Search results

A wetland is a place of tourist attraction, and tourism values play a key role in economic development. Among various services provided by a wetland, recreational services are increasingly valuable in the tourism sector. This paper aims to unfold the potential recreational values of the Dal Lake in Jammu and Kashmir, India.

The study uses individual travel cost methods (TCMs) and assesses its impact on regional development in terms of income and employment generation. A sample of 200 tourists is selected through an on-site survey on Dal Lake, and the demand for recreational visits and its value is estimated by employing the truncated Poisson regression model (TPRM) and un-truncated Poisson regression model (UTPRM). The consumers' surplus is estimated and tourists' benefit to visiting the wetland is explored.

On average, estimated consumers' surplus per visitor is Rs 6,250 (US$96.15) and Rs 25,000 (US$384.61) from respective models. The annual total recreational value of the lake is accounted for Rs 1713m (US$ 26m). This high consumer surplus (CS) and recreational values of the lake indicate large demand for its recreational facilities.

The study is based on primary data and thus, is original. The paper has implications for the policymakers to formulate sustainable management plans for the proper use of Dal Lake and tourism development.

The purpose of this paper is to explore two distinct subsets of dynamic capabilities that need to be deployed when pursuing innovation through inter-organizational activities, respectively, in the contexts of broad networks and specific alliances. The authors draw distinctions and explore potential interdependencies between these two dynamic capability reservoirs, by integrating concepts from the theoretical perspectives they are derived from, but which have until now largely ignored each other – the social network perspective and the dynamic capabilities view.

The authors investigate nanotechnology-driven R&D activities in the 1995–2005 period for 76 publicly traded firms in the electronics and electrical equipment industry and in the chemicals and pharmaceuticals industry, that applied for 580 nanotechnology-related patents and engaged in 2,459 alliances during the observation period. The authors used zero-truncated Poisson regression as the estimation method.

The findings support conceptualizing dynamic capabilities as four distinct subsets, deployed for sensing or seizing purposes, and across the two different inter-organizational contexts. The findings also suggest potential synergies between these subsets of dynamic capabilities, with two subsets being more macro-oriented (i.e. sensing and seizing opportunities within networks) and the two other ones more micro-oriented (i.e. sensing and seizing opportunities within specific alliances).

The authors show that firms differ in their subsets of dynamic capabilities for pursuing different types of inter-organizational, boundary-spanning relationships (such as alliances vs broader network relationships), which ultimately affects their innovation performance.

The authors contribute to the growing body of work on dynamic capabilities and firm-specific advantages by unbundling the dynamic capability subsets, and investigating their complex interdependencies for managing different types of inter-organizational linkages. The main new insight is that the “linear model” of generating more innovations through higher inter-firm collaboration in an emerging field paints an erroneous picture of how high innovation performance is actually achieved.

The purpose of this paper is to measure the technical inefficiency of dairy farms and subsequently investigate the factors affecting technical inefficiency in the Malaysian dairy industry.

This study uses multi-directional efficiency analysis to measure the technical inefficiency scores on a sample of 200 farm observations and single-bootstrap truncated regression model to define factors affecting technical inefficiency.

Managerial and program inefficiency scores are presented for intensive and semi-intensive production systems. The results reveal marked differences in the inefficiency scores across inputs and between production systems.

Intensive systems generally have lowest managerial and program inefficiency scores in the Malaysian dairy farming sector. Policy makers could use this information to advise dairy farmers to convert their farming system to the intensive system.

The results suggest that the Malaysian Government should redefine its policy for providing farm finance and should target young farmers when designing training and extension programs in order to improve the performance of the dairy sector.

The existing literature on Southeast Asian dairy farming has neither focused on investigating input-specific efficiency nor on comparing managerial and program efficiency. This paper aims to fill this gap.

This paper aims to identify the scale efficiency of dry ports in Brazil and its main technological drivers.

This paper uses the Data Envelopment Analysis (DEA) model in two stages. The first stage of the DEA was used to measure the efficiency of the dry ports. In the second stage, the Bootstrap Truncated Regression (BTR) was applied to explore the relationship between efficiency and the factors analyzed. The inputs, outputs and contextual variables for this analysis were extracted from the secondary database provided by Revista Tecnologística.

In the first analysis stage, a high level of idleness was verified in the operations. The contextual variables in the second stage were significant: Certification, Warehouse Management System (WMS), barcode and Radio Frequency Identification (RFID). Results corroborate the positive impact of Information Technology (IT) coordination processes on logistics performance.

