Skip to main content
Log in

Going digital: implications for firm value and performance

  • Published:
Review of Accounting Studies Aims and scope Submit manuscript


We examine firm value and performance implications of the growing trend of nontechnology companies engaging in activities relating to digital technologies. We measure digital activities in firms based on the disclosure of digital words in the business description section of 10-Ks. Digital activities are associated with a market-to-book ratio 8%–26% higher than industry peers, and only 25% of the differences in market-to-book is explained by accounting capitalization restrictions. To control for selection bias, we implement lagged dependent variable and IV regressions, and our market-to-book findings are robust to these specifications. Portfolios formed on digital activity disclosure earn a Daniel et al. The Journal of Finance 52 (3): 1035–1058 (1997)-adjusted return of 30% over a three-year horizon and a monthly alpha of 44-basis-points. On the other hand, we find weak evidence of near-term, positive improvements in fundamental performance, as we find some evidence of interim productivity increases but declines in sales growth conditional on digital activities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

Data are available from the public sources cited in the text.


  1. All dollar figures are denominated in US dollars.

  2. We define the digital terms in Appendix Table 11.

  3. Appendix Table 12 presents the list of industry codes that are used to identify Tech firms.

  4. We use the digital-related patent search terms provided in Bloom et al. (2018) and Webb (2020).

  5. In the rest of the text, we use the same methodology to compute the economic ranges.

  6. As a measure of technology exposure, this instrument proxies for industries that are more likely to benefit from AI, technologies and thus firms within these industries should exhibit a greater likelihood of adoption digital technologies. Consistent with this conjecture, our first-stage regressions show that the measure of AI technology overlap correlates strongly with higher values of the digital score. Moreover, we argue that the assignment of industries that are more exposed to AI technology is fairly exogenous, as many of the patents are filed in universities and other nonprofit organizations.

  7. We study the SRC, as the valuation of sales, unlike book equity and earnings, is not confounded by capitalization restrictions. Thus examining the effects of digital activities on the SRC is a relatively clean way of studying whether digital activities do indeed increase firm valuations.

  8. Abnormal returns are estimated by deducting the firm’s raw returns from the corresponding firm’s size, book-to-market and momentum quintile portfolio returns, following Daniel et al. (1997).

  9. These portfolios hold firms that are in the top tercile of digital disclosers in the long position and firms that do not disclose digital terms in the short position.

  10. Results are also similar without risk factor controls.

  11. The long-short portfolios also yield a significant unadjusted return of 47 basis points.

  12. In our determinants analysis (Table 4), we find that past sales growth and stock returns relate negatively to digital activities.

  13. For example, better reward-punishment practices, performance evaluations.

  14. For NAICS industries, we use the 2017 industry classification and convert industries defined in past versions via the NAICS crosswalks. We also drop firms that do not have at least a four-digit NAICS code. For GICS industries, we define the technology industries based on current (2018 version) and historical GICS codes.

  15. We outline the specific words within these topic groups in Appendix Table 11.

  16. The construction of these variables is detailed in Appendix Table 13.

  17. We report the sample statistics of the tech firms in Table A.1 in the internet appendix.

  18. In Appendix 4, we provide some examples of how these digital terms are used in the firms’ disclosures.

  19. We link the patent data to our dataset with the CRSP-patent link table provided by Kogan et al. (2017), which covers patent data from calendar years 1925–2019 (September). Thus we examine the patent data for a subsample of firm-year observations from fiscal years 2010–2018: 16,315.

  20. We measure the proportion of IT workers from Revelio Labs, which covers a subsample of firms in our sample (11,671 firm-year observations).

  21. The tech portfolio consists of all tech firms classified based on Appendix Table 12. The returns within the portfolio are value-weighted, and we re-balance portfolio weights at the daily-level. The nontech portfolio is defined similarly but consists of firms that are classified as nontech. To reduce the effects of low liquidity stocks from inducing measurement error in the return regressions, we drop penny stock entries with less than $5 in price. Also, to reduce measurement error, betas estimated with less than 200 observations are dropped. Due to these sample restrictions, the analysis is based on a subsample of 17,008 firm-year observations.

