Zins, A. (2012), "Building tourism knowledge through quantitative analysis: the legacy of Josef Mazanec", International Journal of Culture, Tourism and Hospitality Research, Vol. 6 No. 4. https://doi.org/10.1108/ijcthr.2012.32706daa.001Download as .RIS
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Building tourism knowledge through quantitative analysis: the legacy of Josef Mazanec
Article Type: Guest editorial From: International Journal of Culture, Tourism and Hospitality Research, Volume 6, Issue 4
Reviews of individual scholars’ work are rarely published in international academic journals. This Special Issue is a rare exception, and is dedicated to the significant contribution made by Josef Mazanec to tourism research and knowledge throughout his academic career. He was recently acknowledged by a Lifetime Achievement Award from the International Academy of Culture, Tourism and Hospitality Research, but is also known to those he worked with as the prototypical honorable professor. Academics who are familiar with his work, but do not know him personally, may be unaware of this side of the man, and this Special Issue is an opportunity paint a portrait of Josef Mazanec: the honorable professor, true gentleman, and mentor.
Neither this editorial nor the selection of papers published here can do justice to the substantial lifetime contribution of Mazanec. They do, however, point to some areas in which he was particularly interested. Mazanec’s scientific work is always deeply grounded in clear-cut paradigms and principles of the philosophy of science. His early publications – many of which appear in German and in the pre-digital era – address such issues explicitly. These publications exemplify the brand of marketing scholars to which Mazanec belongs.
Several academics have tried to classify marketing scholars into groups by means of logic, a general framework, or by subjective appraisal, but Mazanec (Franke and Mazanec, 2006) developed a new, empirically backed typology for US and German marketing scholars on the basis of different “paradigms”, “schools of thought” and “research approaches”. The six types identified and profiled in that paper (“marketing scientist”, “marketing missionary”, “marketing manager”, “marketing philosopher”, “realist” and “all-round researcher”) may be used as a system to classify Mazanec’s own work. Mazanec as “marketing scientist” probably comes closest, followed by the portrait of the “realist”.
In his paper “Unravelling myths in tourism research,”, which appeared in Tourism Recreation Research (Mazanec, 2009), Mazanec presented the following issues for critical discussion, and these may also help to understand his legacy:
Scientific discourse should not (and cannot) take place without making value judgments. Such starting and viewpoints should be made explicit.
No scientific (not even everyday) discourse takes place without formulating hypotheses.
Quantitative techniques do not only require quantitative data.
Definitions (theoretical constructs) alone do not explain anything.
Detecting causal relationships requires adequate research methods.
Changing model components until the data match the proposed structure has nothing to do with hypothesis testing.
Interrelated hypotheses should be tested simultaneously.
Not every hidden or latent factor is automatically a reflective construct.
Capturing unobserved heterogeneity among consumers directly, instead of analyzing data at the aggregate level, is worthwhile.
Mazanec already held most of these views in the early 1980s, when he was appointed Chair of the Institute of Tourism and Leisure Studies at the University of Economics and Business (WU Wien). He strongly advocated that tools and techniques already known in management science should enter the empirical domain of tourism, and should be exploited to the benefit of more effective decision making at company and destination management level (Ender et al., 1983). Thirty years later, Mazanec reiterated that “If tourism research is expected to contribute to mastering real-world problems it must assist in improving decision making” (Mazanec et al., 2010, p. 17).
From this perspective, Mazanec’s decision to follow the invitation of the editors of International Journal of Information Technology and Tourism to conduct a bibliometric study investigating the usage patterns of advanced analytical methods in tourism research comes as no surprise. The study includes work in six leading journals published between 1988 and 2008 (Mazanec et al., 2010). By analyzing more than 4,600 articles with more than 2,000 applications of advanced (multivariate) methods, the team of authors extended a previous study by Palmer and colleagues, who argue that “the use of statistics in empirical research can be regarded as an indicator of the degree of scientific progress” (Palmer et al., 2005, p. 167). Mazanec and colleagues went further, though, by critically assessing milestone achievements, indicators of progress and blind alleys. They pursued the following key areas:
structural equation modeling; and
The articles published in this special issue offer a cross-section of areas where significant progress has been observed, and areas where more work is required to advance knowledge and decision-making quality in tourism. We also offer some insights into the areas where Mazanec contributed by applying concepts and methods of management science, and deepening our understanding through critical reflection on popular approaches in marketing research.
