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Back to the Future: Does the use of information and communication technology enhance the performance of public historical archives?

Journal of Cultural Economics volume 45, pages 13–43 (2021)Cite this article

Abstract

The diffusion of digital information and communication technologies (ICT) in public services is generally assumed to improve their performance. However, the impact of ICT on the efficiency of public services is rarely verified. This study focuses on public historical archives (PHAs), which are a widely diffuse—yet little explored—public service. It provides an assessment of the impact of a specific ICT on PHAs’ efficiency, namely the introduction of websites. The analysis adopts a two-stage approach involving the estimation of the frontier using Data Envelopment Analysis (DEA) and Window DEA. Results show that the diffusion of ICT is efficiency improving: Italian PHAs having a website are generally more efficient than others. These empirical findings are robust to the use of alternative estimators and empirical strategies in both first and second stage assessment. Finally, we suggest some directions for further research in the field.

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Fig. 1

Source: our elaboration on data provided by web repository (Internet Archive—Wayback Machine) and by browsing PHAs’ websites

Fig. 2

Source: our elaboration on data provided by MiBACT Statistical office

Fig. 3

Source: our computation on data provided by MiBACT Statistical office and web repository (Internet Archive—Wayback Machine) and by browsing PHAs’ websites

Notes

  1. For example, ICT generated novel forms of cultural demand and supply in the cultural sector. See: Bakhshi and Throsby 2012; Borowiecki et al. 2016; Handke et al. 2016.

  2. Several studies have employed frontier approaches to assess the relationship between ICT investment, productivity and technical efficiency: Liao et al. (2016) used stochastic frontier model to assess the impact of ICT for the economic growth in the US whereas Lien and Peng (2001) employed DEA to examine technical efficiency of telecommunications industries in 24 OECD countries.

  3. WDEA has been applied to a wide range of topics related to private firms’ or public sector efficiency (see for instance, Degl’Innocenti et al. 2017; Lv et al. 2017; Zhang and Chen 2018; Phan et al. 2018; Liu et al. 2019).

  4. Table 1 excludes two archives: the National Central Archive in Rome, for its specificity, and the archive of Ferrara for lack of data.

  5. We do not account for a potential source of heterogeneity in the sample deriving by online access which is not equally practiced across archives. However, this aspect does not endanger the reliability of our analysis since the current extent of online access is very limited, and often requires users to access from PHAs’ internet workstations (i.e. such users are also visitors).

  6. See Guccio et al. (2016) for a more detailed description of MiBACT’s projects involving IT and the use of internet.

  7. Since the exact date when a website becomes fully operational is not known, in Sect. 4 we also consider 2008 and 2010 as cut-offs, showing that our cut-off choice does not affect the results.

  8. For further details see Fried et al. (2008), Simar and Wilson (2008).

  9. Simar and Wilson (2007) presents a survey of developments devoted to include noise in non-parametric frontier estimators and proposes a stochastic version of DEA and FDH estimators.

  10. The rationale behind bootstrapping is to simulate a true sampling distribution by simulating their Data Generating Process—DGP—here the outputs from DEA (Simar and Wilson 2008). More in particular, the process relies on constructing a pseudo-data set and re-estimating the DEA model with this new data set. Repeating the process many times allows for achieving a good approximation of the true distribution of the sampling.

  11. See Simar and Wilson (2008) for the technical detail of the bootstrap procedures.

  12. The Banker (1996) test shows that in our sample we can reject the null hypothesis of CRS at 5 percent level of significance. Results are available upon request.

  13. An alternative way to study the efficiency change and the catch up effect is to compute and analyse the Malmquist index. However, Asmild et al. (2004) show that the use of Malmquist index on scores estimated through WDEA is inappropriate and leads to incorrect results.