  22. For the initial activity sample, we drop all observations of subsequent digital activity, which leads the sample size to drop from 20,450 to 16,497. Note that this regression compares first disclosers to nondisclosers who form the majority of the sample. (There are only 548 first disclosers.) This suggests that the majority of the disclosed digital activity is subsequent disclosure, which aligns with the fact that digital disclosure is highly persistent.

  23. We do find that lagged ROA is positively associated with a higher digital score in the full sample. But we do not find a statistically significant effect in the initial activity sample with industry fixed effects, suggesting that this finding is driven by the increase in ROA after digital disclosure is initiated (and this is corroborated in Panel A of Table 9).

  24. We use common equity in Compustat to measure book equity (following, Doyle et al. 2003, Soliman 2008, Frankel et al. 2011, and Lundholm et al. 2017).

  25. We compare ratios relative to industry peers, as investors commonly benchmark accounting and valuation ratios relative to industry peers.

  26. We also examine the valuation effects of digital activity in the cross-section in Table A.5 in the IA. We find firms that are larger, expend more on SG&A, CapEx and in high digital adoption industries, receive higher valuations.

  27. This estimate could be viewed as conservative, as the controls for intangibles also absorb the effects that digital investments may have on future investment opportunities and growth.

  28. In an untabulated analysis of the lagged dependent variable specification, we also find that continuing disclosers tend to exhibit higher market-to-book, relative to those that do not continue to disclose after initial disclosure of digital activities.

  29. This approach corrects for accounting conservatism by first estimating the conservatism correction factor, which is the ratio of the capitalized tangible and intangible assets (via the cost accounting method over the estimated useful life of assets) to capitalized tangible assets (via the straight-line depreciation method over the estimated useful life of assets). Market-to-book is then adjusted by dividing by this ratio. See Section 2 of the internet appendix for more details on the methodology and theory behind the computation of this conservatism correction factor.

  30. As the conservatism-adjustment drops firms with insufficient investment histories, we conduct the analysis on a small subsample of firms. We also follow McNichols et al. (2014) in dropping financial firms (SIC 6000–6779), firms with assets of less than $4 million and a net PPE-to-asset ratio of less than 0.1, as the conservatism correction is less suited for these firms. Consequently, our sample for this analysis consists of 7352 firm-year observations.

  31. This is based on the average market-to-book of this subsample reported in Table A.2 in the internet appendix.

  32. See Table A.2 in the internet appendix for more details on the sample statistics of the conservatism-corrected market-to-book.

  33. For more details on the methodology, please see Section 3 in the internet appendix.

  34. The logic underlying the valuation interpretation of the ERC stems from an accounting literature that views the ERC coefficient as capturing the market’s expectation of the capitalization rate of earnings (Easton and Zmijewski 1989; Collins and Kothari 1989; Dechow et al. 2014).

  35. We chose this return window as the 99th percentile of the lag between earnings announcement and 10-K filing date is 39 days. We drop observations where the lag is greater than 40 days.

  36. Abnormal daily returns are calculated by taking the raw return minus the Fama-French/Carhart four-factor expected returns (Carhart 1997), where the expected returns are estimated with the β’s of the four-factor model that are estimated in a (−280,−60) window.

  37. We remove consensus forecasts that are more than 100 days old at the time of the announcement and remove forecasts in which the price at the end of the fiscal period is less than one dollar and unexpected earnings is greater than the price.

  38. This analysis is conducted on a subsample of firms that are covered by analysts in the IBES and satisfy our forecast filtering requirements. Thus our sample size drops from 20,839 in the market-to-book analysis to 14,361 in the ERC analysis.

  39. To further control for firm-level heterogeneity in the unexpected earnings and returns relationship, we also examine an alternative specification with grouped firm fixed effects based on 10 by 10 size and beta portfolios in Table A.3 in the internet appendix. We find similar results.

