Studying temporal change using Qualitative Comparative Analysis (QCA) allows researchers to examine complex and dynamic causal pathways between configurations of time-based conditions and a desired outcome. No comprehensive QCA technique currently addresses complex temporal changes in a unified manner. To remedy these shortcomings, we introduce Growth Pattern QCA—a mixed-method technique for studying complex growth dynamics. We integrate a quantitative computational toolkit for calculating growth slopes with QCA and demonstrate it using an illustrative multiyear panel. We offer practical guidance for researchers to apply these mixed methods for analyzing and forecasting complex growth patterns in various entrepreneurship research settings. We also review existing techniques and provide a decision roadmap of time-related QCA methods for researchers to use the best option for their research objectives.
AguinisH.BakkerR. M. (2021). Time is of the essence: Improving the conceptualization and measurement of time. Human Resource Management Review, 31(2), 100763. https://doi.org/10.1016/j.hrmr.2020.100763
2.
AldrichH. E. (2008). Organizations and environments. Stanford University Press.
AversaP.FurnariS.HaefligerS. (2015). Business model configurations and performance: A qualitative comparative analysis in formula one racing, 2005–2013. Industrial and Corporate Change, 24(3), 655–676. https://doi.org/10.1093/icc/dtv012
7.
BaumgartnerM.ThiemA. (2020). Often trusted but never (properly) tested: Evaluating qualitative comparative analysis. Sociological Methods & Research, 49(2), 279–311. https://doi.org/10.1177/0049124117701487
BeekunR.GinnG. (1993). Business strategy and interorganizational linkages: An expansion of the Miles and Snow typology. Human Relations, 46(11), 1291–1318. https://doi.org/10.1177/001872679304601102
10.
BeynonM. J.JonesP.PickernellD. (2020). Country-level entrepreneurial attitudes and activity through the years: A panel data analysis using fsQCA. Journal of Business Research, 115, 443–455.
11.
BirdB. J.WestG. P. (1998). Time and entrepreneurship. Entrepreneurship Theory and Practice, 22(2), 5–9.
12.
BlieseP. D. (2013). Multilevel Modeling in R (2.5): A brief introduction to R, the multilevel package and the nlme package. Sage Open.
13.
BlieseP. D.PloyhartR. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustrations. Organizational Research Methods, 5(4), 362–387. https://doi.org/10.1177/10944280223711
14.
BlumbergM.PringleC. D. (1982). The missing opportunity in organizational research: Some implications for a theory of work performance. Academy of Management Review, 7(4), 560–569. https://doi.org/10.5465/amr.1982.4285240
15.
BoivieS.GraffinS. D.GentryR. J. (2016). Understanding the direction, magnitude, and joint effects of reputation when multiple actors’ reputations collide. The Academy of Management Journal, 59(1), 188–206. https://doi.org/10.5465/amj.2014.0521
CarenN.PanofskyA. (2005). TQCA: A technique for adding temporality to qualitative comparative analysis. Sociological Methods & Research, 34(2), 147–172. https://doi.org/10.1177/0049124105277197
18.
ChenG.PloyhartR. E.ThomasH. C.AndersonN.BlieseP. D. (2011). The power of momentum: A new model of dynamic relationships between job satisfaction change and turnover intentions. Academy of Management Journal, 54(1), 159–181. https://doi.org/10.5465/AMJ.2011.59215089
DelmarF.WiklundJ. (2008). The effect of small business managers’ growth motivation on firm growth: A longitudinal study. Entrepreneurship Theory and Practice, 32(3), 437–457.
21.
DessG. G.BeardD. W. (1984). Dimensions of organizational task environments. Administrative Science Quarterly, 29(1), 52–73. https://doi.org/10.2307/2393080
22.
DouglasE. J.ShepherdD. A.PrenticeC. (2020). Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship. Journal of Business Venturing, 35(1), 1–17. https://doi.org/10.1016/j.jbusvent.2019.105970
23.
DuY. Z.KimP. H. (2021). One size does not fit all: Strategy configurations, complex environments, and new venture performance in emerging economies. Journal of Business Research, 124, 272–285. https://doi.org/10.1016/j.jbusres.2020.11.059
24.
