Search results

The purpose of this study is to explore the impact of product category and consumers’ motivations profiles on the determinants of consumers’ preferences and purchase intentions of counterfeits and genuine products, through manipulation of product attributes and purchase situations.

The study relies on an experimental design involving a questionnaire on a convenience sample with two parts: a tradeoff model manipulating three attributes, product type (genuine vs. counterfeit), price (high vs. low) and place of purchase (regular shop, Internet and market) in two product categories, and a scale measuring motivations to purchase counterfeits. Ranking and purchase intentions are analyzed using conjoint analysis and generalized linear mixed model (GLMM).

Ranking reveals a dominant pattern of consumer behavior regarding counterfeiting: product type, price and place of purchase. Product category has a moderating effect on choice criteria: relative importance of place of purchase and price varies according product category. Consumers’ motivations profiles have also a moderating effect on consumer behavior. Some profiles are more receptive to copies. Consumers’ profiles exhibit different hierarchies of purchase criteria and may change them depending upon product category.

Results challenge literature on the dominant role of price among choice criteria. Price alone cannot determine a counterfeit purchase. It is the interaction of price, place of purchase or product type that explains such a behavior. Product category matters: Price and place of purchase importance cannot be considered without accounting for product category. Consumers’ motivations profile matters. Consumers are not homogeneous face to counterfeits.

Fatigue associated with shift work is a well-established and pervasive problem in policing that affects officer performance, safety, and health. It is critical to understand the extent to which fatigue degrades officer driving performance. Drowsy driving among post-shift workers is a well-established risk factor yet no data are available about officer injuries and deaths due to drowsy driving. The purpose of this paper is to assess the impact of fatigue associated with work shift and prior sleep on officers’ non-operational driving using laboratory experiments to assess post-shift drowsy driving risks and the ability of a well-validated vigilance and reaction-time task to assess these risks.

Experienced police patrol officer volunteers (n=78) from all four shifts of a medium-sized city’s police department were tested using a within- and between-subjects design to assess the impact of fatigue on individual officers, as well as the impact of different work shifts, on post-shift driving performance. Controlled laboratory experiments were conducted during which participants drove high-fidelity driving training simulators on two occasions: immediately following five consecutive 10:40-hour patrol shifts (fatigued condition) and again 72 hours after completing the last shift in a work cycle (rested condition).

Generalized linear mixed-model analyses of driving performance showed that officers working night shifts had significantly greater lane deviation during post-shift, non-operational driving than those working day shifts (F=4.40, df=1, 150, p=0.038). The same method also showed that easy to measure psychomotor vigilance test scores for reaction time predicted both lane deviation (F=31.48, df=1, 151, p < 0.001) and collisions (F=14.10, df=1, 151, p < 0.001) during the simulated drives.

Simulated driving tasks done by participants were generally less challenging than patrol or off-duty driving and likely underestimate the impact of fatigue on police driving post-shift or during extended shifts.

This is the first experimental research to assess the impact of shiftwork, fatigue, and extended shifts on police post-shift drowsy driving, a known risk factor for shift workers in general.

Māori (Indigenous New Zealanders) and Pacific students tend not to attain the same levels of educational success as New Zealanders of European descent. Addressing this problem is a particular challenge at tertiary level in science, engineering, and architecture and design (SEAD). Te Rōpū Āwhina (Āwhina), an initiative at Victoria University of Wellington (VUW), aims to produce Māori and Pacific professionals who contribute to Māori and Pacific development and leadership. The objective of this analysis was to summarise quantitative results from the first 11 years of Āwhina and to show they are consistent with an Āwhina ‘effect’; that is, a positive influence on (combined) Māori and Pacific success in the SEAD disciplines. Individual-level records held in the VUW student database were used to generate smoothed trends in SEAD and non-SEAD graduate and postgraduate degree completions since 1991. Substantial improvements in SEAD Māori and Pacific completions occurred between 1999 and 2010, including a 50%- increase in Māori and Pacific postgraduate completions relative to all SEAD postgraduate completions. In the same period, non-SEAD Māori and Pacific postgraduate completions increased at a similar rate to all non-SEAD postgraduate completions. Results were consistent with a strong Āwhina effect, which has important implications for the nature of tertiary institutions, their cultural and social disconnection with Indigenous and minority students, and their social obligations and responsiveness. This analysis did not account for students who did not complete a qualification or include key confounders such as entry qualifications and gender. Definitive confirmation of an Āwhina effect is the subject of ongoing research.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

Branching is not the only way for foreign banks to enter a national market, and it is impractical when there are informational and cultural barriers and asymmetries among countries. The purpose of this paper is to analyze the determinants of cross-border branching in the Latin American banking sector, a region with regulatory disparity and political and economic instability, offering elements to a grounded strategic decision.

