Which Factors Determine the Adoption of the Internet of Things? Impacts and Benefits
-
Miruna Sarbu
Abstract
This paper provides first econometric evidence on the determinants of the Internet of Things among firms and on potential performance impacts. The analysis is based on representative firm-level data from 874 German firms. A probit model and an instrumental variable regression serve as econometric approach. The results reveal that especially collaboration platforms and B2B e-commerce increase the propensity to use the Internet of Things. The results further indicate that product innovation is highest for firms jointly using the Internet of Things and collaboration platforms while a reduction of the workforce is also highest in this case. In contrast, there is no evidence for a potential impact on sales development.
Acknowledgements
I would like to thank Irene Bertschek, Olga Slivko, Philipp Weinschenk and Brian Cooper for helpful comments and suggestions. All errors are my own.
-
Declaration of Interest: I disclose any financial or other substantive conflict of interest. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Distribution of firms across industries.
| Industry | Observations | Percentage |
|---|---|---|
| Consumer goods | 61 | 6.98 |
| Chemical and pharmaceutical industry | 31 | 3.55 |
| Basic materials | 59 | 6.75 |
| Metal industry | 63 | 7.21 |
| Electrical industry | 76 | 8.70 |
| Machinery | 48 | 5.49 |
| Automotive industry | 18 | 2.06 |
| Retail trade | 65 | 7.44 |
| Wholesale trade | 67 | 7.67 |
| Transportation | 52 | 5.95 |
| Content and media | 53 | 6.06 |
| IT and telecommunication | 35 | 4.00 |
| Financial services | 66 | 7.55 |
| Real estate and leasing | 41 | 4.69 |
| Consulting and advertising | 42 | 4.81 |
| Technical services | 64 | 7.32 |
| Enterprise services | 33 | 3.77 |
| Sum | 874 | 100 |
-
Source: ZEW ICT Survey 2014, own calculations.
Distribution of the Internet of Things by industries.
| Industry | Share of firms with the Internet of Things |
|---|---|
| Consumer goods | 0.03 |
| Chemical and pharmaceutical industry | 0.03 |
| Basic materials | 0.05 |
| Metal industry | 0.03 |
| Electrical industry | 0.18 |
| Machinery | 0.25 |
| Automotive industry | 0.22 |
| Retail trade | 0.02 |
| Wholesale trade | 0.00 |
| Transportation | 0.04 |
| Content and media | 0.02 |
| IT and telecommunication | 0.06 |
| Financial services | 0.17 |
| Real estate and leasing | 0.02 |
| Consulting and advertising | 0.05 |
| Technical services | 0.06 |
| Enterprise services | 0.03 |
-
Source: ZEW ICT Survey 2014, own calculations.
Probit estimation: coefficient estimates.
| Dependent variable: dummy for the Internet of Things | |||
|---|---|---|---|
| (1) | (2) | (3) | |
| Constant term |
|
|
|
| Wikis (dummy variable) | 0.291
|
|
0.219
|
| Blogs (dummy variable) |
|
|
|
| Social networks (dummy variable) | 0.115
|
0.014
|
0.058
|
| Collaboration platforms (dummy variable) |
|
|
|
| Employees with PC (share of employees) |
|
|
|
| Employees with Internet (share of employees) |
|
|
|
| Employees with mobile Internet (share of employees) |
|
|
|
| Firms with 50–499 employees (dummy variable) |
|
|
|
| Firms with >500 employees (dummy variable) |
|
|
|
| Highly qualified employees (share of employees) | 0.594
|
0.657
|
|
| Medium-qualified employees (share of employees) |
|
|
|
| Employees
|
|
|
|
| Employees
|
0.035
|
0.290
|
|
| Competitors 0–5 (dummy variable) |
|
|
|
| Competitors >50 (dummy variable) | 0.155
|
0.109
|
|
| IT training (share of employees) | 0.130
|
||
| ERP software (dummy variable) |
|
||
| Big data (dummy variable) | 0.127
|
||
| R&D activity (share of expenditures) |
|
||
| Export activity (dummy variable) | 0.183
|
||
| B2B e-commerce (dummy variable) |
|
||
| B2C e-commerce (dummy variable) |
|
||
| Job rotation (dummy variable) |
|
||
| Self-dependent teams (dummy variable) | 0.137
|
||
| Industry dummies (dummy variables) | No | Yes | Yes |
| Basic materials (dummy variable) |
|
||
| Electrical industry (dummy variable) |
|
||
| Machinery (dummy variable) |
|
||
| Automotive industry (dummy variable) |
|
||
| Observations | 874 | 874 | 874 |
| Log-likelihood at zero | −226.35937 | −221.13618 | −221.13618 |
| Log-likelihood of final model | −209.62023 | −154.27410 | −146.11798 |
|
|
|
|
|
| Pearson
|
|
|
|
-
Significance levels: *
2SLS regression: coefficient estimates – first stage.