The legacy of Josef Mazanec and how we can build on it
Conceptual perspective on modeling tourism behavior
Tourism researchers aim to better understand the demand for particular tourism services and experiences – be they destinations, resorts, amusement parks, or cultural attractions. Most studies look into the past or analyze the present to infer symptomatic patterns determining future behavior. The vast majority of such demand analyses are outcome directed. Hence, the underlying models try to structure the interdependencies of various external stimuli, internal variables (personal characteristics and learning constructs), and resulting states (e.g. brand choice, destination loyalty) without explicitly mapping the flow and sequence of sub-decisions and information-processing steps. Mazanec started to mimic individual travel counseling processes (person-to-person interaction) by deriving and implementing a fuzzy rule base to construct a trip counseling system to assist travel agents in their sales recommendations to customers (Hruschka and Mazanec, 1990).
The technological development of the internet occasioned not only an explosion of available travel information, but also an ever-increasing array of functions and applications improving the human-computer interaction. The so-called “democratization of information” calls for improved and more efficient information retrieval capabilities. Against this background, Mazanec and his research team continued to work on trip counseling systems, thus assisting the improved development of intelligent destination recommender systems (Mazanec, 2002; Fesenmaier et al., 2006).
The conceptual paper by Gretzel, Hwang, and Fesenmaier presents the current state of design aspects of such recommender systems. Their perspective highlights that without understanding the decision-making process of travelers, and without empirical analyses and advanced analytical tools, recommender systems cannot be developed successfully.
Reliability of binary scales
When aiming to generate valid and useful results from empirical research, academics must face and overcome myriad minor and major concerns, obstacles, and challenges. Five papers in this Special Issue deal with a selection of such problems. Reliability and validity represent two of the major issues of empirical research. Dolnicar and Leisch pick up one of Mazanec’s favorite topics: binary measurement and scales. Mazanec has always advocated simpler and less labored data collection methods, and suggests compensating for the potential loss of information by applying more sophisticated data analysis techniques. The paper tests comparative reliability for binary and magnitude format response options using stability as the key criterion. Their findings, which indicate the low reliability of ordinal scales, will no doubt concern those scholars who subscribe to the necessary condition of scale reliability for any measurement deemed valid.
Validity of pictorial versus verbal scales for image measurement
The third paper, by Pezenka and Buchta, addresses the issues presented by scales, validity, and information representation. Mazanec started studying positioning analysis in the mid-1970s when working on attitudinal and image measurement, and the representation of perceptual spaces of products (e.g. Mazanec, 1975). The use and validation of pictures instead of verbal stimuli for image measurement has been on the long-term research agenda at the Institute for Advertising and Marketing Research of WU Wien. Mazanec contributed to this field of research both indirectly, by co-supervising the seminal PhD project by Wusst (1987), and directly, by developing an entire agent-based market simulation environment (Strasser and Mazanec, 2000), based on adaptive and non-parametric techniques. Pezenka and Buchta take up this topic – which has been of interest to Mazanec throughout his entire research career – and apply not only innovative sorting techniques using web interfaces, but also a creative method to compare the equivalence of verbal and non-verbal measurements in the context of city destinations.
Sampling and results accuracy
Surveys represent one of the most important data-gathering instruments in Mazanec’s research. He started his academic career in the field of advertising and marketing research, so sampling, representativeness response biases and many other technical details became part of his operating system as a researcher well before he entered the field of tourism. Some of the early challenges regarding sampling he faced in the context of empirical tourism research include two studies for the tourism industry: an Austrian travel survey in 1981 and the First National Guest Survey 1983. For the latter, he developed a multilayered, concentrated cluster sample that weighted participating overnight visitors to match the official Austrian tourism statistics. In this case, the real structure or sampling frame of overnight visitors was known; at least as far as nationality, type and place of accommodation were concerned. However, one essential parameter was always missing and is still unknown to most of the official tourism authorities: the distribution of the length of stay of their tourists. Zins has also demonstrated that disregarding this important structural characteristic in guest surveys leads to substantial biases in tourism spending estimates (Ganglmair and Zins, 1999). The paper in this Special Issue by Park and Fesenmaier reports similar problems in the context of conversion studies of destination management organizations. They offer two approaches to overcome non-response problems present in such online-administered surveys: post-stratification and propensity score adjustments.
Conceptual definitions – scale development – formative compared to reflective specification
The application of structural equation models (SEMs) in tourism research has increased substantially during the past two decades. The widespread use of powerful PCs and software has facilitated the speed of its adoption. However, together with increased analytical possibilities, the probability of misuse and misspecification has increased, too. Mazanec exhibited a vital and uninterrupted interest in exploiting the power and richness of SEMs for testing sets of hypotheses simultaneously, and his writings address the developments and critical issues related to the practical application of SEMs in tourism (Mazanec et al., 2010) and business administration in general (Mazanec, 2007a). Yet while expressing his concern about the use of SEMs in tourism research, Mazanec has always been keen to advance the field by finding and evaluating better solutions. Research examples about inferred causation theory and non-linear SEM provide evidence for this endeavor (Mazanec, 2007b, c). Mayr and Zins take up some of these topics in their paper on perceived service value in this issue: conceptual rigor, formative compared to reflective specification, replication, and alternatives to covariance-based modeling.
