Does a low-cost carrier lead the domestic tourism demand and growth of New Zealand?

Abstract

This study aims to provide a better understanding of the impact of New Zealand's low-cost carrier (LCC) on domestic tourism demand and growth. The panel data regression model and the two-stage least-square (2SLS) model (aims to control for the endogeneity effects) are used to empirically investigate the impact of LCC and the key determinants affecting New Zealand's domestic tourism using five regions (Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) from June 2009 to July 2015. The findings suggested that the LCC's services, GDP per capita, the regional tourism indicators (accommodation, and food and beverage), and land transport costs affected New Zealand's domestic tourism. The policy implications of the key finding regarding the significance of the LCC's operations on New Zealand's domestic tourism (local/regional tourism authorities and tourism operators), airline competition between incumbent airline (Air New Zealand) and the LCC (Jetstar), and airport authorities are discussed.

Highlights

• •Understand the impact of LCC on New Zealand's domestic tourism demand and growth.
• •Five New Zealand key regions were included in this study.
• •LCC's services, GDP per capita, the regional tourism indicators, and land transport cost affected New Zealand's domestic tourism.
• •Findings have implications for strategic planning for tourism authorities and operators, airlines and airports.

1. Introduction

The successful low-cost airline business model developed by Southwest Airlines in the early 1970s has since spread to different parts of the world (Chiou and Chen, 2010, Francis et al., 2006). Recent literature has suggested that low-cost carriers (LCCs) have become one of the key drivers of tourism development for a country or city (e.g. Chung and Whang, 2011, Dobruszkes and Mondou, 2013, Eugenio-Martin and Inchausti-Sintes, 2016, Graham and Dennis, 2010, Rey et al., 2011, Zhang and Lu, 2013). The emergence of LCCs has attracted different types of air travellers (leisure and business travellers) to visit other destinations with lower airfares, more frequent flights, loyalty programmes, and short turnaround times (e.g. Budd et al., 2014, Chang and Hung, 2013, Dresner, 2006, Mason, 2000). There seems little doubt that the rapid development of LCCs worldwide has had a significant impact on the tourism sector in terms of the transportation of air travellers and the promotion of tourist destinations, as well as the generation of tourists to emerging and mature destinations (Clavé, Saladié, Cortés-Jiménez, Young, & Young, 2015).

New Zealand is an island country but is one of the popular international tourist destinations for visits, adventure, and sightseeing, thanks to this country's varied natural resources and heritage (e.g. Maori culture) (e.g. Balli and Tsui, 2015, Cloke and Perkins, 2002, Pearce, 1999, Ryan, 2002). It is evident that most tourism research of New Zealand has focussed on international tourism, as inbound and outbound international travel is strong. For 2014 and 2015, the total international visitor arrivals to New Zealand increased from approximately 2.86 to 3.13 million (Statistics New Zealand, 2015a). In addition, the total number of New Zealand residents travelling overseas grew to approximately 2.41 million from 2.27 million during the same period (Statistics New Zealand, 2015b). In contrast, domestic tourism in New Zealand has generally been less researched, considering its lesser importance in generating foreign exchange and earnings from the increasing number of international visitor arrivals (Pearce, 1993, Pearce, 1999). Arguably, international tourists to New Zealand cannot always sustain the New Zealand tourism industry, especially in the years of economic turmoil (e.g. the global financial crisis of 2008/09) and other exogenous shocks (e.g. the US 9/11 terrorist attacks in 2001 and the severe acute respiratory syndrome (SARS) outbreak in 2003) (Becken and Gnoth, 2004, Yeoman et al., 2012). Although these events have had negative impacts on international tourism in the short term, New Zealand's tourism industry still looks promising in the long term, especially when domestic tourism is taken into consideration. In this light, a vibrant domestic tourism can bolster New Zealand's tourism industry from the fluctuations of international visitor arrivals, and also generates stability and predictability for its tourism sector (Okello, Kenana, & Kieti, 2012).

In 1996, the first LCC entered in New Zealand's aviation market and had a minimal market share (Francis et al., 2006). However, Jetstar (the subsidiary of Qantas) was the first LCC to start scheduled domestic flight services in New Zealand since June 2009. To the best of our knowledge, only a few studies have investigated the LCC's impact on New Zealand's domestic tourism demand, although this rapidly growing low-cost air transport market deserves a thorough examination. Importantly, New Zealand's domestic tourism industry exhibited strong growth recently, and there were more than 21.5 million domestic guest nights across New Zealand's regions and cities in 2015 (Statistics New Zealand, 2015c).1 This active and strong domestic tourism demand in New Zealand again prompted this study to empirically explore the impact of the LCC (i.e. Jetstar) on domestic tourism demand and growth in New Zealand.

Our contribution to the literature and strategic planning and decision-marking for policy makers (e.g. regional/local tourism authorities and tourism operators, airline management, and airports) has emerged from a thorough investigation of the relationship between domestic tourism development and the LCC's operations in New Zealand. Again, it is generally accepted that the LCC is playing an increasingly important role in New Zealand's domestic tourism sector, and there are only a few empirical studies and reports in the area of New Zealand's domestic tourism sector. Therefore, this study developed the econometric models to investigate the LCC's role in New Zealand's domestic tourism development, given that the five key New Zealand regions and tourist destinations (i.e. Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) are currently served by the LCC (Jetstar); these regions took a combined share of 44.07% and 43.94% of the total domestic guest nights in 2014 and 2015, respectively (Statistics New Zealand, 2015c). Simply, a clear knowledge of the role of the LCC on the tourism demand of New Zealand's major regions and tourist destinations can offer a better estimation of domestic tourist flows with further expansion of the LCC's operations to other smaller regions and/or cities. Thus the LCC's operations will benefit the future growth of New Zealand's domestic tourism demand and local/regional economies.

The format of this paper is structured as follows. Section 2 provides literature review of the relationship between domestic tourism and LCCs, and the determinants that affect domestic tourism. Section 3 provides an overview of the LCC serving New Zealand's five key regions and tourist destinations (i.e. Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) and presents the dataset for analysis. Section 4 describes the methodology used to investigate the role of the LCC on New Zealand's domestic tourism demand and growth. Section 5 presents the empirical findings of the study. Section 6 provides a discussion of the key findings and policy implications of this study. The final section summarises the key findings and indicates a direction for future research.

2. Literature review

Two important aspects of domestic tourism and LCCs are examined in the following review sections, commencing with the relationship between domestic tourism and the impact of LCCs, followed by the determinants affecting domestic tourism.

2.1. Domestic tourism and the impact of LCCs

Airlines (including LCCs) play an important role in linking domestic destinations (Prideaux & Whyte, 2014). Air traffic is a strong predictor of tourist arrivals (Albalate & Fageda, 2016). Tourism and air transport are explicitly linked, especially in the context of leisure traffic, and demand for air transport services is essentially derived from tourism activities (Lei and Papatheodorou, 2010, Papatheodorou and Lei, 2006). Most of the demand for LCC operations has been generated by the leisure market, including travellers on vacation and those visiting friends and relatives (VFR) (Lawton and Solomko, 2005, Mason and Alamdari, 2007). In tourism and air transport literature, researchers have generally achieved a consensus about the positive impact of LCCs on domestic tourism demand and domestic air travel demand. Dresner, Lin, and Windle (1996) suggested that the impact of LCCs on communities in the United States associated with the provision of low-cost services, fare reductions, and flight services between specific city-pairs other than neighbouring city pairs. Dresner (2006) further mentioned that the growth of LCCs has been important to the domestic US market, as LCCs transported a higher percentage of leisure passengers.

