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Copula modeling enables the analysis of multivariate count data that has previously required imposition of potentially undesirable correlation restrictions or has limited attention to models with only a few outcomes. This article presents a method for analyzing correlated counts that is appealing because it retains well-known marginal distributions for each response while simultaneously allowing for flexible correlations among the outcomes. The proposed framework extends the applicability of the method to settings with high-dimensional outcomes and provides an efficient simulation method to generate the correlation matrix in a single step. Another open problem that is tackled is that of model comparison. In particular, the article presents techniques for estimating marginal likelihoods and Bayes factors in copula models. The methodology is implemented in a study of the joint behavior of four categories of US technology patents. The results reveal that patent counts exhibit high levels of correlation among categories and that joint modeling is crucial for eliciting the interactions among these variables.

People with chronic conditions faced a type of double jeopardy during the COVID-19 pandemic. Their pre-existing health conditions made them more likely to become severely ill – and more likely to be admitted to intensive care, intubated, and die – if infected with SARS-CoV-2, the virus that causes COVID-19. At the same time, access to needed screening, testing, and treatment was often limited due to the cancelation of primary care services by healthcare providers and systems overwhelmed by the need to treat patients with COVID-19. Patients with chronic conditions feared being exposed to COVID-19 while receiving care. The resulting stress, fear, and anxiety made the management of chronic diseases even more difficult. Several subsets of patients with certain medical conditions, including immunodeficiencies and disabilities, were particularly impacted. The COVID-19 pandemic, and the response to it, also impacted support and services available to caregivers and heightened stress, particularly among parents and caregivers.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

Examine tax-related decisions of married couples to determine whether decisions are made jointly or if one spouse dominates the decision. We also examine characteristics related to decision styles.

Questionnaires completed independently by both the husband and wife.

Nearly 40 percent of the couples make tax decisions jointly, while the remaining couples allow one spouse to dominate tax-related decisions. The results indicate that when one spouse dominates the decisions, it is most often the wife. We also find that couples are more likely to share tax-related decision responsibility jointly when the husband earns significantly more than the wife, when the couple has greater income as a family, and when they are a new couple.

Prior research has generally not recognized tax decisions by married couples as a joint decision or attempted to determine whether tax decisions are dominated by the husband or wife. This issue has implications for interpreting research results in light of prior research that has found that tax-related decisions vary significantly by gender. The finding that many couples make joint decisions suggests that an interesting avenue for future research would be to determine the nature of that joint decision making and whether it is collaborative, bargaining, or something else.

Prior research on tax-related decisions has not recognized that for approximately 40 percent of tax returns filed, the unit of study is not a single individual but a married couple.

The increased popularity of social media has been prompting the recruitment managers to use social media recruitment. Very little has been studied on the effectiveness of social media recruitment from the recruiter's perspective. Influenced by the diffusion of innovation theory, the study measures the usefulness of social media recruitment through various prehire and posthire recruitment outcomes. The study also used the media richness theory to examine the role of credibility and satisfaction as a mediating variable.

Data has been collected from the recruiters in the public and private sector of India. Available literature is studied to develop survey instrument validated through experts from industry and academia. Pilot study was conducted to test for any construct weaknesses. Data is analyzed using AMOS.

The study result proved that social media recruitment is significantly related to both prehire outcomes and posthire outcomes. The result also proved the mediating effect of credibility and satisfaction and suggests recruitment practitioner to emphasize on disseminating credible, relevant and sufficient information through suitable communication mode.

HR professional to be careful about the information provided through a social media recruitment method. Practitioner to establish credibility of the information to create a sense of satisfaction by the applicants toward the information. Thus, as the information becomes more credible, the attraction to the organization also increases, which in turn results in more applicants applying for the job.

This is the first quantitative study to examine effectiveness of social media recruitment under the influence of mediator – credibility and satisfaction considering the data from the recruiters.

Recruiters’ decision to use recruitment methods (RMs) depends on several expected outcomes such as number applications, quality of applicants, speed of filling up vacancy, post joining job performance, absenteeism, commitment and satisfaction of the applicants. RMs may vary from each other in terms of its capability to communicate different type of information. The current research aims at exploring recruiter's intention to use RMs like job advertisement (JA), online recruitment (OLR) and social media in reference to several recruitment outcomes (ROs). Further, the role of information credibility and sufficiency (ICS) on recruiter's intention to use has been studied.

Data were collected from 242 recruiters from the manufacturing and service sector of India. The survey instrument consists of RMs, recruitment outcome and credibility and satisfaction that are identified following the theory of planned behavior (TPB). Confirmatory factor analysis (CFA) was used for a simultaneous assessment of overall and specific elements of measurement validity and reliability. Structural equation modeling (SEM) is used to test the hypothesized model.

