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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.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

Drawing on legitimacy and upper-echelon theory, this paper aims to investigate the moderating role of corporate governance in the relationship between corporate social performance (CSP) and board gender diversity (BGD).

Using Morgan Stanley Capital International measures of social and governance performance, the authors use 2,950 firm-year observations from US companies for the years 2016–2020 to show that good performance on social issues drives BGD.

The panel data model indicates that the relationship between CSP and BGD is strengthened when firms display robust corporate governance.

This study contributes to the extant literature through empirical consideration of CSP as a predictor of BGD, a relationship that has rarely been examined. It further highlights the significant role of corporate governance in ensuring that women have access to corporate boards. Discussion and findings highlight that social performance and governance may significantly contribute to the diversity of socially cognizant boards.

Previous studies on social influence and virtual product adoption have mainly taken users’ purchase behavior as a dichotomous variable (i.e. purchasing or not). Given the prevalence of competing versions (basic vs upgraded) of a virtual product in online communities, this paper investigated the differences in the effect of social influence on users’ adoption of basic and upgraded choices of a virtual product. It also examined how the effect varies with users’ social status and user-level network density.

A natural experiment was conducted in an online game community. Two competing versions (basic vs upgraded) of a virtual product were provided for in-game purchase while a random set of users selected from 897,765 players received the notification of their friends’ adoption information. A competing-risk model was used to test the hypotheses.

Social influence exerts a stronger positive effect on users’ adoption of the upgraded virtual product than of the basic virtual product. Middle-status users have the greatest (least) susceptibility to social influence in adopting the upgraded (basic) virtual product than low- and high-status users. User’s network density enhances the effect of social influence on adoption of both virtual products, even more for the upgraded one.

This research contributes to the social influence and product adoption literature by disentangling the different effects of social influence on basic and upgraded versions of a virtual product. It also identifies the boundary conditions that social influence works for each version of the virtual product.

Active shooter incidents have risen considerably in recent years, elevating public and law enforcement focus on improving response to these incidents. The contemporary policy for reacting to an active shooter event is for the officers who first arrive on the scene to move quickly to engage and neutralize a shooter, prioritizing victim safety and minimizing loss of life. This study provides a preliminary understanding of the police view regarding their role in active shooter events and their experiences with active shooter training.

We conducted a survey of 413 sworn personnel across three US cities. Analyses examine differences in officers’ training experiences across the agencies and predictors of a sense of duty to prioritize victims’ lives over that of officers.

Officers’ training experiences differed by agency, yet most officers supported a duty to sacrifice their lives to prioritize victims’ safety during active shooter incidents. Officers with more years of experience have lower odds of supporting a duty to sacrifice their lives to save victims during an active shooter situation relative to less experienced officers. Respondents who recall officer safety among the top three most memorable topics from their recent active shooter training also have lower odds of supporting a duty to sacrifice.

The authors add knowledge about police officers’ experiences with active shooter training and officers’ opinions about their role in responding to active shooter incidents. We discuss implications of the findings relative to police culture and training and suggest directions for future research.

This study aims to investigate the relationship between consumer awareness of Zespri International Limited (Zespri™) and its sales promotion in Japan and the recent expansion of New Zealand (NZ) kiwifruit imported into Japan.

Tweets mentioning Zespri™ were utilised as a proxy of such awareness. They were first summarised using two text-mining techniques: tf-idf scoring and a co-occurrence network graph. Afterwards, the authors estimated a tri-variate vector autoregression (VAR) model consisting of the net imports of NZ kiwifruit in Japan, unit import price and number of tweets. Additionally, the occurrence frequency of tweets with “Kiwi Brothers”, promotional characters for Zespri™’s sales, was added to the model, and a tetra-variate VAR model was estimated. Finally, Granger-causality tests, an estimation of the impulse response function and forecast error variance decomposition was conducted.

All these variables were found to Granger-cause each other. Furthermore, a shock in the document frequency of “Kiwi Brothers” significantly affected Japan’s kiwifruit imports from NZ, explaining approximately 20% of future imports. Zespri™’s distinctive sales promotion was, thus, found to contribute in part to the recent increase in NZ’s kiwifruit export to Japan.

This paper is the first to apply text-regression methodology to food consumption research; it contributes to food consumption research by proposing a practical way to combine tweets with outcome variables using a time-series analysis.