  14. In spite of the advantages of WDEA it must be mentioned that, although this method allows to exploit panel data to assess efficiency over time, it is not a dynamic model such as those developed by Färe and Grosskopf (1996), Nemoto and Goto (1999, 2003), Tone and Tsutsui (2010), i.e. it does not model DMUs dynamic behaviour and does not take into account that some inputs are quasi-fixed and carried over along subsequent periods. While dynamic alternatives allow to model choices in a more realistic way in general, their advantage is limited in our context. In fact, dynamic models are designed to deal with strategic long term choices of investment that imply the diversion of resources usually employed to produce the outputs. Thus, investment reduces the output in the short run, resulting in an overestimation of inefficiency through traditional DEA. In our setting WDEA is thus preferable to get a more conservative estimation of the potentially positive impact of websites on PHAs efficiency.

  15. As common in DEA framework we pooled the data for consistency of the efficiency estimates (Simar and Wilson 2008).

  16. The separability condition requires that contextual factors do not affect production possibilities, i.e. the shape and the level of the frontier (Simar and Wilson 2007, Daraio et al. 2018). However, the variables that we employ regard mainly the presence and use of websites, which are related to the demand for such services and thus may reasonably affect the distribution of efficiency scores inside the production possibility set but presumably not the attainable set and its frontier. Furthermore, in what follows, we also perform the Banker and Natarajan (2008) estimator does not require such condition, which makes us quite confident on the robustness of our findings (Banker et al. 2019).

  17. As observed by Wooldridge (2002), feasible generalized least squares estimator is prudently employed when dealing with simple forms of autocorrelation or group wise heteroskedasticity.

  18. A detailed discussion on this estimate and its reliability can be found in Guccio et al. (2018a).

  19. YEAR_SINCE defines a subsample since some PHAs did not have a website even in the sample, as reported in Fig. 2. W_VISIT defines a subsample because observations required additional cleaning procedures and are also characterized by several missing data.

  20. We thank the General Direction for Italian Archives for its support in the collection of data.

  21. Such outcomes are obtained through the Simar and Wilson (2000) bootstrap procedure with 2000 bootstrap draws, estimated by using the software package FEAR 1.15 (Wilson 2008), to check for the robustness of the DEA findings with respect to the sampling variation. However, Simar and Wilson (2008) point out that the bias correction obtained with bootstrap procedures introduces additional “noise” in the efficiency estimates and show that the bias correction provides valid inference only if the ratio of the estimated bias to the variance satisfies some conditions. As a caution rule, they suggest that the bias correction should not be used if correction is larger than four times the variance of efficiency scores obtained from all bootstrapped pseudo-samples, as in our estimation. Thus, we do not focus on such estimates in the remainder of the paper.

  22. We consider three geographical areas: North (Emilia Romagna, Friuli, Liguria, Lombardia, Piemonte, Trentino, Veneto), Centre (Lazio, Marche, Toscana, Umbria), South (Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, Sicilia).

  23. We also run semi-parametric estimates (Simar and Wilson 2007), on the bias-corrected estimates as a robustness check. Results are shown in the “Appendix”.

  24. Results based on T2012 are shown in the “Appendix”.

  25. We have 4 windows over the period 2009–2014 because, when a new period is introduced into the window, the earliest year is dropped. Thus, the first window includes the first 3 years of the period: 2009, 2010 and 2011. In the second window, the year 2009 is excluded and the year 2012 is included and so on. Since WDEA treats a DMU independently across the entire period, 4 three-year windows considerably increase the number of observations of the sample. To provide robustness to our results we also run the estimate setting k = 4. Related estimates, which confirm our main results are available upon request.

References

  • Ancarani, A. (2005). Towards quality e-service in the public sector: The evolution of web sites in the local public service sector. Managing Service Quality: An International Journal, 15(1), 6–23.

    Google Scholar 

  • Arduini, D., Belotti, F., Denni, M., Giungato, G., & Zanfei, A. (2010). Technology adoption and innovation in public services. The case of e-Government in Italy, Information Economics and Policy, 22, 257–275.

    Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–297.

    Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.

    Google Scholar 

  • Asmild, M., Paradi, J. C., Aggarwal, V., & Schanit, C. (2004). Combining DEA window analysis with the Malmquist index approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67–89.

    Google Scholar 

  • Bakhshi, H., & Throsby, D. (2012). New technologies in cultural institutions: theory, evidence and policy implications. International Journal of Cultural Policy, 18(2), 205–222. https://doi.org/10.1080/10286632.2011.587878.

