Benchmarking bed and breakfast industry in a sharing economy: a frontier-based technology forecasting approach

Abstract

The sharing economy affords new opportunities to the global tourism industry, accelerating the development of tourism at a striking rate. This paper examines productivity of the bed and breakfast (B&B) industry and how to improve the performance of the B&B industry in a sharing economy. A modified global Malmquist index using slack-based measures is developed to measure productivity, and benchmark selection models using technology forecasting are proposed to improve future performance. The future technology is forecasted by averaging the frontier shifts (technology changes) in previous periods. This study challenges the implicit assumption that the technology will be stationary in traditional benchmarking, and suggests that how the information of frontier shifts derived from Malmquist index can be used to forecast future technology in a rational manner. The empirical results inform that there is a productivity growth in the B&B industry in a sharing economy, and the main driver of the growth is technology progress rather than performance improvement. In particular, a counterintuitive but interesting result is that the B&B industry experienced a slight productivity growth after the outbreak of COVID-19. The benchmarks in 2021 and 2022 are forecasted to improve the performance of the B&B industry.

Keywords:

• benchmarking
• data envelopment analysis
• sharing economy
• bed and breakfast
• technology forecasting

JEL CODE:

• C14

Data availability statements

The data that support the findings of this study are available from the corresponding author upon request.

Disclosure statement

The authors have no competing interests to declare that are relevant to the content of this article.

Notes

1 Source: http://www.sic.gov.cn/News/557/10779.htm

2 Source: http://www.sic.gov.cn/News/79/8860.htm

3 It deserves mentioning that there are some studies focusing on efficiency forecasting with DEA. For example, Vaninsky (2006) examines the efficiency of electric power generation in the United States from 1991 to 2004. The efficiency of electric power generation for 2010 is also forecasted by an algebraic formulation. However, they do not forecast the production technology for 2010 explicitly in a rational manner.

4 We thank an anonymous reviewer for having pointed out this relevant aspect.

5 As shown later, the conservative estimations work well in the empirical application because significant technology progress is found in the B&B industry from 2014 to 2020.

6 The traditional slack-based measure, i.e., Model (3)(3) $$\begin{array}{c}\begin{array}{c}\begin{array}{c}\mathit{\text{Ef}}{f}^t\left(k,t\right)=\mathrm{\text{min}}\hspace{0.17em}\frac{1-\frac{1}{m}\sum _{i=1}^m\frac{s_{\mathit{\text{ik}}}^{-\left(t\right)}}{x_{\mathit{\text{ik}}}^{\left(t\right)}}}{1+\frac{1}{s}\sum _{r=1}^s\frac{s_{\mathit{\text{rk}}}^{+\left(t\right)}}{y_{\mathit{\text{rk}}}^{\left(t\right)}}}\\ \mathrm{s}.\mathrm{t}.\sum _{j=1}^n{\lambda }_j^{\left(t\right)}{x}_{\mathit{\text{ij}}}^{\left(t\right)}+s_{\mathit{\text{ik}}}^{-\left(t\right)}=x_{\mathit{\text{ik}}}^{\left(t\right)},\\ \sum _{j=1}^{}{\lambda }_j^{\left(t\right)}{y}_{\mathit{\text{rj}}}^{\left(t\right)}-s_{\mathit{\text{rk}}}^{+\left(t\right)}=y_{\mathit{\text{rk}}}^{\left(t\right)},\end{array}\\ \sum _{j=1}^n{\lambda }_j^{\left(t\right)}=1,\\ {\lambda }_j^{\left(t\right)}\ge 0,s_{\mathit{\text{ik}}}^{-\left(t\right)}\ge 0,s_{\mathit{\text{rk}}}^{+\left(t\right)}\ge 0,\end{array}\\ i=1,2,\dots ,m;r=1,2,\dots ,s;j=1,2,\dots ,n;t=1,2,\dots ,T,\end{array}$$(3) , can be used to differentiate the inefficient and efficient cities/ counties.

Additional information

Funding

This work was supported by National Natural Science Foundation of China (Nos. 72171238), Natural Science Foundation of Hunan Province (2021JJ20072), Young Scientific and Technological Talents Program of Hunan Province (2022RC1242).