DuY. Z.KimP. H.AldrichH. E. (2016). Hybrid strategies, dysfunctional competition, and new venture performance in transition economies. Management & Organization Review, 12(3), 469–501. https://doi.org/10.1017/mor.2016.30
25.
DuY. Z.KimP. H.FournéS. P. L.WangX. (2022). In times of plenty: Slack resources, R&D investment, and entrepreneurial firms in challenging institutional environments. Journal of Business Research, 145, 360–376. https://doi.org/10.1016/j.jbusres.2022.03.004
El SawyO. A.MalhotraA.ParkY. K.PavlouP. A. (2010). Seeking the configurations of digital ecodynamics: It takes three to tango. Information Systems Research, 21(4), 835–848. https://doi.org/10.1287/isre.1100.0326
29.
FeldmanB. J.MasynK. E.CongerR. D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45(3), 652–676. https://doi.org/10.1037/a0014851
30.
FissP. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393–420. https://doi.org/10.5465/amj.2011.60263120
31.
FissP.SharapovD.CronqvistL. (2013). Opposites attract? Opportunities and challenges for integrating large-N QCA and econometric analysis. Political Research Quarterly, 66(1), 191–198. https://doi.org/10.1177/1065912912468269e
32.
FurnariS.CrillyD.MisangyiV. F.GreckhamerT.FissP. C.AguileraR. (2021). Capturing causal complexity: Heuristics for configurational theorizing. Academy of Management Review, 46(4), 778–799. https://doi.org/10.5465/amr.2019.0298
33.
Garcia-CastroR.AriñoM. A. (2016). A general approach to panel data set-theoretic research. Journal of Advances in Management Sciences & Information Systems, 2016(2), 63–76. https://doi.org/10.22218/JMDM/22016
34.
GreckhamerT.FurnariS.FissP. C.AguileraR. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16(4), 482–495. https://doi.org/10.1177/1476127018786487
35.
HaefnerN.PalmiéM.LeppänenP. T. (2023). With(out) a little help from my friends? Reconciling incongruous findings on stakeholder management, innovation, and firm performance. Entrepreneurship Theory and Practice, 47(1), 142–171. https://doi.org/10.1177/10422587211024497
36.
HakT.JaspersF.DulJ. (2013). The analysis of temporally ordered configurations: Challenges and solutions. In FissP. C.CambréB.MarxA. (Ed.), Configurational theory and methods in organizational research (chapter 5). Emerald Group Publishing Limited. https://doi.org/10.1108/S0733-558X(2013)0000038009
37.
HinoA. (2009). Time-series QCA: Studying temporal change through Boolean analysis. Sociological Theory and Methods, 24(2), 247–265. https://doi.org/10.11218/ojjams.24.247
38.
HoskissonR. E.EdenL.LauC. M.WrightM. (2000). Strategy in emerging economies. Academy of Management Journal, 43, 249–267. https://doi.org/10.2307/1556394
39.
KierA. S.McMullenJ. S. (2020). Entrepreneurial imaginativeness and new venture ideation in newly forming teams. Journal of Business Venturing, 35(6), 106048. https://doi.org/10.1016/j.jbusvent.2020.106048
40.
KimP. H.LiM. (2014). Seeking assurances when taking action: Legal systems, social trust, and starting businesses in emerging economies. Organization Studies, 35(3), 359–391. https://doi.org/10.1177/0170840613499566
41.
KimP. H.LongestK. C.LippmannS. (2015). The tortoise versus the hare: Progress and business viability differences between conventional and leisure-based founders. Journal of Business Venturing, 30(2), 185–204. https://doi.org/10.1016/j.jbusvent.2020.106048
42.
KimP. H.WennbergK.CroidieuG. (2016). Untapped riches of meso-level applications in multilevel entrepreneurship mechanisms. Academy Management Perspectives, 30(3), 273–291. https://doi.org/10.5465/amp.2015.0137
43.
KraemerT.WeigerW. H.GouthierM.HammerschmidtM. (2020). Toward a theory of spirals: The dynamic relationship between organizational pride and customer-oriented behavior. Journal of the Academy of Marketing Science, 48(6), 1095–1115. https://doi.org/10.1007/s11747-019-00715-0
44.