| Dependent variable: dummy for big data | ||||
|---|---|---|---|---|
| (1) | ||||
| Constant term |
|
|||
| Wikis (dummy variable) | 0.0133
|
|||
| Blogs (dummy variable) | 0.027
|
|||
| Social networks (dummy variable) |
|
|||
| Collaboration platforms (dummy variable) |
|
|||
| Employees with PC (share of employees) |
|
|||
| Employees with Internet (share of employees) |
|
|||
| Employees with mobile Internet (share of employees) | 0.030
|
|||
| Firms with 50–499 employees (dummy variable) |
|
|||
| Firms with >500 employees (dummy variable) |
|
|||
| Highly qualified employees (share of employees) | 0.018
|
|||
| Medium-qualified employees (share of employees) |
|
|||
| Employees
|
0.001
|
|||
| Employees
|
0.055
|
|||
| Competitors 0–5 (dummy variable) | 0.015
|
|||
| Competitors >50 (dummy variable) |
|
|||
| IT training (share of employees) | 0.043
|
|||
| ERP software (dummy variable) |
|
|||
| R&D activity (share of expenditures) |
|
|||
| Export activity (dummy variable) |
|
|||
| B2B e-commerce (dummy variable) | 0.027
|
|||
| B2C e-commerce (dummy variable) |
|
|||
| Job rotation (dummy variable) |
|
|||
| Self-dependent teams (dummy variable) | 0.022
|
|||
| Industry dummies (dummy variables) | Yes | |||
| Wholesale trade (dummy variable) |
|
|||
| Real estate and leasing (dummy variable) |
|
|||
| Systematical search of the Internet for content on firm’s products and services (dummy variable) |
|
|||
| Observations | 874 | |||
| F-statistic (test for weak instruments) |
|
|||
| Partial
|
0.016 | |||
| Minimum eigenvalue test statistic | 12.99 | |||
| 10% | 15% | 20% | 25% | |
| 2SLS size of nominal 5% Wald test | 16.38 | 8.96 | 6.66 | 5.53 |
| LIML size of nominal 5% Wald test | 16.38 | 8.96 | 6.66 | 5.53 |
| Durbin–Hausman test: robust score
|
||||
| Wu–Hausman test: robust regression
|
||||
-
Significance levels: *:
Table of pairwise correlation: correlation coefficients.
| Variable | Sales development | Product innovation | Employment trend |
|---|---|---|---|
| Sales development | 1.0 | ||
| Product innovation | 0.004 | 1.0 | |
| Employment change | 0.72 | −0.02 | |
| Internet of Things | 0.04 | 0.21 | −0.04 |
| Internet of Things | |||
| And B2B e-commerce | 0.03 | 0.19 | −0.06 |
| Internet of Things | |||
| And collaboration platforms | 0.0003 | 0.15 | −0.08 |
-
Source: ZEW ICT Survey 2014, own calculations.
