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Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.

Past studies on foreign corporate investment and wages hypothesize that by expanding into highly concentrated and highly capital intensive industries, foreign owners are better able to pay higher wages than their domestic counterparts. Our study tests this hypothesis by comparing the effects of domestic and foreign acquisition activity on union and non‐union wages. We find strong evidence supporting the ability to pay hypothesis. There is no indication of bargaining strength changing with foreign acquisitions, as such activity is not associated with larger union wage premiums. Union premiums, however, decline with greater domestic acquisition activity.

The purpose of this paper is to estimate the direct effects of foreign direct investment (FDI) on domestic target firms’ profitability gains, in India, post-acquisition. In particular, it focuses on identifying the importance of firms’ heterogeneities on the effects, taking into account the source of FDI, the intensity of firm interaction, and the target firms’ technology-absorptive capacity. Most importantly, the paper investigates whether the estimates depend on a combined rather than single impact of these heterogeneities.

To control for the possibility of selection bias and endogeneity, this empirical analysis uses a methodology that combines propensity score matching and difference-in-differences (PSM–DID) in adopting a comprehensive data set of both foreign- and Indian-acquired firms that were purchased through mergers and acquisitions in India between 1991 and 2013.

The analysis reveals four major findings. First, overall, the post-foreign acquisition target firms’ performance gains were positive and varied by the heterogeneous technology transfer capacity of the foreign investor. Second, it is possible that target firms located in industrial clusters with more foreign agglomeration experienced larger profitability gains through more dynamic firm interactions in terms of spillovers. Third, Indian targets with higher technology-absorptive capacity benefitted in higher profitability gains from acquiring and assimilating the superior technology that is transferred from foreign investors. Finally, an optimal combination of Indian target firms with higher technology-absorptive capacity and foreign investors with higher technology transfer capacity maximizes profitability gains, post-acquisition. This synergy effect is particularly prominent in clusters where more foreign firms agglomerate.

This study captures the true direct effect of FDI by adjusting the combined causal effects of various inherent heterogeneities in the target firms’ performance, thus correcting any possible bias, which few previous studies have addressed.

The purpose of this paper is to investigate the impact of the frequency of information acquisition on the frequency of stock trading. The authors also examined if the Big Five personality traits of investor influence the association between information acquisition and stock trading behavior.

The authors adopted NEO Five-Factor Inventory (Costa and McCrae, 1989) inventory to measure the Big Five personality traits of investors and examined the data collected from 541 individual investors of the Chinese stock market. To overcome the potential endogeneity bias, the authors followed two-stage least square method for estimating endogenous covariate by employing instrumental variable analysis. The authors performed probit regression to evaluate the moderating influence of investor personality traits on the association between information acquisition and stock trading behavior. The authors also performed several other tests to check the robustness of the key findings.

This research confirmed the previous findings that the more frequently investors acquire information, the more often they trade in stocks. Moreover, the authors added to the existing literature by providing empirical evidence that the Big Five personality traits moderate the relationship of information acquisition with stock trading behavior. Information acquisition tends to increase stock trading frequency in investors with conscientiousness, extraversion and agreeableness traits. On the other hand, it also has the tendency to decrease the intensity of stock trading in investors with openness and neuroticism traits.

The theoretical model in this study seeks to explain that the psychological factor, namely, investor personality, influences the way an investor interprets signals from information which in turn influences the investor decision to trade in securities. This research suggests that psychological characteristics of investors can be of relevance for policy makers in their attempts to improve their business in the financial services industry.

This study combines both information search literature and behavioral finance literature to investigate whether or not the information acquisition that relates to investors’ asset allocation decisions is influenced by investor personality. The study offers new theoretical insights into investors’ behavior due to the characteristics of the Chinese stock market which are uniquely different from other stock markets in the world. No previous study has been conducted so far in the Chinese stock market to explore variations in the impact of investors’ information acquisition on their stock trading by the Big Five personality and this paper strives to fill this research gap.

A two‐component framework for strategic marketing research, focused on the corporate level and the business‐unit level, to structure an interpretation of the strategic dimensions of the acquisition in November 1998 of Star Markets, a Boston, USA‐based food retail chain, by J. Sainsbury plc the UK’s second largest food retailer. Set within a broader context of the wave of acquisition‐driven consolidation rapidly transforming the US food retail industry during the late 1990s, the paper considers the extent to which the acquisition of Star Markets represented a strategic fit with Sainsbury’s existing US business, the alternative strategies available to the company at the time of the acquisition, and the resulting strategic centrality of the US business to Sainsbury’s corporate future. Focuses on the highly contested nature of the retail internationalization process and issues of sustaining international expansion during periods of retrenchment and strategic reassessment. Highlights the tensions which can be created within the portfolio of business units of a large multidivisional firm during the internationalization process, and the stresses in the relationship between management and the capital markets which can develop if the internationalization process is perceived, correctly or incorrectly, to threaten the strategic credibility of the firm.

This paper examines the corporate income tax compliance costs and their determinants by analyzing survey and financial statements data from firms operating in Greece. We find that corporate tax compliance costs are of considerable size and vary with several firm-specific characteristics, including the firm’s size, its age, the sector in which it operates, its location, and its legal form. The paper intends to raise awareness regarding the impact of tax compliance costs, especially for countries, such as Greece, that were significantly affected by the economic and financial crisis.

The main purpose of this study is to examine the possibility of prediction of Greek takeover targets that belong to the industrial sector, emphasizing the econometric methodology and the prediction test.

The study uses a sample of 51 targets and 290 non‐targets exclusively from Greek industry over the period 1997‐2005. In order to achieve a better predictive accuracy the paper uses a new econometric methodology, the bootstrap mixed logit and different (more advanced) techniques of prediction test and choice of cutoff values.

The results exhibit that bootstrap mixed logit has significant and valuable predictive ability with respect to the classical conditional logit model. Furthermore, the predictive accuracy is higher than the results of other studies (e.g Palepu and Espahbodi and Espahbodi).

The main contribution of this study is the application of the bootstrap mixed logit in analyzing Greek takeovers. The results change the prediction variables as well as the determinants of the takeover target characteristics for the Greek industry. This is meaningful, not only for the investors that seek to increase the value of their fortune through acquisitions, but also for the managers that can detect if their firm might be considered a takeover target.

While the existing studies largely suggest that valuation uncertainty benefits acquirers, who apply discounts to targets' value attributable to information asymmetry, the authors argue that the opposite may be the case.

Through multivariate econometric analysis of transaction data, the authors establish the link between the degree of valuation uncertainty measured by targets' track of public listing and acquisition premia. The authors use text-mining tools to measure acquirer–target similarity and control for its role in intermediating the posited empirical relationships.

Having analyzed 618 acquisitions involving listed targets from China, the authors find that acquirers pay higher valuation premia for the more recently listed and relatively younger companies than for those with a longer history since floatation. Similar patterns apply to valuation multiples. Higher valuations are partially attributable to premia for control, as acquirers are likelier to buy a majority stake in the recently listed firms, especially if the latter are similar to them. Such transactions take less time to complete and involve a transfer of larger share blocks despite the higher degree of information asymmetry and a frequent lack of targets' operational profitability. The authors also observe a significant premium for target–acquirer similarity: acquirers appear to rush deal completion due to possible overestimation of targets' potential and familiarity bias.

The authors show that acquisition premia may be driven by acquirers' proclivity to place risky investment bets on the growth potential of opaque targets. This pattern may partially explain frequent failures of mergers and acquisitions (M&A).

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.