SYSTEMATIC LITERATURE REVIEW OF TOURISM GROWTH NEXUS: AN OVERVIEW OF THE LITERATURE AND A CONTENT ANALYSIS OF 100 MOST INFLUENTIAL PAPERS

This study performs the first citation-based systematic literature review of the tourism-growth nexus. The citation analysis provides a bird's eye view of this literature, which, in turn, identifies the sources of knowledge in terms of most influential journals, authors, and articles. A detailed content analysis of 100 most influential papers has been generated on the nature of the study, variables used, country of analysis, type of analysis, the methodology, and the direction of causality. In total, 284 papers were found relevant in the Scopus database using a comprehensive list of keywords. The citation analysis reveals that Tourism Management is the leading journal with a total of 2527 citation counts, whereas Tourism Economics is the leading journal with a total of 41 publications on this topic. Juan Gabriel Brida is the most prolific author, whereas Lee and Chang (2008) is recognized as the most influential paper. The content analysis reveals that 58% have applied time series, and 38% have used panel data analysis. Tourism causing growth is the leading result of both time-series and panel studies. International tourism receipts/earnings/expenditure and the number of international tourist arrivals are the most widely used variables to measure tourism.     

The relative importance of journals used in operations management research A citation analysis

Journal rankings are important for a variety of reasons, most importantly as the basis of academic tenure and promotion decisions. A common approach in other fields is the citations methodology which is used in this study to determine the relative importance of various operations management (OM) journals. The citations analysis noted the frequency that all other journals are cited in Decision Sciences, Journal of Operations Management, and Management Science during the period 1992 to 1994. Rankings are provided based on total citations, citations per article, and citations per words published. The study shows that the journals with the most importance to OM research are Management Science, Decisions Sciences and Operations Research. Other journals important to OM research are the Harvard Business Review, Journal of Operations Management, and IIE Transactions.     

JMS at 50: Trends over Time

We present an analysis of the articles published in the Journal of Management Studies since its inception to assess to what extent JMS has: maintained its leading international ranking; maintained its founding mission as a broad based management journal; and remained a broad based management journal compared to other general management journals. In terms of its impact factor and citations despite reaching a low point in 2001, we find that JMS today ranks higher than it has ever done throughout its 50‐year history. From our content analysis covering the life‐cycle of JMS, we find four areas have been the most frequently represented, although their relative importance varies between decades: Organizational Management/Behaviour, Strategy, Human Resource Management, and General Management, accounting for 67 per cent of articles published over the period. JMS has strengthened its international author distribution through the increase in authors from the EU especially; the period 2000–04 which saw the predominance of UK authors was an anomaly. There are marked differences between type of article and author country of origin. Our comparative analysis of the word networks between the journals JMS, AMJ, ASQ, and HRM shows that over each decade the papers in the first three normally form a single cluster, indicating that the words used in the papers in the different journals are similar, while papers from HRM often form an outlining group. Notably, in the early 2000s papers in JMS form a distinct cluster, with papers from HRM paralleling the anomaly identified in the content analysis. Overall, JMS has regained its distinctiveness as a broad‐based international management journal, not favouring any particular theoretical or empirical approach.     

Ranking journal quality by harmonic mean of ranks: an application to ISI statistics & probability

As the preponderance of journal rankings becomes increasingly more frequent and prominent in academic decision making, such rankings in broad discipline categories is taking on an increasingly important role. The paper focuses on the robustness of rankings of academic journal quality and research impact using the widely‐used Thomson Reuters ISI Web of Science citations database (ISI) for the Statistics & Probability category. The paper analyses 110 ISI international journals in Statistics & Probability using quantifiable research assessment measures (RAMs), and highlights the similarities and differences in various RAMs, which are based on alternative transformations of citations and influence. Alternative RAMs may be calculated annually or updated daily to determine when, where and how (frequently) published papers are cited (see Chang, McAleer and Oxley (2011a, b, c) , Chang, Maasoumi and McAleer (2012) ). The RAMs are grouped in four distinct classes that include impact factor, mean citations and non‐citations, journal policy, number of high quality papers, and journal influence and article influence. These classes include the most widely used RAMs, namely the classic 2‐year impact factor including journal self citations (2YIF), 2‐year impact factor excluding journal self citations (2YIF^*), 5‐year impact factor including journal self citations (5YIF), Eigenfactor (or Journal Influence), Article Influence, h‐index, PI‐BETA (Papers Ignored – By Even The Authors), 5YD2 (= 5YIF/2YIF) as a measure of citations longevity, and escalating self citations as a measure of increasing journal self citations. The paper highlights robust rankings based on the harmonic mean of the ranks of RAMs across the 4 classes. It is shown that focusing solely on the 2YIF of a journal, which partly answers the question as to when published papers are cited, to the exclusion of other informative RAMs, which answer where and how (frequently) published papers are cited, can lead to a distorted evaluation of journal quality, impact and influence relative to the more robust harmonic mean of the ranks.     

