Introduction
The spread of COVID-19 hit hard in the hospitality and tourism industry, resulting in a devastating effect on firms' financial performance (Song et al., 2021). In response to the unprecedented crisis, hospitality and tourism firms had to reconfigure themselves to deploy and control resources. Regarding resource allocation, we focus on firms' decision on Information Technology (IT) investment and its impact on hotel property performance, as existing research indicates that IT applications can increase the competitive advantage of the hospitality and tourism businesses by reducing operational costs and improving service productivity (Melián-González & Bulchand-Gidumal, 2016). For example, self-service technology and mobile technology have been successfully implemented allowing firms to achieve efficiency and flexibility for both service providers and customers (Li et al., 2021). Furthermore, firms have recently begun to implement service robots, which are currently the most advanced form of IT tool, automating repetitive frontline tasks (Tussyadiah, 2020).
While a body of research suggests a positive direct impact of IT investment on financial performance (e.g., Hua et al., 2020), the question of whether such an advantage of IT investment could hold during a crisis has not yet been answered. To that end, we put forth the following research question:
RQ: did IT investment by hospitality and tourism firms actually provide a buffer against the global pandemic?
During the COVID-19, the advanced IT tools have rapidly been applied in the hospitality and tourism industry, as they reduce the level of interpersonal contact, streamline service interactions, and consequently affect customers' service experiences (Chan et al., 2021). Customers have become intolerant of contagious risks (Qiu et al., 2020), and frontline service technologies have satisfied customers' increasing demand for safer service encounters by minimizing direct contact with frontline service providers (Wan et al., 2020). Relevant to our research, Sharma et al. (2021) found that hotels' announcements of their service product and process innovations to ensure safety drove strong positive effects on hotel market value.
On the other hand, the human-oriented nature of service provision in the hospitality and tourism industry warns over-reliance on the advanced technologies (Parasuraman et al., 1994). Previous research suggests that downplaying the high-touch nature of service encounters by technologies can elicit negative reactions from customers (Xiong et al., 2021). Moreover, if firms had made rapid decisions in new IT investment to promptly respond to the pandemic, it can result in performance risk (Hua et al., 2021). That is, without careful consideration of an alignment between strategic implementations of IT and firms' business strategy, IT investment might rather have detrimental effects on firm performance (Yin et al., 2020).
In sum, there is a mixed view regarding the effectiveness of IT investment by hospitality and tourism firms during the global pandemic. Adding to this line of research, we aim to examine the impact of IT investment in the U.S. hotel industry on property-level performance by comparing pre- and during-COVID situations and to provide useful insights for businesses in terms of appropriate strategic decisions during a major crisis.
Methodology and results
We obtained data for 6,953 US hotel properties from CBRE Hotels Research from 2017 to 2021, which provide a total of 19,263 property-year observations. Fig. 1 describes the locations of the sampled US hotel properties. For measuring property performance (PERFORMANCE), we used three different performance indicators that have been widely accepted as a measure of financial performance (Benavides-Velasco et al., 2014; Song et al., 2022), namely total revenue (REV), EBITDA, and net income (N.I). We followed Hua et al. (2020) and used the “IT expenditure” data as a measure of IT investment (IT_TOTAL), the sum of all IT expenses in revenue generating departments and IT undistributed expenses. To examine the impact of IT investment on hotel performance in a more comprehensive manner, we also used the change in total IT expenses (IT_CHANGE) between the current year (t) and the previous year (t − 1) since a hotel property's accumulated IT investment likely involves improved productivity and efficiency through its incremental investment in IT (Liang et al., 2010). For addressing unobserved property-specific heterogeneity that possibly confounds the impact of IT expenditure on performance, we conducted a panel regression analysis using a fixed-effects method for coefficient estimation (Wooldridge, 2010).
Fig. 1.
The locations of the sampled hotel properties in the US.
We included a COVID-19 variable (COVID) as a dummy variable, which takes a value of 1 for 2020-2021 and 0 for 2017-2019 as the World Health Organization (WHO) declared the COVID-19 outbreak a public health emergency of international concern on January 30, 2020 (World Health Organization, 2020). Three control variables, namely scale (i.e., a dummy variable equal to 1 if a hotel property's chain scale as luxury or upper upscale, and 0 otherwise), location (i.e., LARGE: a dummy variable equal to 1 if a hotel's location as a large metropolitan area, and 0 otherwise; MID: a dummy variable equal to 1 if a hotel's location as a mid-sized city, and 0 otherwise), and the number of rooms available (ROOMS), were included (Lee & Jang, 2011).
