Norman Au

A neural network model to forecast Japanese demand for travel to Hong Kong.

Abstract Apart from simple guesswork, time-series and regression techniques have largely dominated forecasting models for international tourism demand. This paper presents a new approach that uses a supervised feed-forward neural network model to forecast Japanese tourist arrivals in Hong Kong. The input layer of the neural network contains six nodes: Service Price, Average Hotel Rate, Foreign Exchange Rate, Population, Marketing Expenses, and Gross Domestic Expenditure. The single node in the output layer of the neural network represents the Japanese demand for travel to Hong Kong. Officially published annual data in the period of 1967 to 1996 were used to build the neural network. Estimated Japanese arrivals were compared with actual published Japanese arrivals. Experimental results showed that using the neural network model to forecast Japanese arrivals outperforms multiple regression, naive, moving average, and exponent smoothing.

“Do We Believe in TripAdvisor?” Examining Credibility Perceptions and Online Travelers’ Attitude toward Using User-Generated Content:

Recent press reports and complaints by hoteliers have called into question the credibility of travel-related user-generated content (UGC). Yet our understanding of the role of credibility perceptions in the context of UGC is limited, notwithstanding the rising interest in social media. Using an online survey of 661 travel consumers, this study applies the component-based structural equation modeling technique of partial least squares to examine online travelers’ perceptions of the credibility of UGC sources and how these perceptions influence attitudes and intentions toward UGC utilization in the travel planning process. The model also draws on the theory of homophily to make predictions about the antecedent of credibility. Results validate the Source Credibility Theory in the context of UGC but also accentuate the central mediating role of attitude. In addition, the study finds support for perceptual homophily as a critical determinant of both credibility and attitude. Theoretical and managerial implications are highlighted.

A critical review of end-user information system satisfaction research and a new research framework

This paper presents a critical review of research in end-user information system satisfaction (EUISS). An extensive literature search is conducted from which over 50 EUISS related papers are identified. It is found that the past research is dominated by the expectation disconfirmation approach. To provide more insights into the psychological processing of the information system performance construct and its impact upon EUISS, we propose an integrated conceptual model based on the equity and needs theories. The implications of the proposed model for EUISS are discussed, and suggestions are made for testing the model.

Relationship modeling in tourism shopping: a decision rules induction approach

Abstract Traditional tourism research on relationship modeling has concentrated predominantly on multivariate econometric models, univariate time-series techniques, and gravity approaches. These relationship modeling methods, although have attained a certain degree of success in the tourism paradigm, are primarily based on mathematical functions and are numeric in nature. A major drawback of these mathematical function-based modeling techniques is their inability to handle non-numeric data. This paper presents a new approach that incorporates the rough set theory to model the relations that exist among a set of mixed numeric and non-numeric tourism shopping data. The output of the rough set approach is a group of decision rules that represents the relations in a tourism shopping information system (IS). Officially published data from the Hong Kong Tourist Association for the period 1983–1996 were used to form the decision rules and test the forecasting accuracy of these decision rules. Empirical findings indicated that 94.1 per cent of the testing cases were successfully forecasted and that there was no significant difference between the forecasted values and their actual counterparts.

Categorical classification of tourism dining.

Abstract Traditional quantitative techniques in foodservice and tourism are unable to discover hidden relationships from a database with numeric and non-numeric data. This paper reports on an initial study about applying an alternative approach that incorporates the rough set theory into relationship modeling in tourism dining. This theory deals with the non-numeric classification analysis of imprecise, uncertain, or incomplete knowledge by incorporating the classical set theory. Using officially published data on tourism dining, decision rules were generated which describe the relationship model. Empirical findings indicated that among the classified cases, 83% of the forecast values were identical to their actual counterparts.

Progress and Development of Information Technology in the Hospitality Industry Evidence from Cornell Hospitality Quarterly

In view of the synergistic interaction between technology and hospitality services, this article reports the findings of a study that analyzed articles related to information technology (IT) published in Cornell Hospitality Quarterly (CQ) and its predecessor, Cornell Hotel and Restaurant Administration Quarterly (CHRAQ), a first-tier journal that places emphasis on the application of research to hospitality businesses. Through an examination of content from CHRAQ’s inaugural issue in the early 1960s to the latest volume (in mid-2011), ninety-five articles were identified as relevant. A content analysis revealed that more than half of the articles focused on the hospitality industry in general and that a substantial number of IT-related articles had been published since the 1980s. This article contributes to a better understanding of the progress of the IT research conducted by hospitality researchers over the past five decades.

The Application of Rough Sets to Sightseeing Expenditures

The existing tourism demand forecasting models in tourism are unable to capture useful information from a database with numeric and nonnumeric data. This article presents a new approach that applies the rough set theory to form a forecasting model for sightseeing expenditures in Hong Kong. The rough set theory deals with the classificatory analysis of imprecise, uncertain, or incomplete knowledge (data) by incorporating the classical set theory. Based on officially published tourist sightseeing data, decision rules are generated to represent the relationships between the independent variables and the dependent variable. Experimental results revealed that the forecasting model can classify 94.1% of the testing cases, and that 87.5% of the classified cases were identical to their actual counterparts. There was no significant difference between the actual values and the forecast values. The advantages of using decision rules induced by rough set to forecast sightseeing expenditure were also offered.

Complaints on the Online Environment — The Case of Hong Kong Hotels

Rapid advancement of web technology enabled hotel customers to increasingly use the Internet as a channel for reporting negative service experiences. Prior studies on hospitality complaints on the web are scarce. Through a content analysis of 453 individual complaint cases reported on one of the largest and most popular travel review websites (tripadvisor.com) for Hong Kong hotels, nine e-complaints categories were firstly identified. These nine complaint categories were then examined across different origins of the complaints. The results of a two-way contingency table analysis further revealed that although no significant relationship was found between e-complaint categories and hotel class, the age group of reviewers was significantly associated with specific types of complaints made online. Various kinds of management responses were also explored against each e-complaint category to identify possible managerial reactions. Lastly the paper explores the implications of the findings and makes suggestions for future e-complaint research for the hospitality industry.

Perceptions and strategies of hospitality and tourism practitioners on social media: an exploratory study.

The potential benefits of social media for the promotion of hospitality and tourism businesses have been well acknowledged in the existing literature. Most studies have, however, examined social media use and perceptions from consumers’ perspective. There is limited research addressing how service providers perceive and employ social media to influence the consumer’s decision making process. Using in-depth interviews with hospitality and tourism practitioners in Hong Kong, this study explores practitioners’ perceptions regarding social media and examines the strategies being applied in engaging social media to influence consumers’ decision making. The findings reveal that practitioners generally understand what social media represent and recognise their potential for business promotion as well as inherent challenges. Furthermore, they employ various social media applications and strategies in attempting to influence consumers’ decision making process. The findings hold implications for both practitioners and researchers.

Comparing Chinese and American attitudes towards complaining

Purpose – The current research aims to explore the possible effects of attitude towards complaining (ATC) on intention to remain loyal to hotels and to compare Chinese and American ATC in this perspective.Design/methodology/approach – The sample of the study consisted of Chinese and American graduate students, who were selected through the non‐probability convenience sampling technique. A total of 1,822 questionnaires were found to be useful and data from these questionnaires were tested through SPSS and LISREL statistical software.Findings – Findings indicate that culture clearly affects ATC and general intention to stay loyal which suggests that practitioners should consider the cultural background of their customers while designing and implementing their service recovery systems. Results show that Chinese respondents tend to forgive and forget failures, whereas Americans seek a remedy from third parties.Research limitations/implications – Only relationships between ATC and loyalty were investigated in ...