Search results

This study aims to apply the modified Walker-Unger model to show the degree of attractiveness of a country for Mexican-based money launderers to send their illicit funds for the 2000–2015 time period.

The modified Walker-Unger model is used to conduct the analysis, as it combines several independent variables related to an illicit financial activity. These allow the researcher to investigate the attractiveness of a market to money launderers and the possible economic effects of money laundering. In total, 13 categories of indicators were used, namely, gross national product per capita; banking secrecy; government attitude; society for worldwide interbank financial telecommunication membership; financial deposits; conflict; corruption; Egmont group membership; language; trade; culture, colonial background; and physical distance.

Model results suggest the preferred destinations for Mexican-based money launderers from 2000 to 2015 were Bermuda (i.e. from 2000–2004), Canada (i.e. in 2005 and 2006) and Monaco (i.e. from 2007–2015).

Timing and availability of reliable data after 2015.

Aids in continuing to empirically validate the Walker-Unger model. There is little literature on models that quantify money laundering activity.

May aid policymakers in targeting anti-money laundering policy to more relevant countries.

The first empirical investigation that looks to quantify money launderer activity in Mexico. Contributes to the limited literature of quantitative investigations on money laundering.

Although economists and academics have studied money laundering for several decades, there continues to be gaps in the research due to a lack of reliable data on money laundering activity, and a lack of detailed sources and methods of collection in government-based reporting. The purpose of this study is to apply the Walker-Unger gravity model and examine US-based money launderer preference for the 2000-2020 time frame. This paper then compares those results with previous applications of the model and identifies trends, which may serve as the foundations of a money launderer preference theory. The results of the investigation ranked countries by preference of US-based money launderers and determined that there was consistency in country destination preference even during recessionary periods.

The Walker–Unger gravity model as applied by Roman et al. (2021) is used to conduct the investigation, to maintain consistency in the application of the Walker–Unger model and further the objective of validating the attractiveness simulation. The model tests the predictive capability of the independent variables to establish the degree of attractiveness each country represents for the funds of US-based money launderers. A score is generated by the model, which is then used to analyze and interpret its significance in relation to all sampled countries.

Model results reveal the countries with the highest attractiveness for US-based money launderers during 2000–2020 were Australia, the Bahamas, Bermuda, Canada, Cayman Islands, Norway, Monaco, Puerto Rico, Switzerland and the USA. Model results show that over the two decades the proportion of money flow scores changed but not to a degree that would alter the country preference of US-based money launderers. US-based money launderers tended to use the same countries for their illicit financial activities, regardless of the state of the legitimate economy.

One of the limitations of the model is that it does not show the effect of money laundering on legitimate economic activity.

The model results will give insight into the preferred destination of US-based money launderers and therefore frame one component of money laundering activities in the USA for the examined time period.

A secondary objective of this study is to evaluate if any changes to US-based money launderer preferences occurred during the three most recent periods of economic downturn in the USA.

The model results will give insight into the preferred destination of US-based money launderers and therefore frame one component of money laundering activities in the USA for the examined time period. A secondary objective of this study is to evaluate if any changes to US-based money launderer preferences occurred during the three most recent periods of economic downturn in the USA. The periods chosen are the 2001 9/11 terrorist attacks, the 2007/08 global financial crisis and the COVID-19 pandemic.

The purpose of this paper is to examine the relation between audit tenure and how clients manage the annual earnings surprise.

A sample of 5,029 firm‐year observations from 1996 to 2003 were employed to examine whether audit tenure is negatively related to the incidence of accrual‐based‐upward earnings management to avoid negative earnings surprises; and whether audit tenure is positively related to the incidence of downward forecast guidance to avoid negative earnings surprises.

Empirical results indicate a substitution of downward forecast guidance for upward earnings management as audit tenure lengthens.

The paper provides evidence that, as the auditor‐client relationship lengthens over time, firms turn to downward forecast guidance as a substitute for upward earnings management. One possible limitation of the sample period involves the implementation of the Sarbanes‐Oxley Act (SOX) of 2002. Because of the increased financial reporting scrutiny on both management and auditors that accompanies SOX, it is likely that constraints on earnings misstatements increase after SOX. Any decrease in upward earnings management resulting from SOX would thus work against finding a relation between audit tenure and the substitution of downward forecast guidance to prevent negative earnings surprises.

This paper supports the notion that audit tenure affects firms' choices among various tactics in their attempts to avoid negative earnings surprises. The results also contribute to the ongoing debate on mandatory audit firm rotation by showing that audit quality increases with audit tenure.

