumarA., & KumarV. (2022). Exploring mindfulness and leadership development: Lessons learnt using grounded theory through the study of the performing arts. FIIB Business Review. Early cite. https://doi.org/10.1177/231971452210886
BarneyJ. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
10.
BelyaevaZ., ShamsS. M. R., SantoroG., & GrandhiB. (2020). Unpacking stakeholder relationship management in the public and private sectors: The comparative insights. EuroMed Journal of Business, 15(3), 269–281.
11.
BhattacharjeeK. K., TsaiC. W., & AgrawalA. K. (2021). Impact of peer influence and government support for successful adoption of technology for vocational education: A quantitative study using PLS-SEM technique. Journal of Quality and Quantity, 55, 2041–2064.
12.
BisognoS., CalabreseA., GastaldiM., & Levialdi GhironN. (2016). Combining modelling and simulation approaches: How to measure performance of business processes. Business Process Management Journal, 22(1), 56–74.
13.
BititciU. S., & MuirD. (1997). Business process definition: A bottom-up approach. International Journal of Operations & Production Management, 17(4), 365–374.
14.
BoothC., RowlinsonM., ClarkP., DelahayeA., & ProcterS. (2009). Scenarios and counterfactuals as modal narratives. Futures, 41(2), 87–95.
15.
CarayannisE. G., & ProvanceM. (2008). Measuring firm innovativeness: Towards a composite innovation index built on firm innovative posture, propensity, and performance attributes. International Journal of Innovation and Regional Development, 1(1), 90–107.
16.
Casadesus-MasanellR., & RicartJ. E. (2010). From strategy to business models and onto tactics. Long Range Planning, 43(2–3), 195–215.
17.
ChatterjeeS., KarA. K., & GuptaM. P. (2018). Alignment of IT authority and citizens of proposed smart cities in India: System security and privacy perspective. Global Journal of Flexible Systems Management, 19, 95–107.
18.
ChatterjeeS., GhoshS. K., & ChaudhuriR. (2019). Knowledge management improving business process: An interpretative framework for successful implementation of AI-CRM-KM System in organizations. Business Process Management Journal, 26(6), 1261–1281.
19.
ChatterjeeS., ChaudhuriR., & VrontisD. (2020a). Does data-driven culture impact innovation and performance of a firm? An empirical examination. Annals of Operational Research. https://doi.org/10.1007/s10479-020-03887-z
20.
ChatterjeeS., GhoshS. K., ChaudhuriR., & ChaudhuriS. (2020b). Adoption of AI-integrated CRM system by Indian industry: From security and privacy perspective. Information and Computer Security, 29(1), 1–24.
21.
ChatterjeeS., ChaudhuriR., & VrontisD. (2021a). Knowledge sharing in international markets for product and process innovation: Moderating role of firm’s absorptive capacity. International Marketing Review. https://doi.org/10.1108/IMR-11-2020-0261
22.
ChatterjeeS., ChaudhuriR., VrontisD., & BasileG. (2021b). Digital transformation and entrepreneurship process in SMEs of India: A moderating role of adoption of AI-CRM capability and strategic planning. Journal of Strategy and Management. https://doi.org/10.1108/JSMA-02-2021-0049
23.
ChatterjeeS., ChaudhuriR., & VrontisD. (2022). Big data analytics in strategic sales performance: Mediating role of CRM capability and moderating role of leadership support. EuroMed Journal of Business, 17(3), 295–311.
24.
ChaudhuriR. (2022). Supply chain sustainability during turbulent environment: Examining the role of firm capabilities and government regulation. Operations Management Research, 15, 1081–1095.
25.
ChaudhuriR., ChatterjeeS., & VrontisD. (2021a). Does remote work flexibility enhance organization performance? Moderating role of organization policy and top management support. Journal of Business Research, 139, 1501–1512.
26.
ChaudhuriR., ChatterjeeS., & VrontisD. (2021b). Examining the global retail apocalypse during the COVID-19 pandemic using strategic omnichannel management: A consumers’ data privacy and data security perspective. Journal of Strategic Marketing, 29(7), 617–632.
27.
ChaudhuriR., ChatterjeeS., & VrontisD. (2022). Antecedents of privacy concerns and online information disclosure: Moderating role of government regulation. EuroMed Journal of Business. https://doi.org/10.1108/EMJB-11-2021-0181
28.
