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The purpose of this paper is to develop a radical innovation launch model that shows the relationship of the market, entrepreneurial and learning orientations with each other, with radical innovation launch marketing capabilities and the subsequent effect on radical innovation launch success. It will provide practitioners with best practices and add to current marketing theory.

An online survey was done, resulting in a usable sample of 176 radical innovation launch practitioners from a cross-section of US companies, namely, small to large, business-to-business and business-to-consumer firms offering a variety of products and services. A partial least squares structural equation modeling technique was used to test construct relationships and the effect on each other.

An organizational learning orientation has a direct effect on the market and entrepreneurial orientations. Learning and marketing orientations are critical links to having radical innovation launch marketing capabilities. While an entrepreneurial orientation has a direct effect on radical innovation launch success, proper, dynamic marketing capabilities are a significant driver. Over 40% of the variance in radical innovation launch success is directly or indirectly affected by the three studied strategic orientations and radical innovation launch marketing capabilities.

This study was conducted only in the USA. A cross-cultural study could be undertaken. Type and size of firm, type of external environment, radical innovation department structure, transformational leadership strength and competitive intensity effect could be studied. New, up-to-date adaptable marketing capabilities should be researched and validated.

For radical innovation launch success, it is critical that a firm develop the market, entrepreneurial and learning orientations and have specific, dynamic marketing capabilities in place. Existing managers should be trained, or new talent hired, to give the firm the capability to develop unique, radical innovation launch strategic, brand identity and new target market plans, to select and manage new downstream partners, and to have quick, customer launch feedback mechanisms in place.

An empirical study of the effect of all three strategic orientations on radical innovation launch marketing capabilities and subsequent radical innovation launch success has not been previously addressed.

Bank failures are critical events that have far-reaching implications for the financial system and various stakeholders. This study aims to focus on analyzing the phenomenon of small bank failures in the USA.

This study adopts the coarsened exact matching (CEM) technique to enhance the reliability of the analysis. By matching similar observed characteristics, the CEM approach helps to address potential selectivity bias and facilitates a more accurate estimation of the treatment effect. This study uses a data set covering the period from 2000 through 2019 and includes 523 failed bank observations and 43,605 nonfailed bank observations.

The results reveal several key findings. Small banks, especially those with lower yields on earning assets, those with lower charge-offs on loans and leases, those with higher core capital ratios and those with higher Fed Funds rates are found to be more susceptible to failure.

Some results align with initial predictions, whereas others present contrasting outcomes.

This study underscores the significance of understanding the factors contributing to bank failure and emphasizes the importance of studying small bank failures in particular.

This study uses the CEM method. CEM is a comprehensive approach that combines matching, sample trimming and reweighting techniques. When applying CEM, researchers carefully select a set of core variables to achieve balance between the treated and control groups. The CEM process involves discretizing each continuous variable into distinct bins or categories, a process known as “coarsening.” It then requires an exact match among these binned variables between the treated and control units, which constitutes the matching step in CEM.

Drawing on the resource-based view, this study aims to examine how family involvement in migrant-founded small businesses gives rise to distinctive resources that help these businesses survive.

Using microdata from the 2007 US survey of business owners (SBO), this study uses logit regression modeling to test the hypothesized relationships.

Results show that small businesses founded by migrant entrepreneurs are less likely to survive and that family involvement weakens the negative relationship between founder migrant status and business survivability. In addition, the positive moderating effect associated with family involvement is further strengthened by the use of external/borrowing startup capital, thus migrant families founded small businesses with access to external capital have the highest probability of survival.

This study contributes to the literature on both migrant entrepreneurship and family business. This paper finds family involvement in the business, interacting with the founder’s migrant status, tends to create distinctive resource endowments that help to compensate for the resource constraints associated with migrant entrepreneurs. Such resource endowments may take the form of high levels of solidarity among migrant family members and the spanning role of the migrant kinship networks extended from the country of origin to the country of residence.

This research adopts Hofstede's typology of culture as a framework to test for cultural differences regarding consumer verbal behaviors in the context of a service provider switching incident.

The study includes respondents from five countries selected to provide global diversity by including cultures from Asia, Europe, South America, and North America. Cooperation from a variety of businesses was utilized in each country investigated to survey employees.

Findings indicate there are significant relationships between cultural orientation and: the propensity of engaging in discussion of service switching incidents; communication valence; the social network that private word‐of‐mouth is shared with; the likelihood of public complaint behavior.