Non-linear relationships – forecasting with fuzzy set neural networks
Although Mazanec regularly taught forecasting in tourism classes, he never published in this area. However, the increased availability and popularity of neural computing algorithms, and the growth of evidence for the power of multilayer perceptrons for mapping any class of linear and non-linear functions soon drew his attention. Innovations in forecasting analysis became possible and attracted Mazanec’s interest as a researcher. In his award-winning paper published in Journal of Travel & Tourism Marketing, “Classifying tourists into market segments: a neural network approach”, he introduced this new “technology” for classification purposes into the tourism literature (Mazanec, 1992).
Many other papers followed that exploit clever and innovative applications of neural networks for marketing research purposes (e.g. Davies et al., 1999). The dynamic adaptation of weights for the update rule within these perceptrons has much in common with the updating steps common to many other conventional forecasting routines. Another team of authors in this issue (Huarng, Yu, Moutinho, and Wang) demonstrate the fuzzification of time-series data, and the consecutive fitting of the adaptive weights within a neural network to historical data.
Creative combination of multivariate techniques of analyses
Mapping (perceptual) structures of market identities (e.g. brands, companies, segments of consumers) into a low-dimensional space is another of Mazanec’s hobby horses. Improving the efficiency of analyses and mapping techniques has been an important driving force for two reasons:
to make the data-gathering phase as easy (and free from biases) as possible; and
to increase the accessibility of results to managers.
Mazanec has sought innovative links between different analytical, steps (e.g. segmentation and positioning) to enrich the substance of research outcomes. Two of his former colleagues (Kastner and Stangl) continue to work in this direction, and they apply Rasch analysis (for scale checks), vector quantization (for segmentation), and correspondence analysis (for mapping purposes) in the context of blog-content reading and travel motivation of internet users.
A portrait of an honorable professor
At the very least, an academic’s legacy consists of two dimensions:
their contribution to knowledge or methodology in their chosen field of research; and
their contribution to training and mentoring people who can contribute to knowledge and methodology in the future.
Most tourism researchers are familiar with Mazanec’s work, and his academic contributions are beyond doubt. Very few, however, had the privilege of working with him – either as a collaborator or mentee. As the editors of this Special Issue, we see ourselves as “apprentices” of Mazanec, and we here have an opportunity to paint a picture of the person behind the papers, our “captain”.
Learning to become an academic has a lot in common with being an apprentice learning a trade. No matter how well you have learned the theory of the academic discipline, you must also learn to define a research problem, determine which research design to set up to investigate the research problem, select an analysis method that will produce valid and reliable results, and learn to draw conclusions and recommendations for practitioners that are based on your research, rather than on your personal views. You also need to learn how to structure a manuscript, formulate a coherent argument, and deal with the often-unfriendly reviewers’ comments. Inevitably, along the way, the “academic apprentice” will make mistakes – many mistakes.
Josef Mazanec was a kind and generous master teacher to his apprentices. He never forced learning on them, never prescribed what kind of research they should pursue. But he was always there to provide advice, take his apprentices by the hand and publish with him, so they could see how academic work is done. He was always there to discuss mistakes his apprentices made, to help them learn from them and make fewer mistakes in the future. He never confused apprentices with slaves, and never took credit for the ideas or work of students, staff or collaborators. He often offered his own ideas to help junior researchers identify a research problem or topic worth pursuing, and gave away ideas with no expectation of being acknowledged. Mazanec never published a paper to which he had not contributed, and always strictly used alphabetical author order on articles. When not all authors contributed equally, he would specify contributions in a footnote.
Throughout his career, Mazanec has always been a true academic gentleman, the prototypical honorable professor. He never preached his principles, and never wrote them down. Anyone who worked with him witnessed him living his principles without compromise. Mazanec was the best role model any junior researcher could hope for. He was the textbook professor, and motivated many of his students and staff to pursue an academic career. Many of us want to grow up to be “a Mazanec”, but we hope we can also carry into the future his legacy: by making meaningful contributions to knowledge and by taking a new generation of tourism researchers by the hand and introducing them to the beauty of this academic trade.
Andreas H. ZinsAssociate Professor in the Department of Tourism and Hospitality Management, MODUL University, Vienna, Austria.
Sara DolnicarProfessor of Marketing at the Institute for Innovation in Social and Business Research, University of Wollongong, Wollongong, Australia.
Received March 2011 Revised June 2011 Accepted August 2011
About the authors
Andreas H. Zins is Associate Professor at the Institute for Tourism and Leisure Studies at the Wirtschaftsuniversität Wien (WU) and Full Professor of Tourism Management at MODUL University, Vienna. Dr Zins lectures in entrepreneurship, marketing, tourism marketing, and modeling of consumer and travel behaviour. His research interests include tourism behavior, marketing research, social impacts, computer-assisted and web-based interviewing, theme parks, and related leisure attractions.
