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Public Transport

, Volume 10, Issue 1, pp 107–127 | Cite as

Formative and reflective measurement models for analysing transit service quality

  • Laura Eboli
  • Carmen Forciniti
  • Gabriella Mazzulla
Original Paper
  • 186 Downloads

Abstract

Transit service quality is a complex concept depending on different service aspects, such as service frequency and punctuality, comfort, cleanliness, information and so on. Transit service quality is generally measured through the satisfaction of the users with the service. There are relationships between the overall service quality and the different transit service aspects, and between each aspect and the characteristics describing it. Structural equation models represent a useful tool for exploring this kind of relationship and determining the influence of the different service characteristics on service quality. An investigated issue concerning structural equation models is the contrast between the formative and the reflective approach. The structural models proposed for measuring transit service quality have followed a reflective approach, according to which the latent variable (or the service aspect) is the cause of the observed measures (or the service factors describing the service aspect); but in this paper we investigate on the fact that formative variables could be considered to model the relationship among the service quality characteristics, supposing that the observed measures, which represent the service characteristics, form the latent construct. The findings from the comparison between the results obtained by applying the two different approaches suggest that the reflective model is surely more suitable for describing the phenomenon of passenger satisfaction with transit service quality. However, we retain that if some service aspects can be more conveniently investigated through a reflective approach, other service aspects could follow a formative approach in a better way.

Keywords

Transit service quality Structural equation modelling Formative and reflective models 