References
Acemoglu, D. and Restrepo, P. (2018). The race between man and machine: implications of technology for growth, factor shares, and employment. Am. Econ. Rev. 108: 1488–1542, https://doi.org/10.1257/aer.20160696.Suche in Google Scholar
Aghion, P., Bloom, N., Blundell, R., Griffith, R., and Howitt, P. (2005). Competition and innovation: an inverted U-relationship. Q. J. Econ. 120: 701–728, https://doi.org/10.1093/qje/120.2.701.Suche in Google Scholar
Aral, S., Brynjolfsson, E., and Wu, D.J. (2006). Which came first, IT or productivity? The virtuous cycle of investment and use in enterprise systems. Proceedings of the 27th Conference on Information Systems, Milwaukee, https://doi.org/10.2139/ssrn.942291.Suche in Google Scholar
Arrow, K.J., Bilir, L.K., and Sorensen, A. (2020). The impact of information technology on the diffusion of new pharmaceuticals. Am. Econ. J. Appl. Econ. forthcoming.10.3386/w23257Suche in Google Scholar
Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: a survey. Comput. Network. 54: 2787–2805, https://doi.org/10.1016/j.comnet.2010.05.010.Suche in Google Scholar
Audresch, D. and Feldman, M. (1996). R&D spillovers and the geography of innovation and production. Am. Econ. Rev. 86: 630–640.Suche in Google Scholar
Baldwin, J.R. and Gu, W. (2004). Trade liberalization: export–market participation, productivity growth, and innovation. Oxf. Rev. Econ. Pol. 20: 372–392, https://doi.org/10.2139/ssrn.1375644.Suche in Google Scholar
Baptista, R. (2000). Do innovations diffuse faster within georgraphical clusters? Int. J. Ind. Organ. 18: 515–535, https://doi.org/10.1016/S0167-7187(99)00045-4.Suche in Google Scholar
Bertschek, I. (1995). Product and process innovation as a response to increasing imports and foreign direct investment. J. Ind. Econ. 43: 341–357, https://doi.org/10.2307/2950548.Suche in Google Scholar
Bertschek, I. and Fryges, H. (2002). The adoption of business-to-business E-commerce: empirical evidence for German companies. ZEW Discussion Paper No. 02-05.10.2139/ssrn.327381Suche in Google Scholar
Bertschek, I. and Kaiser, U. (2004). Productivity effects of organizational change: microeconometric evidence. Manag. Sci. 50: 394–404, https://doi.org/10.1287/mnsc.1030.0195.Suche in Google Scholar
Bertschek, I. and Meyer, J. (2010). IT is never too late for changes? Analysing the relationship between process innovation, IT and older workers. ZEW Discussion Paper No. 10-053. Mannheim.10.2139/ssrn.1663662Suche in Google Scholar
Bertschek, I., Ohnemus, J., and Viete, S. (2017). The ZEW ICT survey 2002 to 2015: measuring the digital transformation in German firms. ZEW-Dokumentation No. 17-01. Mannheim.10.1515/jbnst-2016-1005Suche in Google Scholar
Börsch-Supan, A., Düzgün, I., and Weiss, M. (2005). Altern und Produktivität: Zum Stand der Forschung. Discussion Paper 73-05. MEA.Suche in Google Scholar
Brandt, M. (2016). Deutsches Web zu langsam für Weltspitze. Statista. last visited July 2020. http://de.statista.com/infografik/1064/top-10-laender-mit-dem-schnellsten-internetzugang.Suche in Google Scholar
Brettel, M., Friederichsen, N., Keller, M., and Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int. J. Mech. Aerospace Indus. Mechatron. Manuf. Eng. 8: 37–44, https://doi.org/10.5281/zenodo.1336426.Suche in Google Scholar
Bresnahan, T., Brynjolfsson, E., and Hitt, L.M. (2002). Information technology, workplace organization and the demand for skilled labor: firm-level evidence. Q. J. Econ. 117: 339–376, https://doi.org/10.1162/003355302753399526.Suche in Google Scholar
Brynjolfsson, E. and Hitt, L.M. (1995). Information technology as a factor of production: the role of differences among firms. Econ. Innovat. N. Technol. 3: 183–200, https://doi.org/10.1080/10438599500000002.Suche in Google Scholar
