Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects

Abstract

New developments in the fields of artificial intelligence or robotics are receiving considerable attention from businesses, as they promise astonishing gains in process efficiency—sparking a surge of corporate investments in new, digital technologies. Yet, firms did not become per se more productive, as labor productivity growth in various industrial nations has decelerated in recent years. The fact that the adoption of innovative technologies is not accompanied by productivity increases has already been observed during the dawn of the computer age and became known as Solow’s Paradox. Thus, this paper takes stock of what is known about the Solow Paradox, before incorporating the findings into the debate of the current productivity slowdown. Based on an in-depth review of 86 empirical studies at the firm level, this paper uncovers various reasons for the emergence of the Solow Paradox, debates its following reversal marked by the occurrence of excess returns and deduces a model of factors influencing the returns on IT investments. Based on these insights, four overarching explanations of the modern productivity paradox namely adjustment delays, measurement issues, exaggerated expectations and mismanagement are discussed, whereby mismanagement emerges as a currently neglected, but focal issue.

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Notes

  1. 1.

    For an excellent discussion of parametric and non-parametric approaches, see Cardona et al. (2013).

  2. 2.

    Because of this broad definition, the terms IT and information and communication technology (ICT) are used interchangeably within this work.

  3. 3.

    The search process was last updated on 20 February 2019.

  4. 4.

    Rational managers should invest in an input factor until an additional unit of the input creates no more value than its costs, resulting in a net marginal product of zero. Therefore, if the marginal returns outweigh the marginal costs, the difference is referred to as an excess marginal product or excess return (see Lehr and Lichtenberg 1999).

  5. 5.

    Weil (2007) further disaggregated IT-Investments into infrastructural, informational, transactional and strategic ones.

  6. 6.

    These categories are quite similar to the ones proposed by Brynjolfsson (1993), but contain different sub-categories.

  7. 7.

    Meaning a “faster automation than socially desirable” (Acemoglu and Restrepo 2019, p. 210).

References

References marked with an asterisk indicate papers included in the literature review

  1. Abernathy WJ (1978) The productivity dilemma: Roadlock to innovation in the automobile industry. Johns Hopkins University Press, Baltimore

    Google Scholar 

  2. *Aboal D, Tacsir E (2018) Innovation and productivity in services and manufacturing: the role of ICT. Ind Corp Change 27:221–241

    Google Scholar 

  3. Abrahamson E (1991) Managerial fads and fashions: the diffusion and rejection of innovations. Acad Manag Rev 16:586–612

    Google Scholar 

  4. Abrahamson E (1996) Management fashion. Acad Manag Rev 21:254–285

    Google Scholar 

  5. Acemoglu D, Restrepo P (2019) Artificial intelligence, automation and work. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 197–236

    Google Scholar 

  6. Acemoglu D, Restrepo P (2018) The race between man and machine: implications of technology for growth, factor shares, and employment. Am Econ Rev 108:1488–1542

    Google Scholar 

  7. Acemoglu D, Dorn D, Hanson GH, Price B (2014) Return of the Solow paradox? IT, productivity, and employment in US manufacturing. Am Econ Rev 104:394–399

    Google Scholar 

  8. Ackoff RL (1967) Management misinformation systems. Manag Sci 14:B-147–B-156

    Google Scholar 

  9. Aghion P, Jones BF, Jones CI (2019) Artificial intelligence and economic growth. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 237–282

    Google Scholar 

  10. Agarwal R, Audretsch D, Sarkar MB (2010) Knowledge spillovers and strategic entrepreneurship. Strateg Entrep J 4:271–283

    Google Scholar 

  11. Agrawal AK, McHale J, Oettl A (2019a) Finding needles in haystacks: artificial intelligence and recombinant growth. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 149–174

    Google Scholar 

  12. Agrawal AK, Gans JS, Goldfarb A (2019b) Exploring the impact of artificial intelligence: prediction versus judgment. Econ Policy 47:1–6

    Google Scholar 

  13. AIS (2011) Senior scholars’ basket of journals. https://aisnet.org/page/SeniorScholarBasket. Accessed 14 Jan 2018

  14. Alpar P, Kim M (1990) A microeconomic approach to the measurement of information technology value. J Manag Inf Syst 7:55–69

    Google Scholar 

  15. Amit R, Zott C (2010) Business model innovation: creating value in times of change. Working paper

  16. Anderson MC, Banker RD, Ravindran S (2003) The new productivity paradox. Commun ACM 46:91–94

    Google Scholar 

  17. Andrews D, Criscuolo C, Gal PN (2016) The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy. OECD working paper

  18. Aral S, Weill P (2007) IT assets, organizational capabilities, and firm performance: how resource allocations and organizational differences explain performance variation. Organ Sci 18:763–780

    Google Scholar 

  19. Arrow KJ (1962) The economic implications of learning by doing. Rev Econ Stud 29:155–173

    Google Scholar 

  20. Arvanitis S, Loukis EN (2009) Information and communication technologies, human capital, workplace organization and labour productivity: a comparative study based on firm-level data for Greece and Switzerland. Inf Econ Policy 21:43–61