    Article  Google Scholar 

  • Baldersheim, H., & Øgård, M. (2008). Innovation in e-Government: Analysis of municipal web pages in the Nordic countries. Information Polity, 13, 125–137.

    Google Scholar 

  • Banker, R. D. (1996). Hypothesis test using data envelopment analysis. Journal of Productivity Analysis, 7(2), 139–159.

    Google Scholar 

  • Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. International Journal of Operational Research, 56(1), 48–58.

    Google Scholar 

  • Banker, R., Natarajan, R., & Zhang, D. (2019). Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: second stage OLS versus bootstrap approaches. European Journal of Operational Research, 278(2), 368–384.

    Google Scholar 

  • Basso, A., & Funari, S. (2004). A quantitative approach to evaluate the relative efficiency of museums. Journal of Cultural Economics, 28(3), 195–216.

    Google Scholar 

  • Bishop, P., & Brand, S. (2003). The efficiency of museums: a stochastic frontier production function approach. Applied Economics, 35(17), 1853–1858.

    Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.

    Google Scholar 

  • Borowiecki, K. J., Forbes, N., & Fresa, A. (Eds.). (2016). Cultural heritage in a changing world. New York: Springer.

    Google Scholar 

  • Borowiecki, K. J., & Navarrete, T. (2015). Digitization of heritage collections as indicator of innovation, University of Southern Denmark, Discussion Papers on Business and Economics N.14/2015.

  • Brynjolfsson, E., & Hitt, L. (1996). Paradox lost? Firm-level evidence on the returns to information systems spending, Management Science, 42(4), 541–558.

    Google Scholar 

  • Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: Information technology, organizational transformation and business performance. Journal of Economic Perspectives, 14(4), 23–48.

    Google Scholar 

  • Brynjolfsson, E., & Kemerer, C. F. (1996). Network externalities in microcomputer software: An econometric analysis of the spreadsheet market. Management Science, 42(12), 1627–1647.

    Google Scholar 

  • Cainelli, G., Evangelista, R., & Savona, M. (2005). Innovation and economic performance in services: A firm-level analysis. Cambridge Journal of Economics, 30(3), 435–458.

    Google Scholar 

  • Calamai, S., Ginouves, V., & Bertinetto, P. M. (2016). Sound archives accessibility. In K. J. Borowiecki, N. Forbes, & A. Fresa (Eds.), Cultural heritage in a changing world. Berlin: Springer.

    Google Scholar 

  • Charnes, A., & Cooper, W. W. (1985). Preface to topics in data envelopment analysis. Annals of Operation Research, 2, 59–94.

    Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Google Scholar 

  • Charnes, A. C. T. C., Clark, C. T., Cooper, W. W., & Golany, B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces. Annals of Operations Research, 2(1), 95–112.

    Google Scholar 

  • Chen, T. Y. (1997). A measurement of resource utilisation efficiency of university libraries. International Journal of Production Economics, 53, 71–80.

    Google Scholar 

  • Chen, Y., Morita, H., & Zhu, J. (2005). Context-dependent DEA with an application to Tokyo public libraries. International Journal of Information Technology and Decision Making, 4(3), 385–394.

    Google Scholar 

  • Chou, Y. C., Chuang, H. H. C., & Shao, B. B. (2014). The impacts of information technology on total factor productivity: A look at externalities and innovations. International Journal of Production Economics, 158, 290–299.

    Google Scholar 

  • Coelli, T., Rao, D. S. P., & Battese, G. E. (1998). An introduction to efficiency and productivity analysis. Boston: Kluwer Academic.

    Google Scholar 

  • Daraio, C., Simar, L., & Wilson, P. W. (2018). Central limit theorems for conditional efficiency measures and tests of the ‘separability’condition in non-parametric, two-stage models of production. The Econometrics Journal, 21(2), 170–191.

    Google Scholar 

  • De Vries, H., Bekkers, V., & Tummers, L. (2016). Innovation in the public sector: A systematic review and future research agenda. Public Administration, 94(1), 146–166.

    Google Scholar 

  • De Witte, K., & Geys, B. (2011). Evaluating efficient public good provision: Theory and evidence from a generalised conditional efficiency model for public libraries. Journal of Urban Economics, 69(3), 319–327.