Brynjolfsson, E. and Hitt, L.M. (2000). Beyond computation: information technology, organizational transformation and business performance. J. Econ. Perspect. 14: 23–48, https://doi.org/10.1257/jep.14.4.23.Suche in Google Scholar
Brynjolfsson, E. and Saunders, A. (2010). Wired for innovation: how IT is reshaping the economy. Cambridge, MA: The MIT Press, https://doi.org/10.5860/choice.47-3916.Suche in Google Scholar
Brynjolfsson, E. and McAfee, A. (2014). The second machine age: work, progress, and prosperity in a time of brilliant technologies. New York: W. W. Norton, https://doi.org/10.1080/15228053.2014.943094.Suche in Google Scholar
Cameron, A.C. and Trivedi, P.K. (2010). Microeconometrics using STATA. STATA Press.Suche in Google Scholar
Crépon, B., Duguet, E., and Mairesse, J. (1998). Research, innovation and productivity: an econometric analysis at the firm level. Econ. Innovat. N. Technol. 7: 115–158, https://doi.org/10.1080/10438599800000031.Suche in Google Scholar
Dombrowski, U. and Wagner, T. (2014). Mental strain as field of action in the 4th industrial revolution. Proc. CIRP 17: 100–105, https://doi.org/10.1016/j.procir.2014.01.077.Suche in Google Scholar
Dosi, G. and Mohnen, P. (2018). Innovation and employment: an introduction. Ind. Corp. Change 28: 45–49, https://doi.org/10.1093/icc/dty064.Suche in Google Scholar
Engelstätter, B. (2012a). It’s not all about performance gains–enterprise software and innovations. Econ. Innovat. N. Technol. 21: 223–245, https://doi.org/10.1080/10438599.2011.562359.Suche in Google Scholar
Engelstätter, B. and Sarbu, M. (2013). Why adopt social enterprise software? Impacts and benefits. Inf. Econ. Pol. 25: 204–213, https://doi.org/10.1016/j.infoecopol.2012.12.001.Suche in Google Scholar
Espinoza, H., Kling, G., McGroarty, F., O’Mahony, M., and Ziouvelou, X. (2020). Estimating the impact of the internet of things on productivity in Europe. Heliyon 6: 1–7, https://doi.org/10.1016/j.heliyon.2020.e03935.Suche in Google Scholar
Faqihi, R., Ramakrishnan, J., and Mavaluru, D. (2020). An evolutionary study on the threats, trust, security, and challanges in SIoT (social internet of things). Mater. Today Proc., forthcoming, https://doi.org/10.1016/j.matpr.2020.09.618.Suche in Google Scholar
Fallenbeck, N. and Eckert, C. (2014). IT-Sicherheit und cloud computing. Springer Vieweg, pp. 397–431.10.1007/978-3-658-04682-8_20Suche in Google Scholar
Gaggl, P. and Wright, G.C. (2017). A short-run view of what computers do: evidence from a UK tax incentive. Am. Econ. J. Appl. Econ. 9: 262–294, https://doi.org/10.1257/app.20150411.Suche in Google Scholar
Gebhardt, J., Grimm, A., and Neugebauer, L.M. (2015). Entwicklungen 4.0–Ausblicke auf zukünftige Anforderungen an und Auswirkungen auf Arbeit und Ausbildung. J. Tech. Educ. 3: 45–61.Suche in Google Scholar
Gera, S. and Gu, W. (2004). The effect of organizational innovation and information and communications technologies on firm performance. Int. Prod. Mon. 9: 37–51, https://doi.org/10.2139/ssrn.1404689.Suche in Google Scholar
Gilbert, R. (2006). Looking for Mr. Schumpeter: where are we in the competition-innovation debate? In: Jaffe, A.D., Lerner, J., and Stern, S. (Eds.), NBER book series: innovation policy and the economy, Vol. 6. Massachusetts, MIT Press, pp. 159–215.10.1086/ipe.6.25056183Suche in Google Scholar
Gunday, G., Ulusoy, G., Kilic, K., and Alpkan, L. (2011). Effects of innovation types on firm performance. Int. J. Prod. Econ. 133: 662–676, https://doi.org/10.1016/j.ijpe.2011.05.014.Suche in Google Scholar
Haaker, T., Ly, P.T.M., Nguyen-Thanh, N., and Nguyen, H.T.H. (2021). Business model innovation through the application of the internet of things: a comparative analysis. J. Bus. Res. 126: 126–136, https://doi.org/10.1016/j.jbusres.2020.12.034.Suche in Google Scholar
Hempell, T. and Zwick, T. (2008). New technology, work organisation and innovation. Econ. Innovat. N. Technol. 17: 331–354, https://doi.org/10.1080/10438590701279649.Suche in Google Scholar