  40. We also examine the fitted ERC curves for digital and nondigital firms using fractional polynomials to model ERC nonlinearities. Our results, presented in Figure A.1 of the internet appendix, show that digital firms tend to exhibit greater return reactions to both positive and negative unexpected earnings (albeit at the more extreme end for negative earnings), consistent with these firms exhibiting a higher ERC coefficient.

  41. Similarly, we remove consensus forecasts that are more than 100 days old at the time of earnings announcement and remove forecasts in which the price at the end of the fiscal period is less than one dollar and unexpected sales greater than the price.

  42. We also examine the robustness of the SRC results by implementing SRC regressions with grouped fixed effects and by examining the fitted SRC using fractional polynomials in Table A.3 and Figure A.2 of the internet appendix. The inferences from both sets of analysis corroborate the main results presented above.

  43. Furthermore, in an untabulated analysis, we show that continuing disclosers tend to exhibit higher SRCs, relative to those that do not continue to disclose after the initial disclosure of digital activities.

  44. We assume 10-K information to be publicly available by four months after the fiscal year-end.

  45. Following Shumway (1997) and Shumway and Warther (1999), we code the delisting return as −30% and − 55% if the firm delists for performance reasons from NYSE and NASDAQ respectively.

  46. Additionally, to further account for low liquidity and high transactions costs in penny stocks, we also remove stocks with prices below $5 at the portfolio formation date.

  47. We also examine the returns without controls for risk factors and find that the results are relatively unchanged.

  48. The monthly returns on an unadjusted basis is 47 basis points and are statistically significant.

  49. To be clear, we code the continuing and noncontinuing digital disclosure in the following way. For continuing disclosures, we recode the Digitali, t variable in equation 6 as 0 for firms that do not make top-tercile disclosure continuously in the return window, and vice versa for noncontinuing disclosures.

  50. Consistent with managerial expertise playing a key role in digital adoption, in Table A.6 in the internet appendix, we also show that firms with digital activity and tech-related top executives yield higher ROA to peers with digital activity.

  51. In support of this conjecture, we also find that gross margins (defined as revenues minus cost of goods sold, scaled by sales) is lower in firms with digital activity, compared to peers (see Table A.4 in the internet appendix), which suggests competitive price pressures that are eroding margins.

  52. We report these analyses in Tables A.7 and A.8 in the internet appendix.

  53. We caution the reader that real-time profits are likely to be lower than the returns reported in this study, as trading frictions could impose additional costs for the investors.


  • Abis, S., and L. Veldkamp. 2020. The changing economics of knowledge production.

  • Amir, E., and B. Lev. 1996. Value-relevance of nonfinancial information: The wireless communications industry. Journal of Accounting and Economics 22 (1–3): 3–30.

    Article  Google Scholar 

  • Autor, D., D. Dorn, and G. Hanson. 2015. Untangling trade and technology: Evidence from local labour markets. The Economic Journal 125 (584): 621–646.

    Article  Google Scholar 

  • Babina, T., A. Fedyk, A. He, and J. Hodson. 2022. Artificial intelligence, firm growth, and product innovation.

  • Bass, A.S. 2018. Non-tech businesses are beginning to use artificial intelligence at scale. The Economist.

  • Bloom, N., R. Sadun, and J. Van Reenen. 2012. Americans do IT better: US multinationals and the productivity miracle. American Economic Review 102 (1): 167–201.

    Article  Google Scholar 

  • Bloom, N., J. Lerner, N. Short, and M. Webb. 2018. Some facts of high-tech patenting.

  • Bresnahan, T., and S. Greenstein. 1996. Technical progress and co-invention in computing and in the uses of computers. Brookings Papers on Economic Activity: Microeconomics, 1–83.

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Brynjolfsson, E., and L. Hitt. 2003. Computing productivity: Firm-level evidence. Review of Economics and Statistics 85 (4): 793–808.

    Article  Google Scholar 

  • Brynjolfsson, E., and M. Smith. 2000. Frictionless commerce? A comparison of internet and conventional retailers. Management Science 46 (4): 563–585.

    Article  Google Scholar 

  • Brynjolfsson, E., D. Rock, and C. Syverson. 2019. Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda, ed. A. Agrawal, J. Gans, and A. Goldfarb. University of Chicago Press.