The sample, while five‐country in design, is somewhat limited by its representation at the extremes (i.e. most developed and least developed) and from urban populations only. Within‐country differences are also not considered. A broad range of service sectors strengthens the results, but does not allow for sector‐specific conclusions. The results of this study can assist global service providers to better understand the role that culture plays in customer verbal behaviors as well as providing direction to formulate strategies and tactics to better manage the complaint process.

As service organizations become more globally diverse, understanding the subtle influences of cultural differences becomes increasingly important for building effective customer relationships. No study to date has examined consumer post‐switching verbal behaviors across a broad range of cultural settings.

Academic research in the USA and more recently in the UK and Sweden, has highlighted public capital as a significant growth determinant. Public capital, it is argued, has a positive effect on private sector output, productivity and capital formation. However, controversy surrounds the empirical results emerging from this literature. Much of the controversy rests on research methods employed. Adds to this body of literature in two ways. First, estimates aggregate production functions for private sector output using Irish data. The stock of public capital is included as an input to investigate the effects of government investment on private sector productivity. Second, uses modern time‐series techniques to test the hypothesis. Employs the Johansen (1988) cointegration testing procedure and error correction modelling on annual data for the period 1958‐1990. These modern techniques produce empirical results which do not support the public capital hypothesis. Suggests several reasons to explain this outcome, and outlines possible policy implications.

Vintage capital growth models have been at the heart of growth theory in the 1960s. This research line collapsed in the late 1960s with the so-called embodiment controversy and the technical sophisitication of the vintage models. This chapter analyzes the astonishing revival of this literature in the 1990s. In particular, it outlines three methodological breakthroughs explaining this resurgence: a growth accounting revolution, taking advantage of the availability of new time series; an optimal control revolution, allowing to safely study vintage capital optimal growth models; and a vintage human capital revolution, along with the rise of economic demography, accounting for the vintage structure of human capital similarly to physical capital age structuring. The related literature is surveyed.

An understanding of the determination of real wages is crucial in analysing the determination of the natural rate of unemployment or NAIRU. Uses cointegration techniques to examine a core theoretical model of the long‐run determinants of real wages involving unit labour costs, unemployment, union power and the replacement ratio. Considers the different measures of union power and the duration of unemployment and alternative specifications involving the “wedge” but a robust cointegrating relationship is not found. These results can be interpreted in several ways: concepts such as union power or the “generosity” of benefits may be measured inadequately; the theoretical understanding of the long‐run determinants of real earnings may remain seriously incomplete; alternatively the short spans of data examined may be insufficient for the application of cointegration techniques, although the sample sizes examined here are fairly typical of most macroeconomic time series.

Purpose: Risk is a multifaceted concept, and its identification requires complex approaches that are often misunderstood. The consequence is that decisions are based on limited perception rather than the full value and meaning of what risk is, as a result, the way it is being tackled is incorrect. The individuals are often limited in their perceptions and ideas and do not embrace the full multifaceted nature of risk. Regulators and individuals want to follow norms and checklists or overuse models, simulations, and templates, thereby reducing responsibility for decision-making. At the same time, the wider use of technology and rules reduces the critical thinking of individuals. We advance the automation process by building robots that follow protocols and forget about the part of risk assessment that cannot be programed. Therefore, with this study, the objective of this study was to discover how people define risk, the influencing factors of risk perception and how they behave toward this perception. The authors also determine how the perception differed with age, gender, marital status, education level and region. The novelty of the research is related to individual risk perception during COVID-19, as this is a new and unknown phenomenon. Methodology: The research is based on the analysis of the self-administered purposely designed questionnaires we distributed across different social media platforms between February and June 2020 in Europe and in some cases was carried out as a interview over communication platforms such as “Skype,” “Zoom” and “Microsoft Teams.” The questionnaire was divided into four parts: Section 1 was designed to collect demographic information from the participants; Section 2 included risk definition statements obtained from literature and a preliminary discussion with peers; Section 3 included risk behavior statements; and Section 4 included statements on risk perception experiences. A five-point Likert Scale was provided, and participants were required to answer along a scale of “1” for “Strongly Agree” to “5” for “Strongly Disagree.” Participants also had the option to elaborate further and provide additional comments in an open-ended box provided at the end of the section. 466 valid responses were received. Thematic analysis was carried out to analyze the interviews and the open-ended questions, while the questionnaire responses were analyzed using various quantitative methods on IBM SPSS (version 23). Findings: The results of the analysis indicate that individuals evaluate the risk before making a decision and view risk as both a loss and opportunity. The study identifies nine factors influencing risk perception. Nevertheless, it must be emphasized that we can continue to develop models and rules, but as long as the risk is not understood, we will never achieve anything.