References

  1. Anderson JC, Gerbing DW (1988) Structural equation modeling in practice: a review and recommended twostep approach. Psychol Bull 103(3):411–423CrossRefGoogle Scholar
  2. Andreassen TW (1995) (Dis)satisfaction with public services: the case of public transportation. J Serv Mark 9:30–41CrossRefGoogle Scholar
  3. Arbuckle JL, Wothke W (1995) AMOS 4.0 user’s guide. SmallWaters Corporation, ChicagoGoogle Scholar
  4. Bamberg S, Schmidt P (1998) Changing travel-mode choice as rational choice: results from a longitudinal intervention study. Ration Soc 10(2):223–252CrossRefGoogle Scholar
  5. Bearden WO, Netmeyer RG (1999) Handbook of marketing scales. Sage, Thousand OaksCrossRefGoogle Scholar
  6. Blalock HM (1964) Causal inferences in nonexperimental research. University of North Carolina Press, Chapel HillGoogle Scholar
  7. Boari G (2000) Uno sguardo ai modelli per la costruzione di indicatori nazionali di customer satisfaction. In: Valutazione della qualità e customer satisfaction: il ruolo della statistica. Vita e Pensiero, Milano, pp 317–336Google Scholar
  8. Bollen K (1989) Structural equations with latent variables. Wiley, New YorkCrossRefGoogle Scholar
  9. Bollen K, Lennox R (1991) Conventional wisdom on measurement: a structural equation perspective. Psychol Bull 100:305–314CrossRefGoogle Scholar
  10. Borsboom D, Mellenbergh GJ, Heerden JV (2003) The theoretical status of latent variables. Psychol Rev 110(2):203–219CrossRefGoogle Scholar
  11. Borsboom D, Mellenbergh GJ, Heerden JV (2004) The concept of validity. Psychol Rev 111(4):1061–1071CrossRefGoogle Scholar
  12. Browne MW, Cudeck R (1993) Alternative ways of assessing model fit. In: Bollen KA, Long JS (eds) Testing structural equation models. Sage, Newbury Park, pp 136–162Google Scholar
  13. Bruner GCB II, James KE, Hensel PJ (2001) Marketing scales handbook. American Marketing Association, ChicagoGoogle Scholar
  14. Byrne BM (1994) Structural equation modeling with EQS and EQS/WINDOWS: basic concepts, applications and programming, 3rd edn. Sage, Thousand OaksGoogle Scholar
  15. Carreira R, Patrício L Natal, Jorge R, Magee C (2014) Understanding the travel experience and its impact on attitudes, emotions and loyalty towards the transportation provider—a quantitative study with mid-distance bus trips. Transp Policy 31:35–46CrossRefGoogle Scholar
  16. Coltman T, Devinney TM, Midgley DF, Venaik S (2008) Formative versus reflective measurement models: two applications of formative measurement. J Bus Res 61(12):1250–1262CrossRefGoogle Scholar
  17. Comité Européen de Normalisation (CEN) (2002) European Standard EN 13816. Transportation - logistics and services - public passenger transport - service quality definition, targeting and measurementGoogle Scholar
  18. de Abreu e Silva J, Goulias KG (2009) A structural equations model of land use patterns, location choice, and travel behavior in Seattle and comparison with Lisbon. In: Proceedings of the 88th annual transportation research board meeting, 11–15 Jan 2009, Washington DCGoogle Scholar
  19. de Abreu e Silva J, Morency C, Goulias KG (2012) Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal. Transp Res Part A 46:1252–1264Google Scholar
  20. de Oña J, de Oña R, Eboli L, Mazzulla G (2013) Perceived service quality in bus transit service. A structural equation approach. Transp Policy 29:219–226CrossRefGoogle Scholar
  21. de Oña J, de Oña R, Eboli L, Mazzulla G (2016a) Index numbers for monitoring transit service quality. Transp Res Part A 84:18–30Google Scholar
  22. de Oña J, de Oña R, Eboli L, Forciniti C, Mazzulla G (2016b) Transit passengers’ behavioural intentions: the influence of service quality and customer satisfaction. Transportmetrica A 12(5):385–412CrossRefGoogle Scholar
  23. Diamantopoulos A, Siguaw JA (2006) Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br J Manag 17:263–282CrossRefGoogle Scholar
  24. Diamantopoulos A, Winklhofer HM (2001) Index construction with formative indicators: an alternative to scale development. J Mark Res 38(5):269–277CrossRefGoogle Scholar
  25. Eboli L, Mazzulla G (2007) Service quality attributes affecting customer satisfaction for bus transit. J Public Transp 10:21–34CrossRefGoogle Scholar
  26. Eboli L, Mazzulla G (2012) Structural equation modelling for analysing passengers’ perceptions about railway services. Proc-Soc Behav Sci 54:96–106CrossRefGoogle Scholar
  27. Eboli L, Mazzulla G (2015) Relationships between rail passengers’ satisfaction and service quality: a framework for identifying the key service factors. Public Transp Plan Oper 7(2):185–201CrossRefGoogle Scholar
  28. Eboli L, Forciniti C, Mazzulla G (2012) Exploring land use and transport interaction through structural equation modelling. Proc-Soc Behav Sci 54:107–116CrossRefGoogle Scholar
  29. Edwards JR, Bagozzi RP (2000) On the nature and direction of the relationship between constructs and measures. Psychol Methods 5:155–174CrossRefGoogle Scholar
  30. Eskildsen JK, Dahlgaard JJ (2000) A causal model for employee satisfaction. Total Qual Manag 11(8):1081–1094CrossRefGoogle Scholar