    Google Scholar 

  21. Autor DH, Dorn D, Katz LF, Patterson C, van Reenen J (2017) The fall of the labor share and the rise of superstar firms. NBER Working paper no. w23396

  22. Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: an empirical exploration. Q J Econ 118:1279–1333

    Google Scholar 

  23. *Badescu M, Garcés-Ayerbe C (2009) The impact of information technologies on firm productivity: empirical evidence from Spain. Technovation 29:122–129

    Google Scholar 

  24. Baily MN, Gordon RJ, Nordhaus WD, Romer D (1988) The productivity slowdown, measurement issues, and the explosion of computer power. Brookings Pap Econ Act 1988:347–431

    Google Scholar 

  25. *Baker J, Song J, Jones DR (2017) Closing the loop: empirical evidence for a positive feedback model of IT business value creation. J Strateg Inf Syst 26:142–160

    Google Scholar 

  26. *Baker J, Song J, Jones DR, Ford EW (2008) Information systems and healthcare XXIX: information technology investments and returns—uniqueness in the healthcare industry. Commun Assoc Inf Syst 23:375–392

    Google Scholar 

  27. Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17:99–120

    Google Scholar 

  28. *Barua A, Lee B (1997) An economic analysis of the introduction of an electronic data interchange system. Inf Syst Res 8:398–422

    Google Scholar 

  29. Barua A, Kriebel CH, Mukhopadhyay T (1995) Information technologies and business value: an analytic and empirical investigation. Inf Syst Res 6:3–23

    Google Scholar 

  30. Bauer J, Jannach D (2018) Optimal pricing in e-commerce based on sparse and noisy data. Decis Support Syst 106:53–63

    Google Scholar 

  31. *Becchetti L, Dal Bedoya, Paganetto L (2003) ICT Investment, productivity and efficiency: evidence at firm-level using a stochastic frontier approach. J Prod Anal 20:143–167

    Google Scholar 

  32. Berman SJ (2012) Digital transformation: opportunities to create new business models. Strategy Leadersh 40:16–24

    Google Scholar 

  33. *Bertschek I, Kaiser U (2004) Productivity effects of organizational change: microeconometric evidence. Manag Sci 50:394–404

    Google Scholar 

  34. Bharadwaj AS (2000) A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Q 24:169–196

    Google Scholar 

  35. Bharadwaj AS, Bharadwaj SG, Konsynski BR (1999) Information technology effects on firm performance as measured by Tobin’s q. Manag Sci 45:1008–1024

    Google Scholar 

  36. Black SE, Lynch LM (2001) How to compete: the impact of workplace practices and information technology on productivity. Rev Econ Stat 83:434–445

    Google Scholar 

  37. *Bloom N, Sadun R, van Reenen J (2012) Americans do IT better: US multinationals and the productivity miracle. Am Econ Rev 102:167–201

    Google Scholar 

  38. Bloom N, Jones CI, van Reenen J, Webb M (2017) Are ideas getting harder to find? NBER working paper no. 23782

  39. BLS (2019) Nonfarm business—labor productivity (output per hour), percent change from previous quarter—PRS85006092 (including annual average). https://www.bls.gov/lpc/#data. Accessed 30 Apr 2019

  40. Bresnahan TF, Trajtenberg M (1995) General purpose technologies ‘Engines of growth’? J Econom 65:83–108

    Google Scholar 

  41. *Bresnahan TF, Brynjolfsson E, Hitt LM (2002) Information technology, workplace organization, and the demand for skilled labor: firm-level evidence. Q J Econ 117:339–376

    Google Scholar 

  42. Broadbent M, Weill P, Neo BS (1999) Strategic context and patterns of IT infrastructure capability. J Strateg Inf Syst 8:157–187

    Google Scholar 

  43. Brynjolfsson E (1993) The productivity paradox of information technology. Commun ACM 36:66–77

    Google Scholar 

  44. Brynjolfsson E (2003) ROI valuation: the IT productivity gap. Optim Mag 21:1–4

    Google Scholar 

  45. *Brynjolfsson E, Hitt LM (1995) Information technology as a factor of production: the role of differences among firms. Econ Innovat New Tech 3:183–200

    Google Scholar 

  46. *Brynjolfsson E, Hitt LM (1996) Paradox lost? Firm-level evidence on the returns to information systems spending. Manag Sci 42:541–558

    Google Scholar 

  47. Brynjolfsson E, Hitt LM (1998) Beyond the productivity paradox. Commun ACM 41:49–55

    Google Scholar 

  48. Brynjolfsson E, Hitt LM (2000) Beyond computation: information technology, organizational transformation and business performance. J Econ Perspect 14:23–48

    Google Scholar 

  49. *Brynjolfsson E, Hitt LM (2003) Computing productivity: firm-level evidence. Rev Econ Stat 85:793–808

    Google Scholar 

  50. Brynjolfsson E, McAfee A (2012) Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Digital Frontier Press, Lexington

    Google Scholar 

  51. Brynjolfsson E, Mendelson H (1993) Information systems and the organization of modern enterprise. J Organ Comput Electron Commer 3:245–255