Similar situations have been found in Australia. Whyte and Prideaux (2007) suggested that the growth in Australian domestic air travel can be attributed to the two LCCs (Jetstar and Virgin Blue), and both airlines have benefited the regional tourism of Queensland destinations such as Cairns. Koo, Wu, and Dwyer (2009) also suggested that LCCs in Australia have improved air travel access to regions outside the state capital cities in Australia by offering discounted tickets and non-stop services from the key domestic origin markets. In addition, recent research by Williams and Baláž (2009) pointed out that the low-cost flight activities have had substantial impacts on labour migrants, knowledge, business connectivity/investment, and mobile markets, specifically tourism. In the case of Italy, Donzelli (2010) also revealed that LCCs contribute to job creation and increase tourism revenue, as well as having a significant impact on the local economy of Southern Italy. Moreover, Pulina and Cortés-Jiménez (2010) illustrated how LCCs influence the domestic and international tourism demand of the town of Alghero (Italy).

2.2. Determinants affecting domestic tourism

This section reviews the possible determinants that affect domestic tourism. There are relatively few prior studies on domestic tourism demand, and therefore, the literature on both international and domestic tourism is used to form a framework for this study to examine New Zealand's domestic tourism demand and growth empirically. In the tourism literature, habit persistence or repeat visits to key tourist destinations is important for explaining tourism demand (Balli et al., 2016, Garín-Muñoz and Montero-Martín, 2007, Habibi, 2016, Pearce, 2012). Air accessibility (Koo et al., 2009, Tan et al., 2016, Whyte and Prideaux, 2007) and the availability of lower airfare services (e.g. low-cost airlines have had a strong impact on domestic air travel) (Dresner, 2006, Dresner et al., 1996) has promoted the growth of domestic tourism in different countries. Seasonality and calendar patterns of tourism demand are generally believed to constitute important issues for tourist destinations and tourism operators (Chung, 2009, Lim and McAleer, 2008).

It is also notable that economic demand-driven variables, such as tourism price variables, affected the number of international and domestic tourists travelling to a destination. These tourism price variables include destination prices or living costs such as accommodation (e.g. Cracolici and Nijkamp, 2009, Eugenio-Martin, 2003, Narayan, 2004, Witt and Martin, 1987), food and beverage prices (Narayan, 2004, Wen, 1997), and transportation costs (e.g. Divisekera, 2003, Kozak, 2001, Lim, 1999, Prideaux, 2000). It has also been suggested that accommodation and food and beverage are the two major sources of tourism revenue (Wang, Hung, & Shang, 2006). Relating to transportation costs, in the context of tourism demand and air transport demand, distance has been widely applied to estimate domestic and international tourism demand for a destination and/or a city-pair's air traffic volumes based on the gravity model: distances discourage travel between destinations (e.g. Hazledine, 2009, Khadaroo and Seetanah, 2008, Massidda and Etzo, 2012, Morley et al., 2014). In addition, Prideaux (2000) claimed transport access cost represents a significant factor in total holiday costs. Transportation costs can be grouped into air transport cost and land transport cost (Efthymiou and Papatheodorou, 2015, Syriopoulos and Thea Sinclair, 1993, Witt and Martin, 1987). In particular, Witt and Martin (1987) suggested that two points relating to transport cost to a destination need to be aware: (i) the substitutability of air transport for surface transport or land transport; and (ii) the increased and decreased attractiveness of near destinations, which are easily accessible by surface transport as compared with more distant destinations for which airfares increase or decrease relative to surface travel costs.

Income variables (e.g. gross domestic product (GDP) per capita or real income) can also explain domestic tourism demand (Athanasopoulos and Hyndman, 2008, Garín-Muñoz, 2009, Seddighi and Shearing, 1997). In particular, Athanasopoulos and Hyndman (2008) found a negative impact of real GDP per capital on domestic tourism (i.e. the number of nights spent for holiday purposes) in Australia, and they also argued that Australians preferred to travel to foreign destinations than domestic destinations when Australia's economy activities improve. In this context, domestic and international destinations behave as substitute goods (Massidda & Etzo, 2012). Furthermore, the exchange rate (another important economic factor used to analyse international tourism demand) between the origin country and a foreign country has been consistently used in modelling international tourism demand (Song and Li, 2008, Tsui and Balli, 2015) because it has significant effect upon inbound and outbound tourism demand and can be treated as the pull and push factors of tourism demand (e.g. Hanqin & Lam, 1999 2 ; Prideaux, 2005, Schiff and Becken, 2011, Webber, 2001). In addition, Martin and Witt (1988) noted that the cost of travel to a substitute destination could be expected to be a factor in destination selection.

Exogenous shocks and crisis events (e.g. the US 9/11 terrorist attacks in 2001, the SARS outbreaks in 2003, the global financial crisis in 2008/09, the Christchurch earthquakes in 2011) are believed to have negative effects upon tourism demand (Tsui et al., 2014, Voltes-Dorta et al., 2015, Wang, 2009, Yeoman et al., 2012). Regarding the impact of terrorism, Voltes-Dorta et al. (2015) suggested that the ceasefire from the terrorist organisation in Spain has a significant positive impact on the number of visitors. In New Zealand, Tsui, Gilbey and Balli (2014) found that the Christchurch earthquakes in 2011 had a significant adverse effect on passenger numbers via the Christchurch International Airport. Similarly, Mazzocchi and Montini (2001) found that local tourist arrivals in Assisi (Central Italy) fell dramatically after the earthquake.

3. Data sources and descriptive analysis

3.1. Data sources

The panel dataset of New Zealand's five key regions and tourist destinations (i.e. Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) from June 2009 to July 2015 was assembled using a variety of sources (see Table 1 ). Data for the number of domestic guest nights and the total guest nights of the five regions in New Zealand were collected from Statistics New Zealand. In order to measure the impact of the LCC (Jetstar) on New Zealand's domestic tourism demand and growth, I also collected information on available seat kilometres (ASKs) scheduled by the LCC (Jetstar) and all the commercial airlines (Air New Zealand)3 to the airport located in each region from the Official Airline Guide (OAG). It should be noted that there is only one commercial airport per city across New Zealand. Data on the GDP per capita in New Zealand were also collected from Statistics New Zealand for capturing the level of income of the New Zealand population for domestic holidays and travel propensity. In addition, I measured the effect of the exchange rate with regards to the substitution effect of international and domestic vacations; the exchange rate between the New Zealand and US dollars was retrieved from the Reserve Bank of New Zealand. Furthermore, two main regional tourism indicators (RTIs)4 (the tourism price indices: accommodation, food and beverage) were obtained from the Ministry of Business Innovation & Development. Transport costs (aviation fuel price and petrol price) for domestic visitors to travel to a destination were collected from the Ministry of Transport. The two RTIs and transport costs aim to grasp the effect of the price levels that domestic tourists and holidaymakers paid for visiting and travelling to the sampled regions and/or cities in this study.