Hermann, M., Pentek, T., and Otto, B. (2016). Design principles for industrie 4.0 scenarios. 2016 49th Hawaii International Conference on System Sciences (HICSS), https://doi.org/10.1109/HICSS.2016.488.Suche in Google Scholar
ITU (2012). New ITU standards define the internet of things and provide the blueprints for its development. last visited July 2020 http://www.itu.int/ITU-T/newslog/New+ITU+Standards+Define+The+Internet+Of+Things+And+Provide+The+Blueprints+For+Its+Development.aspx.Suche in Google Scholar
Jensen, M.B., Johnson, B., Lorenz, E., and Lundvall, B.A. (2007). Forms of knowledge and modes of innovation. Res. Pol. 36: 680–693, https://doi.org/10.1016/j.respol.2007.01.006.Suche in Google Scholar
Kagermann, H., Wahlster, W., and Helbig, J. (2013). Umsetzungsempfehlung für das Zukunftsprojekt Industrie 4.0. Abschlussbericht des Arbeitskreises Industrie 4.0, Bundesministerium für Bildung und Forschung, last visited July 2020 http://www.bmbf.de/files/Umsetzungsempfehlungen_Industrie4_0.pdf.Suche in Google Scholar
Kretschmer, T. (2012). Information and communication technologies and productivity growth: a survey of the literature, OECD digital economy papers 195. OECD Publishing, https://doi.org/10.1787/5k9bh3jllgs7-en.Suche in Google Scholar
Kumar, R., Rajalakshmy, P., Saranya, M.D., Kirubakaran, S., Elwin, J.G.R., and Marichamy, S. (2020). Process automation through internet of things on copper coating process of stainless steel. Mater. Today Proc., forthcoming.Suche in Google Scholar
Lachmaier, S. and Rottmann, H. (2007). Employment effects of innovation at the firm level. J. Econ. Stat. 227: 254–272, https://doi.org/10.1515/jbnst-2007-0304.Suche in Google Scholar
Lee, J., Bagheri, B., and Kao, H.A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3: 18–23, https://doi.org/10.1016/j.mfglet.2014.12.001.Suche in Google Scholar
Leiponen, A. (2005). Organisation of knowledge and innovation: the case of Finnish business services. Ind. Innovat. 12: 185–203, https://doi.org/10.1080/13662710500087925.Suche in Google Scholar
Milgrom, P. and Roberts, J. (1990). The economics of modern manufacturing: technology, strategy, and organization. Am. Econ. Rev. 80: 511–528.Suche in Google Scholar
Peters, B. (2004), Employment effects of different innovation activities: microeconometric evidence, ZEW Discussion Paper No. 04-73, Mannheim.10.2139/ssrn.604481Suche in Google Scholar
Polder, M., Leeuwen, G.v., Mohnen, P., and Raymond, W. (2010). Product, process and organizational innovation: drivers, complementarity and productivity effects. MERIT Working Papers 035.10.2139/ssrn.1626805Suche in Google Scholar
Rey, A., Panetti, E., Maglio, R., and Ferretti, M. (2021). Determinants in adopting the internet of things in the transport and logistics industry. J. Bus. Res., forthcoming, https://doi.org/10.1016/j.jbusres.2020.12.049.Suche in Google Scholar
Rousseau, M.B., Mathias, B.D., Madden, L.T., and Crook, T.R. (2016). Innovation, firm performance, and appropriation: a meta-analysis. Int. J. Innovat. Manag. 20, https://doi.org/10.1142/S136391961650033X.Suche in Google Scholar
Schlechtendahl, J. and Keinert, M. (2015). Making existing production systems industry 4.0-ready. Prod. Eng. Res. Dev. 9: 143–148, https://doi.org/10.1007/s11740-014-0586-3.Suche in Google Scholar
Schuh, G., Potente, T., Wesch-Potente, C., Weber, A.R., and Prote, J.P. (2014). Collaboration mechanisms to increase productivity in the context of industry 4.0. Proc. CIRP 19: 51–56.10.1016/j.procir.2014.05.016Suche in Google Scholar
Sestino, A., Prete, M.I., Piper, L., and Guido, G. (2020). Internet of things and big data as enablers for business digitalization strategies. Technovation, forthcoming, https://doi.org/10.1016/j.technovation.2020.102173.Suche in Google Scholar