An Empirical Assessment of the Influence of March and Simon’s Organizations: The Realized Contribution and Unfulfilled Promise of a Masterpiece

March and Simon’s Organizations is a seminal work in management studies. We assess the contribution Organizations has made to management studies through a citation context analysis of 1,400 articles from top management journals. By categorizing the content from Organizations cited in subsequent works, we find that two related categories associated with cognitive limits and routines or programs account for half (50.5%) of all citation contexts since 1990. We also investigate whether citations to Organizations have been peripheral or substantial to the citing authors’ claims, how cited content has changed over time, and the extent to which citing works have provided empirical evidence to test March and Simon’s propositions or have been critical of their assertions. Our investigation reveals disturbingly few attempts to empirically validate the assertions made in Organizations. Overall, we provide a critical reflection on how well subsequent scholarship fulfils the aims and promise heralded by this masterpiece.     

An efficient nonparametric estimator for models with nonlinear dependence

We provide a convenient econometric framework for the analysis of nonlinear dependence in financial applications. We introduce models with constrained nonparametric dependence, which specify the conditional distribution or the copula in terms of a one-dimensional functional parameter. Our approach is intermediate between standard parametric specifications (which are in general too restrictive) and the fully unrestricted approach (which suffers from the curse of dimensionality). We introduce a nonparametric estimator defined by minimizing a chi-square distance between the constrained densities in the family and an unconstrained kernel estimator of the density. We derive the nonparametric efficiency bound for linear forms and show that the minimum chi-square estimator is nonparametrically efficient for linear forms.     

Statistical Approaches for Non‐parametric Frontier Models: A Guided Tour

A rich theory of production and analysis of productive efficiency has developed since the pioneering work by Tjalling C. Koopmans and Gerard Debreu. Michael J. Farrell published the first empirical study, and it appeared in a statistical journal (Journal of the Royal Statistical Society), even though the article provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analysing productivity and efficiency of firms. Both parametric and non‐parametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue or profit frontiers, which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labour, energy and capital) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This ‘guided tour’ reviews the development of various non‐parametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative

This paper is concerned with inference about a function g that is identified by a conditional quantile restriction involving instrumental variables. The paper presents a test of the hypothesis that g belongs to a finite-dimensional parametric family against a nonparametric alternative. The test is not subject to the ill-posed inverse problem of nonparametric instrumental variable estimation. Under mild conditions, the test is consistent against any alternative model. In large samples, its power is arbitrarily close to 1 uniformly over a class of alternatives whose distance from the null hypothesis is proportional to n^−1/2, where n is the sample size. Monte Carlo simulations illustrate the finite-sample performance of the test.     