Table 1 shows the outcomes when using IT_TOTAL as a main explanatory variable, which in Columns (1), (3), and (5) indicate a positive impact on all of the property performance measures. When we included the interaction terms between IT_TOTAL and COVID to compare the impact of IT investments between pre-COVID (2017-2019) and during-COIVD (2020-2021), a negative and significant effect on hotel performance resulted, as shown in Columns (2), (4), and (6). That is, compared to the pre-COVID, the positive impact of IT_TOTAL on hotel performance reduced during the COVID.
Table 1.
Results of the main analyses (IT_TOTAL).
| Variables | (1) REV | (2) REV | (3) EBITDA | (4) EBITDA | (5) N.I. | (6) N.I. |
|---|---|---|---|---|---|---|
| IT_TOTAL | 43.766⁎⁎⁎ (2.890) | 26.978⁎⁎⁎ (2.737) | 18.440⁎⁎⁎ (1.294) | 11.351⁎⁎⁎ (1.118) | 18.662⁎⁎⁎ (1.362) | 11.467⁎⁎⁎ (1.150) |
| COVID | −6.655e+06⁎⁎⁎ (256,283) | −978,317⁎⁎⁎ (325,837) | −2.863e+06⁎⁎⁎ (112,299) | −465,670⁎⁎⁎ (159,843) | −2.860e+06⁎⁎⁎ (113,772) | −427,193⁎⁎ (171,167) |
| IT_TOTAL×COVID | −34.402⁎⁎⁎ (2.053) | −14.526⁎⁎⁎ (1.059) | −14.744⁎⁎⁎ (1.120) | |||
| SCALE | 3.669e+06⁎⁎⁎ (1.191e+06) | 2.123e+06 (1.236e+06) | 1.464e+06⁎⁎ (568,465) | 810,849 (632,485) | 1.406e+06⁎⁎ (587,636) | 743,684 (661,125) |
| ROOMS | 84.495 (120.85) | 322.805⁎⁎ (157.116) | 22.461 (49.798) | 123.085 (65.057) | 16.512 (52.919) | 118.645 (69.973) |
| LARGE | 1.753e+07 (9.536e+06) | 1.254e+07⁎⁎⁎ (4.853e+06) | 8.137e+06 (4.411e+06) | 6.028e+06⁎⁎ (2.441e+06) | 8.117e+06 (4.410e+06) | 5.976e+06⁎⁎ (2.413e+06) |
| MID | 6.084e+06 (4.623e+06) | 5.277e+06⁎⁎ (2.383e+06) | 3.126e+06 (2.203e+06) | 2.786e+06⁎⁎ (1.349e+06) | 3.143e+06 (2.224e+06) | 2.797e+06⁎⁎ (1.360e+06) |
| Constant | −1.225e+07 (1.174e+07) | −2.482e+07 (1.351e+07) | −7.179e+06 (4.999e+06) | −1.249e+07⁎⁎ (5.663e+06) | −7.446e+06 (5.223e+06) | −1.28e+07⁎⁎ (6.054e+06) |
| F-value | 319.69⁎⁎⁎ | 654.82⁎⁎⁎ | 255.43⁎⁎⁎ | 430.75⁎⁎⁎ | 241.54⁎⁎⁎ | 410.80⁎⁎⁎ |
| Observations | 19,263 | 19,263 | 19,263 | 19,263 | 19,263 | 19,263 |
Note: Robust standard errors are in parentheses; e+ indicates ten to the nth power.
p < 0.01.
p < 0.05.
Table 2 reports the findings when IT_CHANGE was used as an alternative measurement of IT investment. IT_CHANGE had a positive and significant impact on the dependent variables as shown in Columns (1), (3), and (5). Thus, the more a hotel overinvests in IT compared to the previous year, the better the organizational outcomes turned out, on average. When including the interaction terms between IT_CHANGE and COVID in Columns (2), (4), and (6), they had a consistent and positive impact on the property performance measures. The results indicate that the positive impact of an increase in IT expenses on property performance increased during the COVID compared with the pre-COVID. That is, the more (less) a hotel invests in IT during the COVID, the stronger positive (negative) IT investment effects on the organizational outcomes the hotel experienced.