    Google Scholar 

  • De Witte, K., & Geys, B. (2013). Citizen coproduction and efficient public good provision: Theory and evidence from local public libraries. European Journal of Operational Research, 224, 592–602.

    Google Scholar 

  • Degl’Innocenti, M., Kourtzidis, S. A., Sevic, Z., & Tzeremes, N. G. (2017). Investigating bank efficiency in transition economies: A window-based weight assurance region approach. Economic Modelling, 67, 23–33.

    Google Scholar 

  • del Barrio, M. J., & Herrero, L. C. (2014). Evaluating the efficiency of museums using multiple outputs: evidence from a regional system of museums in Spain. International Journal of Cultural Policy, 20(2), 221–238.

    Google Scholar 

  • Del Barrio-Tellado, M. J., & Herrero-Prieto, L. C. (2019). Modelling museum efficiency in producing inter-reliant outputs. Journal of Cultural Economics, forthcoming. https://doi.org/10.1007/s10824-019-09347-2.

    Article  Google Scholar 

  • Del Barrio-Tellado, M. J., Herrero-Prieto, L. C., & Sanz, J. A. (2009). Measuring the efficiency of heritage institutions: a case study of a regional system of museums in Spain. Journal of Cultural Heritage, 10(2), 258–268.

    Google Scholar 

  • Dias, C., & Escoval, A. (2013). Improvement of hospital performance through innovation: Toward the value of hospital care. Health Care Manager, 32(2), 129–140.

    Google Scholar 

  • Djellal, F., Gallouj, F., & Miles, I. (2013). Two decades of research on innovation in services: Which place for public services? Structural Change and Economic Dynamics, 27, 98–117.

    Google Scholar 

  • Duff, W. M., & Johnson, C. A. (2002). Accidentally found on purpose: Information-seeking behavior of historians in archives. The Library Quarterly, 72(4), 472–496.

    Google Scholar 

  • El-Haddadeh, R., Weerakkody, V., & Al-Shafi, S. (2013). The complexities of electronic services implementation and institutionalisation in the public sector. Information & Management, 50, 135–143.

    Google Scholar 

  • Fallah-Fini, S., Triantis, K., & Johnson, A. L. (2014). Reviewing the literature on non-parametric dynamic efficiency measurement: state-of-the-art. Journal of Productivity Analysis, 41, 51–67.

    Google Scholar 

  • Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers with dynamic DEA. Cambridge: Kluwer.

    Google Scholar 

  • Fernández-Blanco, V., Herrero, L. C., & Prieto-Rodríguez, J. (2013). Performance of cultural heritage institutions. In I. Rizzo & A. Mignosa (Eds.), Handbook on economics of cultural heritage (pp. 470–489). Cheltenham: Edward Elgar.

    Google Scholar 

  • Finocchiaro Castro, M., Guccio, C., & Rizzo, I. (2011). Public intervention on heritage conservation and determinants of heritage authorities performance: a semi-parametric analysis. International Tax and Public Finance, 18(1), 1–16.

    Google Scholar 

  • Finocchiaro Castro, M., & Rizzo, I. (2009). Performance measurement of heritage conservation activity in sicily. International Journal of Arts Management, 11(2), 29–41.

    Google Scholar 

  • Flew, T., & Swift, A. (2013). Cultural policy. In R. Towse & C. Handke (Eds.), Handbook on the digital creative economy (pp. 155–161). Cheltenham: Edward Elgar.

    Google Scholar 

  • Fountain, J. E. (2001). Building the virtual state. Information technology and institutional change. Washington, DC: Brookings Institution Press.

    Google Scholar 

  • Fried, H. O., Knox Lovell, C. A., & Schmidt, S. S. (2008). The measurement of productive efficiency and productivity growth. Oxford: Oxford University Press.

    Google Scholar 

  • Froehle, C. M., & Roth, A. V. (2004). New measurement scales for evaluating perceptions of the technology-mediated customer service experience. Journal of Operations Management, 22(1), 1–21.