A methodology for cluster analysis of citation histories

Citations to all the papers (558) published from 1955 to 1964 by a multidisciplinary (natural sciences) research institute within a ‘scientific periphery’ were collected for the 11-year period after a 10-year lapse since the publication years. All the papers were grouped into 31 research topics, three of which had no such late citations at all. For the remaining 28 groups of papers three indicators were defined: ALPHA, the ratio of the number of papers with citations, to the number of all papers of the particular research topic, indicating thus an overall CITATION EFFICACY; BETA, the ratio of the sum of all citations, to the number of the cited papers, indicating CITATION INTENSITY, and GAMMA, expressing the CITATION LONGEVITY for a given research topic as the incidence (number) of cited papers (irrespective of the number of citations) within the 11-year citing period. In addition, three normalized transformations of the indicator BETA were checked. Two-dimensional (without ALPHA) and three dimensional (with ALPHA, GAMMA, and one of the BETA variants) graphical representations together with a pairwise correlation analysis served as preliminary guidance in the latter statistical analyses by (a) Ward's Hierarchical Cluster Analysis and (b) Polar Taxons Analysis. Both of them resulted in good agreement. Thus, the 28 research topics were found to belong to three clusters. Their composition varied slightly for the original BETA and its three normalized values. It was concluded that ALPHA is not a redundant but quite useful indicator, and that one of the normalized BETA-variants appears most suitable for this kind of studies. In the three-dimensional graphs the clusters may be related to scientific merit as judged in a composite way by all the three indicators. This is done with regard to the diagonal joining the indicators' maximal with their minimal values. The citation LONGEVITY (GAMMA) appears to be most important. Cluster stability tests showed fluctuations of few research topics, which was related to their specific features within the given research setting. It emerges on the whole that the research merit of this (peripheral) scientific production is determined neither by the journals status the papers were published in, nor by the authors' institutional status. Rather, it is the very scientific quality of individual papers within a given research topic that is decisive for the citation ‘survival’.     

On the relation between intellectual collaboration and intellectual output: Evidence from the finance academe

This paper tests the relation between intellectual collaboration and the quality of the intellectual output using academic papers published in prestigious finance journals during 1988–2005. We use the number of authors of a paper to measure the extent of intellectual collaboration and the number of citations that a paper receives (adjusted by the number of years since the paper's publication) as a measure of its intellectual value. Based on empirical tests, we find that papers with more authors are cited more often. This relation does not hold for purely theoretical papers. Coauthoring with a prolific author leads to higher quality papers, but coauthoring with colleagues at the same institution leads to neither higher nor lower quality papers. Papers with four authors are cited most often. Overall, when it comes to intellectual collaboration, our results counter the notion that “too many cooks spoil the broth.”     

DEA, DFA and SFA: A comparison

The nonparametric data envelopment analysis (DEA) model has become increasingly popular in the analysis of productive efficiency, and the number of empirical applications is now very large. Recent theoretical and mathematical research has also contributed to a deeper understanding of the seemingly simple but inherently complex DEA model. Less effort has, however, been directed toward comparisons between DEA and other competing efficiency analysis models. This paper undertakes a comparison of the DEA, the deterministic parametric (DFA), and the stochastic frontier (SFA) models. Efficiency comparisons across models in the above categories are done based on 15 Colombian cement plants observed during 1968–1988.     

On nonparametric estimation of a hedonic price function

Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar (Journal of Applied Econometrics 2007; 22 : 695–699) found that a nonparametric approach for estimating a hedonic house price function is superior to formerly suggested parametric and semiparametric specifications. We carefully reanalyze these specifications for this dataset by applying a recent nonparametric specification test and simulation‐based prediction comparisons. For the case at issue our results suggest that a previously proposed parametric specification does not have to be rejected and we illustrate how nonparametric methods provide valuable insights during all modeling steps. Copyright © 2010 John Wiley & Sons, Ltd.     

Model‐Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout

Estimation with longitudinal Y having nonignorable dropout is considered when the joint distribution of Y and covariate X is nonparametric and the dropout propensity conditional on (Y,X) is parametric. We apply the generalised method of moments to estimate the parameters in the nonignorable dropout propensity based on estimating equations constructed using an instrument Z, which is part of X related to Y but unrelated to the dropout propensity conditioned on Y and other covariates. Population means and other parameters in the nonparametric distribution of Y can be estimated based on inverse propensity weighting with estimated propensity. To improve efficiency, we derive a model‐assisted regression estimator making use of information provided by the covariates and previously observed Y‐values in the longitudinal setting. The model‐assisted regression estimator is protected from model misspecification and is asymptotically normal and more efficient when the working models are correct and some other conditions are satisfied. The finite‐sample performance of the estimators is studied through simulation, and an application to the HIV‐CD4 data set is also presented as illustration.     