Table 1.

Variable definition and sources.

Time series and variables	Definition	Source
ln(Domestic guest nights)it	The logarithm of the number of domestic guest nights for region i at time t	Statistics New Zealand
ln(Domestic guest night)it-1	The previous period's number of domestic guest nights for region i at time t. This variable is created by taking one-period lag of ln(Domestic guest nights)it	Statistics New Zealand
(%) of domestic guest nightsit	Market share of domestic guest nights for region i at time t, calculated as the number of domestic guest nights divided by the total guest nights (domestic and international) region i at time t. This variable is reported as a percentage (%)	Statistics New Zealand, author's own calculation
ln(LCC's ASK)it	The logarithm of available seat kilometres (ASKs) scheduled by the LCC (Jetstar) for the airport of region i at time t	Official Airline Guide
(%) of LCC's ASKit	Market share of the LCC's ASK for the airport of region i at time t, calculated as ASKs scheduled by the LCC divided by the total ASKs scheduled by all commercial airlines for that airport at time t. This variable is reported as a percentage (%)	Official Airline Guide, Author's own calculation
LCC dummyit	A binary variable that takes 1 after the entrance of LCC's services for the airport of region i at time t and 0 otherwise	Author's own calculation
ln(GDP per capita)t	The logarithm of GDP per capita of New Zealand at time t	Statistics New Zealand
ln(RTI-Accommodation)it	The logarithm of the RTI of accommodation costs for domestic tourists for region i at time t	Ministry of Business, Innovation & Development
ln(RTI-Food & Beverage)it	The logarithm of the RTI of food and beverage costs for domestic tourists for region i at time t	Ministry of Business, Innovation & Development
ln(Aviation fuel price)t	The logarithm of aviation jet fuel price per gallon at time t in New Zealand dollars	US EIA Energy information Administration
ln(Petrol price)t	The logarithm of petrol price per litre at time t in New Zealand dollars	Ministry of Transport
ln(HHI index)it	Airport competition between low-cost carrier and incumbent carrier of region i at time t	Author's own calculation
Exchange rate (NZD vs.USD)t	The exchange rate between New Zealand and US dollars at time t	Reserve Bank of New Zealand
Global financial crisis 2008/09	A binary variable that takes 1 for the period of the global financial crisis in 2008/09 and 0 otherwise	Author's own calculation
Christchurch earthquakes 2011	A binary variable that takes 1 for the period of the Christchurch earthquakes between from February 2011 to January 2012 and 0 otherwise	Author's own calculation
Seasonal dummies	A dummy variable for each month of the year	Author's own calculation

As shown in Table 1, I have also created two dummy variables (the global financial crisis 2008/09 and the Christchurch earthquakes 2011) to capture the perceived negative impacts of these exogenous crises on domestic tourism demand in New Zealand. These two dummy variables take the value of 1 when the crises occurred and 0 otherwise. The monthly dummy variables (seasonal dummies) were also created to capture the fluctuation in seasonal patterns of New Zealand's domestic tourism demand.

3.2. Overview of domestic guest nights and ASKs for the regions and descriptive statistics

Auckland, Canterbury/Christchurch, Queenstown, and Wellington regions are always the four most popular domestic tourist destinations in New Zealand. Fig. 1 shows monthly domestic guest nights and monthly ASKs scheduled by the LCC for the studied regions and their respective shares for each of the regions for the period of June 2009–July 2015. In Fig. 1, the time series of the monthly domestic guest nights and the monthly LCC's ASKs for the five New Zealand regions exhibited different patterns, alongside the possibility of seasonal patterns. Note that New Zealand's domestic tourism has the characteristics of shorter stays and a strong visiting friends and family sector (Ministry of Business, Innovation & Employment, 2014).5 The Auckland region leads the group with an average of 298,554 domestic guest nights per month, followed by Canterbury/Christchurch (155,645), Wellington (132,025), Queenstown (74,124), and Dunedin (42,269). Accordingly, the LCC's monthly average ASK for Auckland Airport is approximately 48.02 million, followed by Christchurch Airport (26.42 million), Wellington Airport (16.61 million), Queenstown Airport (10.84 million), and Dunedin Airport (3.76 million). In addition, the shares of domestic guest nights of the total guest nights (domestic and international) for all five regions exceed 56%, except for Queenstown has a share of 33.76%. The lower share of domestic guest nights for the Queenstown region indicates that it handled more international tourists than domestic tourists during the study period. However, the shares of the LCC's services for all the studied airports are between 13.14% and 31.36%: Queenstown Airport (31.36%), Auckland Airport (24.38%), Christchurch Airport (22.10%), Wellington Airport (17.24%), and Dunedin Airport (13.14%). These figures suggest that the LCC plays a significant role in New Zealand's domestic aviation market in transporting air travellers between the key cities and tourist destinations (Official Airline Guides (OAG), 2015, Statistics New Zealand, 2015c).

Fig. 1.

Monthly domestic guest nights and the low-cost carrier's monthly available seat kilometres (ASKs) for New Zealand's five regions and airports (June 2009–July 2015). Remarks: The LCC (Jetstar) only commenced its low-cost flight services for Dunedin Airport in July 2011.

Detailed explanations of all the variables of interest are shown in Table 1. The descriptive statistics for all variables of interest (in logarithmic form) for the period of June 2009–July 2015 are shown in Table 2 . Two dependent variables, ln(Domestic guest nights)_it and (%) of Domestic guest nights _it are the number of domestic guest nights and the share of domestic guest nights of the total guest nights (domestic and international) of a region, respectively. The variables ln(Domestic guest nights)_it-1, ln(LCC's ASK)_it, (%) of LCC's ASK _it, ln(GDP per capita)_t, ln(RTI-Accommodation, Food & Beverage)_it, ln(aviation fuel price)_t, ln(petrol price)_t, and Exchange rate (NZD vs. USD)_t are used as the explanatory variables in the panel data regression models, together with three dummy variables (Global financial crisis 2008/09, Christchurch earthquakes 2011, and Seasonal dummies) to capture their impacts on New Zealand's domestic tourism. The descriptive statistics in Table 2 shows that the distribution of eight time series variables, ln(Domestic guest nights)_it, (%) of Domestic guest nights _it, ln(LCC's ASK)_it, (%) of LCC's ASK _it, ln(RTI-Accommodation)_it, ln(aviation fuel price)_t, ln(petrol price)_t, and Exchange rate (NZD vs. USD)_t are skewed to the left. The remaining variables in this study are skewed to the right and peaked with respect to their respective skewness and kurtosis values. In addition, the multicollinearity among all the variables of interest was verified by the correlation matrix of variables; the test results show no significant correlations among the explanatory variables in the dataset.

Table 2.

Descriptive statistics for variables of New Zealand's domestic tourism (June 2009–July 2015).

