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A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

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.

We show political divisions in perceptions of police officers even before the divisive political and social events of 2016. We do so using respondents' interpretations of surprising and ambiguous headlines involving police officers (e.g., assumptions about what happened or who was involved).

We use affect control theory's ABO event structure and derivations of this structure to construct a set of headlines that describe ostensibly good people (A) doing bad things (B) to other good people (O) or are ambiguous on one or more of these components. We present 517 MTurk respondents with a set of seven headlines and collect quantitative and qualitative data on their reactions to, and interpretations of, these headline events.

Police headlines generate interest among readers. When interpreting events, respondents are less likely to modify or redefine police officers compared to other actors. However, assumptions related to ambiguous events involving police differ by political orientation. Liberals view police more negatively than conservatives, in part because they imagine them doing worse things to slightly better people. Qualitative analyses support and shed light on the mechanisms underlying this and other partisan effects.

The research was designed to examine interest in headline structure, not specific actors. Thus, the patterns unique to police and political differences were not an original focus. We believe these inductive results are informative, but a study expressly designed to test hypotheses regarding perceptions of events with police officers is recommended for future work.

Understanding the political divide in perceptions of police and the potential of media coverage for exacerbating these effects is essential and related to ACTs growing interest in meaning divergence.

This study aims to understand how different levels of streamer channels influence luxury brand sales in live streaming commerce. This study also seeks to understand the conditions under which luxury brands may benefit more from different level streamer channels.

Panel data were collected from 17 international luxury brands on the Douyin live streaming platform in an 18 week period from August to December 2020 and analyzed by using a two-way fixed effects model.

The authors compared different mega-, macro- and micro-streamer channels within live streaming commerce and found that the densities of mega- and macro-streamer channels had significant positive impacts on luxury brand sales in live streaming commerce. Moreover, the effects of the density of streamer channel on luxury brand sales were moderated by such variables as product line breadth, product line depth, product type (star/non-star) and product price (high/low). The authors found that product line breadth and depth could reduce the positive impact of the densities of mega- and macro-streamer channels on luxury brand sales. For star products and high-priced products, the relationship between the density of mega-streamer channel and luxury brand sales was more likely to be observed than for non-star products and low-priced products. The relationship between the density of macro-streamer channel and luxury brand sales was more likely to be observed in low-priced products than in high-priced products.

The findings make important contributions to the literature in that the authors expand the influencer-brand fit theory by proposing a new model of effects of the densities of mega-, macro- and micro-streamer channels on sales performance across different luxury products to improve our understanding of the fit among influencers, brands and products. This helps luxury brands make basic decisions of “who sells” and “sells what” when engaging in live streaming commerce.

In demand-driven markets, customer value, sometimes called perceived use value or consumer surplus, is defined by the customer rather than the firm. The value a firm can appropriate, its profits, is driven by the customer’s willingness to pay for the value they receive, adjusted by costs. This paper introduces a conceptual framework that helps understand value creation and appropriation in demand-driven markets and shows how to influence them through strategic decision-making.

This paper uses an axiomatic approach combined with an extended analytical formulation of the jobs-to-be-done framework to contextualise demand-driven markets. It mathematically derives implications for managerial decision-making concerning selecting customer segments, optimising customer value creation and maximising firm value appropriation in a competitive environment.

Rooting strategic decision-making in the jobs-to-be-done framework allows distinguishing between what customers want to achieve (goal), what product attributes need to be satisfied (opportunity space/constraints) and what value creation criteria related to features are important (utility function). This paper shows that starting from a job-to-be-done, the problem of identifying which customer segments to serve, what product to offer and what price to charge, can be formulated as an optimisation problem that simultaneously (rather than sequentially) solves for the three decision variables, customer segments, product features and price, by maximising the value that a firm can appropriate, subject to maximising customer value creation and constrained by the competitive environment.

Applying the derived results to simultaneously deciding which customer segments to target, what product features to offer and what price to charge, given a set of competing products, allows managers to increase their chances of winning the competitive game.

This paper shows that starting from a job-to-be-done and simultaneously focusing on customers, product features, price and competitors enhances firm profitability. Strategic decision-making is formulated as an optimisation problem based on an axiomatic approach contextualising demand-driven markets.

This study aims to empirically test how sponsored video customization (i.e. the degree to which a sponsored video is customized for a sponsoring brand) affects video shares differently depending on influencer characteristics (i.e. mega influencer and expert influencer) and brand characteristics (i.e. brand establishment and product involvement).

This study uses a unique real-world data set that combines coded variables (e.g. customization) and objective video performance (e.g. sharing) of 365 sponsored videos to test the hypotheses. A negative binomial model is used to analyze the data set.

This study finds that the effect of video customization on video shares varies across contexts. Video customization positively affects shares if they are made for well-established brands and high-involvement products but negatively influences shares if they are produced by mega and expert influencers.

