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This paper aims to identify drivers of efficiency and their influence on airline performances in South Africa. Unfortunately, the methods currently used to measure airline efficiency fail to address the heterogeneity problem, which blurs inefficiency.

To remedy the heterogeneity problem, this paper adopts the meta-frontier framework to identify drivers of efficiency. The interesting feature of the model is that it ensures that heterogeneous airlines are compared based on one homogeneous technology. The model is tested using a panel data sample of nine South African airlines, which operated from 2015 to 2018.

The paper demonstrates that structural drivers, namely, “aircraft size”, and “airline ownership” and one executional driver, namely, “the cost structure” significantly influence (p < 0.05) airline efficiency thereby corroborating evidence from some prior studies.

First, because of the small size of the industry, fewer airlines and a lack of detailed data, the study could not consider other important factors such as optimal routing and network structure. Second, a more rigorous analysis over a period of time would yield better understanding about the growth of the industry in South Africa and recognise the variation in the influence of drivers of efficiency on airline performances over time.

The results have potential policy implications. First, as the market in South Africa is too small to operate with a smaller aircraft probably, for airlines that operate with smaller aircraft to operate efficiently they should first identify niche markets where they can have a route monopoly. Second, while all state-owned airlines are perfect statehood symbols that define and represent countries, most state carriers in South Africa are highly inefficient. The researcher recommends policymakers to privatise state airlines or seek equity partners. Many nationalised airlines have turned losses to profits in the run-up to privatisation. British Airways, once a large burden on the British taxpayer, is now one of the world’s most efficient airlines. After the privatisation of Air France and Iberia, all two turned from loss-making concerns into profitable airlines. It, therefore, makes no sense for the South African government to expect state carriers to pursue a commercial mandate with such political interference. The very notion of efficiency itself is at risk.

This paper is a first attempt to identify drivers of operational efficiency using a bootstrapped meta-frontier approach in the airline industry in South Africa. By applying the meta-frontier approach the paper ensures that all heterogeneous airlines are assessed based on their distance from a common and identical frontier.

The airline industry is structurally challenged by its very nature, because of high overhead and capital costs. This is further exacerbated by macro-predictability and micro-uncertainty, thereby making it difficult for airlines in South Africa to attain operational efficiency. The purpose of this study is to identify drivers of operational efficiency and their impacts on airline performances in South Africa.

An extensive data collection using primary and secondary sources enabled the researchers to gather data on all the airlines operating in South Africa, for the period of 2012-2016, on a variety of parameters. A two-stage empirical analysis was carried out, which involved estimation of operational efficiencies during the first stage by using data envelopment analysis (DEA) and determination of performance drivers during the second stage by using a two-way random-effects generalised least squares regression and also a Tobit model.

From the study, it is clear that two structural drivers, namely, “aircraft size” and “seat load factor”, and two executional drivers, namely, “low cost business model” and “revenue hours per aircraft”, significantly impacted (p < 0.05) positively on airline efficiencies in South Africa. To improve efficiency, management should first concentrate on the drivers that can be changed in the short-term (executional drivers) and later focus on the drivers that require long-term planning (structural drivers). However, among the structural drivers, only “aircraft families” had a negative impact on airline efficiencies, whilst among executional drivers, only “block hours” negatively impacted on airline efficiencies.

Despite the importance of this study, it is not free of limitations. Firstly, because of the small size of the industry, fewer airlines and lack of detailed data, the study could not consider other important factors such as optimal routing and network structure. Secondly, although non-aeronautical revenues have become increasingly important in airline management, they were not included in this study. Further studies may investigate the impact of these factors on airline efficiency.

The results have potential policy implications. Firstly, as the domestic airline market in South Africa is too small to operate with a smaller aircraft efficiently, airlines that intend to make use of smaller aircraft should first identify niche markets where they can have a route monopoly, such as SA Airlink. Secondly, as block time negatively affected airline efficiency, airlines can undertake schedule adjustments to reduce block time and thus improve technical efficiency.

This paper is a first attempt to identify drivers of operational efficiency in the airline industry in South Africa. The results indicate that DEA is a useful tool to identify factors impacting airline efficiency and could improve airline performances in South Africa.