    Google Scholar 

  • Bughin, J., E. Hazan, S. Ramaswamy, M. Chui, T. Allas, P. Dahlstrom, N. Henke, and M. Trench. 2017. Artificial intelligence - the next digital frontier? McKinsey Global Insitute.

    Google Scholar 

  • Campbell, J., H. Chen, D. Dhaliwal, H. Lu, and L. Steele. 2014. The information content of mandatory risk factor disclosures in corporate filings. Review of Accounting Studies 19 (1): 396–455.

    Article  Google Scholar 

  • Carhart, M. 1997. On persistence in mutual fund performance. The Journal of Finance 52 (1): 57–82.

  • Chen, M., Q. Wu, and B. Yang. 2019. How valuable is fintech innovation? Review of Financial Studies 32 (5): 2062–2106.

    Article  Google Scholar 

  • Cockburn, I., R. Henderson, and S. Stern. 2019. The impact of artificial intelligence on innovation: An exploratory analysis. In The economics of artificial intelligence: An agenda, ed. A. Agrawal, J. Gans, and A. Goldfarb. University of Chicago Press.

    Google Scholar 

  • Collins, D., and S. Kothari. 1989. An analysis of intertemporal and cross-sectional determinants of earnings response coefficients. Journal of Accounting and Economics 11 (2–3): 143–181.

    Article  Google Scholar 

  • Cooper, M., O. Dimitrov, and P. Rau. 2001. A by any other name. Journal of Finance 56 (6): 2371–2388.

  • Core, J., W. Guay, and A. Van Buskirk. 2003. Market valuations in the new economy: An investigation of what has changed. Journal of Accounting and Economics 34 (1–3): 43–67.

    Article  Google Scholar 

  • Crawford, S., D. Roulstone, and E. So. 2012. Analyst initiations of coverage and stock return synchronicity. Accounting Review 87 (5): 1527–1553.

    Article  Google Scholar 

  • Curtis, A., S. McVay, and S. Toynbee. 2020. The changing implications of research and development expenditures for future profitability. Review of Accounting Studies 25 (2): 405–437.

    Article  Google Scholar 

  • Daniel, K., M. Grinblatt, S. Titman, and R. Wermers. 1997. Measuring mutual fund performance with characteristic-based benchmarks. The Journal of Finance 52 (3): 1035–1058.

    Google Scholar 

  • Dechow, P., A. Hutton, L. Meulbroek, and R. Sloan. 2001. Short-sellers, fundamental analysis, and stock returns. Journal of Financial Economics 61 (1): 77–106.

    Article  Google Scholar 

  • Dechow, P., R. Sloan, and J. Zha. 2014. Stock prices and earnings: A history of research. Annual Review of Financial Economics 6: 343–363.

    Article  Google Scholar 

  • Dehaan, E., J. Madsen, and J. Piotroski. 2017. Do weather-induced moods affect the processing of earnings news? Journal of Accounting Research 55 (3): 509–550.

    Article  Google Scholar 

  • Doyle, J., R. Lundholm, and M. Soliman. 2003. The predictive value of expenses excluded from pro forma earnings. Review of Accounting Studies 8 (2): 145–174.

    Article  Google Scholar 

  • Dranove, D., C. Forman, A. Goldfarb, and S. Greenstein. 2014. The trillion dollar conundrum: Complementarities and health information technology. American Economic Journal: Economic Policy 6 (4): 239–270.

    Google Scholar 

  • Easton, P., and M. Zmijewski. 1989. Cross-sectional variation in the stock market response to accounting earnings announcements. Journal of Accounting and Economics 11 (2–3): 117–141.

    Article  Google Scholar 

  • Fama, E., and K. French. 2015. A five-factor asset pricing model. Journal of Financial Economics 116 (1): 1–22.

    Article  Google Scholar 

  • Feldman, R., S. Govindaraj, J. Livnat, and B. Segal. 2010. Management’s tone change, post earnings announcement drift and accruals. Review of Accounting Studies 15 (4): 915–953.