Table 2.
Results of the main analyses (IT_CHANGE).
| Variables | (1) REV | (2) REV | (3) EBITDA | (4) EBITDA | (5) N.I. | (6) N.I. |
|---|---|---|---|---|---|---|
| IT_CHANGE | 35.085⁎⁎⁎ (2.939) | −19.194⁎⁎⁎ (4.167) | 16.776⁎⁎* (1.579) | −10.358⁎⁎⁎ (1.916) | 16.546⁎⁎⁎ (1.574) | −10.495⁎⁎⁎ (1.942) |
| COVID | −9.848e+06⁎⁎⁎ (330,314) | −8.517e+06⁎⁎⁎ (287,719) | −4.245e+06⁎⁎⁎ (150,680) | −3.580e+06⁎⁎⁎ (134,066) | −4.292e+06⁎⁎⁎ (154,739) | −3.629e+06⁎⁎⁎ (138,789) |
| IT_CHANGE×COVID | 76.082⁎⁎⁎ (4.464) | 38.033⁎⁎⁎ (2.086) | 37.902⁎⁎⁎ (2.115) | |||
| SCALE | 6.566e+06⁎⁎ (3.339e+06) | 9.175e+06⁎⁎⁎ (3.513e+06) | 2.848e+06 (1.560e+06) | 4.153e+06⁎⁎ (1.711e+06) | 2.403e+06 (1.595e+06) | 3.703e+06⁎⁎ (1.726e+06) |
| ROOMS | −474.702 (245.026) | −242.004 (248.059) | −207.511 (114.233) | −91.186 (117.405) | −195.856 (115.487) | −79.931 (119.102) |
| LARGE | 1.237e+07 (7.621e+06) | 9.520e+06 (6.275e+06) | 5.916e+06 (3.623e+06) | 4.491e+06 (2.914e+06) | 5.976e+06 (3.658e+06) | 4.556e+06 (2.958e+06) |
| MID | 6.714e+06 (4.505e+06) | 4.217e+06 (3.407e+06) | 3.407e+06 (1.967e+06) | 2.160e+06 (1.662e+06) | 3.459e+06 (2.016e+06) | 2.215e+06 (1.715e+06) |
| Constant | 5.209e+07⁎⁎ (2.281e+07) | 3.146e+07 (2.285e+07) | 1.914e+07 (1.065e+07) | 8.830e+06 (1.082e+07) | 1.750e+07 (1.077e+07) | 7.222e+06 (1.097e+07) |
| F-value | 177.99⁎⁎⁎ | 250.00⁎⁎⁎ | 155.04⁎⁎⁎ | 222.88⁎⁎⁎ | 148.98⁎⁎⁎ | 216.01⁎⁎⁎ |
| Observations | 11,796 | 11,796 | 11,796 | 11,796 | 11,796 | 11,796 |
Note: Robust standard errors are in parentheses; e+ indicates ten to the nth power.
p < 0.01.
p < 0.05.
Conclusion
Our findings suggest that while IT investment had positive effects on hotel property performance before COVID-19 emerged, the advantages gained were significantly reduced during the pandemic. Still, continuous and increased IT investment during the pandemic continues to provide a performance advantage, in line with the finding of Sharma et al. (2021). Hence, the current study implies the importance of continuous IT investment by firms during external shocks, providing a better understanding of organizational technology adoption, echoing Yang et al. (2021). We suggest to hotel property management that a certain level of IT investment should be sustained or gradually increased and should not be affected by the weaker financial conditions resulting from an external crisis such as COVID-19.
One of limitations of the current study is the use of dummy variable to illustrate the impact of the COVID-19 pandemic on the U.S. hotel industry, as the sample consists of property-year observations. However, the impact of COVID-19 may differ depending on its stage in terms of the number of confirmed cases in the U.S. Thus, splitting the sample period into sub-periods would be beneficial to provide a more detailed examination.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
We thank the support of CBRE Hotels Research for providing the database.
Biographies
Sungwoo Choi (sungwoochoi@cuhk.edu.hk), Ph.D., has focused on service technology/innovation as his major research interests.
Jihwan Yeon, Ph.D., has focused on sharing economy, corporate social responsibility, and sustainability issues.
