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Why is it that highly trained and seasoned executives fail? On the surface, this doesn’t make sense because they are very successful; yet research in the organization sciences provides no shortage of evidence to prove just that. From the classic Mann Gulch fire disaster of Weick’s famous collapse of sensemaking study, to studies of myopia of learning, escalation of commitment, threat-rigidity, dominant logic, the architecture of simplicity, the Icarus Paradox, to core competencies turning into core rigidities, and navigating new competitive markets using “old” cognitive maps, and many more such examples point to a ubiquitous phenomenon where highly trained and experienced professionals find themselves “stuck” in the heat of battle, unable to move and progress. On the one hand, for some, there is a desperate need for change, but are unable to do so, due to their trained incapacities. On the other hand, some simply cannot see the need for change, and continue with their “business as usual” mentality. For both, their visions of the world shrink, they have a tendency to cling onto their past habitual practices and oversimplify the complexity of the situation. In moments like these: DROP YOUR TOOLS and UNLEARN! This book chapter introduces a framework (grounded in clinical psychology) that has had consistent success in helping seasoned executives and key decision-makers open up the alternatives whenever they find themselves stuck with complexity.

The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

Since the seminal works by Granger and Joyeux (1980) and Hosking (1981), estimations of long-memory time series models have been receiving considerable attention and a number of parameter estimation procedures have been proposed. This paper gives an overview of this plethora of methodologies with special focus on likelihood-based techniques. Broadly speaking, likelihood-based techniques can be classified into the following categories: the exact maximum likelihood (ML) estimation (Sowell, 1992; Dahlhaus, 1989), ML estimates based on autoregressive approximations (Granger & Joyeux, 1980; Li & McLeod, 1986), Whittle estimates (Fox & Taqqu, 1986; Giraitis & Surgailis, 1990), Whittle estimates with autoregressive truncation (Beran, 1994a), approximate estimates based on the Durbin–Levinson algorithm (Haslett & Raftery, 1989), state-space-based maximum likelihood estimates for ARFIMA models (Chan & Palma, 1998), and estimation of stochastic volatility models (Ghysels, Harvey, & Renault, 1996; Breidt, Crato, & de Lima, 1998; Chan & Petris, 2000) among others. Given the diversified applications of these techniques in different areas, this review aims at providing a succinct survey of these methodologies as well as an overview of important related problems such as the ML estimation with missing data (Palma & Chan, 1997), influence of subsets of observations on estimates and the estimation of seasonal long-memory models (Palma & Chan, 2005). Performances and asymptotic properties of these techniques are compared and examined. Inter-connections and finite sample performances among these procedures are studied. Finally, applications to financial time series of these methodologies are discussed.

Diaspora tourism primarily refers to various population groups, including migrants, foreign workers, political refugees, ethnic and religious minorities and overseas communities living away from their ancestral homeland for various reasons. Throughout history, people have been forced to leave their original homeland due to various factors ranging from economic crises to natural and human-made tragedies, including war. The 24 February 2022 unjustified and unproved Russia's war of aggression against Ukraine, which started on the heels of the two-year COVID-19 pandemic, has resulted in massive and terrible consequences for many domains of political, economic and social life. The Russian invasion of Ukraine has generated the largest historical migration flows at a scale unforeseen in Europe since World War II. Since Russia invaded Ukraine, at least 12 million people have been displaced from their homes, according to the United Nations. The unprecedented influx of the Ukrainian people raises concerns about future developments, issues and challenges associated with Ukrainians' presence in other countries, particularly neighbouring ones. Therefore, this chapter analyses the possibility of diaspora tourism for Ukrainians shortly by utilising a critical approach when the situation stabilises in Ukraine. To begin with, this chapter first explains diaspora tourism with its definition and characteristics. Furthermore, it reviews the literature on the Russia–Ukraine war and its impact on Ukrainian tourism. In conclusion, it discusses the new Ukrainian diaspora wave soon.

Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

Drawing from theories of modernization and socioemotional selectivity, this study investigates the effect of familial support on the relationship between immigrant generation and mental health service use for Asian American and Latinx older adults.

Using the data from the National Latino and Asian American Study (NLAAS) 2002–2003, nested logistic regressions (N = 810) were used to test the effects of familial support (parent–child relationship quality) on the relationship between immigrant generation and the use of mental health services. Differences in familial support between older adults and their younger counterparts were also accounted for.

The results indicate that familial support partially attenuates the relationship between immigrant generation and mental health service use, but only for Latinx groups. Familial support was not significantly different for older adults than that of those younger in age.

Findings suggest the need for a better understanding of familial support as it relates to mental health service use for these groups. Approaches to improving the access to, and the overall use of, mental health services should be sensitive to ethnic variation. Immigrant groups may also endure stressors associated with legal and citizenship status. Future research should consider the effect of these political identities on mental health. Studies on parent–child relationship quality should also be longitudinal in order to better understand the dynamic nature of familial support across the life course.

This chapter addresses gaps in the literature as Asian Americans are relatively understudied group with regard to mental health. Previous studies showed that US-born Asian American and Latinx populations are more likely to use mental health services than their foreign-born counterparts, but the effects of generation status and familial support for older adults are unclear.

This paper investigates the impact of the development of capital markets on economic growth in Africa and reports a significant increase in real GDP per capita after stock exchanges are established. This paper also reveals that there are significant improvements in the level of private investments in the post stock market launch era. The results also indicate that stock markets play a complementary role to the banking sector by contributing to the availability of private credit. Although African capital markets are relatively less advanced when compared to capital markets on other continents (particularly in terms of technology, structure, and liquidity), we find that their establishment has been crucial in helping African countries catch up with the rest of the world.