    Google Scholar 

  52. Brynjolfsson E, Saunders A (2009) Wired for innovation: how information technology is reshaping the economy. MIT Press, Cambridge

    Google Scholar 

  53. Brynjolfsson E, Yang S (1996) Information technology and productivity: a review of the literature. In: Zelkowitz M (ed) Advances in computers, vol 43. Academic Press, Cambridge, pp 179–214

    Google Scholar 

  54. Brynjolfsson E, Hitt LM, Yang S (2002) Intangible assets: computers and organizational capital. Brookings Pap Econ Act 2002:137–181

    Google Scholar 

  55. Brynjolfsson E, Collis A, Eggers F (2019a) Using massive online choice experiments to measure changes in well-being. Proc Natl Acad Sci USA 116:7250–7255

    Google Scholar 

  56. Brynjolfsson E, Rock D, Syverson C (2019b) Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 23–57

    Google Scholar 

  57. Brynjolfsson E, Rock D, Syverson C (2018) The productivity J-curve: how intangibles complement general purpose technologies. NBER working paper no. 25148

  58. *Byrd TA, Marshall TE (1997) Relating information technology investment to organizational performance: a causal model analysis. Omega-Int J Manag S 25:43–56

    Google Scholar 

  59. Byrne DM, Fernald JG, Reinsdorf MB (2016) Does the United States have a productivity slowdown or a measurement problem? Brookings Pap Econ Act 2016:109–182

    Google Scholar 

  60. Cardona M, Kretschmer T, Strobel T (2013) ICT and productivity: conclusions from the empirical literature. Inf Econ Policy 25:109–125

    Google Scholar 

  61. Carr NG (2003) IT does not matter. Educause Rev 38:24–38

    Google Scholar 

  62. *Castiglione C, Infante D (2014) ICTs and time-span in technical efficiency gains. A stochastic frontier approach over a panel of Italian manufacturing firms. Econ Model 41:55–65

    Google Scholar 

  63. Chan YE, Reich BH (2007) IT alignment: what have we learned? J Inf Technol 22:297–315

    Google Scholar 

  64. *Chang YB, Gurbaxani V (2012) The Impact of IT-related spillovers on long-run productivity: an empirical analysis. Inf Syst Res 23:868–886

    Google Scholar 

  65. Chang YB, Gurbaxani V (2013) An empirical analysis of technical efficiency: the role of IT intensity and competition. Inf Syst Res 24:561–578

    Google Scholar 

  66. Chesbrough H (2010) Business model innovation: opportunities and barriers. Long Range Plann 43:354–363

    Google Scholar 

  67. *Chowdhury SK (2006) Investments in ICT-capital and economic performance of small and medium scale enterprises in East Africa. J Int Dev 18:533–552

    Google Scholar 

  68. *Chwelos P, Ramirez R, Kraemer KL, Melville NP (2010) Research note—does technological progress alter the nature of information technology as a production input? New evidence and new results. Inf Syst Res 21:392–408

    Google Scholar 

  69. Clemons EK, Row MC (1991) Sustaining IT advantage: the role of structural differences. MIS Q 15:275–292

    Google Scholar 

  70. Cockburn IM, Henderson R, Stern S (2019) The impact of artificial intelligence on innovation. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 115–146

    Google Scholar 

  71. *Commander S, Harrison R, Menezes-Filho N (2011) ICT and productivity in developing countries: new firm-level evidence from Brazil and India. Rev Econ Stat 93:528–541

    Google Scholar 

  72. Corrado C, Hulten C, Sichel D (2009) Intangible capital and US economic growth. Rev Income Wealth 55:661–685

    Google Scholar 

  73. Crafts N (2004) Steam as a general purpose technology: a growth accounting perspective. Econ J 114:338–351

    Google Scholar 

  74. Crafts N (2018) The productivity slowdown: is it the ‘new normal’? Oxf Rev Econ Policy 34:443–460

    Google Scholar 

  75. *Dasgupta S, Sarkis J, Talluri S (1999) Influence of information technology investment on firm productivity: a cross-sectional study. Logist Inf Manag 12:120–129

    Google Scholar 

  76. Davenport TH (1993) Process innovation: reengineering work through information technology. Harvard Business School Press, Boston

    Google Scholar 

  77. Davenport TH, Ronanki R (2018) Artificial intelligence for the real world. Harv Bus Rev 96:108–116

    Google Scholar 

  78. David PA (1990) The Dynamo and the computer: an historical perspective on the modern productivity paradox. Am Econ Rev 80:355–361

    Google Scholar 

  79. Dedrick J, Gurbaxani V, Kraemer KL (2003) Information technology and economic performance: a critical review of the empirical evidence. ACM Comput Surv 35:1–28

    Google Scholar 

  80. Dehning B, Richardson VJ (2002) Returns on investments in information technology: a research synthesis. J Inf Syst 16:7–30

    Google Scholar 

  81. Denison EF (1989) Estimates of productivity change by industry: an evaluation and an alternative. Brookings Institution Press, Washington, D.C.