Restaurants in South Africa have a notoriously high failure rate. This study aims to identify drivers of restaurant efficiency in South Africa.

A stochastic cost frontier function with three inputs (i.e. labour, food and beverage and materials) and one output as the total revenue is specified and used to estimate restaurant efficiency. An extensive data collection using primary and secondary sources enabled the researcher to gather data from 42 restaurants, for the year 2016, on a variety of parameters.

The findings show that on average restaurants were operating at 77%, with the most and least efficient restaurants operating at a 97 and a 43% efficiency level, respectively. From the study, it is clear that two structural drivers, namely, “location” and “operation type”, and two executional drivers, namely, “restaurant type” and “revenue per available seat hour”, significantly impacted (p < 0.05) on restaurant efficiency in South Africa.

Despite the importance of this study, it is not free of limitations. First, the research was based on efficiency drivers for restaurants situated in a specific South African province. Caution is therefore required when generalising the findings of this study to restaurants in other geographic areas, as a replication of this study in other geographic areas might reveal varying levels of efficiency. Second, the measurement of restaurant efficiency was limited to five efficiency drivers. Even though these efficiency drivers were included in other studies as well, there could be other relevant efficiency drivers that are likely to influence restaurant efficiency.

To improve efficiency, restaurateurs should first concentrate on the drivers that can be changed in the short term (executional drivers) and then later focus on the drivers that require long-term planning (structural drivers). Restaurateurs should understand the use of RevPASH strategies to manipulate demand during peak and off-peak periods. Furthermore, restaurants should be able to change the table mix to optimise table configuration. Changing a restaurant’s table configuration during peak times increases efficiency.

This paper is a first attempt to identify drivers of operational efficiency using a stochastic approach in the restaurant industry in South Africa. As restaurants in South Africa have a high failure rate, the results could assist restaurateurs in managing more successful entities.

The purpose of this paper is to explore the intricate relationship between the flagship of the sharing economy, Airbnb and hotel revenue per available room (RevPAR) in South Africa.

To identify the impact of Airbnb on hotel RevPAR, the paper used a triple difference-in-differences framework that compares changes in cities in South Africa where Airbnb started operating relative to areas without Airbnb. A total of 569 hotels were analysed.

While the study finds no evidence of adverse impacts of Airbnb on hotel RevPAR, the findings show that the entry of Airbnb led to a decrease in RevPAR of budget hotels. However, its impact is more pronounced during periods of peak demand, consequently, disrupting the pricing power of hotels.

The research was based on the impact of Airbnb on hotel RevPAR in hotels situated in specific cities in South Africa. Caution is therefore required when generalising the findings of this study to other hotels in other geographic areas. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel RevPAR. However, the findings underscore the notion that innovations are not intrinsically disruptive but only relative to another product. In so doing, the study adds to the limited body of work in the field on disruptive innovation and to the academic discourse on innovation in tourism more broadly.

First, the findings suggest the impact on hotels tends towards Airbnb generally playing a largely complementary role rather than a diversionary one. However, to increase RevPAR, hotels should systematically change their pricing models to account for flexible capacity by rethinking the wisdom of seasonal pricing and reduce prices during peak seasons to avoid inviting more competition from Airbnb.

To the best of the author’s knowledge, this paper is the first to explore the relationship between Airbnb and hotel markets using a triple difference methodology.

Restaurants are characterised by predictable, seasonal factors and unpredictable, individual customer demand, which make it difficult for restaurateurs to attain efficiency. A combination of these two factors, macro-predictability and micro-uncertainty, produces economic risks, which make it difficult for restaurants to attain operational efficiency. The purpose of this study is to identify factors impacting restaurant efficiency in South Africa.

By using primary and secondary sources, data were collected from 16 different types of restaurants in South Africa, for the period 2012-2016, on a variety of parameters. A two-stage empirical analysis was carried out, which involved the estimation of operational efficiencies during the first stage by using data envelopment analysis (DEA) and determination of factors impacting restaurant performance in South Africa during the second stage by using two-way random-effects generalised least squares and Tobit regression models.