ChesbroughH. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43(2/3), 354–363.
29.
ChesbroughH., & RosenbloomR. S. (2002). The role of business model in capturing value from innovation: Evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change, 11(3), 529–555.
30.
CheungY., & BalJ. (1998). Process analysis techniques and tools for business improvement. Business Process Management Journal, 4(4), 274–290.
31.
DasaguptaS., SarkisJ., & TalluriS. (1999). Influence of information technology on firm productivity: A cross-sectional study. Logistic Information Management, 12(1/2), 120–129.
32.
DavenportP. H., & ShortJ. E. (1990, Summer). The new industrial engineering: Information technology and business process redesign. Sloan Management Review, 31, 11–27.
33.
De VreedeG. J. (1998). Collaborative business engineering with animated electronic meetings. Journal of Management Information Systems, 14(3), 141–164.
34.
DebM., & DavidE. L. (2014). An empirical examination of customers’ adoption of m-banking in India. Journal of Marketing Intelligence & Planning, 32(4), 475–494.
35.
DeyP. K., HariharanS., & CleggB. T. (2006). Measuring the operational performance of intensive care units using the analytic hierarchy process approach. International Journal of Operations & Production Management, 26(8), 849–865.
36.
DimitropoulosP., KoroniosK., ThrassouA., & VrontisD. (2019). Cash holdings, corporate performance and viability of Greek SMEs: Implications for stakeholder relationship management. EuroMed Journal of Business, 15(3), 333–348.
37.
DoomunR., & Vunka JungumN. (2008). Business process modelling, simulation, and reengineering: Call centers. Business Process Management Journal, 14(6), 838–848.
38.
EarlM. J. (1994). The new and the old of business process redesign. Journal of Strategic Information Systems, 3(1), 5–22.
39.
EisenhardtK. M., & MartinJ. A. (2000). Dynamic capabilities: What are they?Strategic Management Journal, 21(10/11), 1105–1121.
40.
FerreiraA., & FrancoM. (2019). The influence of strategic alliances on human capital development: A study applied to technology-based SMEs. EuroMed Journal of Business, 15(1), 65–85.
41.
FeyerabendP. (2006). How to defend society against science? In SelingerE. & CreaseR. P. (Eds.), The philosophy of expertise (pp. 358–369). Columbia University Press.
42.
FornellC., & LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
43.
ForresterJ. W. (1976). Business structure, economic cycles, and national policy. Futures, 8(3), 195–214.
44.
GargS. (2019). Research methodology in HR initiatives in building inclusive and accessible workplaces (pp. 61–83). Emerald Publishing Limited.
45.
GiaglisG. M., HlupicV., De VreedeG. J., & VerbraeckA. (2005). Synchronous design of business processes and information systems using dynamic process modelling. Business Process Management Journal, 11(5), 488–500.
46.
HairJ. F.Jr., HultG. T. M., RingleC., & SarstedtM. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
47.
HairJ. F., SarstedtM., RingleC. M., & GuderganS. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Sage Publications.
48.
HammerM., & ChampyJ. (1993). Reengineering the corporation: A manifesto for business revolution. Harper Collins.
49.
HanayshaJ. R., Al-ShaikhM. E., JogheeS., & AlzoubiH. M. (2022). Impact of innovation capabilities on business sustainability in small and medium enterprises. FIIB Business Review, 11(1), 67–78.
50.
HarrisonJ. R., LinZ., CarrollG. R., & CarleyK. M. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32(4), 1229–1245.
51.
HelfatC. E., FinkelsteinS., MitchellW., PeterafM., SinghH., TeeceD., & WinterS. G. (2009). Dynamic capabilities: Understanding strategic change in organizations. John Wiley & Sons.
52.
HlupicV., & RobinsonS. (1998). Business process modelling and analysis using discrete-event simulation. In MedeirosD. J., WatsonE. F., CarsonJ. S. & ManivannanM. S. (Eds.), Proceedings of the 1998 winter simulation conference, Washington, DC, December13–16 (pp. 1363–1369).
53.
HurleyR. F., & HultG. T. M. (1998). Innovation, market orientation, and organizational learning: An integration and empirical examination. Journal of Marketing, 62(3), 42–54.
54.
InyangA. E., AgnihotriR., & MunozL. (2018). The role of manager leadership style in salesperson implementation of sales strategy: A contingency perspective. Journal of Business & Industrial Marketing, 33(8), 1074–1086.