Hyoung Ju Song, Ph.D., has focused on environmental, social, and corporate governance (ESG) issues and corporate level strategy.
Jihao Hu, a Ph.D. candidate, has focused on consumer psychology and information processing.
Associate editor: Yang Yang
References
- Benavides-Velasco C.A., Quintana-García C., Marchante-Lara M. Total quality management, corporate social responsibility and performance in the hotel industry. International Journal of Hospitality Management. 2014;41:77–87. [Google Scholar]
- Chan J., Gao Y.L., McGinley S. Updates in service standards in hotels: how COVID-19 changed operations. International Journal of Contemporary Hospitality Management. 2021;33(5):1668–1687. [Google Scholar]
- Hua N., Huang A., Medeiros M., DeFranco A. The moderating effect of operator type: the impact of information technology (IT) expenditures on hotels' operating performance. International Journal of Contemporary Hospitality Management. 2020;32(8):2519–2541. [Google Scholar]
- Hua N., Zhang T., Jahromi M.F., DeFranco A. The speed of change and performance risk: Examining the impacts of IT spending in the US hotel industry. Journal of Hospitality and Tourism Technology. 2021;12(3):563–579. [Google Scholar]
- Lee S.K., Jang S. Room rates of US airport hotels: Examining the dual effects of proximities. Journal of Travel Research. 2011;50(2):186–197. [Google Scholar]
- Li M., Yin D., Qiu H., Bai B. A systematic review of AI technology-based service encounters: Implications for hospitality and tourism operations. International Journal of Hospitality Management. 2021;95 [Google Scholar]
- Liang T., You J., Liu C. A resource‐based perspective on information technology and firm performance: a meta analysis. Industrial Management & Data Systems. 2010;110(8):1138–1158. [Google Scholar]
- Melián-González S., Bulchand-Gidumal J. A model that connects information technology and hotel performance. Tourism Management. 2016:30–37. [Google Scholar]
- Parasuraman A., Zeithaml V.A., Berry L.L. Reassessment of expectations as a comparison standard in measuring service quality: Implications for further research. Journal of Marketing. 1994;58(1):111–124. [Google Scholar]
- Qiu R.T., Park J., Li S., Song H. Social costs of tourism during the COVID-19 pandemic. Annals of Tourism Research. 2020;84 doi: 10.1016/j.annals.2020.102994. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sharma A., Shin H., Santa-María M.J., Nicolau J.L. Hotels’ COVID-19 innovation and performance. Annals of Tourism Research. 2021;88 doi: 10.1016/j.annals.2021.103180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Song H.J., Yeon J., Lee S. Impact of the COVID-19 pandemic: Evidence from the US restaurant industry. International Journal of Hospitality Management. 2021;92 doi: 10.1016/j.ijhm.2020.102702. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Song H.J., Yeon J., Lee S., Li Z. The effect of an increase in federal minimum wage on the US hotel industry: A difference-in-differences approach. Current Issues in Tourism. 2022;25(6):887–900. [Google Scholar]
- Tussyadiah I. A review of research into automation in tourism: Launching the Annals of Tourism Research Curated Collection on Artificial Intelligence and Robotics in Tourism. Annals of Tourism Research. 2020;81 [Google Scholar]
- Wan L.C., Chan E.K., Luo X. ROBOTS COME to RESCUE: How to reduce perceived risk of infectious disease in Covid19-stricken consumers? Annals of Tourism Research. 2020;88 doi: 10.1016/j.annals.2020.103069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wooldridge J.M. MIT press; 2010. Econometric analysis of cross section and panel data. [Google Scholar]
- World Health Organization WHO coronavirus disease (COVID-19) dashboard. 2020. https://covid19.who.int
- Xiong X., Wong I.A., Yang F.X. Are we behaviorally immune to COVID-19 through robots? Annals of Tourism Research. 2021;91 doi: 10.1016/j.annals.2021.103312. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang Y., Zhang C.X., Rickly J.M. A review of early COVID-19 research in tourism: Launching the Annals of Tourism Research’s Curated Collection on coronavirus and tourism. Annals of Tourism Research. 2021;91 doi: 10.1016/j.annals.2021.103313. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yin J., Wei S., Chen X., Wei J. Does it pay to align a firm's competitive strategy with its industry IT strategic role? Information & Management. 2020;57(8) [Google Scholar]