    Article  Google Scholar 

  • Fitzgerald, T., B. Balsmeier, L. Fleming, and G. Manso. 2021. Innovation search strategy and predictable returns. Management Science 67 (2): 1109–1137.

    Article  Google Scholar 

  • Frankel, R., S. McVay, and M. Soliman. 2011. Non-GAAP earnings and board independence. Review of Accounting Studies 16 (4): 719–744.

    Article  Google Scholar 

  • Freeman, R., and S. Tse. 1992. A nonlinear model of security price responses to unexpected earnings. Journal of Accounting Research 30 (2): 185–209.

    Article  Google Scholar 

  • Gipper, B., C. Leuz, and M. Maffett. 2020. Public oversight and reporting credibility: Evidence from the PCAOB audit inspection regime. The Review of Financial Studies 33 (10): 4532–4579.

    Article  Google Scholar 

  • Healy, P., G. Serafeim, S. Srinivasan, and G. Yu. 2014. Market competition, earnings management, and persistence in accounting profitability around the world. Review of Accounting Studies 19 (4): 1281–1308.

    Article  Google Scholar 

  • Hirshleifer, D., P. Hsu, and D. Li. 2013. Innovative efficiency and stock returns. Journal of Financial Economics 107 (3): 632–654.

    Article  Google Scholar 

  • Hirshleifer, D., P. Hsu, and D. Li. 2018. Innovative originality, profitability, and stock returns. Review of Financial Studies 31 (7): 2553–2605.

    Article  Google Scholar 

  • Hitt, L.M. 1999. Information technology and firm boundaries: Evidence from panel data. Information Systems Research 10 (2): 134–149.

    Article  Google Scholar 

  • Hope, O., D. Hu, and H. Lu. 2016. The benefits of specific risk-factor disclosures. Review of Accounting Studies 21 (4): 1005–1045.

    Article  Google Scholar 

  • Jaffe, A., and J. Lerner. 2004. Innovation and its discontents. Princeton University Press.

    Google Scholar 

  • Kogan, L., D. Papanikolaou, A. Seru, and N. Stoffman. 2017. Technological innovation, resource allocation, and growth. The Quarterly Journal of Economics 132 (2): 665–712.

    Article  Google Scholar 

  • Kogan, L., D. Papanikolaou, L. Schmidt, and B. Seegmiller. 2022. Technology, vintage-specific human capital, and labor displacement: evidence from linking patents with occupations.

  • Koh, P., and D. Reeb. 2015. Missing R&D. Journal of Accounting and Economics 60 (1): 73–94.

    Article  Google Scholar 

  • Lakhani, K., and Marco Iansiti. 2020. Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business School Press.

    Google Scholar 

  • Lee, C., S. Sun, R. Wang, and R. Zhang. 2019. Technological links and predictable returns. Journal of Financial Economics 132 (3): 76–96.

    Article  Google Scholar 

  • Lev, B., and T. Sougiannis. 1996. The capitalization, amortization, and value-relevance of R&D. Journal of Accounting and Economics 21 (1): 107–138.

    Article  Google Scholar 

  • Lev, B., and P. Zarowin. 1999. The boundaries of financial reporting and how to extend them. Journal of Accounting Research 37 (2): 353–385.

    Article  Google Scholar 

  • Li, F. 2008. Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics 45 (2–3): 221–247.

    Article  Google Scholar 

  • Li, F. 2010. The information content of forward-looking statements in corporate filings - a naïve bayesian machine learning approach. Journal of Accounting Research 48 (5): 1049–1102.

    Article  Google Scholar 

  • Li, F., R. Lundholm, and M. Minnis. 2013. A measure of competition based on 10-K filings. Journal of Accounting Research 51 (2): 399–436.

    Article  Google Scholar 

  • Lundholm, R., N. Rahman, and R. Rogo. 2017. The foreign investor bias and its linguistic origins. Management Science 64 (9): 4433–4450.

    Article  Google Scholar 

  • McNichols, M., M. Rajan, and S. Reichelstein. 2014. Conservatism correction for the market-to-book ratio and tobin’s q. Review of Accounting Studies 19 (4): 1393–1435.