    Google Scholar 

  • Gallouj, F., & Zanfei, A. (2013). Innovation in public services: Filling a gap in the literature. Structural change and economic dynamics, 27, 89–97.

    Google Scholar 

  • Giardina, E., Mazza, I., Pignataro, G., & Rizzo, I. (2016). Voluntary provision of public goods and technology. International Advances in Economic Research, 22, 321–332.

    Google Scholar 

  • Gil-Garcia, J. R., Dawes, S. S., & Pardo, T. A. (2018). Digital government and public management research: finding the crossroads. Public Management Review, 20(5), 633–646.

    Google Scholar 

  • Guccio, C., Lisi, D., Mignosa, A., & Rizzo, I. (2018a). Does cultural heritage monetary value have an impact on visits? An assessment using Italian official data. Tourism Economics, 24(3), 297–318.

    Google Scholar 

  • Guccio, C., Martorana, M. F., Mazza, I., & Rizzo, I. (2016). Technology and public access to cultural heritage: The Italian experience on IT for public historical archives. In K. J. Borowiecki, N. Forbes, & A. Fresa (Eds.), Cultural heritage in a changing world (pp. 55–75). New York: Springer.

    Google Scholar 

  • Guccio, C., Mignosa, A., & Rizzo, I. (2018b). Are public state libraries efficient? An empirical assessment using network data envelopment analysis. Socio-Economic Planning Sciences, 64, 78–91.

    Google Scholar 

  • Guccio, C., Pignataro, G., & Rizzo, I. (2014). Evaluating the efficiency of public procurement contracts for cultural heritage conservation works in Italy. Journal of Cultural Economics, 38(1), 43–70.

    Google Scholar 

  • Hammond, C. J. (2002). Efficiency in the provision of public services: a data envelopment analysis of UK public library systems. Applied Economics, 34(5), 649–657.

    Google Scholar 

  • Handke, C., Stepan, P., & Towse, R. (2016). Cultural Economics and the Internet. In J. M. Bauer & M. Latzer (Eds.), Handbook on the economics of the internet (pp. 146–162). Cheltenham: Edward Elgar.

    Google Scholar 

  • Kao, C., & Lin, Y. C. (2004). Evaluation of the university libraries in Taiwan: Total measure versus ratio measure. Journal Operational Research Society, 55(12), 1256–1265.

    Google Scholar 

  • Law, R., Qi, S., & Buhalis, D. (2010). Progress in tourism management: A review of website evaluation in tourism research. Tourism Management, 31(3), 297–313.

    Google Scholar 

  • Liao, H., Wang, B., Li, B., & Weyman-Jones, T. (2016). ICT as a general-purpose technology: The productivity of ICT in the United States revisited. Information Economics and Policy, 36, 10–25.

    Google Scholar 

  • Lien, D., & Peng, Y. (2001). Competition and production efficiency: Telecommunications in OECD countries. Information Economics and Policy, 13(1), 51–76.

    Google Scholar 

  • Lin, W. T., & Chiang, C. Y. (2011). The impacts of country characteristics upon the value of information technology as measured by productive efficiency. International Journal of Production Economics, 132(1), 13–33.

    Google Scholar 

  • Lin, W. T., & Shao, B. B. M. (2000). Relative sizes of information technology investments and productive efficiency: Their linkage and empirical evidence. Journal of the Association for Information Systems, 1(7), 1–35.

    Google Scholar 

  • Lin, W. T., & Shao, B. B. M. (2006a). Assessing the input effect on productive efficiency in production systems: The value of information technology capital. International Journal of Production Research, 44(09), 1799–1819.

    Google Scholar 

  • Lin, W. T., & Shao, B. B. M. (2006b). The business value of information technology and inputs substitution: The productivity paradox revisited. Decision Support Systems, 42(2), 493–507.

    Google Scholar 

  • Liu, C. C., Wang, T. Y., & Yu, G. Z. (2019). Using AHP, DEA and MPI for governmental research institution performance evaluation. Applied Economics, 51(10), 983–994.

    Google Scholar 

  • Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). A survey of DEA applications. Omega, 41(5), 893–902.