This study uses data from six Latin American countries. To account for the preponderance of zero counts, classes of zero-inflated models are applied (Poisson, negative binomial, and mixed). Model fit indicators obtained from differences between observed and estimated counts are used for comparisons, considering branches in each region established by banks from every other foreign region of the sample.

Branching by foreign banks is positively correlated with the population, GDP per capita, household disposable income, and economic freedom score of the host country. The opposite holds for the unemployment rate and entry regulations of the host country.

Few paper address cross-border banking in emerging economies. This paper analyzes cross-border branching in Latin America in the context of the current financial integration and bank strategy. Econometrically, its pioneering design allows modeling of inflation of zeros, over-dispersion, and the multilevel data structure. This design allowed testing of a novel country-level variable: the host country’s economic freedom score.

This paper aims to test the political-economy hypothesis that country sizes are related with constraints associated with Chinese trade.

This study applies a generalized linear mixed approach on panel data of Southern African (henceforth SADC) economies from 2001 to 2014 to observe common Chinese trade patterns among SADC countries.

Empirical results support the hypothesis that structural differences exist and smaller SADC countries are disadvantaged in their trade relations with China.

This paper is exploratory by nature. Its scope and the depth of analysis is constrained by data availability.

The manuscript has been approved by the author and has never been published, or has been considered for publication elsewhere.

Smart manufacturing can lead to disruptive changes in production technologies and business models in the manufacturing industry. This paper aims to identify technological topics in smart manufacturing by using patent data, investigating technological trends and exploring potential opportunities.

The latent Dirichlet allocation (LDA) topic modeling technique was used to extract latent technological topics, and the generalized linear mixed model (GLMM) was used to analyze the relative emergence levels of the topics. Topic value and topic competitive analyses were developed to evaluate each topic's potential value and identify technological positions of competing firms, respectively.

A total of 14 topics were extracted from the collected patent data and several fast growth and high-value topics were identified, such as smart connection, cyber-physical systems (CPSs), manufacturing data analytics and powder bed fusion additive manufacturing. Several leading firms apply broad R&D emphasis across a variety of technological topics, while others focus on a few technological topics.

The developed methodology can help firms identify important technological topics in smart manufacturing for making their R&D investment decisions. Firms can select appropriate technology strategies depending on the topic's emergence position in the topic strategy matrix.

Previous research studies have not analyzed the maturity levels of technological topics. The topic-based patent analytics approach can complement previous studies. In addition, this study provides a multi-valuation framework for exploring technological opportunities, thus providing valuable information that supports a more robust understanding of the technology landscape of smart manufacturing.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

This integrative review aimed to identify and synthesis literature on analysis techniques and methodological approaches used to analyse consumer measures in mental health research.

The review included papers published up to January 2020 across seven databases: CINAHL, Web of Science, Medline, PsycINFO, EMBASE, Scopus and Google Scholar. Data search and extraction was conducted according to the recommended guidelines for conducting review by Cochrane and Joanna Briggs Institute. Mixed method synthesis was used to integrate both qualitative and quantitative data into a single synthesis.

The initial search yielded a total of 2,282 papers. A total of 32 papers were included in the synthesis. Most of the included papers (25/32; 78.12%) focused on psychometric properties, whereas 14% (5/32) targeted analysis techniques, and 6.3% (2/32) addressed methodological justification. The measurement models (e.g. psychometric properties) were analysed through validity and reliability testing as part of instrument development and adaptation. The structural models were analysed using techniques such as structural equation modelling, multivariable regression models, intraclass correlation coefficient and partial least squares–structural equation modelling.

Although consumer-reported instruments are analysed using techniques involving linear, hierarchical and longitudinal effects, no attempt has been given to procedures that applied complex data mining or machine learning. Consumer researchers, clinicians and quality management are encouraged to apply rigorous analysis techniques to critically evaluate consumer outcome measures.

This review provides evidence on the analysis techniques in mental health research to inform the training of mental health professionals, students and quality assessment practitioners.