KrausS.Ribeiro-SorianoD.SchüsslerM. (2018). Fuzzy-set qualitative comparative analysis (fsQCA) in entrepreneurship and innovation research–the rise of a method. International Entrepreneurship and Management Journal, 14, 15–33. https://doi.org/10.1007/s11365-017-0461-8
45.
KumarS.SahooS.LimW. M.KrausS.BamelU. (2022). Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecasting and Social Change, 178, 121599. https://doi.org/10.1016/j.techfore.2022.121599
46.
LévesqueM.StephanU. (2020). It’s time we talk about time in entrepreneurship. Entrepreneurship Theory and Practice, 44(2), 163–184. https://doi.org/10.1177/1042258719839711
47.
LiY. H.MoS. J.SongY. F.WangM. (2016). Longitudinal analysis in occupational health psychology: A review and tutorial of three longitudinal modeling techniques. Applied Psychology, 65(2), 379–411. https://doi.org/10.1111/apps.12055
48.
LinderC.LechnerC.PelzelF. (2020). Many roads lead to Rome: How human, social, and financial capital are related to new venture survival. Entrepreneurship Theory and Practice, 44(5), 909–932.
49.
LitricoJ.LeeM.KossekE. (2011). Cross-level dynamics between changing organizations and career patterns of reduced-load professionals. Organization Studies, 32(12), 1678–1697. https://doi.org/10.1177/0170840611421250
50.
LiuD.MitchellT. R.LeeT. W.HoltomB. C.HinkinT. R. (2012). When employees are out of step with coworkers: How job satisfaction trajectory and dispersion influence individual- and unit-level voluntary turnover. Academy of Management Journal, 55(6), 1360–1380. https://doi.org/10.5465/amj.2010.0920
51.
LubkeG. H. (2010). Latent variable mixture modeling. In HancockG. R.MuellerR. O. (Eds.), The reviewer’s guide to quantitative methods in the social sciences. New York, NY: Routledge.
52.
MasynK. E. (2013). Latent class analysis and finite mixture modeling. In T. D.Little (Ed.), The Oxford handbook of quantitative methods (Vol. 2, pp. 551–611), Oxford University Press.
53.
MessersmithJ. G.LeeJ. Y.GuthrieJ. P.JiY. Y. (2014). Turnover at the top: Executive team departures and firm performance. Organization Science, 25(3), 653–967. https://doi.org/10.1287/orsc.2013.0864
54.
MeuerJ. (2017). Exploring the complementarities within high-performance work systems: A set-theoretic analysis of UK firms. Human Resource Management, 56(4), 651–672. https://doi.org/10.1002/hrm.21793
55.
MeuerJ.RupiettaC. (2017). Integrating QCA and HLM for multilevel research on organizational configurations. Organizational Research Methods, 20(2), 324–342. https://doi.org/10.1177/1094428116665465
56.
MeyerA. D.TsuiA. S.HiningsC. R. (1993). Configurational approaches to organizational analysis. Academy of Management Journal, 36(6), 1175–1195. https://doi.org/10.5465/256809
57.
MisangyiV. F.GreckhamerT.FurnariS.FissP. C.CrillyD.AguileraR. (2017). Embracing causal complexity: The emergence of a neo-configurational perspective. Journal of Management, 43(1), 255–282. https://doi.org/10.1177/0149206316679252
58.
ÖlanderF.ThøgersenJ. (1995). Understanding of consumer behavior as a prerequisite for environmental protection. Journal of Consumer Policy, 18(4), 345–385. https://doi.org/10.1007/BF01024160
59.
OanaI.SchneiderC. Q. (2021). A robustness test protocol for applied QCA: Theory and R software application. Sociological Methods & Research, 53(1), 57–88. https://doi.org/10.1177/00491241211036158
60.
PacheA. C.SantosF. (2013). Inside the hybrid organization: Selective coupling as a response to competing institutional logics. Academy of Management Journal, 56(4), 972–1001. https://doi.org/10.5465/amj.2011.0405
61.
