Abstract
This study examines the impact of ICT readiness on the tourism industry and how it leads to growing competitiveness by deploying three-panel data analysis techniques (pooled OLS, fixed effects, dynamic GMM) with 177 nations for the period 2011 to 2019. ICT readiness is gauged using the World Economic Forum's Travel and Tourism Competitiveness Index, whereas tourism's contribution towards economic progress is gauged by overall international traveler arrival. The observations indicate that ICT readiness causes a statistically significant effect on tourism's role in economic progress. Tourism policy effects and guidelines for future works are discussed as well.
TopIntroduction
Tourism is a concept involving various aspects and it has emerged as a main industry in the global economy (Aramendia-Muneta & Ollo-Lopez, 2013; Mazzola, Pizzuto & Ruggieri, 2019; Suhel & Bashir 2018). Indeed, its significance is growing continuously. For the past two years, a 4% annual growth rate has been recorded in the tourism sector as 300 million more tourists travelled internationally during 2008 to 2019 (UNWTO, 2019). The travel and tourism sector contributes significantly to the GDP, exports and employment as against other main industries (WTTC, 2019). In 2016, the sector’s contribution to global GDP was almost 10.2% and its growth patterns and outlooks mostly exceeded that of other main industries (Crotti & Misrahi, 2015, 2017). According to the World Travel and Tourism Council (WTTC), tourism is the centre piece of fostering the development scheme of nations as the sector’s significance prompted the National Tourism Organizations (NTOs) to spend more than US$ 4 billion annually during 2008 to 2019 on marketing and advertising programs. Hence, governments worldwide should think about inclining towards policies for bolstering the development of the industry, enabling their respective nations to reap a broader suite of advantages in the whole economy.
The tourism industry is one of the initial services sectors to espouse and utilise information and communication technology (ICT) for endorsing its services (Adeola & Evans, 2020). Today, ICT has profoundly impacted the manner in which business is carried out and entities compete (Porter, 2001). Conventionally, the travel distribution function has been executed by outbound tour operators, travel agencies, and inbound travel managers or handling agencies (Buhalis & Laws, 2001, 2008). However, advancements in internet and electronic commerce in the late 1990s and the advent of tourism as one of the main Business to Consumers (B2C) and Business to Business (B2B) applications have altered the situation quickly (O’Connor, 1999; Smith et al., 1998). Internet has emerged as a key tool and one which is extensively employed by the tourism industry. ICT allows tourists to access dependable and precise information and to make reservations quickly, saving on cost and avoiding inconvenience triggered by orthodox methods (O’Connor, 1999).
Progresses in technology and the needs for an improved lifestyle have fostered an extremely competitive environment at a huge scale for firms, organisations, enterprises, companies, nations, regions, sectors and individuals as a whole. This is also applicable to the travel and tourism sector which has recorded key changes because of several aspects such as technology advancements. Gooroochurn and Sugiyarto (2005) studied determinants of the travel and tourism sector and observed that tourism competitiveness is a blend of eight key determinants: technology, price, social development, environment, openness, human resources, infrastructure and the human tourism indicator. Moreover, technology and the social aspect significantly impact the sector’s competitiveness. On one hand, Aramendia-Muneta and Ollo-Lopez (2013) observed that internet and communication technologies (ICTs) deliver new prospects by enhancing output, generating competitive edge, fostering new businesses and enabling new modes of management (Buhalis, 2008; Gruescu, Nanu, & Pirvu, 2009; Ion & Andreea, 2008; Irvine & Anderson, 2008). Conversely, Harvey (2010) emphasised the various facets of society being part of an overall social system as far as political economy is concerned. This shows how governments, by viewing tourism as an instrument and contributor, could affect their nation’s development, considering that the general social theory of political economy deals with how politics affects choices in society (Bramwell, 2011). The government’s role is essential in tourism planning and development, and thus government involvement is directed through formal establishments like ministries (Nunkoo, 2015; Wang & Bramwell, 2012). Governments make choices for the economy in general, and this comprises the objective development of ICT infrastructures, and also the decision to endorse infrastructural ventures.
Key Terms in this Chapter
Gross Domestic Product (GDP): Gross domestic product (GDP) is the standard measure of the value added created through the production of goods and services in a country during a certain period. As such, it also measures the income earned from that production, or the total amount spent on final goods and services (less imports).
Ordinary Least Squares (OLS): Ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model.
Information and Communication Readiness: Refer to one’s ability to converse with people through various technologies. Similar to information technology (IT), ICT refers to technology use for regular, everyday tasks: sending an email, making a video call, searching the internet, using a tablet or mobile phone, and more.
Panel Data: Panel data is a subset of longitudinal data where observations are for the same subjects each time. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for the former, one time point for the latter).
Generalized Method of Moments (GMM): Generalized method of moments (GMM) is a generic method for estimating parameters in statistical models. Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.
Tourism Industry: The tourism industry, also known as the travel industry, is linked to the idea of people travelling to other locations, either domestically or internationally, for leisure, social or business purposes. It is closely connected to the hotel industry, the hospitality industry and the transport industry, and much of it is based around keeping tourists happy, occupied and equipped with the things they need during their time away from home.
Autoregression (AR): Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems.