This paper seeks to empirically examine diffusion models and Google Trends’ ability to explain and nowcast the new product growth phenomenon. In addition to the selected diffusion models and Google Trends, this study proposes a new model that incorporates the two. The empirical analysis is based on the cases of the iPhone and the iPad. The results show that the new model exhibits a better curve fit among all the studied ones. In terms of nowcasting, although the performance of the new model differs from that of Google Trends in the two cases, they both produce more accurate results than the selected diffusion models.
AlbuquerqueP.BronnenbergB.J.CorbettC.J.A spatiotemporal analysis of the global diffusion of ISO 9000 and ISO 14000 certificationManag. Sci.5332007451–468
2.
ArmstrongJ.S.GreenK.C.GraefeA.Golden rule of forecasting: Be conservativeJ. Bus. Res.68820151717–1731
3.
Bangwayo-SkeeteP.F.SkeeteR.W.Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approachTour. Manag.462015454–464
4.
BarreiraN.GodinhoP.MeloP.Nowcasting unemployment rate and new car sales in south-western Europe with Google TrendsNetnomics1432013129–165
5.
BassF.M.A new product growth for model consumer durablesManag. Sci.1551969215–227
6.
BassF.M.Comments on “A new product growth for model consumer durables”: the bass modelManag. Sci.501220041833–1840
7.
BassF.M.KrishnanT.V.JainD.C.Why the Bass model fits without decision variablesMark. Sci.1331994203–223
8.
Carrière-SwallowY.LabbéF.Nowcasting with Google trends in an emerging marketJ. Forecast.3242013289–298
9.
ChatterjeeR.EliashbergJ.RaoV.R.Dynamic models incorporating competitionMahajanV.MullerE.WindY.New Product Diffusion Models2000Kluwer Academic PublishersNew York
10.
ChienC.-F.ChenY.-J.PengJ.-T.Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycleInt. J. Prod. Econ.12822010496–509
11.
ChoiH.VarianH.A.L.Predicting the present with Google trendsEcon. Rec.8820122–9
12.
DanaherP.J.HardieB.G.S.PutsisW.P.Jr.Marketing-mix variables and the diffusion of successive generations of a technological innovationJ. Mark. Res.3842001501–514
13.
DeckerR.Gnibba-YukawaK.Sales forecasting in high-technology markets: a utility-based approachJ. Prod. Innov. Manag.2712010115–129
GeroskiP.A.Models of technology diffusionRes. Policy294-52000603–625
16.
GivonM.MahajanV.MullerE.Software piracy: estimation of lost sales and the impact on software diffusionJ. Mark.591199529–37
17.
GuoX.A novel Bass-type model for product life cycle quantification using aggregate market dataInt. J. Prod. Econ.1582014208–216
18.
HuY.DuR.Y.DamangirS.Decomposing the impact of advertising: augmenting sales with online search dataJ. Mark. Res.5132014300–319
19.
HuangX.ZhangL.DingY.The Baidu index: uses in predicting tourism flows – A case study of the forbidden cityTour. Manag.582017301–306
20.
JiangZ.JainD.C.A generalized Norton–Bass model for multigeneration diffusionManag. Sci.581020121887–1897
21.
JunS.-P.ParkD.-H.Consumer information search behavior and purchasing decisions: Empirical evidence from KoreaTechnol. Forecast. Soc. Change107201697–111
22.
JunS.-P.ParkD.-H.YeomJ.The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preferenceTechnol. Forecast. Soc. Change862014237–253
23.
JunS.-P.SungT.-E.ParkH.-W.Forecasting by analogy using the web search trafficTechnol. Forecast. Soc. Change115201737–51
24.
KieslingE.GüntherM.StummerC.WakolbingerL.Agent-based simulation of innovation diffusion: a reviewCent. Eur. J. Oper. Res.2022012183–230
25.
KimN.ChangD.R.ShockerA.D.Modeling intercategory and generational dynamics for a growing information technology industryManag. Sci.4642000496–512
26.
KimS.ShinD.H.Forecasting short-term air passenger demand using big data from search engine queriesAutom. Constr.70201698–108
27.
KironD.Organizational alignment is key to big data successMIT Sloan Manag. Rev.54320131
28.
KumarV.KrishnanT.V.Multinational diffusion models: an alternative frameworkMark. Sci.2132002318–330
29.
LambaK.SinghS.P.Big data in operations and supply chain management: current trends and future perspectivesProd. Plan. Control2811-122017877–890
30.
LiD.-C.ChenW.-C.ChangC.-J.ChenC.-C.WenI.H.Practical information diffusion techniques to accelerate new product pilot runsInt. J. Prod. Res.531720155310–5319
31.
LiH.AminiM.A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategyInt. J. Prod. Res.501120123152–3171
32.
LiX.PanB.LawR.HuangX.Forecasting tourism demand with composite search indexTour. Manag.59201757–66
33.
LibaiB.MullerE.PeresR.The diffusion of servicesJ. Mark. Res.4622009163–175
34.
LibaiB.MullerE.PeresR.The role of within-brand and cross-brand communications in competitive growthJ. Mark.733200919–34
35.