55.
IraniZ., HlupicV., BaldwinL. P., & LoveP. E. D. (2000). Re-engineering manufacturing processes through simulation modelling. Logistics Information Management, 13(1), 7–13.
56.
JanuszczakJ. (2011). Simulation for business process management. In FischerL. (Ed.), BPMN 2.0 handbook (2nd ed., pp. 43–57). Future Strategies.
57.
KamarudeenN., SundarakaniB., & ManikasI. (2020). An assessment of the Dubai metro service’s performance using SCOR model and ARENA simulation. FIIB Business Review, 9(3), 167–180.
58.
KisslerS. M., TedijantoC., GoldsteinE., GradY. H., & LipsitchM. (2020). Projecting the transmission dynamics of SARS-CoV-2 through the post pandemic period. Science, 368(6493), 860–868.
59.
LinW. L., YipN., HoJ.-A., & SambasivanM. (2020). The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective. Industrial Marketing Management, 89, 61–71.
60.
Lopez-NicolasC., NikouS., Molina-CastilloF.-J., & BouwmanH. (2020). Gender differences and business model experimentation in European SMEs. Journal of Business & Industrial Marketing, 35(7), 1205–1219.
61.
LoveP. E. D., GunasekaranA., & LiH. (1998). Putting an engine into re-engineering: Toward a process-based organization. International Journal of Operation & Production Management, 18(9), 937–949.
62.
MaheshwariP., ChaudhuriR., & ShahM. (2022). Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership. Computers & Industrial Engineering, 168, 108058.
63.
MajumdarR., & MittalA. (2023). Startup financing: Some evidence from the Indian venture capital industry. FIIB Business Review. https://doi.org/10.1177/231971452211421
64.
MellahiK., & HarrisL. C. (2016). Response rates in business and management research: An overview of current practice and suggestions for future directions. British Journal of Management, 27(2), 426–437.
65.
MukherjeeS., & ChittipakaV. (2022). Analysing the adoption of intelligent agent technology in food supply chain management: An empirical evidence. FIIB Business Review, 11(4), 438–454.
66.
NguyenB. (2021). Value co-creation and social media at bottom of pyramid (BOP). The Bottom Line, 34(2), 101–123.
67.
NogesteK., & WalkerD. H. T. (2006). Using knowledge management to revise software-testing processes. Journal of Workplace Learning, 18(1), 6–27.
68.
O’KaneJ. F. (2003). Simulation as an enabler for organizational excellence. Measuring Business Excellence, 7(4), 12–19.
69.
OmraniD. (1992). Business process reengineering: A business revolution?Management Services, 36, 12–16.
70.
PallG. A. (1987). Quality press management. Prentice-Hall.
71.
PandaB. K. (2020). Application of business model innovation for new enterprises: A case study of digital business using a freemium business model. Journal of Management Development, 39(4), 517–524.
72.
PauwelsK., & WeissA. (2008). Moving from free to fee: How online to change their firms market business model successfully. Journal of Marketing, 72(3), 14–31.
73.
PegdenC. D., ShannonR. E., & SadowskiR. P. (1995). Introduction to simulation using SIMAN. McGraw-Hill.
74.
PiddM. (2009). Tools for thinking; modelling in management science. John Wiley and Sons.
75.
PrabhuN., RamaprasadB. S., PrasadK., & ModemR. (2021). Does workplace spirituality influence reflexivity in ongoing teams? Examining the impact of shared transformational leadership on team performance. South Asian Journal of Business Studies. https://doi.org/10.1108/SAJBS-07-2020-0246
76.
ReckerJ. C. (2014). Suggestions for the next wave of BPM research: Strengthening the theoretical core and exploring the protective belt. Journal of Information Technology Theory and Application, 15(2), 5–20.
77.
RobinsonS. (2014). Simulation: The practice of model development and use. John Wiley & Sons.
78.
RosemannM., & Vom BrockeJ. (2015). The six core elements of business process management. In Vom BrockeJ. & RosemannM. (Eds.), Handbook on business process management (pp. 105–122). Springer.
79.
RothS., SchwedeP., ValentinovV., Pérez-VallsM., & Kaivo-OjaJ. (2020). Harnessing big data for a multifunctional theory of the firm. European Management Journal, 38(1), 54–61.
80.
