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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.

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.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.