Spath, D., Ganschar, O., Gerlach, S., Hämmerle, M., Krause, T., and Schlund, S. (2013). Produktionsarbeit der Zukunft – industrie 4.0. Stuttgart: Fraunhofer Verlag.Suche in Google Scholar
Statista Research Department (2019). Prognose zur Anzahl der vernetzten Geräte im Internet der Dinge (IoT) weltweit in den Jahren 2016 bis 2020, https://de.statista.com/statistik/daten/studie/537093/umfrage/anzahl-der-vernetzten-geraete-im-internet-der-dinge-iot-weltweit/ (Accessed 21 January 2021).Suche in Google Scholar
Statista Research Department (2020). Ausgaben für das Internet der Dinge (IoT) weltweit im Jahr 2019 und Prognose für 2020 (in Milliarden US-Dollar), https://de.statista.com/statistik/daten/studie/537226/umfrage/prognose-zu-den-ausgaben-fuer-das-internet-der-dinge/ (Accessed 21 January 2021).Suche in Google Scholar
Stock, T. and Selinger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Proc. CIRP 40: 536–541.10.1016/j.procir.2016.01.129Suche in Google Scholar
Thether, B.S. (2005). Do services innovate (differently)? Insights from the European innobarometer survey. Ind. Innovat. 12: 153–184, https://doi.org/10.1080/13662710500087891.Suche in Google Scholar
Veuve, A. and Simon, J. (2012). E-commerce. Erfolg im Online–Vertriebskanal. Best practice xperts. BPX-Edition.Suche in Google Scholar
Wang, S., Wan, J., Zhang, D., Li, D., and Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Network. 101: 158–168, https://doi.org/10.1016/j.comnet.2015.12.017.Suche in Google Scholar
Wooldridge, J.M. (2010). Econometric analysis of cross section and panel data, 2nd ed. The MIT Press.Suche in Google Scholar
Wortmann, F. and Flüchter, K. (2015). Internet of things-technology and value added. Bus. Inf. Syst. Eng. 57: 221–224, https://doi.org/10.1007/s12599-015-0383-3.Suche in Google Scholar
Wozniak, G.D. (1987). Human capital, information, and the early adoption of new technology. J. Hum. Resour. 22: 101–112, https://doi.org/10.2307/145869.Suche in Google Scholar
Yang, Y., Wu, L., Yin, G., Li, L., and Zhao, H. (2017). A survey on security and privacy issues in internet-of-things. IEEE Internet Things J. 4: 1250–1258, https://doi.org/10.1109/JIOT.2017.2694844.Suche in Google Scholar
Yang, H., Kumara, S., Bukkapatnam, S.T.S., and Tsung, F. (2019). The internet of things for smart manufacturing: a review. IISE Trans. 51: 1190–1216, https://doi.org/10.1080/24725854.2018.1555383.Suche in Google Scholar
ZEW–Centre for European Economic Research (2015). IKT-report, last visited August 2020 http://ftp.zew.de/pub/zew-docs/div/IKTRep/IKT_Report_2015.pdf.Suche in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Original Articles
- Human Capital and Economic Growth in OECD Countries Revisited: Initial Stock versus Changes in the Stock of Human Capital Effects
- The Macroeconomic Determinants of House Prices and Rents
- The Exposure Geography of Italian Local Economies to Major Foreign Ones. Evidences from a Multiscale Spatial Experiment Based on Granularity
- Which Factors Determine the Adoption of the Internet of Things? Impacts and Benefits
- Under Debate
- What Determines COVID-19 Vaccination Rates in Germany?
- Data Observer
- IOER Monitor: A Spatio-Temporal Research Data Infrastructure on Settlement and Open Space Development in Germany
Artikel in diesem Heft
- Frontmatter
- Original Articles
- Human Capital and Economic Growth in OECD Countries Revisited: Initial Stock versus Changes in the Stock of Human Capital Effects
- The Macroeconomic Determinants of House Prices and Rents
- The Exposure Geography of Italian Local Economies to Major Foreign Ones. Evidences from a Multiscale Spatial Experiment Based on Granularity
- Which Factors Determine the Adoption of the Internet of Things? Impacts and Benefits
- Under Debate
- What Determines COVID-19 Vaccination Rates in Germany?
- Data Observer
- IOER Monitor: A Spatio-Temporal Research Data Infrastructure on Settlement and Open Space Development in Germany