Sara Dolnicar completed her PhD at the Vienna University of Economics and Business Administration. She is currently Professor of Marketing at the University of Wollongong in Australia, and the Director of the Institute for Innovation in Business and Social Research (IIBSoR). Her research interests include market segmentation, quantitative methodology in marketing research, answer format effects and response styles, and tourism marketing.
Davies, F., Goode, M., Mazanec, J.A. and Moutinho, L. (1999), “LISREL and neural network modelling: two comparison studies”, Journal of Retailing and Consumer Services, Vol. 6 No. 4, pp. 249–61
Ender, W., Fuhri, R., Mazanec, J.A. and Steiner, M. (1983), “Von der Hotelbetriebslehre zur Management Science des Tourismus? – Zeitgemäße Aufgaben einer Betriebswirtschaftslehre des Fremdenverkehrs”, der markt, Vol. 85, pp. 36–46
Fesenmaier, D.R., Mirzadeh, N., Ricci, F., Rumetshofer, H., Schaumlechner, E., Venturini, A., Wöber, K.W. and Zins, A.H. (2006), “DieToRecs: a case-based travel advisory system”, in Fesenmaier, D.R., Werthner, H. and Wöber, K.W. (Eds), Destination Recommendation Systems: Behavioural Foundations and Applications, CAB International, London, pp. 227–39
Franke, N. and Mazanec, J.A. (2006), “The six scientific identities of marketing: a vector quantization of research approaches”, European Journal of Marketing, Vol. 40 Nos 5/6, pp. 634–61
Ganglmair, A. and Zins, A.H. (1999), Reiseausgaben im österreichischen Tourismus, 2nd ed., Österreichische Gesellschaft für Angewandte Fremdenverkehrswissenschaft, Wien
Hruschka, H. and Mazanec, J.A. (1990), “Computer-assisted travel counseling”, Annals of Tourism Research, Vol. 7 No. 2, pp. 208–27
Mazanec, J.A. (1975), “Einstellungsmessung in der Marketingforschung: eindimensional-summativ oder mehrdimensional?”, der markt, Vol. 55 No. 3, pp. 89–92
Mazanec, J.A. (1992), “Classifying tourists into market segments: a neural network approach”, Journal of Travel and Tourism Marketing, Vol. 1 No. 1, pp. 39–59
Mazanec, J.A. (2002), “Introducing learning and adaptivity into web-based recommender systems for tourism and leisure services”, Tourism Review/Zeitschrift für Tourismus, Vol. 57 No. 4, pp. 8–14
Mazanec, J.A. (2007a), “Zauberlehrlings BeSEM – oder was Anwender über Ge- und Missbrauch des Structural Equation Modeling in der betriebswirtschaftlichen Forschung wissen sollten”, Werbeforschung and Praxis, Vol. 52 No. 1, pp. 25–30
Mazanec, J.A. (2007b), “Exploring tourist satisfaction with nonlinear structural equation modeling and inferred causation analysis”, Journal of Travel and Tourism Marketing, Vol. 21 No. 4, pp. 73–90
Mazanec, J.A. (2007c), “New frontiers in tourist behavior research: steps toward causal inference from non-experimental data”, Asia Pacific Journal of Tourism Research, Vol. 12 No. 3, pp. 223–35
Mazanec, J.A. (2009), “Unravelling myths in tourism research”, Tourism Recreation Research, Vol. 34 No. 3, pp. 319–23
Mazanec, J.A., Ring, A., Stangl, B. and Teichmann, K. (2010), “Usage patterns of advanced analytical methods in tourism research 1988-2008: a six journal survey”, Information Technology and Tourism, Vol. 12 No. 1, pp. 17–46
Palmer, A.L., Sesé, A. and Montano, J.J. (2005), “Tourism and statistics. Bibliometric study 1998-2002”, Annals of Tourism Research, Vol. 32 No. 1, pp. 167–78
Strasser, H. and Mazanec, J.A. (2000), A Nonparametric Approach to Perception-Based Market Segmentation: Foundations, Springer, New York, NY
Wusst, C. (1987), “Ansätze zur Image- und Einstellungsmessung am Beispiel Österreich”, dissertation, Wirtschaftsuniversität Wien, Vienna
Dolnicar, S., Grabler, K. and Mazanec, J.A. (1999), “Analyzing destination images: a perceptual charting approach”, Journal of Travel and Tourism Marketing, Vol. 8 No. 4, pp. 43–57
Song, H. and Li, G. (2008), “Tourism demand modeling and forecasting – a review of recent research”, Tourism Management, Vol. 29, pp. 203–20