The results clearly show that the ability of restaurants to succeed will not be determined by their size but by their type, location and revenue per available seat. While the study finds various factors impacting on operational efficiency, the survival of restaurants in South Africa seem to be determined by cost efficiency, which brings in better market performance through lowering cost of sales.

The results have implications for restaurant managers in that if they want to improve cost efficiency, they must manage restaurant capacity and customer demand in a way that maximises revenue. To stimulate demand during periods of low demand, management could consider strategies that attract more customers or encourage upselling, whereas during periods of high demand, management may consider raising prices or reducing meal durations. The results indicate that DEA is a useful tool to identify factors impacting restaurant efficiency and could enhance the service data and revenue management with regards to restaurant efficiency in South Africa.

To the best of the author’s knowledge, this paper is the first that attempts to identify factors impacting restaurant efficiency in South Africa by using DEA. The findings could enhance the service data and revenue management with regards to restaurant efficiency in South Africa.

The sharing economy has caught great attention from researchers and policymakers. However, due to the dearth of available data, not much empirical evidence has been provided. This paper aims to empirically assess the impacts of Airbnb on hotel performances in South Africa.

Using South Africa as a case study, the study measures the impacts of Airbnb on hotel performances on three key metrics, namely, room prices, occupancy and Revenue per available room (RevPAR). A difference-in-difference model is estimated using a population-based data set of 809 hotels from 2016 to 2018.

The results reveal that despite Airbnb significantly and negatively impacting on hotel occupancies it has a non-significant effect on hotel prices and RevPAR. Although from the theoretical perspective a disruptive innovation business model such as Airbnb can possibly have a negligible effect on hotel performances because it may attract a different group of customers and create a new market, the empirical findings of this study fail to support this theoretical hypothesis. Consequently, the findings diverge with newly developed knowledge in other markets and point to nuanced and contextual complementary effects.

Although some interesting findings are revealed into his study, some caveats remain. For instance, the study relied on data from hotels not from Airbnb. If the data of Airbnb can become available, it would be interesting to further examine whether the aggregated RevPAR of Airbnb can compensate for the aggregated loss of hotel RevPAR. This type of analysis could provide a broader evaluation scope regarding the overall effect of Airbnb on hotel performances. Moreover, if a longer time series data set of hotels in the post-Airbnb time period could become available, it would be interesting to further investigate the time-varying dynamic effects of Airbnb on hotel performances.

While hotels have launched a campaign to portray Airbnb as being commercial operators looking to compete illegally with hotels for the same segment of customers, this study shows that the rhetoric has been exaggerated. Airbnb, and more broadly, vacation rentals do not represent a war with hotels. They represent an answer to a different need. Indeed, the study reveals that Airbnb’s offer is a mere supplement to the market contrary to media rhetoric that it is meant to substitute hotels. The study has several implications for practitioners. First, these results are important because they serve as evidence against news articles that claim Airbnb is driving hotels out of business. They also show that if current trends continue, employees in the hotel industry in South Africa do not need to be concerned about losing their jobs because of Airbnb’s emergence. It is also important information for investors who may be concerned that Airbnb is hurting the hotel industry’s bottom line. Second, as the share of Airbnb listings on the accommodation market varies dramatically between cities, it is likely that eventual regulations/restrictions should be introduced in the provincial levels, while most of the cities continue benefiting from the increasing number of Airbnb visitors.

To the best of the author’s knowledge, this study is the first in South Africa to provide empirical evidence that Airbnb is significantly changing consumption patterns in the hotel industry, as opposed to generating purely incremental economic activity.

The purpose of this paper is to examine the impacts of Airbnb on hotel employment.

A triple-difference regression framework was used to compare changes in hotel employment in three cities in South Africa where Airbnb started operating relative to cities without this home-sharing platform. Data on hotel employment was drawn from the survey on hotel service conducted by Smith Travel Research whereas the data on Airbnb’s listings was drawn from the Airbnb’s homepage for the period between July 2015 and December 2018. The analysis was restricted to 792 hotels.