PagliarinS.GerritsL. (2020). Trajectory-based qualitative comparative analysis: Accounting for case-based time dynamics. Methodological Innovations, 13(3), 1–11. https://doi.org/10.1177/2059799120959170
62.
ParkY.FissP. C.El SawyO. A. (2020). Theorizing the multiplicity of digital phenomena: The ecology of configurations, causal recipes, and guidelines for applying QCA. MIS Quarterly, 44(4), 1493–1520. https://doi.org/10.25300/MISQ/2020/13879
63.
RaginC. C. (1987). The comparative method: Moving beyond qualitative and quantitative strategies. University of California Press.
64.
RaginC. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.
65.
RaginC. C. (2014). QCA and Panel Data. In 2nd International QCA Expert Workshop, Zurich, Switzerland, 5–7November. ETH.
66.
RaginC. C.StrandS. I. (2008). Using qualitative comparative analysis to study causal order: Comment on Caren and Panofsky (2005). Sociological Methods & Research, 36(4), 431–441. https://doi.org/10.1177/0049124107313903
67.
ReynoldsP.BosmaN.AutioE.HuntS.BonoN. D.ServaisI.Lopez-GarciaP.ChinN. (2005). Global entrepreneurship monitor: Data collection design and implementation 1998–2003. Small Business Economics, 24, 205–231. https://doi.org/10.1007/S11187-005-1980-1
68.
RogosaD.WillettJ. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203–228. https://doi.org/10.1007/BF02294247
69.
RogosaD.BrandtD.ZimowskiM. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92(3), 726–748. https://doi.org/10.1037/0033-2909.92.3.726
70.
SchneiderC. Q.MakszinK. (2014). Forms of welfare capitalism and education-based participatory inequality. Socio-Economic Review, 12(2), 437–462. https://doi.org/10.1093/ser/mwu010
71.
SchneiderC. Q.WagemannC. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge University Press.
72.
SimonH. A. (1996). The sciences of the artificial. MIT Press.
73.
SlevinD. P.CovinJ. G. (1998). Time, growth, complexity, and transitions: Entrepreneurial challenges for the future. Entrepreneurship Theory and Practice, 22(2), 53–68. https://doi.org/10.1177/104225879802200205
74.
SteffensP.DavidssonP.FitzsimmonsJ. (2009). Performance configurations over time: Implications for growth–and profit–oriented strategies. Entrepreneurship Theory and Practice, 33(1), 125–148. https://doiorg/10.1111/j.1540-6520.2008.00283.x
75.
StreetC. T.GallupeR. B. (2009). A proposal for operationalizing the pace and scope of organizational change in management studies. Organizational Research Methods, 12(4), 720–737. https://doi.org/10.1177/1094428108327881
76.
TangZ. Y.ChenL. D.ZhouZ. Y.WarkentinaM.GillensonM. L. (2019). The effects of social media use on control of corruption and moderating role of cultural tightness-looseness. Government Information Quarterly, 36(4), 101384. https://doi.org/10.1016/j.giq.2019.06.001
77.
VedulaS.KimP. H. (2019). Gimme shelter or fade away: The impact of regional entrepreneurial ecosystem quality on venture survival. Industrial and Corporate Change, 28(4), 827–854. https://doi.org/10.1093/icc/dtz032
78.
VisB.WoldendorpJ.KemanH. (2013). Examining variation in economic performance using fuzzy-sets. Quality & Quantity, 47, 1971–1989. https://doi.org/10.1007/s11135-011-9637-4
79.
WellmanN.TrösterC.GrimesM.RobersonQ.RinkF.GruberM. (2023). Publishing multimethod research in AMJ: A review and best-practice recommendations. Academy of Management Journal, 66(4), 1007–1015. https://doi.org/10.5465/amj.2023.4004
80.
WennbergK.DelmarF.McKelvieA. (2016). Variable risk preferences in new firm growth and survival. Journal of Business Venturing, 31(4), 408–427. https://10.1016/j.jbusvent.2016.05.001
81.
WoodsideA. G. (2017). The complexity turn: Cultural, management, and marketing applications. Springer International Publishing.