Time series and variables	Observations	Mean	Standard deviation	Maximum	Minimum	Skewness	Kurtosis
ln(Domestic guest nights)it	370	11.63	0.69	12.85	10.39	-0.09	1.95
(%) of Domestic guest nightsit	370	55.39%	13.22%	78.18%	23.20%	-0.52	2.45
ln(LCC's ASK)it	345 a	16.68	0.74	17.97	0.74	-0.10	1.94
(%) of LCC's ASKit	370	0.22	0.08	0.50	0.00	-0.62	4.78
ln(GDP per capita)t	370	8.29	0.06	8.40	8.20	0.22	1.88
ln(RTI-Accommodation)it	370	4.55	0.21	5.05	4.01	-0.19	2.66
ln(RTI-Food & Beverage)it	370	4.73	0.19	5.31	4.20	0.13	3.15
ln(Aviation fuel price)t	370	1.17	0.17	1.44	0.67	-0.83	2.99
ln(Petrol price)t	370	0.70	0.09	0.80	0.50	-0.97	2.45
ln(HHI index)it	370	8.81	0.14	0.921	8.52	1.30	5.18
Exchange rate (NZD vs.USD)t	370	0.78	0.06	0.87	0.64	-0.53	2.47
Global financial crisis 2008/09	370	0.09	0.29	1.00	0.00	2.77	8.68
Christchurch earthquakes 2011	370	0.16	0.37	1.00	0.00	1.83	4.36

Remarks.

^aThe total of 345 observations of ln(LCC's ASK)_it is because the LCC (Jetstar) only started to provide low-cost flight services for Dunedin Airport from July 2011. Available seat kilometres (ASKs) equals the number of seats scheduled by an airline multiplied by the flying distance to a destination.

To estimate the panel data regression models, all the variables of interest need to be stationary in order to avoid the problem of spurious correlation (Alba and Papell, 2007, Balli et al., 2016). Therefore, the panel unit root tests were performed to test the stationarity of all the variables of interest investigated in this study. The results of panel unit root tests show that all the variables of interest are stationary, except the variables ln(GDP per capita)_t, ln(RTI-Accommodation)_it, ln(RTI-Food & Beverage)_it, ln(aviation fuel price)_t, ln(aviation fuel price)_t, and Exchange rate (NZD vs. USD)_t. Therefore, first-order differencing was applied to these six non-stationary variables to make them become stationary.6

4. Model specifications and econometric method

Prior literature shows that LCCs can have a significant impact on the tourism demand and tourism growth of a county or city. As the case for other destinations, New Zealand's domestic tourism demand and growth may be affected by the factors and variables discussed in Section 2. Therefore, this study developed econometric models to investigate New Zealand's domestic tourism demand and growth. Following the demand function of Chi, Koo, and Lim (2010), this study specified New Zealand's domestic tourism demand and growth as a function of the LCC's seat capacity (ASKs), GDP per capita, two RTIs (accommodation, food and beverage), transport costs (aviation fuel price and petrol price), the exchange rate, other exogenous variables (the global financial crisis of 2008/09 and the Christchurch earthquakes of 2011), and seasonal dummies. Importantly, the rapid development and expansion of the LCC's operations (Jetstar) within New Zealand's domestic aviation market and its impact on domestic tourism have been less studied than international tourism. This study aimed to fill this gap and used the LCC's seat capacity/per region panel dataset to investigate its impact on New Zealand's domestic tourism demand and growth, as well as identifying the variables that influenced tourism demand for the five key New Zealand regions and tourist destinations (i.e. Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) for the period of June 2009–July 2015.

Two panel data regression models were used to investigate the impact of LCC's services on New Zealand's domestic tourism demand and growth as specified as below:

Model 1:

$$\ln {\left(Domesticguestnights\right)}_{it}={\beta }_0+{\beta }_1\ln {(LCC\text{'}sASK)}_{it}+{\beta }_2\ln {\left(GDPpercapita\right)}_t+{\beta }_3\ln {(RTI-Accommodation)}_{it}+{\beta }_4\ln {(RTI-Food\&Beverage)}_{it}+{\beta }_5\ln {\left(Aviationfuelprice\right)}_t+{\beta }_6\ln {\left(Petrolprice\right)}_t+{\beta }_{7}Exchangerate{(NZDvs.USD)}_t+{\beta }_{8}Globalfinancialcrisis2008/09+{\beta }_{9}Christchurchearthquakes2011+{\beta }_{10}Seasonaldummie{s}_t+{\epsilon }_{it}$$

Model 2:

$$\left(\%\right)ofDomesticguestnight{s}_{it}={\beta }_0+{\beta }_1\left(\%\right)ofDomesticguestnight{s}_{it-1}+{\beta }_2\left(\%\right)ofLCC\text{'}sAS{K}_{it}+{\beta }_3\ln {\left(GDPpercapita\right)}_t+{\beta }_4\ln {\left(RTI-Accommodation\right)}_{it}+{\beta }_5\ln {\left(RTI-Food\&Beverage\right)}_{it}+{\beta }_6\ln {\left(Aviationfuelprice\right)}_t+{\beta }_7\ln {\left(Petrolprice\right)}_t+{\beta }_{8}Exchangerate{\left(NZDvs.USD\right)}_t+{\beta }_{9}Globalfinancialcrisis2008/09+{\beta }_{10}Christchurchearthquakes2011+{\beta }_{11}Seasonaldummie{s}_t+{\epsilon }_{it}$$

where i and t denote region i and month t, respectively; ln denotes the logarithm; β _s indicates the coefficients to be estimated; ε _it is the disturbance term. The statistical program of Eviews 8 was used for the estimation. It should be noted that the variable ln(Domestic guest nights)_it in Model 1 represents the number of domestic guest nights (a proxy for the number of domestic tourists and holidaymakers) of region i at month t, and the variable (%) of Domestic guest nights _it in Model 2 represents the share of domestic guest nights of the total guest nights of region i at month t. In addition, the variable (%) of LCC's ASK _it in Model 2 indicates the share of the LCC's seat capacity (Jetstar) provided at an airport; this variable is a measure of dominance which is a percentage of the total LCC's ASK in a particular region (Vowles, 2000), and also it indicates the LCC's position against the incumbent airline (Air New Zealand) in transporting domestic tourists and holidaymakers between New Zealand's regions and cities.

5. Empirical findings

Table 3 presents the estimation results of the panel data regression models and the two-stage least-square (2SLS) models: Model (1) - ln(Domestic guest nights)_it and Model (2) - (%) of Domestic guest nights _it. During the panel data regression analysis, the problem of endogeneity in Model 1A may provide inconsistent estimators or biased estimation results. Note that the endogeneity effect violates the assumptions of ordinary least squares (OLS), and thus the OLS estimator is biased, inconsistent and no longer the best linear unbiased estimator (BLUE) (Chi et al., 2010). In this study, the variable ln(LCC's ASK)_it in Model 1A can be endogenous, as an increase in airline seat capacity for an airport increases the number of tourists and holidaymakers using airline services to travel to the region where the airport is located, which, in turn, influences the total seat capacity scheduled by airlines. Thus this study used the instrumental variable (IV) technique to correct the problem of endogeneity (e.g. Borenstein, 1989, Hsiao, 2014, Tsui et al., 2016, Wooldridge, 2002).

Table 3.

Panel data regression models of the impacts of low-cost carrier on New Zealand's domestic tourism (June 2009–July 2015).