Although prior studies have showed that Airbnb has an adverse impact on hotel performances, the findings show that total hotel employment expanded after the entry of Airbnb, accompanied by a marked relative shift towards self-employment. Airbnb supply negatively affected permanent employment, which was compensated by increase in temporary employment and self-employment. While the paper finds no evidence of adverse employment impacts, the hourly earnings declined for hotel employees were compensated by increase in income among self-employed Airbnb hosts. Although these findings might be contradictory to the general conjecture, such evidence calls for a comprehensive investigation of Airbnb’s overall economic impact.

First, the study was based on the impact of Airbnb on hotel employment in South Africa. Caution is therefore required when generalising the findings of this study in other geographic areas. Second, some variables that may affect employment have not been included in this study because of the unavailability of data.

The results show that the sharing economy plays an important role in solving the unemployment problem, particularly in South Africa, where there is a high rate of unemployment. More practical implications are further discussed.

To the best of author’s knowledge, this paper provides the first systematic evidence of the impact of Airbnb on labour market outcomes in the hotel industry in South Africa.

本论文研究Airbnb对酒店就业的影响。

研究采用三重差分回归模型, 对南非三个有Airbnb经营的城市与其他没有Airbnb经营的城市做酒店就业情况的对比分析。酒店就业数据来自于Smith Travel Research进行的Hotel Service问卷。Airbnb数据来自于2015年七月至2018年十二月之间的Airbnb主页上的租赁房屋数据。共792家酒店参与了数据分析。

尽管之前的研究表明Airbnb对酒店绩效有着负面影响, 但是本论文研究表明, 在Airbnb进驻城市后, 该城市的酒店就业提升了, 并伴随着显著的自营就业。然而, Airbnb最终还是对长期的就业有着负面影响, 只是短期就业和自营就业的比重增大而已。虽然本论文未发现Airbnb对就业有负面影响, 但是酒店员工的时薪降低了, 随之弥补的是自营就业的收入增加。尽管本研究结果对整体的文献有着矛盾的地方, 但是仍需针对Airbnb经济影响的全面调研。

首先, 本论文仅研究了南非Airbnb对酒店就业情况的影响。其研究结果在其他地区的推广性则有待考察。第二, 受到数据限制, 一些可能会影响就业率的因素未被收录在本研究样本内。

研究结果表明共享经济对解决就业问题有着重要影响, 尤其是在失业率较高的南非地区。更多的实际意义可以被深入讨论。

本论文是首篇系统分析了Airbnb对南非酒店业劳动市场影响的文章。

Although a substantial amount of research on the effect of demographics on service quality perception can be found in the extant literature, practitioners and researchers in the built environment, especially property managers, know little or nothing about the influence of demographic characteristics on service quality in Nigeria. Hence, the purpose of this paper is to examine the effect of demographic variables of the occupiers of shopping complexes on the perception of service quality of property managers.

Primary data were used for the study through questionnaire administration. Ibadan was stratified into five axes using existing major roads where shopping complexes were highly concentrated. Based on the Oyo State Ministry of Land, Housing and Survey (2017) database, there were 33, 65, 48, 64 and 66 shopping complexes on each identified major road. A systematic sampling technique was adopted to select 57 (20 per cent) out of 276 shopping complexes and 192 (10 per cent) out of 1,919 occupiers of the shopping complexes in the study area. In total, 157 occupiers responded to the questionnaire, and data were analysed using mean, independent t-test, and one-way ANOVA.

The study found that the gender and education of the occupiers significantly influenced service quality perception, whereas the age of the respondents was insignificant. Specifically, female occupiers rated the service quality of property managers better than their male counterparts. Through η², the effect size of gender on service quality perception was small (17 per cent) and effect size on the level education of the occupiers was also small (13 per cent). The age of occupiers did not significantly influence service quality perception. It is expected that the findings of this study will help property managers to understand the effect of various demographic variables on service quality perception for enhanced property management practice.

The findings suggested that the property managers of shopping complex should structure their services to best accommodate occupiers’ demographics. Moreover, property managers can use the findings to guide demographic-driven marketing strategy to target and attract more tenants efficiently.

The study is one of the few studies that examined the effect of the demographic characteristics of occupiers on service quality perception that could enhance given individualised attention to occupiers.