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Using Internet‐enabled mobile handheld devices to access the World Wide Web is a promising addition to the Web and traditional e‐commerce. Mobile handheld devices provide convenience and portable access to the huge information on the Internet for mobile users from anywhere and at anytime. However, mobile commerce has not enjoyed the same level of success as the e‐commerce has so far because mobile Web contents are scarce and mostly awkward for browsing. The major reason of the problems is most software engineers are not familiar with handheld devices, let alone programming for them. To help software engineers better understand this subject, this article gives a comprehensive study of handheld computing and programming for mobile commerce. It includes five major topics: (i) mobile commerce systems, (ii) mobile handheld devices, (iii) handheld computing, (iv) server‐side handheld computing and programming, and (v) client‐side handheld computing and programming. The most popular server‐side handheld applications are mostly functioning through mobile Web contents, which are constructed by using only few technologies and languages. On the other hand, various environments/languages are available for client‐side handheld computing and programming. Five of the most popular are (i) BREW, (ii) J2ME, (iii) Palm OS, (iv) Symbian OS, and (v) Windows Mobile. They are using either C/C++ or Java programming languages. This article will explain J2ME, a micro version of Java, and Palm OS programming, using C, by giving step‐by‐step procedures of J2ME and Palm application development.

The purpose of this paper is to explore teachers' behaviours in completing an internet survey using decision trees. Furthermore, to reduce the complexity of the decision trees, a statistical technique was used to decrease the number of input variables in the decision trees.

A dataset of 47,647 samples was used to build the decision trees. These samples were collected from an internet survey of teachers in Taiwan. The output of the decision trees was the answering time (the time taken to complete the internet questionnaire). Eight variables were selected as the inputs for the decision trees. Two techniques were employed to build the decision trees – the exhaustive chi‐squared automatic interaction detector (ECHAID) and classification and regression tree (CRT) analysis. To reduce the complexity of the decision models, factor analysis technique was used to decrease the data dimensions (number of input variables) and to obtain a simplified decision model. One‐way ANOVA was used to validate the effects of the dimension reduction.

From the results of the factor analysis, a simplified decision tree is recommended using four input variables – teaching years, school level, sex and area. The classification accuracy of the simplified model is statistically equivalent to that of the original one, which used eight input variables.

The complexity of decision trees theoretically depends on the number of input variables. This study used a statistical technique to decrease the number of input variables and thereby reduce the complexity of the decision trees. A statistical technique was employed to validate that the classification accuracy is not statistically different between the original decision model and the simplified one. The decision models proposed in this paper can be applied in estimating the answering time for completing a questionnaire during an internet survey.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

The purpose of this paper is to examine the effect of parental and peer norms on idol worship as well as the effect of idol worship on the intention to purchase and obtain the idol’s music products legally and illegally.

A stratified, two-stage, cluster sampling procedure was applied to a list of high schools obtained from the Ministry of Education in Taiwan. A return rate of 80 per cent yielded 723 usable questionnaires, the data from which were analysed by the LISREL structural equation modelling software.

The results suggest that both social worship and personal worship have a significant and positive impact on the intention to purchase music. However, personal worship has a negative impact on the intention to pirate music while social worship appears to strengthen it.

The findings suggest that idol worship is more complex than previously understood. The constructs chosen in this research should be seen only as a snapshot but other variables such as vanity trait, autonomy, romanticism or involvement are not taken into account. Future studies would benefit from inclusion of these variables and a wider geographical scope.

The findings contain many implications to help marketing executives and planners better revise their existing marketing and communication strategies to increase their revenue.

Existing research has tended to examine the impact of idol worship as a whole on the reduction of music piracy, but overlook the two-dimensional aspects of idol worship, hence ignoring the fact that many music firms have not properly utilised idol worship to deal with the challenges associated with music piracy. The findings broaden existing understanding about the causes of two different dimensions of idol worship and their different impacts on the intention to music piracy.