Dependent variables	Model 1			Model 2
	ln(Domestic guest nights)it			(%) of Domestic guest nightsit
Explanatory variables	Model - 1A	2SLS Model - 1B	2SLS Model - 1C	2SLS Model - 2A
	Coefficients	Coefficients	Coefficients	Coefficients
Constant	-2.0913** (-2.428)	-2.1247** (-2.415)	-0.0627 (-1.015)	0.0032 (0.282)
ln(Domestic guest nights)it-1	–	–	0.9395*** (82.407)	–
% of (Domestic guest nights)it-1	–	–	–	0.9691*** (68.127)
ln(LCC's ASK)it	0.8268*** (16.302)	0.8305*** a (15.885)	0.0493*** a (4.128)	–
% of ln(LCC's ASK)it	–	–	–	0.2320*** b (2.618)
ln(GDP per capita)t	5.9124*** (3.136)	11.5800*** (3.881)	0.7709 (0.532)	0.3545 (0.330)
ln(RTI -Accommodation)it	0.1877*** (5.705)	0.2155*** (4.742)	0.3342*** (5.924)	0.0245 (1.336)
ln(RTI -Food & Beverage)it	0.1753*** (6.455)	0.2889*** (5.746)	0.3843*** (6.010)	0.0405** (2.131)
ln(Aviation fuel price)t	0.0135 (0.119)	-0.1731 (-0.961)	-0.0375 (-0.507)	-0.0317 (-0.765)
ln(Petrol price)t	1.1410*** (3.183)	0.8084** (2.141)	0.2831 (1.348)	0.1503 (1.140)
Exchange rate (NZD vs.USD)t	0.2665 (0.539)	-0.8195 (-1.046)	-0.3787 (-1.354)	-0.0775 (-0.625)
Global financial crisis 2008/09	0.1872** (2.091)	0.0754 (0.795)	0.0074 (0.917)	-0.0014 (-0.160)
Christchurch earthquakes 2011	-0.0349 (-0.892)	-0.0796 (-1.773)	-0.0040 (-0.695)	0.0011 (0.188)
Seasonal dummy(1)	0.1272*** (4.756)	0.0743 (1.402)	0.1168** (2.308)	0.0144 (1.190)
Seasonal dummy(2)	0.0258 (0.565)	-0.0101 (-0.160)	-0.2066*** (-2.853)	-0.0141 (-1.195)
Seasonal dummy(3)	-0.0180 (-0.345)	0.0046 (0.075)	-0.0691 (-1.578)	0.0421*** (3.667)
Seasonal dummy(4)	0.0057 (0.084)	0.0093 (0.116)	-0.0413 (-1.383)	0.0657*** (5.900)
Seasonal dummy(5)	-0.0940 (-1.626)	-0.1945** (-2.298)	-0.2047*** (-3.786)	0.0528*** (4.901)
Seasonal dummy(6)	-0.1060 (-1.608)	-0.2179** (-2.259)	-0.0398 (-0.896)	0.0415*** (3.742)
Seasonal dummy(7)	-0.0451 (-0.588)	-0.0352 (-0.380)	0.0455*** (2.584)	-0.0005 (-0.048)
Seasonal dummy(8)	-0.0543 (-0.636)	-0.0401 (-0.354)	-0.0928*** (-2.750)	0.0086 (0.771)
Seasonal dummy(9)	-0.0837 (-1.674)	-0.1220 (-1.788)	-0.0450 (-1.188)	-0.0017 (-0.151)
Seasonal dummy(10)	-0.0485 (-1.132)	-0.0714 (-1.066)	-0.0353 (-0.791)	0.0056 (0.500)
Seasonal dummy(11)	-0.0072 (-0.172)	-0.0118 (-0.195)	-0.0696 (-1.593)	-0.0415*** (-3.754)
Adjusted R2	0.896	0.877	0.985	0.933
F-statistics	147.936	122.206	10.37.99	243.440
Observations	341	341	341	365

Remarks: ** and *** indicate that the explanatory variable is significant at the 0.05 and 0.01 significance level, respectively. t-statistics are printed in parentheses. The results of Hausman test verified that all the models favour using the random-effect models.

^ain Models 1B and 1C represent the predicted values of ln(LCC's ASK)_it, which are computed by using the instrumental variable ln(HHI Index)_it during the first-stage regression analysis. Results for the first-stage regression analysis are unreported for the sake of brevity.

^bThe results of the Hausman test verified that (%) of ln(LCC's ASK)_it in Model 2A is not an endogenous variable.

The IV technique used to correct the problem of endogeneity is shown below:

As mentioned above, the variable ln(LCC's ASK)_it is likely to be an endogenous variable in Model 1A. For the first-stage regression analysis, the variable ln(LCC's ASK)_it is regressed with the Herfindahl–Hirschman Index (HHI Index), which is an instrumental variable to measure airline market competition (it is calculated on the basis of the total ASKs an airline scheduled for an airport as a share of the whole) in New Zealand's domestic aviation market: the LCC (Jetstar) vs the incumbent airline (Air New Zealand).7 The predicted variable ln(LCC's ASK)_it was also obtained. As expected, the HHI Index is to have a negative coefficient during the first-stage regression analysis. As a region becomes more competitive, competition increases and LCC's ASK decreases (Fageda, Jiménez, & Perdiguero, 2011). The endogenous variable ln(LCC's ASK)_it is replaced with its predicted value during the second-stage regression analysis. Because the predicted values are uncorrelated with the error terms in the regression models, this IV technique can produce an unbiased coefficient from which the causality between ln(Domestic guest nights)_it and ln(LCC's ASK)_it can be inferred.

It should be noted that Model 1A contains the endogenous variable ln(LCC's ASK)_it, and the OLS estimators in the model are biased and inconsistent. Therefore, the subsequent interpretation and further analyses of the empirical results in this study were based on the 2SLS models (1B and 1C). The estimates obtained from these two 2SLS models are quite consistent and perform satisfactorily, as the magnitude and the signs of the coefficients seem to be theoretically reasonable and significant. In addition, the estimators can be interpreted, as elasticities as the model specification of Model 1 is in double-logarithmic form.

In examining the effect of total seat capacity scheduled by the LCC on New Zealand's domestic tourism demand and growth, the variable ln(LCC's ASK)_it is reported to be a highly significant factor in all models affecting the variable ln(Domestic guest nights)_it in all the regions. For example, Models 1B and 1C suggest that a 1% increase in the total seat capacity scheduled by the LCC (Jetstar) to the sampled regions is associated with a 0.05–0.83% increase in the number of domestic guest nights (meaning more domestic tourists visited and stayed at the studied regions). The elasticities of the variables ln(LCC's ASK)_it and ln(Domestic guest nights)_it reported in both models (1B and 1C) are in line with the study of Vowles (2000), claiming that a LCC provides low-cost service will have a strong effect in airfare price of a destination. Similarly, the study of Ryan and Birks (2006) claimed that airfare of a LCC is significant below that charged by full-service carriers in trans-Transman route, and their finding supported Australasian experience and the European experience.

Considering the effect of repeat visits to the sampled regions in this study, the variable ln(Domestic guest nights)_it-1 (the one-period lag of the dependent variable) is added to Model 1C as another explanatory variable. This variable aims to capture the dynamic information of the tourist numbers dataset, and the result shows that repeat visits or habit persistence is important in explaining the number of domestic guest nights of New Zealand's domestic tourism.