The result shows that RMs significantly relates with ROs. In detail, social media recruitment (SMR) significantly relates with pre ROs and post ROs; OLR significantly relates with pre ROs and post ROs and JA significantly relates post ROs. Only JA insignificantly relates with pre ROs. The result also supports the hypothesis that ICS acts as a mediator between the influences of RMs on ROs.

The result of the study has important theoretical and managerial implications. The theoretical implication is explained from the perspective of signaling theory (ST) and elaboration likelihood model (ELM) theory.

The study is unique as multiple RMs have been studied with reference to both pre and post ROs using the data collected from the recruiters.

This chapter investigates the effects of the corporate sector on the effectiveness of selected tax compliance instruments in the context of large corporate taxpayers belonging to the finance, manufacturing, and service sectors. Applying multilevel logit models based on real tax office and survey data from Bangladesh, it is found that the filing compliance of large corporate taxpayers is influenced by penalty, tax audit, and taxpayer services, while reporting compliance is influenced by tax audit, criminal prosecution, and tax simplification. In the case of payment compliance, two coercive instruments – penalty and tax audit – have been found to be statistically significant. However, when sector characteristics are considered, the extent of the influence of these instruments, and, in some cases, their statistical significance changes. This suggests that the effectiveness of tax compliance instruments, among other things, largely depends on the sector affiliation of corporate taxpayers. Overall, this study establishes that corporate sector plays an important role in the effectiveness of tax compliance instruments, with the caveat that findings might be different if working definitions of the study variables were measured differently.

The current study extends the technology acceptance model (TAM) and information system success model (ISSM) to the context of branded sport applications. Specifically, the study examined the influences of app system success dimensions and TAM determinants on branded sport app usage intention. Moreover, the current study examined the gender differences regarding the relative importance of the drivers and predictors of usage intention.

Data collection (n = 256) was conducted using convenience sampling in South Korea. The data were primarily analyzed via partial least squares structural equation modeling (PLS-SEM), multi-group analysis and importance–performance map analysis (IPMA) using SmartPLS 3.0.

App users who viewed branded sport apps as having a higher level of system and information quality were likely to have stronger perceptions of enjoyment, usefulness, and ease of use. Among the TAM determinants, perceived enjoyment most significantly influenced their usage intention, followed by perceived usefulness and perceived ease of use. Multigroup analysis revealed that some relationships between app system success dimensions and TAM determinants were significantly different by gender. In addition, IPMA indicated that perceived enjoyment and system quality of branded sport apps were relatively more important than the other predictors.

The current study contributes to the literature by incorporating both TAM and ISSM and extending the TAM with the perceived enjoyment construct to examine the key determinants of usage intention in the context of branded sport apps.

Generational theory research suggests that the arrival of the Millennial generation into adulthood will have significant effects on society because of their differing values and attitudes. We examine whether this generation has differing perceptions of tax fairness as well as their attitudes towards tax compliance as compared to other generations by administering an instrument to a sample of 303 taxpayers, distributed approximately equally across three generational groups: Baby Boomers, Generation X, and Millennials. The results suggest that there are significant differences in the viewpoints toward vertical equity and progressive taxation among the three generations. More specifically, the Millennial generation was less likely to recommend progressive taxation than the other two generations. In addition, there were significant differences between the groups on an exchange equity question as well. However, in this situation, it was the Baby Boomers that were significantly different from the other two generations. The results also suggest that the Millennials have attitudes that are more accepting of noncompliance than both the Generation X participants and the Baby Boomer participants. However, a significant difference does not exist between the Baby Boomer participants and Generation X participants on their attitudes towards compliance.

The transition from multichannel to omnichannel retailing requires a better conceptualisation, especially for customer experience in smart shopping malls. Therefore, this study aims to propose a theoretical model that captures customers’ omnichannel experiences in smart shopping malls in terms of personal interaction, physical environment and virtual environment encounters. It examines the mediating role of flow experience on the relationship between the three types of encounters and customers’ intention to revisit smart shopping malls.

The study draws on four key theories: the service encounter model, trust-commitment theory, flow theory and experiential value theory. A total of 553 completed questionnaires were collected from customers (millennials) in the United Kingdom (UK). The data was analysed using partial least squares-structural equation modelling.

The findings show that physical environment encounters and personal interaction encounters play a significant role in customers’ omnichannel experiences in smart malls. Also, of significance are the following aspects of virtual environment encounters: interface design, personalisation, trust, privacy, consumer–peer interaction and relationship commitment. The findings highlight the significant mediating role of flow on the relationships between these three types of encounters and intention, and the effect of flow on omnichannel service usage in smart shopping malls.

The research contributes to the existing literature by proposing a conceptual model: the smart shopping mall omnichannel customer experience (SSMCE) model. The findings offer practical guidance to shopping malls and retailers who wish to enhance the customer omnichannel experience.