    Article  Google Scholar 

  • Nissim, D., and S. Penman. 2001. Ratio analysis and equity valuation: From research to practice. Review of Accounting Studies 6: 109–154.

    Article  Google Scholar 

  • Philippon, T. 2016. The fintech opportunity.

  • Piotroski, J., and D. Roulstone. 2004. The influence of analysts, institutional investors, and insiders on the incorporation of market, industry, and firm-specific information into stock prices. Accounting Review 79 (4): 1119–1151.

    Article  Google Scholar 

  • Resutek, R. 2021. Is R&D really that special? A fixed-cost explanation for the empirical patterns of R&D firms. Contemporary Accounting Research 39 (1): 721–749.

    Article  Google Scholar 

  • Rock, Daniel. 2019. Engineering value: The returns to technological talent and investments in artificial intelligence.

  • Seamans, R., and M. Raj. 2019. Artificial intelligence, labor, productivity and the need for firm-level data. In The economics of artificial intelligence: An agenda, ed. A. Agrawal, J. Gans, and A. Goldfarb. University of Chicago Press.

    Google Scholar 

  • Shumway, T. 1997. The delisting bias in CRSP data. The Journal of Finance 52 (1): 327–340.

    Article  Google Scholar 

  • Shumway, T., and V. Warther. 1999. The delisting bias in CRSP’s Nasdaq data and its implications for the size effect. The Journal of Finance 54 (6): 2361–2379.

    Article  Google Scholar 

  • Soliman, M. 2003. Using industry-adjusted DuPont analysis to predict future profitability and returns. Ph.D. Diss., University of Michigan.

  • Soliman, M. 2008. The use of DuPont analysis by market participants. The Accounting Review 83 (3): 823–853.

    Article  Google Scholar 

  • Tambe, P. 2014. Big data investment, skills, and firm value. Management Science 60 (6): 1452–1469.

    Article  Google Scholar 

  • Tambe, P., L. Hitt, D. Rock, and E. Brynjolfsson. 2019. IT, AI and the growth of intangible capital.

  • Trueman, B., F. Wong, and X. Zhang. 2000. The eyeballs have it: Searching for the value in internet stocks. Journal of Accounting Research 38 (Supplement): 137–162.

    Article  Google Scholar 

  • Trueman, B., F. Wong, and X. Zhang. 2001. Back to basics: forecasting the revenues of internet firms.” Review of Accounting Studies 6 (2–3): 305–329.

  • Webb, M. 2020. The impact of artificial intelligence on the labor market.

  • You, H., and X. Zhang. 2009. Financial reporting complexity and investor underreaction to 10-K information. Review of Accounting Studies 14 (4): 559–586.

    Article  Google Scholar 

Download references


We are grateful to Russell Lundholm (editor) and an anonymous referee for suggestions that have greatly improved this paper. We also thank workshop participants at Harvard Business School, 2019 MIT–Asia Conference, The Second Conference on Intelligent Information Retrieval in Accounting and Finance, the 2020 American Accounting Association Conference, the HBS Digital Initiative Doctoral Workshop, the 2021 Hawaii Accounting Research Conference, the Korean Accounting Information Association Webinar, and the 2022 Stanford Accounting Summer Camp for insightful comments and suggestions. The authors acknowledge research support from Harvard Business School. All errors are our own.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Wilbur Chen.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information


(PDF 425 kb)