The income level variable ln(GDP per capita)_t was also found to boost New Zealand's domestic tourism demand and growth, with a statistically significant parameter in Model 1B. This result is consistent with our expectation and indicates that a 1% increase in the income per capita of the New Zealand population stimulates a 11.58% increase in domestic guest nights of the sampled regions; this also implies that the growth in the income level of the New Zealand population will increase domestic tourism demand. In contrast to other studies of domestic tourism demand, the estimation results show that two regional tourism indicator variables ln(RTI-Accommodation)_it and ln(RTI-Food & Beverage)_it for domestic tourists and holidaymakers to stay and visit the sample regions are reported to have positive significant estimators in Models 1B and 1C. These elasticities suggest that a 1% increase in accommodation prices and food and beverage prices still produce a 0.22–0.33% and 0.29–0.38% increase in the number of domestic guest nights for the sampled regions, respectively. This may be because New Zealand's domestic tourists and holidaymakers are not particularly concerned with increasing tourism-related costs and expenditure during their domestic trips even after the improvement in income per capital and higher spending power. It should be noted that New Zealand's GDP per capita grew from approximately NZ$43,845 in 2009 to NZ$53,012 in 2015, equalling a 20.91% increase (New Zealand Government Treasury, 2015).

Furthermore, two transport costs paid by domestic tourists to travel to a destination: air transport cost variable ln(Aviation fuel price)_t could not be found to be a significant variable affecting New Zealand's domestic tourism demand in all the models. Land transport cost variable ln(Petrol price)_t was found to have a significant impact on New Zealand's domestic tourism demand in Model 1B only but not in other models. This interesting empirical finding of ln(Petrol price)_t is logical and expected by considering driving is the most common form of transport among New Zealand residents and domestic tourists to travel to their holiday destinations. However, while discussing the amount of transport costs required for domestic trips, it is important to note the choice of transport (air transport or land transport) will depend on how quickly travellers want to get from Points A to B in terms of times and distances (New Zealand Tourism, 2016). For instance, air transport will be a preferable choice or a substitute of land transport for domestic tourists travelling from Auckland at North Island to Queenstown at South Island of New Zealand, as the trip itself is approximately 21 hours by driving and 1553 kilometres. On the other hand, land transport may be the preferable choice for domestic tourists who travel from Christchurch to Queenstown at South Island with approximately 6 hours by driving and 484 kilometres.8

In addition, the estimation results for all models show that the variable Exchange rate (NZD vs. USD)_t is not an important factor for explaining New Zealand's domestic tourism demand and growth. It should be noted that this variable is used as the proxy for the “substitute goods” of international vacations over domestic vacations by considering the strength of the New Zealand currency against the US dollar (one of the most commonly used currencies worldwide). This result is consistent with our expectation that the fluctuation (stronger or weaker) in New Zealand currency would not affect domestic tourism demand and growth, as the number of domestic tourist guest nights for the five sampled regions maintained their growth trends at different magnitudes from the exchange rate's trend (see Fig. 1). The dummy variables Global financial crisis 2008/09 and Christchurch earthquakes 2011 have been included to capture their negative impacts on New Zealand's domestic tourism. Their expected significant negative impacts on domestic guest nights are not reported in the models. In addition, the Seasonal dummies variables are included for controlling the fluctuation in domestic tourism patterns and demand in New Zealand but found mixed results across the models due to different explanatory variables included in the models.

Importantly, looking at the position of LCC (Jetstar) in New Zealand's domestic aviation market to serve and support domestic tourism in Model 2A, the variable % of ln(LCC's ASK)_it is reported to be statistically significant and positive. This positive significant estimator further support the findings of the two 2SLS models (1B and 1C), in which the variable ln(LCC's ASK)_it has a significant impact on the variable ln(Domestic guest nights)_it or the LCC positively affected New Zealand's domestic tourism demand and growth.

5.1. Robustness check

As a robustness check, this study also applied the difference-in-difference (DiD) approach to identify and verify the LCC's impact (the treatment) on New Zealand's domestic tourism demand and growth (i.e. domestic guest nights). The basic intuition of the DiD approach is that to study the impact of the ‘treatment’, one compares the performance of the treatment group during the pre-and post-LCC periods relative to the performance of control group during the per- and post-LCC periods (Albalate and Fageda, 2016, Slaughter, 2001, Wooldridge, 2002). However, for this assumption to be valid it is necessary to demonstrate that both treatment and control groups are similar (or is the same) in the absence of treatment (the entry of LCC).9 Note that Jetstar started its low-cost services (the treatment) in New Zealand since June 2006. To perform the DiD analysis, ten New Zealand regions/airports were selected and divided into two groups: (1) treatment regions/airports have LCC's services (Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington); and (2) control regions/airports have no LCC's services (Hamilton, Palmerston North, Rotorua, Taupo, and Whangarei). The analysis period of the DiD estimation covers January 2008 to July 2015. That is, the difference between domestic guest nights for treatment regions and control regions during the pre- and post-periods of LCC's services.

The DiD model was used to investigate the impact of LCC on New Zealand's domestic tourism demand and growth as specified as below:

Model 3:

$$\ln {\left(Domesticguestnights\right)}_{it}={\beta }_0+{\beta }_{1}Trend+{\beta }_{2}d{(Treat\_region)}_{it}+{\beta }_{3}d{(Post\_LCC)}_{it}+{\beta }_{4}d(Treat\_regio{n)}_{it}\ast d{(Post\_LCC)}_{it}+{\beta }_5\ln {\left(GDPpercapita\right)}_t+{\beta }_6\ln {(RTI-Accommodation)}_{it}+{\beta }_7\ln {(RTI-Food\&Beverage)}_{it}+{\beta }_8\ln {\left(Aviationfuelprice\right)}_t+{\beta }_9\ln {\left(Petrolprice\right)}_t+{\beta }_{10}Exchangerate{(NZDvs.USD)}_t+{\beta }_{11}Globalfinancialcrisis2008/09+{\beta }_{12}Christchurchearthquakes2011+{\beta }_{13}\ln {\left(HHIIndex\right)}_{it}+{\epsilon }_{it}$$

where i and t denote region i and month t, respectively; β _s indicates the coefficients to be estimated; the variable Trend denotes the time trend that is common to the treatment and control regions or it tries to control the general changes (a trend) across the treatment and control regions over time; the dummy variable d(Treat_region)_it denotes the treatment regions have LCC's services (treatment regions with LCC's services equals to 1 and control regions without LCC's services equals to 0); d(Post_LCC)_it is the dummy variable for the post-LCC period (the post-LCC period equals to 1 and 0 otherwise); the β ₃ is the parameter of interest (i.e. the DiD estimator), which is the interaction term d(Treat_region)_it * d(Post_LCC)_it, providing the difference in domestic guest nights between the treatment and control regions before and after receiving LCC's services; ε _it is the disturbance term. The DiD estimator is reported to be a statistically significant and positive coefficient in Table 4 , which suggest that there is a positive impact of LCC's services on the number of domestic guest nights in New Zealand. Importantly, the significant DiD estimator obtained from the DiD approach is consistent with the empirical findings of panel data regression analysis reported in Table 3 (Models 1B and 1C).

Table 4.

Difference-in-Difference (DiD) estimation and panel data regression model of the impact of low-cost carrier on New Zealand's domestic tourism (January 2008–July 2015).

