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Many researchers suggest that investment bankers underprice IPOs. However, from 1989 to 1996, all Japanese IPOs were auctioned, reducing the role of underwriters. Initial returns of Japanese price-competitive IPOs are not found lower than underwriter-priced U.S. IPOs. Issue size, firm size, general market movements, insider sales levels, and underwriter quality are not highly related to initial returns under price-competitive auctions. However, there appears to be a strong partial adjustment phenomenon. Thus, price-competitive auctions did not result in significantly lower initial returns, but did reduce the impacts of many traditional variables found to significantly affect initial returns in U.S. underwriter-priced IPOs.

This study examines the impact of the trading volume on Initial Public Offering (IPO) initial return in the context of an emerging market from January 2006 to December 2016. Models consist of hierarchical and multiple regressions have been evaluated. Our results show, firstly, IPO provides an average of 21.90% of initial return to investors on the first trading day, 9.08% of return on the second day of trading, and 7.12% of return on the third day of return. Secondly, there is a positive relationship between the oversubscription ratio and initial return and no relationship between trading volume and initial return on the first three trading day. Thirdly, the trading volume does not act as a moderator that worsens the relationship between the oversubscription ratio and initial return. Lastly, this study shows that investors should actively participate in the subsequent trading of an IPO. Higher participation will bring greater liquidity and shareholder wealth in the stock market. To the authors' knowledge, this is the first study on the moderating effect of trading volume on IPO initial return in an emerging market.

While extant entry theory has long prescribed a niche approach for new ventures, a preponderance of empirical research has found that broad strategies may be the key to new venture success. This study examines the difference between entry theory and empirical evidence by considering the moderating impact of initial financial resources on the effectiveness of venture strategy. Examining new, independent firms at the point of inception, we find that initial financial resources moderate the relationship between strategic breadth and performance, implying that the returns to a broad initial strategy increase with the level of initial capital. Contrary to popular niche prescriptions for new ventures, we did not find support for the belief that firms with low initial financial resources should pursue niche strategies and suggest that it may be time to re-examine theory on the nature of the relationship between entry strategies and performance.

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

The purpose of this paper is to analyze numerically the blowup in finite time of the solutions to a one-dimensional, bidirectional, nonlinear wave model equation for the propagation of small-amplitude waves in shallow water, as a function of the relaxation time, linear and nonlinear drift, power of the nonlinear advection flux, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of three types of initial conditions.

An implicit, first-order accurate in time, finite difference method valid for semipositive relaxation times has been used to solve the equation in a truncated domain for three different initial conditions, a first-order time derivative initially equal to zero and several constant wave speeds.

The numerical experiments show a very rapid transient from the initial conditions to the formation of a leading propagating wave, whose duration depends strongly on the shape, amplitude and width of the initial data as well as on the coefficients of the bidirectional equation. The blowup times for the triangular conditions have been found to be larger than those for the Gaussian ones, and the latter are larger than those for rectangular conditions, thus indicating that the blowup time decreases as the smoothness of the initial conditions decreases. The blowup time has also been found to decrease as the relaxation time, degree of nonlinearity, linear drift coefficient and amplitude of the initial conditions are increased, and as the width of the initial condition is decreased, but it increases as the viscosity coefficient is increased. No blowup has been observed for relaxation times smaller than one-hundredth, viscosity coefficients larger than ten-thousandths, quadratic and cubic nonlinearities, and initial Gaussian, triangular and rectangular conditions of unity amplitude.

The blowup of a one-dimensional, bidirectional equation that is a model for the propagation of waves in shallow water, longitudinal displacement in homogeneous viscoelastic bars, nerve conduction, nonlinear acoustics and heat transfer in very small devices and/or at very high transfer rates has been determined numerically as a function of the linear and nonlinear drift coefficients, power of the nonlinear drift, viscosity coefficient, viscous attenuation, and amplitude, smoothness and width of the initial conditions for nonzero relaxation times.

The paper analyses the effects of individual and household characteristics on current poverty status, while controlling for initial conditions, past poverty status and unobserved heterogeneity in 14 European countries for the period 1994–2001, using the European Community Household Panel. The distinction between true state dependence and individual heterogeneity has important policy implications, since if the former is the main cause of poverty it may be crucial to break the ‘vicious circle’ of poverty using income-supporting social policies, whereas if it is the latter anti-poverty policies should focus primarily on education, training, development of personal skills and other labour market oriented policies. The empirical results are similar in qualitative terms but rather different in quantitative terms across the EU countries covered in the paper. State dependence remains significant in all model specifications, even after controlling for unobserved heterogeneity or when removing possible endogeneity bias. Higher poverty rates and higher poverty persistence are associated with particular welfare state regimes, although the link is substantially weakened when other explanatory variables are included in the analysis.

This paper aims to study the initial trust of the owner to the contractor, establish the initial trust mechanism, explore the factors that affect the initial trust of the owner to the contractor and analyze its influence mechanism. Based on this, it is easy for the owners and contractors to take targeted measures to improve the initial trust, which is conducive to the sustainable development of the project.

On the basis of reading a large amount of literature, this paper constructs the occurrence mechanism of the owner's initial trust to the contractor from the five factors of trust propensity, trust belief, trustee’s characteristics, institution-based trust, trust motivation and from the perspective of the owner using the structural equation model for questionnaire survey and empirical analysis.

The results of this paper show that the institution-based trust, the trustee’s characteristics and the trust belief of the trustor clearly have a positive effect on trust motivation, and the trustee’s characteristics have the most significant effect on the trust motivation. The influence of trust propensity on trust motivation was not significant.

This paper studies the occurrence mechanism of the owner's initial trust in the contractor, discusses its influencing factors and analyzes the influence of these factors on the initial trust, which enriches the theoretical system of initial trust research. The results of this study can help owners and contractors to develop targeted measures to build good initial trust.

The purpose of this study is to describe and analyse the effect of a set of determinants on initial trust and behavioural intention to use financial robo-advisors (FRAs).

The theory of perceived risk and the behavioural finance paradigm were used to develop a conceptual model of retail investors’ initial trust in FRAs. Data collected from 554 young retail investors (YRIs) from Sweden and Malaysia were analysed using structural equation modelling.

The results of this study indicate that the amount of public information, social media information-seeking and a rational decision style are significantly related to initial trust in FRAs, which in turn is significantly and positively related to the behavioural intention to use this technology. However, none of the risks under study significantly affect the initial trust in FRAs.

Information is vital to inducing YRIs to rely on FRAs, so the more public and social media information is available, the higher their intention to use this technology. However, YRIs vary in decision style, and the results suggest implementing a more sophisticated system than the current “one-size-fits-all” approach to YRI behaviour.

The empirical-based model enhances the knowledge of the initial phase of trust-building, when YRIs lack sufficient experience of FRAs. By collecting data from two countries, the study’s novel conclusions may help in developing effective FRA services for the youth segment.

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.