    Google Scholar 

  82. *Devaraj S, Kohli R (2000) Information technology payoff in the health-care industry: a longitudinal study. J Manag Inf Syst 16:41–67

    Google Scholar 

  83. Devaraj S, Kohli R (2003) Performance impacts of information technology: is actual usage the missing link? Manag Sci 49:273–289

    Google Scholar 

  84. Dewan S, Kraemer KL (2000) Information technology and productivity: evidence from country-level data. Manag Sci 46:548–562

    Google Scholar 

  85. *Dewan S, Min C-k (1997) The substitution of information technology for other factors of production: a firm level analysis. Manag Sci 43:1660–1675

    Google Scholar 

  86. *Dewan S, Shi C, Gurbaxani V (2007) Investigating the risk–return relationship of information technology investment: firm-level empirical analysis. Manag Sci 53:1829–1842

    Google Scholar 

  87. Diewert WE, Fox KJ (1999) Can measurement error explain the productivity paradox? Can J Econ 32:251–280

    Google Scholar 

  88. Diewert WE, Fox KJ, Schreyer P (2018) The digital economy, new products and consumer welfare. Working paper

  89. *Doms ME, Jarmin RS, Klimek SD (2004) Information technology investment and firm performance in US retail trade. Econ Innovat New Tech 13:595–613

    Google Scholar 

  90. Dos Santos B, Sussman L (2000) Improving the return on IT investment: the productivity paradox. Int J Inf Manag 20:429–440

    Google Scholar 

  91. *Dunne T, Foster L, Haltiwanger J, Troske KR (2004) Wage and productivity dispersion in United States manufacturing: the role of computer investment. J Labor Econ 22:397–429

    Google Scholar 

  92. Eppler MJ, Mengis J (2004) The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inform Soc 20:325–344

    Google Scholar 

  93. Feldstein M (2015) The US underestimates growth. Wall Str J 18:2015

    Google Scholar 

  94. Fisch C, Block J (2018) Six tips for your (systematic) literature review in business and management research. Manag Rev Q 68:103–106

    Google Scholar 

  95. *Francalanci C, Galal H (1998a) Aligning IT investments and workforce composition: the impact of diversification in life insurance companies. Eur J Inf Syst 7:175–184

    Google Scholar 

  96. *Francalanci C, Galal H (1998b) Information technology and worker composition: determinants of productivity in the life insurance industry insurance companies. MIS Q 22:227–241

    Google Scholar 

  97. Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Change 114:254–280

    Google Scholar 

  98. Gal P, Nicoletti G, Renault T, Sorbe S, Timiliotis C (2019) Digitalisation and productivity: In search of the holy grail—firm-level empirical evidence from EU countries. OECD working paper

  99. *Gargallo-Castel A, Galve-Górriz C (2007) Information technology, complementarities and three measures of organizational performance: empirical evidence from Spain. J Inf Technol Impact 7:43–58

    Google Scholar 

  100. Gartner (2019) Gartner says global IT spending to reach $3.8 trillion in 2019. https://www.gartner.com/en/newsroom/press-releases/2019-01-28-gartner-says-global-it-spending-to-reach–3-8-trillio. Accessed 05 Mar 2019

  101. Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116

    Google Scholar 

  102. *Giuri P, Torrisi S, Zinovyeva N (2008) ICT, skills, and organizational change: evidence from Italian manufacturing firms. Ind Corp Change 17:29–64

    Google Scholar 

  103. Gordon RJ (2000) Does the “new economy” measure up to the great inventions of the past? J Econ Perspect 14:49–74

    Google Scholar 

  104. Gordon RJ (2004) Five puzzles in the behavior of productivity, investment, and innovation. NBER Working paper no. 10660

  105. Gordon RJ (2016) The rise and fall of American growth: The US standard of living since the civil war. Princeton University Press, Princeton

    Google Scholar 

  106. Greenana N, Mairesse J (2000) Computers and productivity in France: some evidence. Econ Innovat New Tech 9:275–315

    Google Scholar 

  107. Gurbaxani V, Whang S (1991) The impact of information systems on organizations and markets. Commun ACM 34:59–73

    Google Scholar 

  108. *Hall BH, Lotti F, Mairesse J (2013) Evidence on the impact of R&D and ICT investments on innovation and productivity in Italian firms. Econ Innovat New Tech 22:300–328

    Google Scholar 

  109. Hammer M (1990) Reengineering work: do not automate, obliterate. Harv Bus Rev 68:104–112

    Google Scholar 

  110. Hartmann PM, Zaki M, Feldmann N, Neely A (2016) Capturing value from big data–a taxonomy of data-driven business models used by start-up firms. Int J Oper Prod Manag 36:1382–1406

    Google Scholar 

  111. *Hempell T (2005a) Does experience matter? Innovations and the productivity of information and communication technologies in German services. Econ Innovat New Tech 14:277–303

    Google Scholar 

  112. *Hempell T (2005b) What’s spurious, what’s real? Measuring the productivity impacts of ICT at the firm-level. Empir Econ 30:427–464

    Google Scholar 

  113. Henderson JC, Venkatraman N (1993) Strategic alignment: leveraging information technology for transforming organizations. IBM Syst J 32:4–16