Appendix 1

Table 11 Digital Terms Regex Definitions

Appendix 2

Table 12 Tech Industry Classification Codes

Appendix 3

Table 13 Variable Definitions

Appendix 4: Examples of Digital Disclosure

1.1 Mistras Group Inc., Fiscal Year: 2011

Historically, NDT solutions predominantly used qualitative testing methods aimed primarily at detecting defects in the tested materials. This methodology, which we categorize as traditional NDT, is typically labor intensive and, as a result, considerably dependent upon the availability and skill level of the certified technicians, engineers and scientists performing the inspection services. The traditional NDT market is highly fragmented, with a significant number of small vendors providing inspection services to divisions of companies or local governments situated in close proximity to the vendor s field inspection engineers and scientists. Today, we believe that customers are increasingly looking for a single vendor capable of providing a wider spectrum of asset protection solutions for their global infrastructure that we call one source . This shift in underlying demand, which began in the early 1990s, has contributed to a transition from traditional NDT solutions to more advanced solutions that employ automated digital sensor technologies and accompanying enterprise software, allowing for the effective capture, storage, analysis and reporting of inspection and engineering results electronically and in digital formats. These advanced techniques, taken together with advances in wired and wireless communication and information technologies, have further enabled the development of remote monitoring systems, asset-management and predictive maintenance capabilities and other data analytics and management. We believe that as advanced asset protection solutions continue to gain acceptance among asset-intensive organizations, only those vendors offering broad, complete and integrated solutions, scalable operations and a global footprint will have a distinct competitive advantage. Moreover, we believe that vendors that are able to effectively deliver both advanced solutions and data analytics, by virtue of their access to customers data, develop a significant barrier to entry for competitors, and so develop the capability to create significant recurring revenues.

1.2 Korn Ferry International, Fiscal Year: 2014

1.2.1 Talent Analytics

Companies are increasingly leveraging big data and analytics to measure the influence of activities across all aspects of their business, including HR. They expect their service providers to deliver superior metrics and measures and better ways of communicating results. Korn Ferry’s go-to-market approach is increasingly focused on talent analytics we are injecting research-based intellectual property into all areas of our business, cascading innovation and new offerings up to our clients.

1.3 Insperity Inc., Fiscal Year: 2015

Our long-term strategy is to provide the best small and medium-sized businesses in the United States with our specialized human resources service offering and to leverage our buying power and expertise to provide additional valuable services to clients. Our most comprehensive HR services offerings are provided through our Workforce Optimization and Workforce Synchronization solutions (together, our PEO HR Outsourcing solutions), which encompass a broad range of human re- sources functions, including payroll and employment administration, employee benefits, workers compensation, government compliance, performance management and training and development services, along with our cloud-based human capital management platform, the Employee Service Center (ESC). Our Workforce Optimization solution is our most comprehensive HR outsourcing solution and is our primary offering. Our Workforce Synchronization solution, which is generally offered only to our mid-market client segment, is a lower cost offering with a longer commitment that includes the same compliance and administrative services as our Workforce Optimization solution and makes available, for an additional fee, the strategic HR products and organizational development services that are included with our Workforce Optimization solution.

1.4 TransUnion, Fiscal Year: 2015

Our addressable market includes the big data and analytics market, which continues to grow as companies around the world recognize the benefits of building an analytical enterprise where decisions are made based on data and insights, and as consumers recognize the importance that data and analytics play in their ability to procure goods and services and protect their identities. International Data Corporation (“IDC”) estimates worldwide spending on big data and analytics services to be approximately $52 billion in 2014, growing at a projected compounded annual growth rate (CAGR) of approximately 15% from 2014 through 2018. There are several underlying trends supporting this market growth, including the creation of large amounts of data, advances in technology and analytics that enable data to be processed more quickly and efficiently to provide business insights, and growing demand for these business insights across industries and geographies. Leveraging our 48-year operating history and our established position as a leading provider of risk and information solutions, we have evolved our business by investing in a number of strategic initiatives, such as transitioning to the latest big data and analytics technologies, expanding the breadth and depth of our data, strengthening our analytics capabilities and enhancing our business processes. As a result, we believe we are well positioned to expand our share within the markets we currently serve and capitalize on the larger big data and analytics opportunity.

1.5 Camping World Holdings, Inc., Fiscal Year: 2017

Customer Database. We have over 15.1 million unique RV contacts in our database of which approximately 3.6 million are Active Customers related to our RV products. We use a customized CRM system and database analytics to track customers and selectively market and cross-sell our offerings. We believe our customer database is a competitive advantage and significant barrier to entry.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, W., Srinivasan, S. Going digital: implications for firm value and performance. Rev Account Stud 29, 1619–1665 (2024).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


JEL classification