    Google Scholar 

  • Lv, K., Yu, A., & Bian, Y. (2017). Regional energy efficiency and its determinants in China during 2001–2010: A slacks-based measure and spatial econometric analysis. Journal of Productivity Analysis, 47(1), 65–81.

    Google Scholar 

  • Mairesse, F., & Eeckaut, P. V. (2002). Museum assessment and FDH technology: Towards a global approach. Journal of Cultural Economics, 26(4), 261–286.

    Google Scholar 

  • Martinéz-Núñez, M., & Pérez-Aguiar, W. S. (2014). Efficiency analysis of information technology and online social networks management: An integrated DEA-model assessment. Information & Management, 51, 712–725.

    Google Scholar 

  • McDonald, J. (2009). Using least squares and tobit in second stage DEA efficiency analyses. European Journal of Operational Research, 197, 792–798.

    Google Scholar 

  • Navarrete, T. (2013a). Digital cultural heritage. In I. Rizzo & A. Mignosa (Eds.), Handbook on the economics of cultural heritage (pp. 251–271). Cheltenham: Edward Elgar.

    Google Scholar 

  • Navarrete, T. (2013b). Museums. In R. Towse & C. Handke (Eds.), Handbook on the digital creative economy (pp. 330–343). Cheltenham: Edward Elgar.

    Google Scholar 

  • Nemoto, J., & Goto, M. (1999). Dynamic data envelopment analysis modeling intertemporal behavior of a firm in the presence of productive inefficiencies. Economic Letters, 64, 51–56.

    Google Scholar 

  • Nemoto, J., & Goto, M. (2003). Measuring dynamic efficiency in production: an application of data envelopment analysis to Japanese electric utilities. Journal of Productivity Analysis, 19, 191–210.

    Google Scholar 

  • OECD. (2015). Government at a Glance 2015. Paris: OECD Publishing. https://doi.org/10.1787/gov_glance-2015-en.

    Book  Google Scholar 

  • Osborne, S. P., & Brown, L. (2011). Innovation, public policy and public services delivery in the UK. The word that would be king? Public Administration, 89(4), 1335–1350.

    Google Scholar 

  • Paolini, P., Mitroff, Silvers D., & Proctor, N. (2013). Technologies for cultural heritage. In I. Rizzo & A. Mignosa (Eds.), Handbook on the economics of cultural heritage (pp. 272–289). Cheltenham: Edward Elgar.

    Google Scholar 

  • Phan, H. T., Anwar, S., & Alexander, W. R. J. (2018). The determinants of banking efficiency in Hong Kong 2004-2014. Applied Economics Letters, 25(18), 1323–1326.

    Google Scholar 

  • Pignataro, G. (2002). Measuring the efficiency of museums: A case study in Sicily. In I. Rizzo & R. Towse (Eds.), The economics of heritage. A study in the political economy of culture in Sicily (pp. 65–78). Cheltenham: Edward Elgar.

    Google Scholar 

  • Pignataro, G. (2011). Performance indicators. In R. Towse (Ed.), A handbook of cultural economics (2nd ed., pp. 332–338). Cheltenham: Edward Elgar.

    Google Scholar 

  • Reggi, L., Arduini, D., Biagetti, M., & Zanfei, A. (2014). How advanced are Italian regions in terms of public e-services? The construction of a composite indicator to analyse patterns of innovation diffusion in the public sector, Telecommunications Policy, 38, 514–529.

    Google Scholar 

  • Reichmann, G. (2004). Measuring university library efficiency using data envelopment analysis. Libri, 54(2), 136–146.

    Google Scholar 

  • Rizzo, I. (2016). Technological Perspectives for Cultural Heritage. In I. Rizzo & R. Towse (Eds.), The artful economist (pp. 197–214). New York: Springer.

    Google Scholar 

  • Salaün, J. M. (2013). The immeasurable economics of libraries. In I. Rizzo & A. Mignosa (Eds.), Handbook on the economics of cultural heritage (pp. 290–305). Cheltenham: Edward Elgar.

    Google Scholar 

  • Shao, B. B. M., & Lin, W. T. (2002). Technical efficiency analysis of information technology investments: a two-stage empirical investigation. Information & Management, 39, 391–401.