    Google Scholar 

  114. *Hitt LM, Brynjolfsson E (1996) Productivity, business profitability, and consumer surplus: three different measures of information technology value. MIS Q 20:121–142

    Google Scholar 

  115. *Hitt LM, Wu DJ, Zhou X (2002) Investment in enterprise resource planning: business impact and productivity measures. J Manag Inf Syst 19:71–98

    Google Scholar 

  116. Ho JL, Wu A, Xu SX (2011) Corporate governance and returns on information technology investment: evidence from an emerging market. Strat Manag J 32:595–623

    Google Scholar 

  117. *Hu Q, Plant R (2001) An empirical study of the casual relationship between IT investment and firm performance. Inf Resour Manag J 14:15–26

    Google Scholar 

  118. *Huang T-H (2005) A Study on the productivities of IT capital and computer labor: firm-level evidence from Taiwan’s banking industry. J Prod Anal 24:241–257

    Google Scholar 

  119. *Huang CJ, Fu T-T, Lai H-P, Yang Y-L (2017) Semiparametric smooth coefficient quantile estimation of the production profile. Empir Econ 52:373–392

    Google Scholar 

  120. IDC (2019) Worldwide blockchain spending forecast to reach $2.9 billion in 2019, according to new IDC spending guide. https://www.idc.com/getdoc.jsp?containerId=prUS44898819. Accessed 05 Mar 2019

  121. Jensen MC, Meckling WH (1976) Theory of the firm: managerial behavior, agency costs and ownership structure. J Financ Econ 3:305–360

    Google Scholar 

  122. Jorgenson DW, Stiroh KJ, Gordon RJ, Sichel DE (2000) Raising the speed limit: US economic growth in the information age. Brookings Pap Econ Act 2000:125–235

    Google Scholar 

  123. Kart L, Heudecker N, Buytendijk F (2013) Survey analysis: big data adoption in 2013 shows substance behind the hype. https://www.gartner.com/doc/2589121/survey-analysis-big-data-adoption. Accessed 1 Dec 2018

  124. Kauffman R, Weill P (1989) An evaluative framework for research on the performance effects of information technology investment. In: Proceedings of the 10th international conference on information systems, pp 377–388

  125. *Khanna R, Sharma C (2018) Testing the effect of investments in IT and R&D on labour productivity: new method and evidence for Indian firms. Econ Lett 173:30–34

    Google Scholar 

  126. *Kılıçaslan Y, Sickles RC, Atay Kayış A, Üçdoğruk Gürel Y (2017) Impact of ICT on the productivity of the firm: evidence from Turkish manufacturing. J Prod Anal 47:277–289

    Google Scholar 

  127. *Kim C-S, Davidson LF (2004) The effects of IT expenditures on banks’ business performance: using a balanced scorecard approach. Manag Finance 30:28–45

    Google Scholar 

  128. *Ko M, Osei-Bryson K-M (2004) Using regression splines to assess the impact of information technology investments on productivity in the health care industry. Inf Syst J 14:43–63

    Google Scholar 

  129. *Ko M, Osei-Bryson K-M (2006) Analyzing the impact of information technology investments using regression and data mining techniques. J Enterp Inf Manag 19:403–417

    Google Scholar 

  130. *Ko M, Osei-Bryson K-M (2008) Reexamining the impact of information technology investment on productivity using regression tree and multivariate adaptive regression splines (MARS). Inf Technol Manag 9:285–299

    Google Scholar 

  131. Kohli R, Devaraj S (2003) Measuring information technology payoff: a meta-analysis of structural variables in firm-level empirical research. Inf Syst Res 14:127–145

    Google Scholar 

  132. Kohli R, Grover V (2008) Business value of IT: an essay on expanding research directions to keep up with the times. J Assoc Inf Syst 9:23–39

    Google Scholar 

  133. KPMG (2018) Ready, Set, Fail?: Avoiding setbacks in the intelligent automation race. https://advisory.kpmg.us/content/advisory/en/index/articles/2018/new-study-findings-read-ready-set-fail.html. Accessed 19 Nov 2018

  134. *Kudyba S, Diwan R (2002) Research report: increasing returns to information technology. Inf Syst Res 13:104–111

    Google Scholar 

  135. Kwon MJ, Stoneman P (1995) The impact of technology adoption on firm productivity. Econ Innovat New Tech 3:219–234

    Google Scholar 

  136. *Lee B, Barua A (1999) An integrated assessment of productivity and efficiency impacts of information technology investments: old data, new analysis and evidence. J Prod Anal 12:21–43

    Google Scholar 

  137. *Lee B, Menon NM (2000) Information technology value through different normative lenses. J Manag Inf Syst 16:99–119

    Google Scholar 

  138. *Lee S, Xiang JY, Kim JK (2011) Information technology and productivity: empirical evidence from the Chinese electronics industry. Inf Manag 48:79–87

    Google Scholar 

  139. *Lee J, McCullough JS, Town RJ (2013) The impact of health information technology on hospital productivity. RAND J Econ 44:545–568