Results show that dry ports operate below their technical and operational capacity and that the sector's lack of regulation in Brazil can facilitate and encourage the use of ports and marine terminals by importers and exporters.

Application of two-stage DEA measures efficiency as a sectoral benchmarking tool.

This paper investigates the development of efficiency and the progress of banking integration in the European Union by checking for convergence among banks of European and Eurozone countries as well as contrasting the results with those of United States banks.

Initially, we employ the two-stage semi-parametric double bootstrap DEA method, which absorbs the effects of possible integration barriers in the measurement of efficiency. Afterwards, we apply a panel data model, in order to investigate the process of banking integration by testing for convergence and for convergent clusters in banking efficiency.

Our main findings show that the bank efficiency of the US is considerably higher than that of the Eurozone and the European Union. Although there is no evidence of convergence across the banking groups, our results indicate the presence of club convergence. We also conclude that the US banking system is closer to convergence than the Eurozone and the European Union banks. Nevertheless, this outcome is subject to change in the future due to the fact that Eurozone and European Union banks' speed of convergence is higher than that of US banks.

Our survey is unique in trying to check for convergence while controlling for country-specific and bank-specific factors that affect the efficiency of European and Eurozone banks. Moreover, recent literature does not compare the convergence of efficiency of Eurozone, European and US banking. Finally, in our paper special consideration was given to the comparison of commercial, cooperative and savings banks, as subsets of our banking groups.

The purpose of this paper is to analyze a variety of factors that can explain the differences in commercial bank efficiency among 17 countries in Latin America (LatAm).

In a first stage, data envelopment analysis (DEA) and conditional efficiency analysis techniques are used to assess the relative efficiency level of 409 banks for the 2014-2016 period. The conditional efficiency approach considers environmental variables (that are beyond the manager’s control), which could influence the shape and the level of the boundary of the attainable set. In the second stage, the resulting conditional efficiency scores are correlated with internal variables (those that are under the manager’s control), which might affect the distribution of the inefficiencies. For this purpose, an econometric approach developed by Simar and Wilson (2007) is used.

First stage scores reveal the heterogeneity of average efficiency within the region. Regarding the factors that may explain the differences in performance in the LatAm banking sector, the results allow us to state that certain internal variables such as bank size, the ratio of loans to total assets and the ratio of non-performing loans show the expected relationship to efficiency, in line with much of the previous literature.

This is the first time that conditional efficiency and Simar and Wilson (2007) approaches have been applied at the same time to analyse the LatAm banking industry.

This paper uses various Data Envelopment Analysis (SBM-DEA) approaches to study the efficiency of major airlines in Asia-Pacific region. To evaluate the operation efficiency of fourteen major airlines in Asia-Pacific region from 2003-2011, Available Seat Kilometers(ASK), Available Ton Kilometers(ATK), the number of employees are used as input factors, Revenue Passenger Kilometers(RPK), Revenue Ton Kilometers(RTK), the amount of Sales are used as output factors.

The non-radial SBM-DEA (Slacks-based Measure of Efficiency) model was able to provide a more comprehensive efficiency of combining economic performance and regional difference. And it was also able to capture slack values in input excess and output shortage.

The results demonstrate that Korea and Japan airlines are operated efficiently and could be regarded as the benchmarking airlines. On the other hand, most of the China and ASEAN airlines are deemed to be inefficient. Also analyzing slacks may be more suitable way for the evaluation or suggestion of an improvement scheme for the inefficient airlines. The excess of labor is the major cause of the airlines’ inefficiency.

This study, using a contingent valuation approach, aims to shed light on the economic evaluation of online learning during the first wave of the pandemic.

A sample of 959 higher education students was asked about their willingness-to-accept (WTA) a monetary compensation for the loss of well-being resulting from the unexpected and mandatory transition to the online space. In explaining WTA determinants, the authors test the appropriateness of the double-hurdle model against the alternative of a Tobit model and find that the factors affecting the participation decision are not the same as those that affect the quantity decision.

Results show that a vast majority of the respondents think that the abrupt transition to online learning is detrimental to them, while those willing to accept a monetary compensation account for 77% of the sample, being the mean WTA between €448 and €595. As expected, WTA decreases with income and age, and it increases if some member of the family unit is unemployed. By aggregating the mean WTA by the population affected, total loss of well-being is obtained.

To the best of the authors’ knowledge, to date, this method has not been used to value online learning in a WTA framework, much less in the particular context of the pandemic. Thus, based on the understanding that the economic evaluation of online learning could be very useful in providing guidance for decision-making, this paper contributes to the literature on the economic evaluation of higher education.

The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.

The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).

The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models.

The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.