Dependent variable	ln(Domestic guest nights)it
	DiD estimation	Panel data regression model
Explanatory variables	Coefficients	Coefficients
Constant	-41.2378*** (-3.94)	11.3305*** (210.46)
Trend	-0.0128*** (-3.09)	–
Treat_regionit	0.2517*** (5.39)	–
Post_LCCit	0.3429*** (3.28)	–
Treat_regionit* Post_LCCit	0.0001*** (28.71)	–
LCC_dummyit	–	0.0716** (2.16)
ln(GDP per capita)t	3.9980*** (3.15)	3.7379*** (3.64)
ln(RTI -Accommodation)it	0.2578*** (2.81)	0.1769*** (5.36)
ln(RTI -Food & Beverage)it	0.4901*** (4.16)	0.2485*** (3.28)
ln(Aviation fuel price)t	-0.1715 (-0.80)	-0.0465 (-1.14)
ln(Petrol price)t	-0.0267 (-0.05)	-0.1720 (-1.36)
Exchange rate (NZD vs.USD)t	0.0207 (0.07)	-0.3479** (-2.45)
Global financial crisis 2008/09	-0.0325 (-0.49)	-0.0096 (-0.35)
Christchurch earthquakes 2011	0.0086 (0.20)	-0.0503*** (-4.68)
ln(HHI Index)it	1.7944*** (8.14)	0.2794*** (2.74)
Seasonal dummy(1)	–	0.2414*** (4.65)
Seasonal dummy(2)	–	0.0504 (1.26)
Seasonal dummy(3)	–	0.0207 (0.37)
Seasonal dummy(4)	–	0.0609 (1.36)
Seasonal dummy(5)	–	-0.1479** (-2.45)
Seasonal dummy(6)	–	-0.2403*** (-2.90)
Seasonal dummy(7)	–	-0.1096 (-1.18)
Seasonal dummy(8)	–	-0.1363 (-1.45)
Seasonal dummy(9)	–	-0.1265 (-1.45)
Seasonal dummy(10)	–	-0.0744 (-1.03)
Seasonal dummy(11)	–	-0.0777 (-1.33)
Adjusted R2	0.649	0.939
Observations	910	910

Remarks: ** and *** indicate that the explanatory variable is significant at the 0.05 and 0.01 significance level, respectively. t-statistics are printed in parentheses. The difference-in-difference estimation is based on the OLS estimation. The results of Hausman test verified that the panel data regression model favoured using the random-effect model.

To take advantage of the larger dataset established for the DiD analysis above, this study also applied the similar model specification used in Model 1 in Section 4 (Model specifications and econometric method) and incorporated the variable LCC dummy _it into Model 4 below. Note that this variable LCC dummy _it replaces the variable ln(LCC's ASK)_it in Model 1 in Section 4, and it is a binary variable to capture the impact of LCC's services upon New Zealand's domestic tourism demand and growth (Auckland, Canterbury/Christchurch, Dunedin, Hamilton, Palmerston North, Queenstown, Rotorua, Taupo, Wellington, and Whangarei) for the period of January 2008–July 2015.

Model 4:

$$\ln {\left(Domesticguestnights\right)}_{it}={\beta }_0+{\beta }_1{\left(LCCdummy\right)}_{it}+{\beta }_2\ln {\left(GDPpercapita\right)}_t+{\beta }_3\ln {(RTI-Accommodation)}_{it}+{\beta }_4\ln {(RTI-Food\&Beverage)}_{it}+{\beta }_5\ln {\left(Aviationfuelprice\right)}_t+{\beta }_6\ln {\left(Petrolprice\right)}_t+{\beta }_{7}Exchangerate{(NZDvs.USD)}_t+{\beta }_{8}Globalfinancialcrisis2008/09+{\beta }_{9}Christchurchearthquakes2011+{\beta }_{10}\ln {\left(HHIIndex\right)}_{it}+{\beta }_{11}Seasonaldummie{s}_t+{\epsilon }_{it}$$

where i and t denote region i and month t, respectively; β _s indicates the coefficients to be estimated; the variable LCC dummy _it is a binary variable that takes 1 after the entrance of LCC's services for the airport of region i at time t and 0 otherwise. In Table 4, most significant coefficients reported in the panel data regression model are largely consistent with those of the DiD estimation, particularly the key variable LCC dummy _it was reported to be a statistically significant and positive coefficient in the panel data regression model. Coupled with the empirical findings of the DiD analysis, the results of the panel data regression model in Table 4 also provide strong evidence regarding the LCC (Jetstar) has a significant impact on Zealand's domestic tourism demand and growth.

6. Discussion and policy implications

This study reported that the low-cost air transport services provided by the LCC (Jetstar) in New Zealand's domestic aviation market had a significant effect on domestic tourism demand and growth. Importantly, the evidence of the LCC's significance for domestic tourism demand and growth in this study is consistent with the findings of other recent literature that investigated the relationship between LCCs' expansion and tourism and economic growth in different countries (e.g. Donzelli, 2010, Koo et al., 2009, Pulina and Cortés-Jiménez, 2010, Whyte and Prideaux, 2007, Williams and Baláž, 2009).

Considering the key findings of this study, special consideration must be given to the recent rapid development of Jetstar and its future potential role in New Zealand's domestic tourism and aviation sectors with new connectivity to more regional destinations. It is recognised that Jetstar adopted aggressive marketing strategies to launch its scheduled budget air travel services for air travellers to fly to four more domestic cities in New Zealand, namely Napier, Nelson, New Plymouth, and Palmerston North, starting from late 2015 and early 2016 (Australian Aviation, 2015). This rapid expansion of LCC's services in New Zealand's domestic aviation market is likely to stimulate and boost air travel demand with discounted tickets; this will continuously develop and benefit New Zealand's regional economies as well as the tourism and aviation sectors, particularly considering the significant domestic tourism expenditure and the significant number of staff employed in the tourism- and aviation-related sectors. For the amount of domestic tourism expenditure generated, there was approximately NZ$15,342 million and NZ$18,080 million in domestic tourism expenditure recorded in 2009 and 2015, respectively, equalling the growth of 17.85% over the years (Statistics New Zealand, 2015e). This situation is echoed by the increasing growth trends for the total guest nights (domestic and international guest nights) and the number of guest arrivals for all of the five key regions and tourist destinations in New Zealand that are connected by Jetstar's networks between 2009 and 2015 (see Fig. 2 ). In comparison with international tourism expenditure, domestic tourism expenditure plays an even more significant role in contributing towards New Zealand's economy during the same period: domestic and international tourism expenditure contributed a total of 18,080 million (60.59%) and 11,748 million (39.41%) to New Zealand's total tourism expenditure in 2015, respectively (see Fig. 3 ).

Fig. 2.

Annual total guest nights and annual total guest arrivals for five New Zealand regions (2009–2015). Source: Statistics New Zealand, 2015a, Statistics New Zealand, 2015c.

Fig. 3.

Total tourism expenditure for New Zealand (2009–2015). Source: Statistics New Zealand (2015b).