    Google Scholar 

  140. *Lehr W, Lichtenberg FR (1998) Computer Use and productivity growth in US Federal Government Agencies, 1987–92. J Ind Econ 46:257–279

    Google Scholar 

  141. *Lehr B, Lichtenberg F (1999) Information technology and its impact on productivity: firm-level evidence from government and private data sources, 1977–1993. Can J Econ 32:335–362

    Google Scholar 

  142. *Licht G, Moch D (1999) Innovation and information technology in services. Can J Econ 32:363–383

    Google Scholar 

  143. *Lichtenberg FR (1995) The output contributions of computer equipment and personnel: a firm-level analysis. Econ Innovat New Tech 3:201–218

    Google Scholar 

  144. Lim J-H, Stratopoulos TC, Wirjanto TS (2013) Sustainability of a firm’s reputation for information technology capability: the role of senior IT executives. J Manag Inf Syst 30:57–96

    Google Scholar 

  145. *Liu T-K, Chen J-R, Huang CJ, Yang C-H (2014) Revisiting the productivity paradox: a semiparametric smooth coefficient approach based on evidence from Taiwan. Technol Forecast Soc Change 81:300–308

    Google Scholar 

  146. *Loveman GW (1994) An assessment of the productivity impact of information technologies. In: Allen T, Scott M (eds) Information technology and the corporation of the 1990s: research studies. MIT Press, Cambridge, pp 88–110

    Google Scholar 

  147. *Loukis E, Sapounas I, Aivalis K (2007) The effect of generalized competition and strategy on the business value of information and communication technologies. J Enterp Inf Manag 21:24–38

    Google Scholar 

  148. *Luo Y, Bu J (2016) How valuable is information and communication technology? A study of emerging economy enterprises. J World Bus 51:200–211

    Google Scholar 

  149. Macdonald S, Anderson P, Kimbel D (2000) Measurement or management?: Revisiting the productivity paradox of information technology. Vierteljahresh Wirtschaftsforsch 69:601–617

    Google Scholar 

  150. *Mahmood MA (1993) Associating organizational strategic performance with information technology investment: an exploratory research. Eur J Inf Syst 2:185–200

    Google Scholar 

  151. *Mahmood MA, Mann GJ (1993) Measuring the organizational impact of information technology investment: an exploratory study. J Manag Inf Syst 10:97–122

    Google Scholar 

  152. *Mahmood MA, Mann GJ (2005) Information technology investments and organizational productivity and performance: an empirical investigation. J Organ Comput Electron Commer 15:185–202

    Google Scholar 

  153. Malone TW, Yates J, Benjamin RI (1987) Electronic markets and electronic hierarchies. Commun ACM 30:484–497

    Google Scholar 

  154. Mata FJ, Fuerst WL, Barney JB (1995) Information technology and sustained competitive advantage: a resource-based analysis. MIS Q 19:487–505

    Google Scholar 

  155. Melville N, Kraemer K, Gurbaxani V (2004) Review: information technology and organizational performance—an integrative model of IT business value. MIS Q 28:283–322

    Google Scholar 

  156. *Melville N, Gurbaxani V, Kraemer K (2007) The productivity impact of information technology across competitive regimes: the role of industry concentration and dynamism. Decis Support Syst 43:229–242

    Google Scholar 

  157. *Menon NM, Lee B, Eldenburg L (2000) Productivity of information systems in the healthcare industry. Inf Syst Res 11:83–92

    Google Scholar 

  158. *Menon NM, Yaylacicegi U, Cezar A (2009) Differential effects of the two types of information systems: a hospital-based study. J Manag Inf Syst 26:297–316

    Google Scholar 

  159. Milgrom P, Roberts J (1990) The economics of modern manufacturing: technology, strategy, and organization. Am Econ Rev 80:511–528

    Google Scholar 

  160. *Mithas S, Tafti A, Bardhan I, Goh JM (2012) Information technology and firm profitability: mechanisms and empirical evidence. MIS Q 36:205–224

    Google Scholar 

  161. Mitra S, Chaya AK (1996) Analyzing cost-effectiveness of organizations: the impact of information technology spending. J Manag Inf Syst 13:29–57

    Google Scholar 

  162. Mokyr J (2014) Secular stagnation? Not in your life. In: Teulings C, Baldwin R (eds) Secular stagnation: facts, causes and cures. CEPR Press, London, pp 83–89

    Google Scholar 

  163. Moshiri S, Simpson W (2011) Information technology and the changing workplace in Canada: firm-level evidence. Ind Corp Change 20:1601–1636

    Google Scholar 

  164. Nakata C, Zhu Z (2006) Information technology and customer orientation: a study of direct, mediated, and interactive linkages. J Marketing Manag 22:319–354

    Google Scholar 

  165. *Neirotti P, Paolucci E (2007) Assessing the strategic value of information technology: an analysis on the insurance sector. Inf Manag 44:568–582

    Google Scholar 

  166. Nordhaus WD (2015) Are we approaching an economic singularity? Information technology and the future of economic growth. NBER working paper no. 21547