MahajanV.MullerE.Timing, diffusion, and substitution of successive generations of technological innovations: The IBM mainframe caseTechnol. Forecast. Soc. Change5121996109–132
ManyikaJ.ChuiM.BrownB.BughinJ.DobbsR.RoxburghC.ByersA.H.Big Data: The Next Frontier for Innovation, Competition, and Productivity2011McKinsey Global Institute
38.
MarmierF.CheikhrouhouN.Structuring and integrating human knowledge in demand forecasting: a judgemental adjustment approachProd. Plan. Control2142010399–412
39.
MascleC.GosseJ.Inventory management maximization based on sales forecast: case studyProd. Plan. Control251220141039–1057
40.
MavraganiA.TsagarakisK.P.YES or NO: predicting the 2015 GReferendum results using Google trendsTechnol. Forecast. Soc. Change10920161–5
41.
MeadeN.IslamT.Modelling and forecasting the diffusion of innovation – A 25-year reviewInt. J. Forecast.2232006519–545
42.
MoatH.S.CurmeC.AvakianA.KenettD.Y.StanleyH.E.PreisT.Quantifying wikipedia usage patterns before stock market movesSci. Rep.32013
43.
NegahbanA.YilmazL.NallT.Managing production level in new product diffusion: an agent-based simulation approachInt. J. Prod. Res.521720144950–4966
44.
NortonJ.A.BassF.M.A diffusion theory model of adoption and substitution for successive generations of high-technology productsManag. Sci.33919871069–1086
45.
NunanD.Addressing the market research skills gapInt. J. Mark. Res.5722015177–178
46.
NunanD.Di DomenicoM.Market research and the ethics of big dataInt. J. Mark. Res.55420132–13
47.
PanagiotopoulosP.BigdeliA.Z.SamsS.Citizen–government collaboration on social media: The case of Twitter in the 2011 riots in EnglandGov. Inf. Q.3132014349–357
48.
PappasS.2014. Data Fail! How Google Flu Trends Fell Way Short.
49.
ParkerP.GatignonH.Specifying competitive effects in diffusion models: an empirical analysisInt. J. Res. Mark.111199417–39
50.
PeresR.MullerE.MahajanV.Innovation diffusion and new product growth models: a critical review and research directionsInt. J. Res. Mark.272201091–106
51.
PerlinM.S.CaldeiraJ.F.SantosA.A.P.PontuschkaM.Can we predict the financial markets based on Google's search queries?J. Forecast.3642017454–467
PreisT.MoatH.S.BishopS.R.TreleavenP.StanleyH.E.Quantifying the digital traces of hurricane sandy on FlickrSci. Rep.32013
54.
PreisT.MoatH.S.StanleyH.E.Quantifying trading behavior in financial markets using Google trendsSci. Rep.32013
55.
PutsisW.P.BalasubramanianS.KaplanE.H.SenS.K.Mixing behavior in cross-country diffusionMark. Sci.1641997354–369
56.
QinR.NembhardD.A.Demand modeling of stochastic product diffusion over the life cycleInt. J. Prod. Econ.13722012201–210
57.
RadasS.ShuganS.M.Seasonal marketing and timing new product introductionsJ. Mark. Res.3531998296–315
58.
RiveraR.A dynamic linear model to forecast hotel registrations in Puerto Rico using Google trends dataTour. Manag.57201612–20
59.
Samuel SaleR.MesakH.I.InmanR.A.A dynamic marketing-operations interface model of new product updatesEur. J. Oper. Res.25712017233–242
60.
SantillanaM.NguyenA.T.DredzeM.PaulM.J.NsoesieE.O.BrownsteinJ.S.Combining search, social media, and traditional data sources to improve influenza surveillancePLoS Comput. Biol.11102015 e1004513
61.
SchaerO.KourentzesN.FildesR.Demand forecasting with user-generated online informationInt. J. Forecast.3512018197–212
62.
SchneiderM.J.GuptaS.Forecasting sales of new and existing products using consumer reviews: a random projections approachInt. J. Forecast.3222016243–256
63.
ShiX.FernandesK.ChumnumpanP.Diffusion of multi-generational high-technology productsTechnovation3432014162–176
64.
SmithP.Google's MIDAS touch: predicting UK unemployment with internet search dataJ. Forecast.3532016263–284
65.
StummerC.KieslingE.GüntherM.VetscheraR.Innovation diffusion of repeat purchase products in a competitive market: an agent-based simulation approachEur. J. Oper. Res.24512015157–167
66.
TsaiB.-H.Modeling diffusion of multi-generational LCD TVs while considering generation-specific price effects and consumer behaviorsTechnovation3310-112013345–354
67.
Van den BulteC.JoshiY.V.New product diffusion with influentials and imitatorsMark. Sci.2632007400–421
68.
VenkatesanR.KrishnanT.V.KumarV.Evolutionary estimation of macro-level diffusion models using genetic algorithms: an alternative to nonlinear least squaresMark. Sci.2332004451–464
69.
VosenS.SchmidtT.Forecasting private consumption: survey-based indicators vs. Google trendsJ. Forecast.3062011565–578
70.
WhitneyL.iPad Sheds More Market Share but Still Dominates2014CNET
71.
YanH.-S.MaK.-P.Competitive diffusion process of repurchased products in knowledgeable manufacturingEur. J. Oper. Res.20832011243–252