SakkaG., VrontisD., SiachouE., ChaudhuriR., & GhoshA. (2022). Societal effects of social media in organizations: Reflective points deriving from a systematic literature review and a bibliometric meta-analysis. European Management Journal, 40(2), 151–162.
81.
SamuelP., & ChackoM. (2019). Feature intersection for agent-based customer churn prediction. Data Technologies and Applications, 53(3), 318–332.
82.
SerranoA., & den HengstM. (2005). Modelling the integration of BP and IT using business process simulation. Journal of Enterprise Information Management, 18(6), 740–759.
83.
SheshadriC. (2019). Impact of AI regulation on intention to use robots: From citizens and government perspective. International Journal of Intelligent Unmanned Systems, 8(2), 97–114.
84.
SheshadriS. (2020). Antecedents of phubbing: From technological and psychological perspectives. Journal of Systems and Information Technology, 22(2), 161–178.
85.
ShimS. J., & KumarA. (2010). Simulation for emergency care process reengineering in hospitals. Business Process Management Journal, 16(5), 795–805.
86.
SidaniY., & ReeseS. (2020). Nancy Dixon: Empowering the learning organization through psychological safety. The Learning Organization, 27(3), 259–266.
87.
SuzukiY., YahyaeiM., JinY., KoyamaH., & KangG. (2012). Simulation based process design: Modeling and applications. Advanced Engineering Informatics, 26(4), 763–781.
ThakurR., AngriawanA., & SummeyJ. H. (2016). Technological opinion leadership: The role of personal innovativeness, gadget love, and technological innovativeness. Journal of Business Research, 69(8), 2764–2773.
90.
ThrassouA., ChaudhuriR., & VrontisD. (2021). Adoption of robust business analytics for product innovation and organizational performance: The mediating role of organizational data-driven culture. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04407-3
91.
ThrassouA., ChaudhuriR., & VrontisD. (2022). Antecedents of obesity among Indian youth: From consumer psychological and lifestyle perspective. In Press. British Food Journal. https://doi.org/10.1108/BFJ-10-2021-1151
92.
TosiniD. (2020). Social immunology: A theory of the immune processes of social systems. Systems Research and Behavioral Science, 38, 50–60.
93.
TrkmanP. (2010). The critical success factors of business process management. International Journal of Information Management, 30(2), 125–134.
94.
UtterbackJ. M., & AbernathyW. J. (1975). A dynamic model of process and product innovation. Omega, 3(6), 639–656.
95.
VallejoC., RomeroD., & MolinaA. (2012). Enterprise integration engineering reference framework and toolbox. International Journal of Production Research, 50(6), 1489–1511.
96.
VergidisK., TurnerC. J., & TiwariA. (2008). Business process perspectives: Theoretical developments vs. real-world practice. International Journal of Production Economics, 114(1), 91–104.
97.
VernadatF., ShahL., EtienneA., & SiadatA. (2013). VR-PMS: A new approach for performance measurement and management of industrial systems. International Journal of Production Research, 51(23/24), 7420–7438.
98.
Vom BrockeJ., SchmiedelT., ReckerJ., TrkmanP., MertensW., & ViaeneS. (2014). Ten principles of good business process management. Business Process Management Journal, 20(4), 530–548.
99.
WambaS. F., DubeyR., GunasekaranA., & AkterS. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498.
100.
WangC. R., & JeongM. (2018). What makes you choose Airbnb again? An examination of users’ perceptions toward the website and their stay. International Journal of Hospitality Management, 74, 162–170.
101.
WangT., GuinetA., BelaidiA., & BesombesB. (2009). Modelling and simulation of emergency services with ARIS and Arena. Case study: The emergency department of Saint Joseph and Saint Luc Hospital. Production Planning & Control, 20(6), 484–495.
102.
WernerfeltB. (1984). A resource-based view of the firm. Strategic Management, 5(2), 171–180.
103.
WillabyH. W., CostaD. S. J., BurnsB. D., MacCannC., & RobertsR. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 73–78.
104.
WuF., YeniyurtS., KimD., & CavusgilS. T. (2005). The impact of information technology on supply chain capabilities and firm performance: A resource-based view. Industrial Marketing Management, 35(4), 493–504.
105.
ZackeryA., ShariatpanahiP., ZolfagharzadehM. M., & PourezzatA. A. (2016). Toward a simulated replica of futures: Classification and possible trajectories of simulation in futures studies. Futures, 81, 40–53.