  167. Obermaier R (2019) Industrie 4.0 und Digitale Transformation als unternehmerische Gestaltungsaufgabe. In: Obermaier R (ed) Handbuch Industrie 4.0 und Digitale Transformation: Betriebswirtschaftliche, technische und rechtliche Herausforderungen, 1st edn. Springer, Wiesbaden, pp 3–46

    Google Scholar 

  168. OECD (2019) Labour productivity and utilisation: labour productivity, annual growth rate (%), 2000–2018. https://data.oecd.org/lprdty/labour-productivity-and-utilisation.htm. Accessed 30 Apr 2019

  169. O’Reilly CA III (1980) Individuals and information overload in organizations: is more necessarily better? Acad Manag J 23:684–696

    Google Scholar 

  170. *Osei-Bryson K-M, Ko M (2004) Exploring the relationship between information technology investments and firm performance using regression splines analysis. Inf Manag 42:1–13

    Google Scholar 

  171. Oz E (2005) Information technology productivity: in search of a definite observation. Inf Manag 42:789–798

    Google Scholar 

  172. Polák P (2017) The productivity paradox: a meta-analysis. Inf Econ Policy 38:38–54

    Google Scholar 

  173. Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harv Bus Rev 92:64–88

    Google Scholar 

  174. Porter ME, Heppelmann JE (2015) How smart, connected products are transforming companies. Harv Bus Rev 93:96–114

    Google Scholar 

  175. Radner R (1993) The organization of decentralized information processing. Econometrican 61:1109–1146

    Google Scholar 

  176. *Rai A, Patnayakuni R, Patnayakuni N (1996) Refocusing where and how IT value is realized: an empirical investigation. Omega-Int J Manag S 24:399–412

    Google Scholar 

  177. *Rai A, Patnayakuni R, Patnayakuni N (1997) Technology investment and business performance. Commun ACM 40:89–97

    Google Scholar 

  178. *Ramirez R, Melville N, Lawler E (2010) Information technology infrastructure, organizational process redesign, and business value: an empirical analysis. Decis Support Syst 49:417–429

    Google Scholar 

  179. Ransbotham S, Gerbert P, Reeves M, Kiron D, Spira M (2018) Artificial intelligence in business gets real: research report. https://sloanreview.mit.edu/projects/artificial-intelligence-in-business-gets-real/. Accessed 10 Jan 2019

  180. Ray G, Muhanna WA, Barney JB (2005) Information technology and the performance of the customer service process: a resource-based analysis. MIS Q 29:625–652

    Google Scholar 

  181. *Ross A (2002) A multi-dimensional empirical exploration of technology investment, coordination and firm performance. Int J Phys Distr Log Manag 32:591–609

    Google Scholar 

  182. Ross SA (1973) The economic theory of agency: the principal’s problem. Am Econ Rev 63:134–139

    Google Scholar 

  183. Sabherwal R, Jeyaraj A (2015) Information technology impacts on firm performance: an extension of Kohli and Devaraj (2003). MIS Q 39:809–836

    Google Scholar 

  184. Schick AG, Gordon LA, Haka S (1990) Information overload: a temporal approach. Account Organ Soc 15:199–220

    Google Scholar 

  185. Schryen G (2013) Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. Eur J Inf Syst 22:139–169

    Google Scholar 

  186. Shao BBM, Lin WT (2002) Technical efficiency analysis of information technology investments: a two-stage empirical investigation. Inf Manag 39:391–401

    Google Scholar 

  187. Shapiro C, Carl S, Varian HR (1999) Information rules: a strategic guide to the network economy. Harvard Business Press, Boston

    Google Scholar 

  188. *Shu W, Strassmann PA (2005) Does information technology provide banks with profit? Inf Manag 42:781–787

    Google Scholar 

  189. Simon HA (1987) The steam engine and the computer: what makes technology revolutionary. Educom Bulletin 22:2–5

    Google Scholar 

  190. *Sircar S, Choi J (2009) A study of the impact of information technology on firm performance: a flexible production function approach. Inf Syst J 19:313–339

    Google Scholar 

  191. *Sircar S, Turnbow JL, Bordoloi B (2000) A framework for assessing the relationship between information technology investments and firm performance. J Manag Inf Syst 16:69–97

    Google Scholar 

  192. Soh C, Markus ML (1995) How IT creates business value: a process theory synthesis. In: Proceedings of the 4th international conference on information systems, pp 29–41

  193. Solow RM (1957) Technical change and the aggregate production function. Rev Econ Stat 39:312–320

    Google Scholar 

  194. Solow RM (1987) We’d better watch out. New York Times, New York

    Google Scholar 

  195. Spitz-Oener A (2006) Technical change, job tasks, and rising educational demands: looking outside the wage structure. J Labor Econ 24:235–270

    Google Scholar 

  196. *Stare M, Jaklič A, Kotnik P (2006) Exploiting ICT potential in service firms in transition economies. Serv Ind J 26:287–302

    Google Scholar 

  197. Statcounter (2018) Search engine market share worldwide. http://gs.statcounter.com/search-engine-market-share. Accessed 13 Dec 2018

