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The purpose of this chapter is to make sense of the cultural distance paradox through a basic assessment of the cross-cultural comparability of cultural distance measures. Cultural distance between a base country and partner countries is a key construct in international business (IB). However, we propose that what exactly is measured by cultural distance is unique for each country that is chosen as the base country to/from which cultural distance to a set of partner countries is calculated.

We use a mathematical argument to establish that cultural distance may correlate rather differently with the culture of partner countries depending on which base country one considers, for example, the United States or China. We then use empirical analysis to show the relevance of this argument, using Hofstede’s data on national culture for 69 countries.

Results show that cultural distance indeed has very different correlations with partner country culture, depending on which country one selects as the base country in one’s distance calculations.

Implication of our findings is that measured cultural distance is not equivalent across different base countries. The effect of cultural distance on such issues as foreign market entry mode or market selection, therefore, lacks international generalizability.

This chapter presents the first assessment of the cross-cultural comparability of cultural distance. Paradoxical findings that plague extant cultural distance research may be understood from the found lack of measurement equivalence.

The public’s awareness of noise and vibration forms a significant barrier to further development of railways. This chapter begins with a short introduction to the main fundamental aspects of acoustics, including decibels, frequency analysis, the propagation of sound with distance and common measurement quantities. The main sources of railway noise are discussed, including rolling noise, impact noise, curve squeal and aerodynamic noise. Simple calculation procedures are described that can be used to assess the impact of railway noise and to compare it with legal limits. The final section is devoted to ground vibration, which is a related form of environmental disturbance.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

Each of the four objectives can be applied within the military training environment. Military training often requires that soldiers achieve specific levels of performance or proficiency in each phase of training. For example, training courses impose entrance and graduation criteria, and awards are given for excellence in military performance. Frequently, training devices, training media, and training evaluators or observers also directly support the need to diagnose performance strengths and weaknesses. Training measures may be used as indices of performance, and to indicate the need for additional or remedial training.

This chapter bridges the gap between two distinct approaches to the concept of psychic distance – measuring it in terms of people’s perceptions of distance or in terms of exogenous national-level differences. The two approaches are reconciled in a “refined and integrative” definition of the concept, which is tested empirically using a mediating model.

Structural equation modeling is used on a bilateral sample of 25 countries to test whether perceptions of psychic distance mediate the relationships between national-level differences and bilateral trade and investment.

By testing for alternative direct paths, the chapter confirms that for the main forms of national-level differences, culture, socioeconomic development, language, and religion, psychic distance fully mediates their relationships with both trade flows and investment patterns. However, for geographic distance, while the relationship is fully mediated for investment, it is only partially mediated for exports. Two asymmetric “distance-bridging” factors are also found to be significant antecedents of psychic distance.

This chapter is the first to empirically demonstrate the mediating relationship between exogenous national-level differences and perceptions of psychic distance, and thus, provides new insights into the debate over which measurement approach is more appropriate. Perceptions of psychic distance, even if measured by expert panels rather than the actual decision-makers, fully capture the impact of national-level differences on trade and FDI flows; however, if such measures of perceptions are not available, a simple selection of four national-level differences will still capture 80% of the same effect.

This chapter describes how micro-locational factors affect international alliance formation. The authors focus specifically on the role of global cities, which are studied from a distance perspective. The authors argue that distances must be apprehended not at the country level but at the city level. The chapter is an attempt to provide a better understanding of the complex, multilevel factors that interact when firms select an alliance partner in a particular location. The authors take an explorative methodological approach through a configurational analysis of international alliances made by American companies in 2015. The main contribution is the proposition of a typology of micro-locational characteristics to help understand international alliance formation at a city level.

A method is derived for estimating a discrete choice model incorporating heteroscedasticities to reflect repeated measurement problems. Heterogeneity of each observation is characterised by a specific scale function and individual heterogeneity is introduced in the random utility choice model. This research proves that the unobserved influences affecting a specific individuals' mode choice are correlated from one of his or her selections to the next repeated questions. This research also suggest a strong evidence of learning effect, implying variances would be decrease as the responses faces repeated questions.

Understanding institutional distance – i.e., the difference in institutional context between countries – is critical for firms whose operations span national boundaries. Institutional distance impacts the relative attractiveness of country markets, trade-offs among foreign market entry strategies, the management of subsidiaries abroad, and ultimately, firm performance. Although scholars in various disciplines have made great advances in defining and measuring the institutional characteristics of nations, we contend that many of these advances have occurred in a parallel fashion. Moreover, extant empirical studies focus disproportionately on the impact of different subsets of the identified institutional characteristics. We suggest that it is time to find common ground across the disparate literatures. Doing so would allow us to view differences in institutional contexts more holistically, and refine our understanding of their implications for multinational corporations.