As indicated in Section 1 (Introduction), the empirical findings of this study have important implications for strategic planning and policy-setting by the local/regional tourism authorities and tourism operators, airline management, and airports regarding the best strategies and/or best approaches for attracting and transporting more tourists for visitation purposes. First, this study shows the existence of a strong relationship between the LCC's services (or the proliferation of air travel) and New Zealand's domestic tourism demand and growth, and this will help provide a more accurate and robust assessment of the impact of the LCC's operations and the necessary resources for advertising and promotion campaigns by the local/regional tourism authorities and tourism operators to promote their regions as attractive tourist destinations for domestic and international tourists. Second, in light of the substantial growth in domestic and international tourists and holidaymakers travelling within New Zealand, fierce competition between the incumbent carrier (Air New Zealand) and the LCC (Jetstar) will become apparent to capture the market share using different approaches (e.g. fare levels, destinations served, and frequency improvement, etc). It is expected that airline competition between Air New Zealand and Jetstar is likely to benefit and boost New Zealand's domestic tourism demand. Given the nature of domestic tourism in New Zealand and its unique geographical landscape with two separate islands, it is also likely that the future growth of Jetstar's operations and networks will further affect Air New Zealand's operations in terms of its schedule planning and fare levels for the trunk and regional routes. It is evident that Air New Zealand is now planning a significant shakeup to its domestic networks involving additional capacity being added onto the main trunk routes (e.g. Auckland, Christchurch, and Wellington) and some regional routes against its domestic rival Jetstar (Australian Aviation, 2016). Similarly, the rapid growth of LCC's operations in New Zealand's domestic aviation market will also impact other travel modes (e.g. land transport) to transport travellers and tourists across regions and cities. Research into the impact of LCC's services on other choices of transport (or intermodal competition) is not within the scope of this study. Third, for the airports in other smaller regions in New Zealand, significant investment in the re-design and upgrade of terminal spaces and the purchase of other equipment is required to facilitate the entry of the LCC (Jetstar), as its ability to transport more tourist arrivals (particularly budget travellers) who pass through the airports is vital.

7. Concluding remarks

LCCs worldwide have achieved substantial growth in recent years, bringing significant impacts and benefits to local/regional tourism and economies. The panel data regression model and the panel data 2SLS model (which aims to control for the endogeneity effects) were used to empirically investigate the impact of LCC's services and the key determinants affecting New Zealand's domestic tourism demand and growth using five key New Zealand regions and tourist destinations (Auckland, Canterbury/Christchurch, Dunedin, Queenstown, and Wellington) for the period of June 2009–July 2015. First, the empirical results of this study revealed that the LCC (Jetstar) has significantly increased and boosted New Zealand's domestic tourism demand. The empirical findings of this study regarding the significant impact of LCC's services on New Zealand's domestic tourism are largely consistently with prior literature, regarding regional tourism growth and economy development resulting from the improvement of air access and the provision of discounted tickets for budget travellers. In addition, GDP per capita, the RTIs (accommodation, and food and beverage), land transport costs are also found to have affected New Zealand's domestic tourism during the study period.

The message underlying the key findings of this study is that the local/regional tourism authorities and tourism operators in New Zealand should further recognise the significant role of air transport (especially the LCC (Jetstar) offers low-cost domestic air travel with discounted tickets as well as the likely impact of intermodal competition between air transport and land transport on tourism demand and growth). This study also allows better assessment of the growth of tourist arrivals (domestic and international) and tourism activity for a region or city with the entry of the LCC, particularly considering the recent rapid expansion of LCC's operations in New Zealand's domestic aviation market. For the growth of low-cost air travel and the associated benefits for the local/regional tourism industry and economy, it is important for the local/regional governments and airport authorities to plan and provide sufficient airport capacity and better equipment to accommodate existing and future LCC's operations, and to handle the increasing amount of tourist numbers and budget travellers.

Two potential limitations of this study are observed: (i) data of domestic guest nights in this study did not indicate the length of stay (e.g. less than one day) and types of accommodation (e.g. hotels, motels, backpackers, and holiday parks) where domestic tourists have stayed, which may have limited the robustness of the empirical findings of LCC's impact on New Zealand's domestic tourism. This is because the length of stay and types of accommodation stayed at a destination can be affected by travel purposes (e.g. VFR, work and business, holiday, and backpacking). However, the current approaches in this study to estimate the LCC's impact on domestic guest nights in New Zealand are largely decided by the availability of tourism data from Statistics New Zealand. As the extension of this study, an in-depth study is recommended to forecast the number of visitor arrivals in different classes (e.g. VFR, business visitors, holiday visitors, and backpacker visitors) for all the regions in New Zealand that have and will be served by the LCC. This would further allow insight to the impact of LCC's services (especially the impact of budget fares on different classes of tourists) on New Zealand's regional tourism sector and economic growth; and (ii) this study did not investigate the effect of tourism demand spillover upon tourist flows between the regions (i.e. the geographical effect). Yang and Wong (2012) claimed that the term of spillover effect refers to the indirect and unintentional effects that a region's sector exerts on tourism flows to other regions. For example, if Queenstown (the popular tourist destination in New Zealand) cannot accommodate the increase in domestic and international tourist arrivals during the peak travel seasons with sufficient star-rated hotel rooms. As the result, many tourists may choose to stay at Christchurch and drive to Queenstown as the driving time is approximately 6 hours. Therefore, a further analysis is recommended to investigate the spillover of tourist flows across New Zealand using the similar approach of Yang and Wong (2012). This would allow insights to domestic and international tourist spillovers for the popular tourist destinations in New Zealand. Notably, tourism demand spillover (or passenger demand spillover of New Zealand's domestic tourism) will also impact airline operations and revenues while considering the difference between observed demand (bookings) and true demand for a particular flight and/or destination. For example, if Air New Zealand's scheduled seat capacity cannot meet the true demand of international and domestic tourists to a destination (e.g. Queenstown) during the peak travel seasons because of capacity constraints or booking control limits imposed on various fare classes. Three common situations can then be observed: revenue losses, the displacement between international and domestic tourists, or the spilling of demand through refusing further reservations from tourists or passenger demand turned away. In particular, spilled tourists may be lost to Jetstar or may be accommodated on other flights of Air New Zealand (Belobaba & Farkas, 1999). The challenges facing an airline (irrespective of Air New Zealand or Jetstar in this study) is to use revenue management to better allocate and sell airline seat inventory while improving passenger revenues; prior literature shows that airlines with revenue management will benefit incremental revenue gains of 2–5% (Belobaba, 1992). To do this, Air New Zealand and Jetstar need to know the distribution for the expected high-fare true demand and schedule the popular destinations in New Zealand with larger airplanes through fleet assignment during the peak travel seasons (Swan, 2002). Importantly, an accurate and robust forecasting of passenger demand is the heart of a successful airline revenue management to ensure an airline's capacity for a particular flight and/or destination meeting true demand.

Biography

Dr. Kan Wai Hong Tsui PhD is a Senior Lecturer, teaching aviation operations, and airline and airport strategies. His research covers different areas, and includes airline and airport demand forecasting, airport productivity and efficiency, and tourism activities and future trends and its relationship with air transport industry.

Discipline: Aviation / Air transport / Tourism

Qualification/s: PhD (Massey University, New Zealand)

Location: The Social Science Tower, Level 8, the School of Aviation, Massey University, Palmerston North, New Zealand.

Phone: +64 6 9518649

Email: W.H.K.Tsui@massey.ac.nz