  198. Stiroh KJ (2005) Reassessing the impact of IT in the production function: a meta-analysis and sensitivity tests. Ann Econ Stat 79:529–561

    Google Scholar 

  199. *Strassmann PA (1985) Information payoff: the transformation of work in the electronic age. Macmillan Publishers, London

    Google Scholar 

  200. *Strassmann PA (1990) The business value of computers: an executive’s guide. Information Economics Press, New Canaan

    Google Scholar 

  201. Syverson C (2011) What determines productivity? J Econ Lit 49:326–365

    Google Scholar 

  202. Syverson C (2017) Challenges to mismeasurement explanations for the US productivity slowdown. J Econ Perspect 31:165–186

    Google Scholar 

  203. Tallon PP, Kraemer KL, Gurbaxani V (2000) Executives’ perceptions of the business value of information technology: a process-oriented approach. J Manag Inf Syst 16:145–173

    Google Scholar 

  204. *Tam KY (1998) Analysis of firm-level computer investments: a comparative study of three Pacific-rim economies. IEEE Trans Eng Manag 45:276–286

    Google Scholar 

  205. *Tambe P, Hitt LM (2012) The productivity of information technology investments: new evidence from IT labor data. Inf Syst Res 23:599–617

    Google Scholar 

  206. *Tambe P, Hitt LM (2014a) Job hopping, information technology spillovers, and productivity growth. Manag Sci 60:338–355

    Google Scholar 

  207. *Tambe P, Hitt LM (2014b) Measuring information technology spillovers. Inf Syst Res 25:53–71

    Google Scholar 

  208. *Tambe P, Hitt LM, Brynjolfsson E (2012) The extroverted firm: how external information practices affect innovation and productivity. Manag Sci 58:843–859

    Google Scholar 

  209. Trajtenberg M (2019) Artificial intelligence as the next GPT: a political-economy perspective. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence: an agenda. University of Chicago Press, Chicago, pp 175–186

    Google Scholar 

  210. Thong JYL, Yap C-S, Raman KS (1996) Top management support, external expertise and information systems implementation in small businesses. Inf Syst Res 7:248–267

    Google Scholar 

  211. Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14:207–222

    Google Scholar 

  212. Triplett JE (1999) The Solow productivity paradox: what do computers do to productivity? Can J Econ 32:309–334

    Google Scholar 

  213. Van Alstyne MW, Parker GG, Choudary SP (2016) Pipelines, platforms, and the new rules of strategy. Harv Bus Rev 94:54–62

    Google Scholar 

  214. *Vinekar V, Teng JTC (2012) IT impacts in information and physical product industries. J Comput Inf Sys 53:65–71

    Google Scholar 

  215. Wade M, Hulland J (2004) The resource-based view and information systems research: review, extension, and suggestions for future research. MIS Q 28:107–142

    Google Scholar 

  216. Wang P (2010) Chasing the hottest IT: effects of information technology fashion on organizations. MIS Q 34:63–85

    Google Scholar 

  217. *Wang T, Wang Y, McLeod A (2018) Do health information technology investments impact hospital financial performance and productivity? Int J Account Inf Syst 28:1–13

    Google Scholar 

  218. Webster J, Watson RT (2002) Analyzing the past to prepare for the future: Writing a literature review. MIS Q 26:13–23

    Google Scholar 

  219. *Weill P (1992) The relationship between investment in information technology and firm performance: a study of the valve manufacturing sector. Inf Syst Res 3:307–333

    Google Scholar 

  220. Weill P, Olson MH (1989) Managing investment in information technology: mini case examples and implications. MIS Q 13:3–17

    Google Scholar 

  221. Wiengarten F, Humphreys P, Cao G, McHugh M (2013) Exploring the important role of organizational factors in IT business value: taking a contingency perspective on the resource-based view. Int J Manag Rev 15:30–46

    Google Scholar 

  222. Willcocks L, Lester S (1996) Beyond the IT productivity paradox. Eur Manag J 14:279–290

    Google Scholar 

  223. *Wilson DJ (2009) IT and beyond: the contribution of heterogeneous capital to productivity. J Bus Econ Stat 27:52–70

    Google Scholar 

  224. *Wu L, Jin F, Hitt LM (2018) Are all spillovers created equal? A network perspective on information technology labor movements. Manag Sci 64:3168–3186

    Google Scholar 

  225. *Yorukoglu M (1998) The information technology productivity paradox. Rev Econ Dyn 1:551–592

    Google Scholar 

  226. *Zhu K (2004) The complementarity of information technology infrastructure and e-commerce capability: a resource-based assessment of their business value. J Manag Inf Syst 21:167–202

    Google Scholar 

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Schweikl, S., Obermaier, R. Lessons from three decades of IT productivity research: towards a better understanding of IT-induced productivity effects. Manag Rev Q 70, 461–507 (2020). https://doi.org/10.1007/s11301-019-00173-6

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Keywords

  • IT investment
  • Information technology
  • Productivity
  • IT productivity paradox
  • Solow Paradox
  • Industry 4.0

JEL Classification

  • A12
  • O31
  • O33