  31. Fillone AM, Montalbo CM, Tiglao NC (2005) Assessing urban travel: a structural equations modeling (SEM) approach. Proc East Asia Soc Transp Stud 5:1050–1064Google Scholar
  32. Fornell C (1982) A second generation of multivariate analysis. Praeger, New YorkGoogle Scholar
  33. Grace JB, Pugesek BH (1997) A structural equation model of plant species richness and its application to a coastal wetland. Am Nat 149(3):436–460CrossRefGoogle Scholar
  34. Hair JF, Anderson RE, Tatham RL, Black WC (2009) Multivariate data analysis. Prentice Hall, Englewood CliffsGoogle Scholar
  35. Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model: Multidiscip J 6(1):1–55CrossRefGoogle Scholar
  36. Irfan SM, Mui HKD, Shahbaz S (2011) Service quality in rail transport of Pakistan: a passenger perspective. In: Proceedings of 3rd SAICON: international conference on management, business ethics and economics (ICMBEE), 28–29 Dec 2011, Lahore, PakistanGoogle Scholar
  37. Joreskog KG (1973) Analysis of covariance structures. In: Krishnaiah PR (ed) Multivariate analysis-III. Academic Press, New York, pp 263–285CrossRefGoogle Scholar
  38. Karlaftis MG, Golias J, Papadimitriou E (2001) Transit quality as an integrated traffic management strategy: measuring perceived service. J Public Transp 4(1):27–44CrossRefGoogle Scholar
  39. Kline RB (1998) Principles and practice of structural equation modeling. Guilford Press, New YorkGoogle Scholar
  40. MacCallum RC, Austin JT (2000) Applications of structural equation modelling in psychological research. Annu Rev Psychol 51:201–226CrossRefGoogle Scholar
  41. MacCallum RC, Browne MW, Sugawara HM (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1(2):130–149CrossRefGoogle Scholar
  42. MacLean S, Gray K (1998) Structural equation modelling in market research. J Aust Mark Res Soc 6(1):17–32Google Scholar
  43. Manaresi A, Marzocchi G, Tassinari G (2000) La soddisfazione del cliente dei servizi di segreteria universitaria: un modello a equazioni strutturali. In: Valutazione della qualità e customer satisfaction: il ruolo della statistica. Vita e Pensiero, Milano, pp 291–316Google Scholar
  44. Mitchell RJ (1992) Testing evolutionary and ecological hypotheses using path analysis and structural equation modelling. Funct Ecol 6:123–129CrossRefGoogle Scholar
  45. Muthén B, Kaplan D, Hollis M (2006) On structural equation modelling with data that are not missing completely at random. Psychometrika 52(3):431–462CrossRefGoogle Scholar
  46. Netmeyer RG, Bearden WO, Sharma S (2003) Scaling procedures: issues and applications. Sage, Thousand OaksCrossRefGoogle Scholar
  47. Ngatia GJ, Okamura T, Nakamura F (2010) The structure of users’ satisfaction on urban public transport service in developing country: the case of Nairobi. J East Asia Soc Transp Stud 8:1288–1300Google Scholar
  48. Rossiter JR (2002) The C-OAR-SE procedure for scale development in marketing. Int J Res Mark 19(4):1–31CrossRefGoogle Scholar
  49. Schumacker RE, Lomax RG (2004) A beginner’s guide to structural equation modeling, 2nd edn. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  50. Simonetto A (2012) Formative and reflective models: state of the art. Electron J Appl Stat Anal 5(3):452–457Google Scholar
  51. Spector PE (1992) Summated rating scale construction. Sage, Newbury ParkCrossRefGoogle Scholar
  52. Steiger JH (1990) Structural model evaluation and modification: an interval estimation approach. Multivar Behav Res 25:173–180CrossRefGoogle Scholar
  53. Stuart KR, Mednick M, Bockman J (2000) Structural equation model of customer satisfaction for the New York City subway system. Transp Res Rec 1735:133–137CrossRefGoogle Scholar
  54. Tam Mei Ling, Tam Mei Lang, Lam WHK (2005) Analysis of airport access mode choice: a case study in Hong Kong. J East Asia Soc Transp Stud 6:708–723Google Scholar
  55. Transportation Research Board (2003) Transit capacity and quality of service manual. TCRP report 100. National Academy Press, Washington, DCGoogle Scholar
  56. Tschopp M, Axhausen KW (2007) Transport infrastructure and spatial development in Switzerland between 1950 and 2000. In: Proceedings of 86th annual meeting of the transportation research board, Jan 2007, Washington, DCGoogle Scholar
  57. Ullman JB (2001) Structural equation modeling. In: Tabachnick BG, Fidell LS (eds) Using multivariate statistics, 4th edn. Allyn and Bacon, Needham Heights, pp 653–771Google Scholar
  58. Van Acker V, Witlox F, Van Wee B (2007) The effects of the land use system on travel behavior: a structural equation modeling approach. Transp Plan Technol 30:331–353CrossRefGoogle Scholar
  59. Wiley DE (1973) The identification problem for structural equation models with unmeasured variables. In: Goldberger AS, Ducan OD (eds) Structural equation models in the social science. Seminar Press, New York, pp 69–83Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Laura Eboli
    • 1
  • Carmen Forciniti
    • 1
  • Gabriella Mazzulla
    • 1
  1. 1.University of CalabriaRendeItaly

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