This study extends the influencer marketing literature by focusing on a new media modality – sponsored video. Drawing on the multiple inference model and the persuasion knowledge theory, this study teases out different conditions under which video customization is more or less likely to foster audience engagement, which both influencers and brands care about. The chosen research setting may limit the generalizability of the findings of this study.

The findings suggest that mega and expert influencers need to consider if their endorsement would backfire on a highly customized video. Brands that aim to engage customers with highly-customized videos should gauge their decision by taking into consideration their years of establishment and product involvement. For video-sharing platforms, especially those that are planning to expand their businesses to include “matching-making services” for brands and influencers, the findings provide theory-based guidance on optimizing such matches.

This paper fulfills an urgent research need to study how brands and influencers should produce sponsored videos to achieve optimal outcomes.

This study aims to regarding the application of traditional pulse frequency modulation control full-bridge LLC resonant converters in wide output voltage fields such as on-board chargers, there are issues with wide frequency adjustment ranges and low conversion efficiency.

To address these issues, this paper proposes a fixed-frequency pulse width modulation (PWM) control strategy for a full-bridge LLC resonant converter, which adjusts the gain by adjusting the duty cycle of the switches. In the full-bridge LLC converter, the two switches of the lower bridge arm are controlled by a fixed-frequency and fixed duty cycle, with their switching frequency equal to the resonant frequency, whereas the two switches of the upper bridge arm are controlled by a fixed-frequency PWM to adjust the output voltage. The operation modes of the converter are analyzed in detail, and a mathematical model of the converter is established. The gain characteristics of the converter under the fixed-frequency PWM control strategy are deeply analyzed, and the conditions for implementing zero-voltage switching (ZVS) soft switching in the converter are also analyzed in detail. The use of fixed-frequency PWM control simplifies the design of resonant parameters, and the fixed-frequency control is conducive to the design of magnetic components.

According to the fixed-frequency PWM control strategy proposed in this paper, the correctness of the control strategy is verified through simulation and the development and testing of a 500-W experimental prototype. Test results show that the primary side switches of the converter achieve ZVS and the secondary side rectifier diodes achieve zero-current switching, effectively reducing the switching losses of the converter. In addition, the control strategy reduces the reactive circulating current of the converter, and the peak efficiency of the experimental prototype can reach 95.2%.

The feasibility of the fixed-frequency PWM control strategy was verified through experiments, which has significant implications for improving the efficiency of the converter and simplifying the design of resonant parameters and magnetic components in wide output voltage fields such as on-board chargers.

This paper aims to investigate how user expectations affect value-added service (VAS) investment and pricing decisions of two-sided platforms. It draws on the information asymmetry theory and offers suggestions on how platform operators can manage user expectations.

According to the game theory, this study considers three user expectations (responsive, passive and wary). By framing the Hotelling duopoly model and comparing the VAS investment, price and platform profits, the optimal platform decision is analyzed and discussed.

The conclusions demonstrate that the monopolistic two-sided platform obtains more profits from the informed users with responsive expectations than uninformed users with passive or wary expectations. The marginal investment cost and cross-network externalities are two key factors that determine the platform's VAS investment and pricing strategies of passive or wary users. Furthermore, considering the expectation preferences, i.e. the uniformed users hold wary expectations with more information and hold passive expectations with less or no information, the results suggest that the proportion of wary users to all uninformed users increases the platform's VAS investment, profits and the price of informed users, and increase (decrease) the price of uninformed users when the cross-network externalities of informed users are relatively small (larger).

These results can provide insightful enlightenment into how platform operators utilize bilateral users' expectations and information level to guide their VAS investment and pricing decisions.

This paper is one of the first to explore the impact of three user expectations and the heterogeneity of preferences in informing users' passive or wary expectations, based on different levels of information on the decision-making of two-sided platforms regarding VAS.

The study aims to explore the conditional relationships between product modularization and new product development (NPD) efficiency. It is postulated that R&D outsourcing plays an important mediating role. Furthermore, the level of competency trust is considered an essential factor in moderating the indirect effect of product modularization on NPD efficiency via R&D outsourcing practices.

Drawing on transaction cost economics theory, this study suggests a moderated mediation model that addresses how product modularization effectively promotes NPD efficiency via outsourcing practices. The hierarchical regression and PROCESS macro model were conducted to test the hypotheses based on survey data from 273 manufacturing firms in China.

Product modularization enhances NPD efficiency directly and indirectly through the external collaboration of R&D outsourcing. Furthermore, the role of product modularization in R&D outsourcing practices is more effective when the competency trust in R&D outsourcing partners is high.

By showing the critical role of external collaboration, this study provides valuable insights into how manufacturing firms utilize product modularization to achieve desired NPD performance more effectively.