A single conceptualization of mental health based on empirical research has yet to be adopted by researchers and practitioners. This paper aims to explore how diverse Americans define mental health. The aim of the study was to build a conceptualization of the term “mental health”, using qualitative and quantitative methods, on the basis of definitions provided by an ethnically diverse sample of lay-people and professionals.

Concept mapping methods, including multidimensional scaling and hierarchical cluster analysis, were applied to 146 statements generated by 125 participants of diverse American racial and ethnic groups. The resulting concept map was inspected visually, quantitatively and qualitatively.

Out of the 146 statements, 8 overarching themes emerged from multidimensional scaling and hierarchical cluster analysis. Themes include Well-being, balance, coping, adaptability, relational, self, lack of mental illness and physical. T-tests revealed statistically significant differences on ratings of importance for statements within the theme “Lack of Mental Illness” from those included in “Well-Being”, “Balance” and “Coping.” Statements included in the theme “Lack of Mental Illness” were rated least essential to the construct of mental health. The “Self” cluster appeared at the center of the data visualization, suggesting that Americans believe that self is essential to the construct in question.

This mixed-method study is consistent with prior evidence that mental health and mental illness might best be considered separate constructs (Westerhof and Keyes, 2010). A logical follow-up might examine why the concept of “Self” emerges centrally as it would help mental health practitioners and policymakers to focus their understanding of mental health to improve mental health interventions.

Understanding that the concept of “Self” is central to Americans’ conceptualizing of mental health may help mental health practitioners and policymakers to focus their efforts in delivering targeted mental health interventions.

This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members.

As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation.

The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association.

This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus.

The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA.

Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs.

This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

The purpose of this paper is to identify the variables that influence the international assignment – expatriate learning relationship and the expatriate learning – organizational learning relationship.

The paper contains a literature review of the research on expatriates' learning and organizational learning

The paper provides an integrative framework that identifies the moderating variables that influence both the relationship between the expatriate international assignment and expatriate learning as well as the relationship between expatriate learning and organizational learning. In addition, this framework specifies the process by which the international assignment influences organizational learning and shows that expatriate learning mediates this relationship. Several hypotheses were generated to provide avenues for future investigation.

The paper does not provide an exhaustive set of the moderating variables and does not focus on the interaction between situational and individual differences moderators.

Managers should pay attention to the selection, maintenance and repatriation of expatriates and facilitate the conditions under which expatriate learning and organizational learning can be maximized. To remain competitive, managers should engage in the continuous process of assessing the effectiveness of international assignments in enhancing expatriate and organizational learning.

The present research identifies the conditions that facilitate or hinder expatriate learning and organizational learning as well as the process by which international assignments influence organizational learning. Expatriate learning and organizational learning are critical for the continuous growth and competitive advantage of organizations, and, accordingly, it is imperative to study the factors and the process that influence learning in organizations, especially in response to the increasing popularity of globalization and the pressure to remain competitive.

The 1990 International Conference of The Planning Forum held in Washington, D. C. set a new attendance record. Some 1300 senior executives exchanged war stories, business theories, and points of view while attending three days of presentations by major company CEOs, consultants to leading corporations, noted academics, and practitioners of strategic management. Many of the star presenters had the discerning Bernard C. Reimann, chairman of the Department of Management at Cleveland State University, in their audience busily taking notes. Professor Reimann regularly reports on major strategic management conferences for Planning Review.

Outlines the key features of the Ohlson (1995) and Feltham and Ohlson (1995, 1996) models, which relate share values to current accounting numbers, and reviews research on their empirical application. Identifies some inconsistencies in results and directions for future research.

The extant new service development (NSD) literature tends to assume that the key practices for NSD identified in one context apply for all services, and has failed to sufficiently consider differences in NSD between service types. The purpose of this paper is to explore the nature of NSD across different service types.

An extensive, cross-sectoral survey was conducted in seven countries. Data from 1,333 NSD projects were analyzed to empirically derive a service typology and examine if and how different types of services vary in terms of NSD resources, practices, methods, and results.

Based on six service characteristics, the study identifies four service types: routine-intensive, technology-intensive, contact-intensive, and knowledge-intensive services. The study also identifies specific NSD resources, practices, methods, and results that are prevalent across the service typology. The evidence indicates that the use of advanced practices and methods differs dramatically between service types.

The paper enables practitioners to expand their current understanding on NSD by providing insights into the variability of NSD across service types. The results suggest that either service-type-specific models or a configurable model for NSD should be developed.

This study provides one of the first empirically derived service typologies for NSD. The study demonstrates that NSD resources, practices, methods, and results differ across service types, thereby challenging the “one size fits all” assumption evident in current NSD research.