    Google Scholar 

  • Simar, L., & Wilson, P. (2000). Statistical inference in nonparametric frontier Models: The State of the Art. Journal of Productivity Analysis, 13, 49–78.

    Google Scholar 

  • Simar, L., & Wilson, P. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31–64.

    Google Scholar 

  • Simar, L., & Wilson, P. (2008). Statistical inference in nonparametric frontier models: recent developments and perspectives. In H. O. Fried, C. A. Knox Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 421–521). New York: Oxford University Press.

    Google Scholar 

  • Simar, L., & Wilson, P. (2011). Two-stage DEA: Caveat emptor. Journal of Productivity Analysis, 36, 205–218.

    Google Scholar 

  • Simon, J., Simon, C., & Arias, A. (2011). Changes in productivity of Spanish university libraries. Omega, 39, 578–588.

    Google Scholar 

  • Sircar, S., Turnbow, J. L., & Bordoloi, B. (2000). A framework for assessing the relationship between information technology investments and firm performance. Journal of management information systems, 16(4), 69–97.

    Google Scholar 

  • Stroobants, J., & Bouckaert, G. (2014). Benchmarking local public libraries using non-parametric frontier methods: A case study of Flanders. Library & Information Science Research, 36(3), 211–224.

    Google Scholar 

  • Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38, 145–156.

    Google Scholar 

  • Towse, R., & Handke, C. (Eds.). (2013). Handbook on the digital creative economy. Cheltenham: Edward Elgar.

    Google Scholar 

  • Turner, S., Allen, P., Bartlett, W., & Pérotin, V. (2011). Innovation and the English National Health Service: A qualitative study of the independent sector treatment centre programme. Social Science and Medicine, 73(4), 522–529.

    Google Scholar 

  • Vaughan, L., & Wu, G. (2004). Links to commercial websites as a source of business information. Scientometrics, 60(3), 487–496.

    Google Scholar 

  • Vitaliano, D. F. (1998). Assessing public library efficiency using data envelopment analysis. Annals of Public and Cooperative Economics, 69, 107–122.

    Google Scholar 

  • Walker, R. M. (2006). Innovation type and diffusion: An empirical analysis of local government. Public Administration, 84(2), 311–335.

    Google Scholar 

  • Wilson, P. W. (2008). FEAR 1.0: A software package for frontier efficiency analysis with R. Socio-Economic Planning Sciences, 42, 247–254.

    Google Scholar 

  • Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MIT Press.

    Google Scholar 

  • Zhang, Y. J., & Chen, M. Y. (2018). Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function. European Journal of Operational Research, 269(1), 64–78.

    Google Scholar 

  • Zieba, M. (2011). An analysis of technical efficiency and efficiency factors for austrian and swiss non-profit theatres. Swiss Journal of Economics and Statistics, 147(2), 233–274.

    Google Scholar 

Download references

Acknowledgments

We are thankful to the Italian Directorate General of Archives (DGA) and the Public Historical Archive of Catania for the support in the collection of the data. We also wish to thank two anonymous referees for their careful review and the Editor Professor Samuel Cameron for his advice. The usual disclaimer applies.

Funding

No funding was received.

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Authors and Affiliations

  1. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Calogero Guccio, Marco Martorana, Isidoro Mazza & Ilde Rizzo

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  1. Calogero Guccio
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  2. Marco Martorana
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  3. Isidoro Mazza
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  4. Ilde Rizzo
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Correspondence to Calogero Guccio.

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Appendix

Appendix

See Tables 11, 12, 13 and 14.

Table 11 Results from truncated regression with bootstrap.
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Table 12 Results from truncated regression with bootstrap with further controls on heritage magnitude and value.
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Table 13 Results from truncated regression with bootstrap for T2012.
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Table 14 Robust LSDV for T2012.
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Guccio, C., Martorana, M., Mazza, I. et al. Back to the Future: Does the use of information and communication technology enhance the performance of public historical archives?. J Cult Econ 45, 13–43 (2021). https://doi.org/10.1007/s10824-020-09385-1

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Keywords

  • Innovation
  • Public services
  • Cultural heritage
  • Archives
  • Non-parametric frontier

JEL Classification

  • Z1
  • D24