One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient,root-<Emphasis Type="Italic">n</Emphasis> consistent StoNEZD method

Understanding the effects of operational conditions and practices on productive efficiency can provide valuable economic and managerial insights. The conventional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be statistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymptotically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n ^−1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.     

The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)

Based on the seminal paper of Farrell (J R Stat Soc Ser A (General) 120(3):253–290, 1957), researchers have developed several methods for measuring efficiency. Nowadays, the most prominent representatives are nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA), both introduced in the late 1970s. Researchers have been attempting to develop a method which combines the virtues—both nonparametric and stochastic—of these “oldies”. The recently introduced Stochastic non-smooth envelopment of data (StoNED) by Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) is such a promising method. This paper compares the StoNED method with the two “oldies” DEA and SFA and extends the initial Monte Carlo simulation of Kuosmanen and Kortelainen (J Prod Anal 38(1):11–28, 2012) in several directions. We show, among others, that, in scenarios without noise, the rivalry is still between the “oldies”, while in noisy scenarios, the nonparametric StoNED PL now constitutes a promising alternative to the SFA ML.     

The origins,development and future directions of data envelopment analysis approach in transportation systems

Over the last two decades, application of Data envelopment analysis (DEA) in transportation problems have gained considerable research attention. This paper presents a literature review and classification of the applications of DEA in transportation systems (TSs). First by classifying 40 papers from 2007 to 2018, the origins of DEA in transportation problems have been reviewed. Then the development and an overall view of DEA applications in TSs have been presented. We have classified the applications of DEA into six different contexts. In each context, published papers have deeply been analyzed. Content of analysis includes “Number of published papers during the time”, “target journals”, “countries”, “keyword frequency”, “most cited papers”, “map of most co-cited publications”. More important, we reported the “inputs and outputs variables” used in each paper. Further “a review of the selected papers” and “gaps/future research directions” have been given within each cluster. The results show that DEA is one of the most useful approach in evaluating TSs for policy makers. On the other hand, DEA can help the decision makers in transportation especially regarding environmental factors, sustainable development and eco-design. Finally, we proposed subjects for future researches including guidance for new studies in the field of DEA applications in TSs.     

ON THE SOURCES OF HETEROGENEITY IN BANKING EFFICIENCY LITERATURE

This study reviews the empirical literature on banking efficiency by conducting a meta‐regression analysis. The meta‐dataset consists of 1661 observations retrieved from 120 papers published over the period 2000–2014. While the role of study design and method‐specific characteristics of primary studies is evaluated, the focus concerns regulation in banking. The results are fourfold. First, parametric methods always yield lower levels of banking efficiency than non‐parametric studies. Second, banking efficiency is higher in studies using the value‐added approach rather than the intermediation method. Third, efficiency scores also depend on the journal's ranking and on the number of observations and variables used in the primary papers. Finally, regulation matters: primary papers focusing on countries with a liberalized banking industry provide higher values for efficiency scores.     

Nonparametric efficiency analysis: A multivariate conditional quantile approach

This paper focuses on nonparametric efficiency analysis based on robust estimation of partial frontiers in a complete multivariate setup (multiple inputs and multiple outputs). It introduces α-quantile efficiency scores. A nonparametric estimator is proposed achieving strong consistency and asymptotic normality. Then if α increases to one as a function of the sample size we recover the properties of the FDH estimator. But our estimator is more robust to the perturbations in data, since it attains a finite gross-error sensitivity. Environmental variables can be introduced to evaluate efficiencies and a consistent estimator is proposed. Numerical examples illustrate the usefulness of the approach.     

Predictor relevance and extramarital affairs

We revisit Fair's ( 1978 ) ‘theory of extramarital affairs’ using robust nonparametric methods developed for the analysis of categorical data. We find evidence suggesting that the number of years married is not a relevant predictor of the propensity to engage in extramarital affairs having controlled for other factors. This finding runs counter to the prevailing wisdom gleaned from misspecified parametric models. Copyright © 2004 John Wiley & Sons, Ltd.